Merge pull request #273 from MervinPraison/develop

fixing internet search tool

.env.example

@@ -0,0 +1,6 @@
+OPENAI_MODEL_NAME="gpt-4o"
+OPENAI_API_KEY="Enter your API key"
+OPENAI_API_BASE="https://api.openai.com/v1"
+CHAINLIT_USERNAME=admin
+CHAINLIT_USERNAME=admin
+CHAINLIT_AUTH_SECRET="chainlit create-secret to create"

.eslintrc

@@ -0,0 +1,10 @@
+{
+    "extends": ["plugin:mdx/recommended"],
+    // optional, if you want to lint code blocks at the same time
+    "settings": {
+      "mdx/code-blocks": true,
+      // optional, if you want to disable language mapper, set it to `false`
+      // if you want to override the default language mapper inside, you can provide your own
+      "mdx/language-mapper": {}
+    }
+  }

.github/FUNDING.yml

@@ -0,0 +1,2 @@
+patreon: MervinPraison
+ko_fi: MervinPraison

.github/workflows/docker-publish.yml

@@ -0,0 +1,44 @@
+name: Daily Publish Docker
+
+on:
+  push:
+    branches:
+      - main
+  schedule:
+    - cron: '0 0 * * *'
+
+jobs:
+  check-and-build:
+    runs-on: ubuntu-latest
+    
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Check last commit time
+        id: check_commit
+        run: |
+          last_commit_time=$(git log -1 --format=%ct)
+          current_time=$(date +%s)
+          time_diff=$((current_time - last_commit_time))
+          if [ $time_diff -ge 120 ] && [ $time_diff -lt 240 ]; then
+            echo "should_run=true" >> $GITHUB_OUTPUT
+          else
+            echo "should_run=false" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Set up Docker Buildx
+        if: steps.check_commit.outputs.should_run == 'true'
+        uses: docker/setup-buildx-action@v1
+
+      - name: Log in to the GitHub Container Registry
+        if: steps.check_commit.outputs.should_run == 'true'
+        run: echo ${{ secrets.GH_TOKEN }} | docker login ghcr.io -u USERNAME --password-stdin
+
+      - name: Build and push Docker image
+        if: steps.check_commit.outputs.should_run == 'true'
+        uses: docker/build-push-action@v2
+        with:
+          context: .
+          push: true
+          tags: ghcr.io/mervinpraison/praisonai:latest

.github/workflows/python-package.yml

@@ -0,0 +1,40 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "develop" ]
+  pull_request:
+    branches: [ "develop" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    # - name: Lint with flake8
+    #   run: |
+    #     # stop the build if there are Python syntax errors or undefined names
+    #     flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+    #     # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+    #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    # - name: Test with pytest
+    #   run: |
+    #     pytest

.github/workflows/python-publish.yml

@@ -0,0 +1,39 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}

.github/workflows/release.yml

@@ -0,0 +1,524 @@
+name: Release Workflow
+
+on:
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Tag to create release with (e.g., v2.0.10)'
+        required: true
+      fork_owner:
+        description: 'GitHub username of the fork owner'
+        required: true
+        default: 'tosin2013'
+
+jobs:
+  test-ubuntu:
+    strategy:
+      matrix:
+        os: [ubuntu-22.04, ubuntu-24.04]
+        framework: ['crewai', 'autogen']
+      fail-fast: false
+
+    runs-on: ${{ matrix.os }}
+    timeout-minutes: 90
+
+    steps:
+    # First checkout the upstream repo at the specified tag
+    - name: Checkout upstream repo
+      uses: actions/checkout@v4
+      with:
+        repository: MervinPraison/PraisonAI
+        ref: ${{ github.event.inputs.tag }}
+        fetch-depth: 0
+
+    # Get the commit hash from the tag
+    - name: Get commit hash
+      id: get_commit
+      run: |
+        COMMIT_HASH=$(git rev-parse HEAD)
+        echo "commit_hash=$COMMIT_HASH" >> $GITHUB_OUTPUT
+        echo "Found commit: $COMMIT_HASH for tag ${{ github.event.inputs.tag }}"
+
+    # Now checkout the fork at main branch
+    - name: Checkout fork
+      uses: actions/checkout@v4
+      with:
+        repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
+        ref: main
+        fetch-depth: 0
+        clean: true
+
+    # Sync with upstream commit
+    - name: Sync with upstream
+      run: |
+        git config --global user.name "GitHub Actions"
+        git config --global user.email "[email protected]"
+        
+        # Add upstream remote
+        git remote add upstream https://github.com/MervinPraison/PraisonAI.git
+        
+        # Fetch from upstream
+        git fetch upstream --tags --force
+        
+        # Reset to upstream commit
+        git reset --hard ${{ steps.get_commit.outputs.commit_hash }}
+
+    - name: Setup Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.11'
+        cache: 'pip'
+
+    - name: Install Poetry
+      run: |
+        curl -sSL https://install.python-poetry.org | python3 -
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+        poetry config virtualenvs.in-project true
+        poetry config virtualenvs.create true
+        poetry --version
+        poetry config --list
+
+    - name: Install Dependencies
+      run: |
+        poetry lock #--no-update
+        poetry install --all-extras
+        poetry env info
+        # Verify venv exists
+        test -d .venv || { echo "Virtual environment not created!"; exit 1; }
+        test -f .venv/bin/activate || { echo "Activation script not found!"; exit 1; }
+
+    - name: Run setup script
+      timeout-minutes: 20
+      run: |
+        set -e  # Exit on any error
+        curl -OL https://raw.githubusercontent.com/tosin2013/PraisonAI/refs/heads/main/setup.sh
+        chmod +x setup.sh
+        ./setup.sh --all --cicd || { echo "Setup script failed"; exit 1; }
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Basic Functionality
+      run: |
+        set -e  # Exit on any error
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        lsof -ti:8084 | xargs -r kill -9 || true
+        source .venv/bin/activate || { echo "Failed to activate virtual environment"; exit 1; }
+        mkdir -p test_output || { echo "Failed to create test_output directory"; exit 1; }
+        
+        echo "Testing basic CLI functionality..."
+        
+        # Test auto generation with explicit framework
+        praisonai --framework ${{ matrix.framework }}  --auto "create a short test script" 2>&1 | tee test_output/auto_test.log
+        
+        # Verify the output file exists and has content
+        if [[ ! -s test_output/auto_test.log ]]; then
+          echo "Error: No output generated from auto test"
+          exit 1
+        fi
+        
+        # Test auto generation without framework
+        echo "Basic functionality tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Framework-specific Features
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        lsof -ti:8084 | xargs -r kill -9 || true
+        praisonai --framework ${{ matrix.framework }} --auto "create a movie script about Robots in Mars" 2>&1 | tee test_output/framework_output.txt || echo "Framework-specific test failed" >> test_output/errors.log
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Core Functionality
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing core functionality..."
+        
+        # Test package imports
+        python3 -c "from praisonai.cli import PraisonAI; from praisonai.auto import AutoGenerator; from praisonai.agents_generator import AgentsGenerator" || exit 1
+        
+        # Test configuration loading
+        python3 -c "from praisonai.inc.config import generate_config; print(generate_config())" || exit 1
+        
+        # Test tools loading
+        python3 -c "from praisonai.inbuilt_tools import *" || exit 1
+        
+        echo "Core functionality tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Agent Generation
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing agent generation..."
+        
+        # Create a test YAML file
+        cat > test_agents.yaml <<EOF
+        topic: "Test automation task"
+        framework: "${{ matrix.framework }} "
+        roles:
+          - name: "Tester"
+            goal: "Test the system"
+            backstory: "Expert system tester"
+            tasks:
+              - description: "Verify system functionality"
+                expected_output: "Test results"
+        EOF
+        
+        # Test YAML loading and agent generation
+        python3 -c '
+        from praisonai.agents_generator import AgentsGenerator
+        config = [{"model": "llama-3.3-70b-versatile", "base_url": "https://api.groq.com/openai/v1/"}]
+        generator = AgentsGenerator("test_agents.yaml", "${{ matrix.framework }} ", config)
+        print("Agent generation test passed!")
+        ' || exit 1
+        
+        echo "Agent generation tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Custom Tools Integration
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing custom tools integration..."
+        
+        # Create a test tool
+        cat > test_tool.py <<EOF
+        from duckduckgo_search import DDGS
+        from praisonai_tools import BaseTool
+
+        class InternetSearchTool(BaseTool):
+            name: str = "Internet Search Tool"
+            description: str = "Search Internet for relevant information"
+
+            def _run(self, query: str):
+                return {"status": "success", "query": query}
+                
+            def test(self):
+                """Test method to verify tool setup"""
+                test_query = "test query"
+                result = self._run(test_query)
+                assert result["status"] == "success"
+                assert result["query"] == test_query
+                return True
+        EOF
+        
+        # Test tool loading and initialization
+        python3 -c '
+        import importlib.util
+        import sys
+        
+        # Load the test tool
+        spec = importlib.util.spec_from_file_location("test_tool", "test_tool.py")
+        module = importlib.util.module_from_spec(spec)
+        sys.modules["test_tool"] = module
+        spec.loader.exec_module(module)
+        
+        # Initialize and test the tool
+        tool = module.InternetSearchTool()
+        assert tool.test(), "Tool test failed"
+        print("Custom tool test passed!")
+        ' || exit 1
+        
+        echo "Custom tools integration test passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test PraisonAI Agents Configuration
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing agents configuration..."
+        mkdir -p test_output || { echo "Failed to create test_output directory"; exit 1; }
+        
+        # Create test script
+        cat > test_agents.py <<EOF
+        from langchain_openai import ChatOpenAI
+        from crewai import Agent, Task, Crew
+        import os
+        import logging
+        import time
+        import random
+
+        logging.basicConfig(level="DEBUG")
+        logger = logging.getLogger(__name__)
+
+        def random_sleep(min_seconds=15, max_seconds=30):
+            """Sleep for a random duration to prevent API rate limiting"""
+            duration = random.uniform(min_seconds, max_seconds)
+            logger.info(f"Rate limit protection: Sleeping for {duration:.2f} seconds...")
+            time.sleep(duration)
+
+        class RateLimitedChatOpenAI(ChatOpenAI):
+            """ChatOpenAI with built-in rate limiting"""
+            def __call__(self, *args, **kwargs):
+                random_sleep()
+                return super().__call__(*args, **kwargs)
+
+        def run_test():
+            logger.info("Initializing test components...")
+            
+            # Initialize the LLM with rate limiting
+            llm = RateLimitedChatOpenAI(
+                model=os.getenv('OPENAI_MODEL_NAME', 'llama-3.3-70b-versatile'),
+                base_url=os.getenv('OPENAI_API_BASE', 'https://api.groq.com/openai/v1/'),
+                api_key=os.getenv('OPENAI_API_KEY'),
+                temperature=0.7
+            )
+
+            random_sleep()
+            logger.info("Creating researcher agent...")
+            researcher = Agent(
+                role="Test Automation Expert",
+                goal="Verify system functionality and performance",
+                backstory="""You are an expert in test automation and system verification,
+                skilled in identifying potential issues and validating functionality.""",
+                verbose=True,
+                allow_delegation=False,
+                llm=llm
+            )
+
+            random_sleep()
+            logger.info("Creating validator agent...")
+            validator = Agent(
+                role="Quality Assurance Specialist",
+                goal="Validate test results and ensure system reliability",
+                backstory="""You are a QA specialist focused on ensuring
+                system reliability and proper functionality.""",
+                verbose=True,
+                allow_delegation=False,
+                llm=llm
+            )
+
+            random_sleep()
+            logger.info("Creating test tasks...")
+            test_task = Task(
+                description="Run a basic system verification test",
+                expected_output="A report on system functionality verification",
+                agent=researcher
+            )
+
+            random_sleep()
+            validation_task = Task(
+                description="Validate the test results and compile findings",
+                expected_output="A validation report of test findings",
+                agent=validator
+            )
+
+            random_sleep()
+            logger.info("Initializing crew...")
+            crew = Crew(
+                agents=[researcher, validator],
+                tasks=[test_task, validation_task],
+                verbose=2
+            )
+
+            logger.info("Starting crew execution...")
+            result = crew.kickoff()
+            logger.info("Test execution completed.")
+            logger.info(f"Result: {result}")
+            return result
+
+        if __name__ == "__main__":
+            try:
+                run_test()
+            except Exception as e:
+                logger.error(f"Test failed with error: {str(e)}", exc_info=True)
+                raise
+        EOF
+        
+        # Run the test script with increased timeout
+        echo "Testing with Python API..."
+        timeout 1800 python3 test_agents.py 2>&1 | tee test_output/agents_test.log
+        
+        # Verify the output
+        if [[ ! -s test_output/agents_test.log ]]; then
+          echo "Error: No output generated from agents test"
+          exit 1
+        fi
+        
+        echo "Agents configuration tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Upload test artifacts
+      if: always()
+      uses: actions/upload-artifact@v3
+      with:
+        name: praisonai-output-${{ matrix.os }}-${{ matrix.framework }}
+        path: test_output/
+        retention-days: 14
+
+  create-release:
+    needs: [test-ubuntu]
+    runs-on: ubuntu-latest
+    if: success()
+    steps:
+    # First checkout the fork's main branch
+    - name: Checkout fork repo
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+        token: ${{ secrets.PAT_TOKEN }}
+        repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
+        ref: main
+
+    # Add upstream and get commit from tag
+    - name: Get upstream tag commit
+      id: get_upstream_commit
+      run: |
+        # Add upstream remote
+        git remote add upstream https://github.com/MervinPraison/PraisonAI.git
+        
+        # Delete local tag if it exists
+        git tag -d ${{ github.event.inputs.tag }} || true
+        
+        # Fetch from upstream including tags with force
+        git fetch upstream --tags --force
+        
+        # Verify tag exists in upstream
+        if ! git ls-remote --tags upstream | grep -q "refs/tags/${{ github.event.inputs.tag }}$"; then
+          echo "::error::Tag ${{ github.event.inputs.tag }} not found in upstream repository"
+          exit 1
+        fi
+        
+        # Get the commit hash from the upstream tag
+        UPSTREAM_COMMIT=$(git rev-list -n 1 refs/tags/${{ github.event.inputs.tag }})
+        if [ -z "$UPSTREAM_COMMIT" ]; then
+          echo "::error::Could not find commit for tag ${{ github.event.inputs.tag }}"
+          exit 1
+        fi
+        echo "upstream_commit=$UPSTREAM_COMMIT" >> $GITHUB_OUTPUT
+        echo "Found commit: $UPSTREAM_COMMIT for tag ${{ github.event.inputs.tag }}"
+
+    # Create tag at the same commit
+    - name: Create tag at commit
+      run: |
+        git config --global user.name "GitHub Actions"
+        git config --global user.email "[email protected]"
+        
+        # Remove tag if it exists locally
+        git tag -d ${{ github.event.inputs.tag }} || true
+        
+        # Remove tag if it exists remotely
+        git push origin :refs/tags/${{ github.event.inputs.tag }} || true
+        
+        # Fetch the specific commit from upstream
+        git fetch upstream ${{ steps.get_upstream_commit.outputs.upstream_commit }} --force
+        
+        # Reset to upstream commit
+        git reset --hard ${{ steps.get_upstream_commit.outputs.upstream_commit }}
+        
+        # Create and push tag
+        git tag -a ${{ github.event.inputs.tag }} -m "Release ${{ github.event.inputs.tag }} (synced from upstream MervinPraison/PraisonAI@${{ github.event.inputs.tag }})"
+        git push origin ${{ github.event.inputs.tag }}
+      env:
+        GIT_ASKPASS: /bin/echo
+        PAT_TOKEN: ${{ secrets.PAT_TOKEN }}
+
+    # Create the release
+    - name: Create Release
+      uses: actions/create-release@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.PAT_TOKEN }}
+      with:
+        tag_name: ${{ github.event.inputs.tag }}
+        release_name: Release ${{ github.event.inputs.tag }}
+        draft: false
+        prerelease: false
+        body: |
+          Release ${{ github.event.inputs.tag }} (synced from upstream)
+          
+          This release is synchronized with MervinPraison/PraisonAI@${{ github.event.inputs.tag }}
+          Commit: ${{ steps.get_upstream_commit.outputs.upstream_commit }}
+          
+          For installation:
+          ```bash
+          pip install praisonai
+          ```
+          
+          Changes from upstream v2.0.10:
+          - Updated agent configuration tests
+          - Added rate limiting protection
+          - Improved test reliability
+          
+          Forked from: MervinPraison/PraisonAI

.github/workflows/unittest.yml

@@ -0,0 +1,36 @@
+name: Run specific unittest
+
+on: [push, pull_request]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.11
+
+    - name: Install Poetry
+      run: |
+        curl -sSL https://install.python-poetry.org | python3 -
+        export PATH="$HOME/.local/bin:$PATH"
+
+    - name: Install dependencies
+      run: |
+        poetry install
+        poetry run python -m pip install duckduckgo_search
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME }}" >> $GITHUB_ENV
+
+    - name: Run specific unittest
+      run: |
+        poetry run python -m unittest tests.test.TestExamples

.gitignore

@@ -1,23 +1,68 @@
-# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
-
-# dependencies
-/node_modules
-/.pnp
-.pnp.js
-
-# testing
-/coverage
-
-# production
-/build
-
-# misc
+.vscode
+crewai
+.cache
+__pycache__
+chroma.sqlite3
+test
+dist
+.env
+assets
+.idea
 .DS_Store
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
+.pytest_cache
+praisonAI.egg-info
+flagged
+test.yaml
+db
+praisonai_prompt.txt
+watch.sh
+docs.sh
+other
+output
 
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
+.files
+threads.db
+threads.db-journal
+
+.chainlit
+!praisonai/ui/config/.chainlit
+
+site
+# Ignore Sphinx build directory
+docs/_build/
+docs/source/
+docs/build/
+
+# Ignore Sphinx cache
+docs/_build/doctrees/
+docs/_build/doctrees/*
+docs/_build/doctrees/.doctrees
+
+# Ignore Sphinx logs and local build files
+docs/_build/.buildinfo
+docs/_build/.buildinfo/*
+docs/_build/.buildinfo/.buildinfo
+
+praisonai/doc
+
+# agentops
+trained_agents_data.pkl
+agentops.log
+
+# crewAI
+crewAI
+
+# virtualenv
+.venv
+
+# Credentials
+credentials.json
+
+# node_modules
+node_modules
+
+outputs
+agents/praisonaiagents/build
+agents/praisonaiagents/dist
+agents/praisonaiagents/praisonaiagents.egg-info
+.codegpt

.praisoncontext

@@ -0,0 +1,4 @@
+# praisonai/ui/context.py
+# /Users/praison/miniconda3/envs/praisonai-package/lib/python3.11/site-packages/llama_index/packs/code_hierarchy/code_hierarchy.py
+praisonai/ui/components/aicoder.py
+praisonai/ui/code.py

.praisonignore

@@ -0,0 +1,4 @@
+cookbooks
+tests
+docs
+praisonai/inbuilt_tools/*

.praisoninclude

@@ -0,0 +1,3 @@
+# praisonai/ui/context.py
+# /Users/praison/miniconda3/envs/praisonai-package/lib/python3.11/site-packages/llama_index/packs/code_hierarchy/code_hierarchy.py
+praisonai/setup

.readthedocs.yml

@@ -0,0 +1,23 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+  jobs:
+    post_install:
+    - bash docs/generate_docs.sh
+
+mkdocs:
+  configuration: mkdocs.yml
+  fail_on_warning: true
+
+python:
+  install:
+    - requirements: docs/requirements.txt

Dockerfile

@@ -0,0 +1,6 @@
+FROM python:3.11-slim
+WORKDIR /app
+COPY . .
+RUN pip install flask praisonai==2.0.35 gunicorn markdown
+EXPOSE 8080
+CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

Dockerfile.chat

@@ -0,0 +1,24 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    python3-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install --no-cache-dir \
+    praisonaiagents>=0.0.4 \
+    praisonai_tools \
+    "praisonai==2.0.18" \
+    "praisonai[chat]" \
+    "embedchain[github,youtube]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "chat"]

Dockerfile.dev

@@ -0,0 +1,28 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    python3-dev \
+    portaudio19-dev \
+    python3-pyaudio \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install --no-cache-dir \
+    praisonaiagents>=0.0.4 \
+    praisonai_tools \
+    "praisonai==2.0.18" \
+    "praisonai[ui]" \
+    "praisonai[chat]" \
+    "praisonai[realtime]" \
+    "embedchain[github,youtube]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "ui"]

Dockerfile.ui

@@ -0,0 +1,24 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    python3-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install --no-cache-dir \
+    praisonaiagents>=0.0.4 \
+    praisonai_tools \
+    "praisonai==2.0.18" \
+    "praisonai[ui]" \
+    "praisonai[crewai]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "ui"]

LICENSE

@@ -0,0 +1,20 @@
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -1,70 +1,582 @@
-# Getting Started with Create React App
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="docs/logo/dark.png" />
+    <source media="(prefers-color-scheme: light)" srcset="docs/logo/light.png" />
+    <img alt="PraisonAI Logo" src="docs/logo/light.png" />
+  </picture>
+</p>
 
-This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
+<p align="center">
+<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" /></a>
+<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" /></a>
+<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a>
+</p>
 
-## Available Scripts
+<div align="center">
 
-In the project directory, you can run:
+# Praison AI
 
-### `npm start`
+<a href="https://trendshift.io/repositories/9130" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison%2FPraisonAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
-Runs the app in the development mode.\
-Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
+</div>
 
-The page will reload when you make changes.\
-You may also see any lint errors in the console.
+PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration.
 
-### `npm test`
+<div align="center">
+  <a href="https://docs.praison.ai">
+    <p align="center">
+      <img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
+    </p>
+  </a>
+</div>
 
-Launches the test runner in the interactive watch mode.\
-See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
+## Key Features
 
-### `npm run build`
+- 🤖 Automated AI Agents Creation
+- 🔄 Self Reflection AI Agents
+- 🧠 Reasoning AI Agents
+- 👁️ Multi Modal AI Agents
+- 🤝 Multi Agent Collaboration
+- ⚡ AI Agent Workflow
+- 🔄 Use CrewAI or AutoGen Framework
+- 💯 100+ LLM Support
+- 💻 Chat with ENTIRE Codebase
+- 🎨 Interactive UIs
+- 📄 YAML-based Configuration
+- 🛠️ Custom Tool Integration
+- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
+- 🖼️ Vision Language Model (VLM) Support
+- 🎙️ Real-time Voice Interaction
 
-Builds the app for production to the `build` folder.\
-It correctly bundles React in production mode and optimizes the build for the best performance.
+## Using No Code
 
-The build is minified and the filenames include the hashes.\
-Your app is ready to be deployed!
+### Auto Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
 
-See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
+### Initialise Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --init create a movie script about Robots in Mars
+praisonai
+```
 
-### `npm run eject`
+## Using Coding
 
-**Note: this is a one-way operation. Once you `eject`, you can't go back!**
+Light weight package dedicated for coding:
+```bash
+pip install praisonaiagents
+```
 
-If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
 
-Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
 
-You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
+# 1. Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    llm="gpt-4o",
+    markdown=True
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    llm="gpt-4o",
+    markdown=True
+)
 
-## Learn More
+# 2. Define Tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher
+)
 
-You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long.""",
+    expected_output="A blog post of at least 4 paragraphs",
+    agent=writer,
+)
 
-To learn React, check out the [React documentation](https://reactjs.org/).
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2],
+    verbose=False,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
 
-### Code Splitting
+result = agents.start()
+```
 
-This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
+Run:
+```bash
+python app.py
+```
 
-### Analyzing the Bundle Size
+## Ollama Integration
+```bash
+export OPENAI_BASE_URL=http://localhost:11434/v1
+```
 
-This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
+## Groq Integration
+Replace xxxx with Groq API KEY:
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxx
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
 
-### Making a Progressive Web App
+## Logging
+```bash
+export LOGLEVEL=info
+```
 
-This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
+Advanced logging:
+```bash
+export LOGLEVEL=debug
+```
 
-### Advanced Configuration
+<div align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="docs/images/architecture-dark.png" />
+    <source media="(prefers-color-scheme: light)" srcset="docs/images/architecture-light.png" />
+    <img alt="PraisonAI Architecture" src="docs/images/architecture-light.png" />
+  </picture>
+</div>
 
-This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
+## Different User Interfaces:
 
-### Deployment
+| Interface | Description | URL |
+|---|---|---|
+| **UI** | Multi Agents such as CrewAI or AutoGen | [https://docs.praison.ai/ui/ui](https://docs.praison.ai/ui/ui) |
+| **Chat** | Chat with 100+ LLMs, single AI Agent | [https://docs.praison.ai/ui/chat](https://docs.praison.ai/ui/chat) |
+| **Code** | Chat with entire Codebase, single AI Agent | [https://docs.praison.ai/ui/code](https://docs.praison.ai/ui/code) |
+| **Realtime** | Real-time voice interaction with AI | [https://docs.praison.ai/ui/realtime](https://docs.praison.ai/ui/realtime) |
 
-This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
+| Other Features | Description | Docs |
+|---|---|---|
+| **Train** | Fine-tune LLMs using your custom data | [https://docs.praison.ai/train](https://docs.praison.ai/train) |
 
-### `npm run build` fails to minify
 
-This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
+## Google Colab Multi Agents
+
+|               | Cookbook        | Open in Colab                                                                                                                                                                                                                                  |
+| ------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Basic         | PraisonAI       | <a target="_blank" href="https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-googlecolab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>       |
+| Include Tools | PraisonAI Tools | <a target="_blank" href="https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-tools-googlecolab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a> |
+
+## Installation Options
+
+### Using pip
+```bash
+pip install praisonai
+```
+
+### Framework-specific Installation
+```bash
+# Install with CrewAI support
+pip install "praisonai[crewai]"
+
+# Install with AutoGen support
+pip install "praisonai[autogen]"
+
+# Install with both frameworks
+pip install "praisonai[crewai,autogen]"
+```
+
+### UI and Additional Features
+```bash
+# Install UI support
+pip install "praisonai[ui]"
+
+# Install Chat interface
+pip install "praisonai[chat]"
+
+# Install Code interface
+pip install "praisonai[code]"
+
+# Install Realtime voice interaction
+pip install "praisonai[realtime]"
+
+# Install Call feature
+pip install "praisonai[call]"
+```
+
+## Quick Start
+
+```bash
+# Set your OpenAI API key
+export OPENAI_API_KEY="Enter your API key"
+
+# Initialize with CrewAI (default)
+praisonai --init "create a movie script about dog in moon"
+
+# Or initialize with AutoGen
+praisonai --framework autogen --init "create a movie script about dog in moon"
+
+# Run the agents
+praisonai
+```
+
+## Full Automatic Mode
+
+```bash
+# With CrewAI (default)
+praisonai --auto "create a movie script about Dog in Moon"
+
+# With AutoGen
+praisonai --framework autogen --auto "create a movie script about Dog in Moon"
+```
+
+## Framework-specific Features
+
+### CrewAI
+When installing with `pip install "praisonai[crewai]"`, you get:
+- CrewAI framework support
+- PraisonAI tools integration
+- Task delegation capabilities
+- Sequential and parallel task execution
+
+### AutoGen
+When installing with `pip install "praisonai[autogen]"`, you get:
+- AutoGen framework support
+- PraisonAI tools integration
+- Multi-agent conversation capabilities
+- Code execution environment
+
+
+
+## TL;DR Multi Agents
+
+```bash
+pip install praisonai
+export OPENAI_API_KEY="Enter your API key"
+praisonai --init create a movie script about dog in moon
+praisonai
+```
+
+## Table of Contents
+
+- [Installation](#installation)
+- [Initialise](#initialise)
+- [Run](#run)
+- [Full Automatic Mode](#full-automatic-mode)
+- [User Interface](#user-interface)
+- [Praison AI Chat](#praison-ai-chat)
+- [Create Custom Tools](#create-custom-tools)
+- [Agents Playbook](#agents-playbook)
+- [Include praisonai package in your project](#include-praisonai-package-in-your-project)
+- [Commands to Install Dev Dependencies](#commands-to-install-dependencies)
+- [Other Models](#other-models)
+- [Contributing](#contributing)
+- [Star History](#star-history)
+
+## Installation Multi Agents
+
+```bash
+pip install praisonai
+```
+
+## Initialise
+
+```bash
+export OPENAI_API_KEY="Enter your API key"
+```
+
+Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
+
+Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
+
+```bash
+praisonai --init create a movie script about dog in moon
+```
+
+This will automatically create agents.yaml file in the current directory.
+
+### To initialise with a specific agent framework (Optional):
+
+```bash
+praisonai --framework autogen --init create movie script about cat in mars
+```
+
+## Run
+
+```bash
+praisonai
+```
+
+or
+
+```bash
+python -m praisonai
+```
+
+### Specify the agent framework (Optional):
+
+```bash
+praisonai --framework autogen
+```
+
+### Full Automatic Mode
+
+```bash
+praisonai --auto create a movie script about Dog in Moon
+```
+
+## User Interface
+
+## PraisonAI User Interfaces:
+
+| Interface | Description                                | URL                                                                   |
+| --------- | ------------------------------------------ | --------------------------------------------------------------------- |
+| **UI**    | Multi Agents such as CrewAI or AutoGen     | [https://docs.praisonai.com/ui/ui](https://docs.praison.ai/ui/ui)     |
+| **Chat**  | Chat with 100+ LLMs, single AI Agent       | [https://docs.praisonai.com/ui/chat](https://docs.praison.ai/ui/chat) |
+| **Code**  | Chat with entire Codebase, single AI Agent | [https://docs.praisonai.com/ui/code](https://docs.praison.ai/ui/code) |
+
+```bash
+pip install -U "praisonai[ui]"
+export OPENAI_API_KEY="Enter your API key"
+chainlit create-secret
+export CHAINLIT_AUTH_SECRET=xxxxxxxx
+praisonai ui
+```
+
+or
+
+```
+python -m praisonai ui
+```
+
+## Praison AI Chat
+
+- https://docs.praison.ai/chat/
+
+```bash
+pip install "praisonai[chat]"
+export OPENAI_API_KEY="Enter your API key"
+praisonai chat
+```
+
+### Internet Search
+
+Praison AI Chat and Praison AI Code now includes internet search capabilities using Crawl4AI and Tavily, allowing you to retrieve up-to-date information during your conversations.
+
+### Vision Language Model Support
+
+You can now upload images and ask questions based on them using Vision Language Models. This feature enables visual understanding and analysis within your chat sessions.
+
+## Praison AI Code
+
+```bash
+pip install "praisonai[code]"
+export OPENAI_API_KEY="Enter your API key"
+praisonai code
+```
+
+### Internet Search
+
+Praison AI Code also includes internet search functionality, enabling you to find relevant code snippets and programming information online.
+
+## Create Custom Tools
+
+- https://docs.praison.ai/tools/custom/
+
+## Agents Playbook
+
+### Simple Playbook Example
+
+```yaml
+framework: crewai
+topic: Artificial Intelligence
+roles:
+  screenwriter:
+    backstory: "Skilled in crafting scripts with engaging dialogue about {topic}."
+    goal: Create scripts from concepts.
+    role: Screenwriter
+    tasks:
+      scriptwriting_task:
+        description: "Develop scripts with compelling characters and dialogue about {topic}."
+        expected_output: "Complete script ready for production."
+```
+
+## Use 100+ Models
+
+- https://docs.praison.ai/models/
+
+## Include praisonai package in your project
+
+- https://docs.praison.ai/developers/wrapper
+- https://docs.praison.ai/developers/wrapper-tools/
+
+## Option 1: Using RAW YAML
+
+```python
+from praisonai import PraisonAI
+
+# Example agent_yaml content
+agent_yaml = """
+framework: "crewai"
+topic: "Space Exploration"
+
+roles:
+  astronomer:
+    role: "Space Researcher"
+    goal: "Discover new insights about {topic}"
+    backstory: "You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos."
+    tasks:
+      investigate_exoplanets:
+        description: "Research and compile information about exoplanets discovered in the last decade."
+        expected_output: "A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth."
+"""
+
+# Create a PraisonAI instance with the agent_yaml content
+praisonai = PraisonAI(agent_yaml=agent_yaml)
+
+# Run PraisonAI
+result = praisonai.run()
+
+# Print the result
+print(result)
+```
+
+## Option 2: Using separate agents.yaml file
+
+Note: Please create agents.yaml file before hand.
+
+```python
+from praisonai import PraisonAI
+
+def basic(): # Basic Mode
+    praisonai = PraisonAI(agent_file="agents.yaml")
+    praisonai.run()
+
+if __name__ == "__main__":
+    basic()
+```
+
+## Commands to Install Dependencies:
+
+### Using uv
+```bash
+# Install uv if you haven't already
+pip install uv
+
+# Install from requirements
+uv pip install -r pyproject.toml
+
+# Install with extras
+uv pip install -r pyproject.toml --extra code
+uv pip install -r pyproject.toml --extra "crewai,autogen"
+```
+
+## Contributing
+
+- Fork on GitHub: Use the "Fork" button on the repository page.
+- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
+- Create a branch: `git checkout -b new-feature`
+- Make changes and commit: `git commit -am "Add some feature"`
+- Push to your fork: `git push origin new-feature`
+- Submit a pull request via GitHub's web interface.
+- Await feedback from project maintainers.
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
+
+## License
+
+Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
+
+## Video Tutorials
+
+| Topic | Video |
+|-------|--------|
+| Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
+| Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
+| Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
+| Firecrawl Integration | [![Firecrawl](https://img.youtube.com/vi/UoqUDcLcOYo/0.jpg)](https://www.youtube.com/watch?v=UoqUDcLcOYo) |
+| User Interface | [![UI](https://img.youtube.com/vi/tg-ZjNl3OCg/0.jpg)](https://www.youtube.com/watch?v=tg-ZjNl3OCg) |
+| Crawl4AI Integration | [![Crawl4AI](https://img.youtube.com/vi/KAvuVUh0XU8/0.jpg)](https://www.youtube.com/watch?v=KAvuVUh0XU8) |
+| Chat Interface | [![Chat](https://img.youtube.com/vi/sw3uDqn2h1Y/0.jpg)](https://www.youtube.com/watch?v=sw3uDqn2h1Y) |
+| Code Interface | [![Code](https://img.youtube.com/vi/_5jQayO-MQY/0.jpg)](https://www.youtube.com/watch?v=_5jQayO-MQY) |
+| Mem0 Integration | [![Mem0](https://img.youtube.com/vi/KIGSgRxf1cY/0.jpg)](https://www.youtube.com/watch?v=KIGSgRxf1cY) |
+| Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
+| Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
+| Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
+
+## License
+
+Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
+
+## Local Docker Development with Live Reload
+
+To facilitate local development with live reload, you can use Docker. Follow the steps below:
+
+1. **Create a `Dockerfile.dev`**:
+    ```dockerfile
+    FROM python:3.11-slim
+
+    WORKDIR /app
+
+    COPY . .
+
+    RUN pip install flask praisonai==2.0.18 watchdog
+
+    EXPOSE 5555
+
+    ENV FLASK_ENV=development
+
+    CMD ["flask", "run", "--host=0.0.0.0"]
+    ```
+
+2. **Create a `docker-compose.yml`**:
+    ```yaml
+    version: '3.8'
+
+    services:
+      app:
+        build:
+          context: .
+          dockerfile: Dockerfile.dev
+        volumes:
+          - .:/app
+        ports:
+          - "5555:5555"
+        environment:
+          FLASK_ENV: development
+        command: flask run --host=0.0.0.0
+
+      watch:
+        image: alpine:latest
+        volumes:
+          - .:/app
+        command: sh -c "apk add --no-cache inotify-tools && while inotifywait -r -e modify,create,delete /app; do kill -HUP 1; done"
+    ```
+
+3. **Run Docker Compose**:
+    ```bash
+    docker-compose up
+    ```
+
+This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.

agents-advanced.yaml

@@ -0,0 +1,67 @@
+framework: crewai
+topic: research about the causes of lung disease
+roles:
+  research_analyst:
+    backstory: Experienced in analyzing scientific data related to respiratory health.
+    goal: Analyze data on lung diseases
+    role: Research Analyst
+    llm:  
+      model: "groq/llama3-70b-8192"
+    function_calling_llm: 
+      model: "google/gemini-1.5-flash-001"
+    allow_delegation: False
+    max_execution_time: 
+    max_iter: 
+    max_rpm: 
+    verbose: True
+    cache: True
+    system_template: 
+    prompt_template: 
+    response_template: 
+    tasks:
+      data_analysis:
+        description: Gather and analyze data on the causes and risk factors of lung
+          diseases.
+        expected_output: Report detailing key findings on lung disease causes.
+        config: 
+          max_iter: 5
+        output_file: "output/data_analysis.txt"
+    tools:
+    - 'InternetSearchTool'
+  medical_writer:
+    backstory: Skilled in translating complex medical information into accessible
+      content.
+    goal: Compile comprehensive content on lung disease causes
+    role: Medical Writer
+    llm:  
+      model: "anthropic/claude-3-haiku-20240307"
+    function_calling_llm: 
+      model: "openai/gpt-4o"
+    tasks:
+      content_creation:
+        description: Create detailed content summarizing the research findings on
+          lung disease causes.
+        expected_output: Document outlining various causes and risk factors of lung
+          diseases.
+        async_execution: True
+        output_file: "output/medical_writer.txt"
+        create_directory: 
+        context: 
+          - data_analysis
+    tools:
+    - ''
+  editor:
+    backstory: Proficient in editing medical content for accuracy and clarity.
+    goal: Review and refine content on lung disease causes
+    role: Editor
+    llm:  
+      model: "cohere/command-r"
+    tasks:
+      content_review:
+        description: Edit and refine the compiled content on lung disease causes for
+          accuracy and coherence.
+        expected_output: Finalized document on lung disease causes ready for dissemination.
+        output_file: "output/editor.txt"
+    tools:
+    - ''
+dependencies: []

agents.yaml

@@ -0,0 +1,43 @@
+framework: praisonai
+process: sequential
+topic: create movie script about cat in mars
+roles:
+  researcher:
+    backstory: Skilled in research, with a focus on gathering accurate and relevant
+      information.
+    goal: Gather information about Mars and cats
+    role: Researcher
+    min_reflect: 1
+    max_reflect: 2
+    tasks:
+      research_task:
+        description: Research about Mars, its environment, and the feasibility of
+          a cat being on Mars. Also, research about cat behavior and characteristics.
+        expected_output: Document with research findings on Mars and cats.
+    tools:
+    - search_tool
+  narrative_designer:
+    backstory: Skilled in narrative development, with a focus on creating engaging
+      stories.
+    goal: Create a story concept for a movie about a cat in Mars
+    role: Narrative Designer
+    tasks:
+      story_concept_development:
+        description: Use the research findings to craft a unique story concept for
+          a movie about a cat in Mars.
+        expected_output: Document with narrative arcs, character bios, and settings.
+    tools:
+    - ''
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tasks:
+      scriptwriting_task:
+        description: Turn the story concept into a production-ready movie script,
+          including dialogue and scene details.
+        expected_output: Final movie script with dialogue and scene details.
+    tools:
+    - ''
+dependencies: []

agents/.gitignore

@@ -0,0 +1,86 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+.env
+.venv
+env.bak/
+venv.bak/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+.project
+.pydevproject
+.settings/
+
+# Testing
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Documentation
+docs/_build/
+docs/generated/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*.ipynb
+
+# Logs and databases
+*.log
+*.sqlite
+*.db
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+.test
+praisonaiaagents.egg-info
+praisonaiagents.egg-info
+app.py
+outputs
+praisonaiagents/build
+praisonaiagents/dist
+praisonaiagents/praisonaiagents.egg-info
+dist
+build
+*.mp4 
+*.png 
+graph.py

agents/async_example.py

@@ -0,0 +1,233 @@
+import asyncio
+import time
+from typing import List, Dict
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from praisonaiagents.main import (
+    display_error,
+    display_interaction,
+    display_tool_call,
+    display_instruction,
+    error_logs,
+    Console
+)
+from duckduckgo_search import DDGS
+from pydantic import BaseModel
+
+console = Console()
+
+# 1. Define output model for structured results
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, str]]
+    total_results: int
+
+# 2. Define both sync and async tools
+def sync_search_tool(query: str) -> List[Dict]:
+    """
+    Synchronous search using DuckDuckGo.
+    Args:
+        query (str): The search query.
+    Returns:
+        list: Search results
+    """
+    display_tool_call(f"Running sync search for: {query}", console)
+    time.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+    except Exception as e:
+        error_msg = f"Error during sync search: {e}"
+        display_error(error_msg, console)
+        error_logs.append(error_msg)
+        return []
+
+async def async_search_tool(query: str) -> List[Dict]:
+    """
+    Asynchronous search using DuckDuckGo.
+    Args:
+        query (str): The search query.
+    Returns:
+        list: Search results
+    """
+    display_tool_call(f"Running async search for: {query}", console)
+    await asyncio.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+    except Exception as e:
+        error_msg = f"Error during async search: {e}"
+        display_error(error_msg, console)
+        error_logs.append(error_msg)
+        return []
+
+# 3. Define both sync and async callbacks
+def sync_callback(output: TaskOutput):
+    display_interaction("Sync Callback", f"Processing output: {output.raw[:100]}...", markdown=True, console=console)
+    time.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        display_tool_call(f"Processed JSON result: {output.json_dict}", console)
+    elif output.output_format == "Pydantic":
+        display_tool_call(f"Processed Pydantic result: {output.pydantic}", console)
+
+async def async_callback(output: TaskOutput):
+    display_interaction("Async Callback", f"Processing output: {output.raw[:100]}...", markdown=True, console=console)
+    await asyncio.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        display_tool_call(f"Processed JSON result: {output.json_dict}", console)
+    elif output.output_format == "Pydantic":
+        display_tool_call(f"Processed Pydantic result: {output.pydantic}", console)
+
+# 4. Create agents with different tools
+sync_agent = Agent(
+    name="SyncAgent",
+    role="Synchronous Search Specialist",
+    goal="Perform synchronous searches and return structured results",
+    backstory="Expert in sync operations and data organization",
+    tools=[sync_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+async_agent = Agent(
+    name="AsyncAgent",
+    role="Asynchronous Search Specialist",
+    goal="Perform asynchronous searches and return structured results",
+    backstory="Expert in async operations and data organization",
+    tools=[async_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+# 5. Create tasks with different configurations
+sync_task = Task(
+    name="sync_search",
+    description="Search for 'Python programming' using sync tool and return structured results",
+    expected_output="SearchResult model with query details and results",
+    agent=sync_agent,
+    async_execution=False,
+    callback=sync_callback,
+    output_pydantic=SearchResult
+)
+
+async_task = Task(
+    name="async_search",
+    description="Search for 'Async programming' using async tool and return structured results",
+    expected_output="SearchResult model with query details and results",
+    agent=async_agent,
+    async_execution=True,
+    callback=async_callback,
+    output_pydantic=SearchResult
+)
+
+# 6. Create workflow tasks
+workflow_sync_task = Task(
+    name="workflow_sync",
+    description="Workflow sync search for 'AI trends' with structured output",
+    expected_output="SearchResult model with AI trends data",
+    agent=sync_agent,
+    async_execution=False,
+    is_start=True,
+    next_tasks=["workflow_async"],
+    output_pydantic=SearchResult
+)
+
+workflow_async_task = Task(
+    name="workflow_async",
+    description="Workflow async search for 'Future of AI' with structured output",
+    expected_output="SearchResult model with Future of AI data",
+    agent=async_agent,
+    async_execution=True,
+    output_pydantic=SearchResult
+)
+
+# 7. Example usage functions
+def run_sync_example():
+    """Run synchronous example"""
+    display_instruction("\nRunning Synchronous Example...", console)
+    agents = PraisonAIAgents(
+        agents=[sync_agent],
+        tasks=[sync_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = agents.start()
+    display_interaction("Sync Example", f"Result: {result}", markdown=True, console=console)
+
+async def run_async_example():
+    """Run asynchronous example"""
+    display_instruction("\nRunning Asynchronous Example...", console)
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=[async_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = await agents.astart()
+    display_interaction("Async Example", f"Result: {result}", markdown=True, console=console)
+
+async def run_mixed_example():
+    """Run mixed sync/async example"""
+    display_instruction("\nRunning Mixed Sync/Async Example...", console)
+    agents = PraisonAIAgents(
+        agents=[sync_agent, async_agent],
+        tasks=[sync_task, async_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = await agents.astart()
+    display_interaction("Mixed Example", f"Result: {result}", markdown=True, console=console)
+
+async def run_workflow_example():
+    """Run workflow example with both sync and async tasks"""
+    display_instruction("\nRunning Workflow Example...", console)
+    agents = PraisonAIAgents(
+        agents=[sync_agent, async_agent],
+        tasks=[workflow_sync_task, workflow_async_task],
+        verbose=1,
+        process="workflow"
+    )
+    result = await agents.astart()
+    display_interaction("Workflow Example", f"Result: {result}", markdown=True, console=console)
+
+# 8. Main execution
+async def main():
+    """Main execution function"""
+    display_instruction("Starting PraisonAI Agents Examples...", console)
+    
+    try:
+        # Run sync example in a separate thread to not block the event loop
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, run_sync_example)
+        
+        # Run async examples
+        await run_async_example()
+        await run_mixed_example()
+        await run_workflow_example()
+
+        if error_logs:
+            display_error("\nErrors encountered during execution:", console)
+            for error in error_logs:
+                display_error(error, console)
+    except Exception as e:
+        display_error(f"Error in main execution: {e}", console)
+        error_logs.append(str(e))
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main()) 

agents/async_example_full.py

@@ -0,0 +1,152 @@
+import asyncio
+import time
+from typing import List, Dict
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from duckduckgo_search import DDGS
+from pydantic import BaseModel
+
+# 1. Define output model for structured results
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, str]]
+    total_results: int
+
+# 2. Define async tool
+async def async_search_tool(query: str) -> Dict:
+    """Perform asynchronous search and return structured results."""
+    await asyncio.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        
+        return {
+            "query": query,
+            "results": results,
+            "total_results": len(results)
+        }
+    except Exception as e:
+        print(f"Error during async search: {e}")
+        return {
+            "query": query,
+            "results": [],
+            "total_results": 0
+        }
+
+# 3. Define async callback
+async def async_callback(output: TaskOutput):
+    await asyncio.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        print(f"Processed JSON result: {output.json_dict}")
+    elif output.output_format == "Pydantic":
+        print(f"Processed Pydantic result: {output.pydantic}")
+
+# 4. Create specialized agents
+async_agent = Agent(
+    name="AsyncSearchAgent",
+    role="Search Specialist",
+    goal="Perform fast parallel searches with structured results",
+    backstory="Expert in efficient data retrieval and parallel search operations",
+    tools=[async_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+summary_agent = Agent(
+    name="SummaryAgent",
+    role="Research Synthesizer",
+    goal="Create concise summaries from multiple search results",
+    backstory="Expert in analyzing and synthesizing information from multiple sources",
+    self_reflect=True,
+    verbose=True,
+    markdown=True
+)
+
+# 5. Create async tasks
+async_task = Task(
+    name="async_search",
+    description="Search for 'Async programming' and return results in JSON format with query, results array, and total_results count.",
+    expected_output="SearchResult model with structured data",
+    agent=async_agent,
+    async_execution=True,
+    callback=async_callback,
+    output_json=SearchResult
+)
+
+async def run_parallel_tasks():
+    """Run multiple async tasks in parallel"""
+    print("\nRunning Parallel Async Tasks...")
+    
+    # Define different search topics
+    search_topics = [
+        "Latest AI Developments 2024",
+        "Machine Learning Best Practices",
+        "Neural Networks Architecture"
+    ]
+    
+    # Create tasks for different topics
+    parallel_tasks = [
+        Task(
+            name=f"search_task_{i}",
+            description=f"Search for '{topic}' and return structured results with query details and findings.",
+            expected_output="SearchResult model with search data",
+            agent=async_agent,
+            async_execution=True,
+            callback=async_callback,
+            output_json=SearchResult
+        ) for i, topic in enumerate(search_topics)
+    ]
+    
+    # Create summarization task
+    summary_task = Task(
+        name="summary_task",
+        description="Analyze all search results and create a concise summary highlighting key findings, patterns, and implications.",
+        expected_output="Structured summary with key findings and insights",
+        agent=summary_agent,
+        async_execution=False,
+        callback=async_callback,
+        context=parallel_tasks
+    )
+    
+    # Create a single PraisonAIAgents instance with both agents
+    agents = PraisonAIAgents(
+        agents=[async_agent, summary_agent],
+        tasks=parallel_tasks + [summary_task],
+        verbose=1,
+        process="sequential"
+    )
+    
+    # Run all tasks
+    results = await agents.astart()
+    print(f"Tasks Results: {results}")
+    
+    # Return results in a serializable format
+    return {
+        "search_results": {
+            "task_status": {k: v for k, v in results["task_status"].items() if k != summary_task.id},
+            "task_results": [str(results["task_results"][i]) if results["task_results"][i] else None 
+                           for i in range(len(parallel_tasks))]
+        },
+        "summary": str(results["task_results"][summary_task.id]) if results["task_results"].get(summary_task.id) else None,
+        "topics": search_topics
+    }
+
+# 6. Main execution
+async def main():
+    """Main execution function"""
+    print("Starting Async AI Agents Examples...")
+    
+    try:
+        await run_parallel_tasks()
+    except Exception as e:
+        print(f"Error in main execution: {e}")
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())

agents/async_example_full_multigroups.py

@@ -0,0 +1,248 @@
+import asyncio
+import time
+from typing import List, Dict
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from duckduckgo_search import DDGS
+from pydantic import BaseModel
+
+# 1. Define output model for structured results
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, str]]
+    total_results: int
+
+# 2. Define async tool
+async def async_search_tool(query: str) -> Dict:
+    """
+    Asynchronous search using DuckDuckGo.
+    Args:
+        query (str): The search query.
+    Returns:
+        dict: Search results in SearchResult model format
+    """
+    await asyncio.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        
+        # Format response to match SearchResult model
+        return {
+            "query": query,
+            "results": results,
+            "total_results": len(results)
+        }
+    except Exception as e:
+        print(f"Error during async search: {e}")
+        return {
+            "query": query,
+            "results": [],
+            "total_results": 0
+        }
+
+# 3. Define async callback
+async def async_callback(output: TaskOutput):
+    await asyncio.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        print(f"Processed JSON result: {output.json_dict}")
+    elif output.output_format == "Pydantic":
+        print(f"Processed Pydantic result: {output.pydantic}")
+
+# 4. Create specialized agents
+async_agent = Agent(
+    name="AsyncSearchAgent",
+    role="Asynchronous Search Specialist",
+    goal="Perform fast and efficient asynchronous searches with structured results",
+    backstory="Expert in parallel search operations and data retrieval",
+    tools=[async_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+summary_agent = Agent(
+    name="SummaryAgent",
+    role="Research Synthesizer",
+    goal="Create comprehensive summaries and identify patterns across multiple search results",
+    backstory="""Expert in analyzing and synthesizing information from multiple sources.
+Skilled at identifying patterns, trends, and connections between different topics.
+Specializes in creating clear, structured summaries that highlight key insights.""",
+    self_reflect=True,  # Enable self-reflection for better summary quality
+    verbose=True,
+    markdown=True
+)
+
+# 5. Create async tasks
+async_task = Task(
+    name="async_search",
+    description="""Search for 'Async programming' and return results in the following JSON format:
+{
+    "query": "the search query",
+    "results": [
+        {
+            "title": "result title",
+            "url": "result url",
+            "snippet": "result snippet"
+        }
+    ],
+    "total_results": number of results
+}""",
+    expected_output="SearchResult model with query details and results",
+    agent=async_agent,
+    async_execution=True,
+    callback=async_callback,
+    output_json=SearchResult
+)
+
+# 6. Example usage functions
+async def run_single_task():
+    """Run single async task"""
+    print("\nRunning Single Async Task...")
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=[async_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = await agents.astart()
+    print(f"Single Task Result: {result}")
+
+async def run_parallel_tasks():
+    """Run multiple async tasks in parallel"""
+    print("\nRunning Parallel Async Tasks...")
+    
+    # Define different search topics
+    search_topics = [
+        "Latest AI Developments 2024",
+        "Machine Learning Best Practices",
+        "Neural Networks Architecture"
+    ]
+    
+    # Create tasks for different topics
+    parallel_tasks = [
+        Task(
+            name=f"search_task_{i}",
+            description=f"""Search for '{topic}' and return results in the following JSON format:
+{{
+    "query": "{topic}",
+    "results": [
+        {{
+            "title": "result title",
+            "url": "result url",
+            "snippet": "result snippet"
+        }}
+    ],
+    "total_results": number of results
+}}""",
+            expected_output="SearchResult model with detailed information",
+            agent=async_agent,
+            async_execution=True,
+            callback=async_callback,
+            output_json=SearchResult
+        ) for i, topic in enumerate(search_topics)
+    ]
+    
+    # Create summarization task with the specialized summary agent
+    summary_task = Task(
+        name="summary_task",
+        description="""As a Research Synthesizer, analyze the search results and create a comprehensive summary. Your task:
+
+1. Analyze Results:
+   - Review all search results thoroughly
+   - Extract key findings from each topic
+   - Identify main themes and concepts
+
+2. Find Connections:
+   - Identify relationships between topics
+   - Spot common patterns or contradictions
+   - Note emerging trends across sources
+
+3. Create Structured Summary:
+   - Main findings per topic
+   - Cross-cutting themes
+   - Emerging trends
+   - Practical implications
+   - Future directions
+
+4. Quality Checks:
+   - Ensure all topics are covered
+   - Verify accuracy of connections
+   - Confirm clarity of insights
+   - Validate practical relevance
+
+Present the summary in a clear, structured format with sections for findings, patterns, trends, and implications.""",
+        expected_output="""A comprehensive research synthesis containing:
+- Detailed findings from each search topic
+- Cross-topic patterns and relationships
+- Emerging trends and their implications
+- Practical applications and future directions""",
+        agent=summary_agent,  # Use the specialized summary agent
+        async_execution=False,  # Run synchronously after search tasks
+        callback=async_callback
+    )
+    
+    # First run parallel search tasks
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=parallel_tasks,  # Only run search tasks first
+        verbose=1,
+        process="sequential"
+    )
+    search_results = await agents.astart()
+    print(f"Search Tasks Results: {search_results}")
+    
+    # Create task objects with results for context
+    completed_tasks = []
+    for i, topic in enumerate(search_topics):
+        task = Task(
+            name=f"search_task_{i}_result",
+            description=f"Search results for: {topic}",
+            expected_output="Search results from previous task",
+            agent=async_agent,
+            result=search_results["task_results"][i]
+        )
+        completed_tasks.append(task)
+    
+    # Update summary task with context from search results
+    summary_task.context = completed_tasks
+    
+    # Run summarization task with summary agent
+    summary_agents = PraisonAIAgents(
+        agents=[summary_agent],  # Use summary agent for synthesis
+        tasks=[summary_task],
+        verbose=1,
+        process="sequential"
+    )
+    summary_result = await summary_agents.astart()
+    print(f"Summary Task Result: {summary_result}")
+    
+    # Return results in a serializable format
+    return {
+        "search_results": {
+            "task_status": search_results["task_status"],
+            "task_results": [str(result) if result else None for result in search_results["task_results"]]
+        },
+        "summary": str(summary_result),
+        "topics": search_topics
+    }
+
+# 7. Main execution
+async def main():
+    """Main execution function"""
+    print("Starting Async AI Agents Examples...")
+    
+    try:
+        # Run different async patterns
+        await run_single_task()
+        await run_parallel_tasks()
+    except Exception as e:
+        print(f"Error in main execution: {e}")
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())

agents/code_agents_example.py

@@ -0,0 +1,50 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import json
+from e2b_code_interpreter import Sandbox
+
+def code_interpreter(code: str):
+    """
+    A function to demonstrate running Python code dynamically using e2b_code_interpreter.
+    """
+    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+    exec_result = Sandbox().run_code(code)
+    if exec_result.error:
+        print("[Code Interpreter error]", exec_result.error)
+        return {"error": str(exec_result.error)}
+    else:
+        results = []
+        for result in exec_result.results:
+            if hasattr(result, '__iter__'):
+                results.extend(list(result))
+            else:
+                results.append(str(result))
+        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+        return json.dumps({"results": results, "logs": logs})
+
+code_agent = Agent(
+    name="code_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in writing Python scripts",
+    self_reflect=False
+)
+execution_agent = Agent(
+    name="execution_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in executing Python scripts",
+    self_reflect=False,
+    tools=[code_interpreter]
+)
+
+code_agent_task = Task(
+    description="Write a simple Python script to print 'Hello, World!'",
+    expected_output="A Python script that prints 'Hello, World!'",
+    agent=code_agent
+)
+execution_agent_task = Task(
+    description="Execute the Python script",
+    expected_output="The output of the Python script",
+    agent=execution_agent
+)
+
+agents = PraisonAIAgents(agents=[code_agent, execution_agent], tasks=[code_agent_task, execution_agent_task])
+agents.start()

agents/example-sequential.py

@@ -0,0 +1,141 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
+from duckduckgo_search import DDGS
+
+def my_callback(output):
+    print(f"Callback Task output: {output}")
+
+def internet_search_tool(query) -> list:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles and URLs.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
+# Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    allow_delegation=False,
+    tools=[internet_search_tool],
+    llm="gpt-4o",
+    markdown=True,
+    reflect_llm="gpt-4o",
+    min_reflect=2,
+    max_reflect=4
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    verbose=True,
+    allow_delegation=True,
+    llm="gpt-4o",
+    tools=[],
+    markdown=True
+)
+
+# Create tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher,
+    tools=[internet_search_tool]
+)
+
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long. 
+    Also, call the get_weather tool to get the weather in Paris.""",
+    expected_output="A blog post of at least 4 paragraphs, and weather in Paris",
+    agent=writer,
+    context=[task1],
+    callback=my_callback,
+    tools=[]
+)
+
+task3 = Task(
+    name="json_task",
+    description="""Create a json object with a title of "My Task" and content of "My content".""",
+    expected_output="""JSON output with title and content""",
+    agent=researcher,
+)
+
+task4 = Task(
+    name="save_output_task",
+    description="""Save the AI blog post to a file""",
+    expected_output="""File saved successfully""",
+    agent=writer,
+    context=[task2],
+    output_file='outputs/ai_blog_post.txt',
+    create_directory=True
+)
+
+# Create and run agents manager
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2, task3, task4],
+    verbose=False,
+    process="sequential",  # "sequential" or "hierarchical"
+    manager_llm="gpt-4o"
+)
+
+result = agents.start()
+
+# Print results and error summary
+print("\n=== Task Results ===")
+for task_id, task_status in result['task_status'].items():
+    print(f"Task {task_id}: {task_status}")
+    if task_result := result['task_results'].get(task_id):
+        print(f"Output: {task_result.raw[:200]}...")  # Show first 200 chars
+
+# Print task details
+print("\n=== Task Details ===")
+for i in range(4):
+    print(agents.get_task_details(i))
+
+# Print agent details
+print("\n=== Agent Details ===")
+print(agents.get_agent_details('Researcher'))
+print(agents.get_agent_details('Writer'))
+
+# Print any errors
+if error_logs:
+    print("\n=== Error Summary ===")
+    for err in error_logs:
+        print(f"- {err}")
+        if "parsing self-reflection json" in err:
+            print("  Reason: The self-reflection JSON response was not valid JSON.")
+        elif "Error: Task with ID" in err:
+            print("  Reason: Task ID referenced does not exist.")
+        elif "saving task output to file" in err:
+            print("  Reason: Possible file permissions or invalid path.")
+        else:
+            print("  Reason not identified")

agents/example2.py

@@ -0,0 +1,74 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from typing import List, Dict
+from duckduckgo_search import DDGS
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles, URLs, and snippets.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+    
+# 2. Agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organising internet data.",
+    tools=[internet_search_tool],
+    self_reflect=False
+)
+
+# 3. Tasks
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+    is_start=True,
+    next_tasks=["validate_data"]
+)
+
+validate_task = Task(
+    description="""Validate the collected data. Check if:
+    1. At least 5 results are returned.
+    2. Each result contains a title and a URL.
+    Return validation_result as 'valid' or 'invalid' only no other text.""",
+    expected_output="Validation result indicating if data is valid or invalid.",
+    agent=data_agent,
+    name="validate_data",
+    task_type="decision",
+    condition={
+        "valid": [],  # End the workflow on valid data
+        "invalid": ["collect_data"]  # Retry data collection on invalid data
+    },
+)
+
+# 4. Workflow
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task, validate_task],
+    verbose=1,
+    process="workflow"
+)
+
+agents.start()

agents/example_callback.py

@@ -0,0 +1,204 @@
+from praisonaiagents import (
+    Agent, 
+    Task, 
+    PraisonAIAgents, 
+    error_logs, 
+    register_display_callback,
+    sync_display_callbacks,
+    async_display_callbacks
+)
+from duckduckgo_search import DDGS
+from rich.console import Console
+import json
+from datetime import datetime
+import logging
+
+# Setup logging
+logging.basicConfig(
+    filename='ai_interactions.log',
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+# Callback functions for different display types
+def interaction_callback(message=None, response=None, markdown=None, generation_time=None):
+    """Callback for display_interaction"""
+    logging.info(f"""
+    === INTERACTION ===
+    Time: {datetime.now()}
+    Generation Time: {generation_time}s
+    Message: {message}
+    Response: {response}
+    Markdown: {markdown}
+    """)
+
+def error_callback(message=None):
+    """Callback for display_error"""
+    logging.error(f"""
+    === ERROR ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def tool_call_callback(message=None):
+    """Callback for display_tool_call"""
+    logging.info(f"""
+    === TOOL CALL ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def instruction_callback(message=None):
+    """Callback for display_instruction"""
+    logging.info(f"""
+    === INSTRUCTION ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def self_reflection_callback(message=None):
+    """Callback for display_self_reflection"""
+    logging.info(f"""
+    === SELF REFLECTION ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def generating_callback(content=None, elapsed_time=None):
+    """Callback for display_generating"""
+    logging.info(f"""
+    === GENERATING ===
+    Time: {datetime.now()}
+    Content: {content}
+    Elapsed Time: {elapsed_time}
+    """)
+
+# Register all callbacks
+register_display_callback('interaction', interaction_callback)
+register_display_callback('error', error_callback)
+register_display_callback('tool_call', tool_call_callback)
+register_display_callback('instruction', instruction_callback)
+register_display_callback('self_reflection', self_reflection_callback)
+# register_display_callback('generating', generating_callback)
+
+def task_callback(output):
+    """Callback for task completion"""
+    logging.info(f"""
+    === TASK COMPLETED ===
+    Time: {datetime.now()}
+    Description: {output.description}
+    Agent: {output.agent}
+    Output: {output.raw[:200]}...
+    """)
+
+def internet_search_tool(query) -> list:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles and URLs.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
+def main():
+    # Create agents
+    researcher = Agent(
+        name="Researcher",
+        role="Senior Research Analyst",
+        goal="Uncover cutting-edge developments in AI and data science",
+        backstory="""You are an expert at a technology research group, 
+        skilled in identifying trends and analyzing complex data.""",
+        verbose=True,
+        allow_delegation=False,
+        tools=[internet_search_tool],
+        llm="gpt-4o",
+        markdown=True,
+        reflect_llm="gpt-4o",
+        min_reflect=2,
+        max_reflect=4
+    )
+    
+    writer = Agent(
+        name="Writer",
+        role="Tech Content Strategist",
+        goal="Craft compelling content on tech advancements",
+        backstory="""You are a content strategist known for 
+        making complex tech topics interesting and easy to understand.""",
+        verbose=True,
+        allow_delegation=True,
+        llm="gpt-4o",
+        tools=[],
+        markdown=True
+    )
+
+    # Create tasks with callbacks
+    task1 = Task(
+        name="research_task",
+        description="""Analyze 2024's AI advancements. 
+        Find major trends, new technologies, and their effects.""",
+        expected_output="""A detailed report on 2024 AI advancements""",
+        agent=researcher,
+        tools=[internet_search_tool],
+        callback=task_callback
+    )
+
+    task2 = Task(
+        name="writing_task",
+        description="""Create a blog post about major AI advancements using the insights you have.
+        Make it interesting, clear, and suited for tech enthusiasts. 
+        It should be at least 4 paragraphs long.""",
+        expected_output="A blog post of at least 4 paragraphs",
+        agent=writer,
+        context=[task1],
+        callback=task_callback,
+        tools=[]
+    )
+
+    task3 = Task(
+        name="json_task",
+        description="""Create a json object with a title of "My Task" and content of "My content".""",
+        expected_output="""JSON output with title and content""",
+        agent=researcher,
+        callback=task_callback
+    )
+
+    task4 = Task(
+        name="save_output_task",
+        description="""Save the AI blog post to a file""",
+        expected_output="""File saved successfully""",
+        agent=writer,
+        context=[task2],
+        output_file='test.txt',
+        create_directory=True,
+        callback=task_callback
+    )
+
+    # Create and run agents manager
+    agents = PraisonAIAgents(
+        agents=[researcher, writer],
+        tasks=[task1, task2, task3, task4],
+        verbose=True,
+        process="sequential",
+        manager_llm="gpt-4o"
+    )
+
+    agents.start()
+
+if __name__ == "__main__":
+    main()

agents/mini_agents_example.py

@@ -0,0 +1,6 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents.start()

agents/multimodal.py

@@ -0,0 +1,59 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Vision Analysis Agent
+vision_agent = Agent(
+    name="VisionAnalyst",
+    role="Computer Vision Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+    name="analyze_landmark",
+    description="Describe this famous landmark and its architectural features.",
+    expected_output="Detailed description of the landmark's architecture and significance",
+    agent=vision_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=vision_agent,
+    images=["image.jpg"] 
+)
+
+# 3. Task with Video File
+task3 = Task(
+    name="analyze_video",
+    description="""Watch this video and provide:
+    1. A summary of the main events
+    2. Key objects and people visible
+    3. Any text or important information shown
+    4. The overall context and setting""",
+    expected_output="Comprehensive analysis of the video content",
+    agent=vision_agent,
+    images=["video.mp4"]  
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[vision_agent],
+    tasks=[task1, task2, task3],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+result = agents.start()
+
+# Print results
+for task_id, task_result in result["task_results"].items():
+    print(f"\nTask {task_id} Result:")
+    print(task_result.raw)

agents/praisonaiagents/__init__.py

@@ -0,0 +1,47 @@
+"""
+Praison AI Agents - A package for hierarchical AI agent task execution
+"""
+
+from .agent.agent import Agent
+from .agents.agents import PraisonAIAgents
+from .task.task import Task
+from .tools.tools import Tools
+from .main import (
+    TaskOutput,
+    ReflectionOutput,
+    display_interaction,
+    display_self_reflection,
+    display_instruction,
+    display_tool_call,
+    display_error,
+    display_generating,
+    clean_triple_backticks,
+    error_logs,
+    register_display_callback,
+    sync_display_callbacks,
+    async_display_callbacks,
+)
+
+# Add Agents as an alias for PraisonAIAgents
+Agents = PraisonAIAgents
+
+__all__ = [
+    'Agent',
+    'PraisonAIAgents',
+    'Agents',
+    'Tools',
+    'Task',
+    'TaskOutput',
+    'ReflectionOutput',
+    'display_interaction',
+    'display_self_reflection',
+    'display_instruction',
+    'display_tool_call',
+    'display_error',
+    'display_generating',
+    'clean_triple_backticks',
+    'error_logs',
+    'register_display_callback',
+    'sync_display_callbacks',
+    'async_display_callbacks',
+] 

agents/praisonaiagents/agent/__init__.py

@@ -0,0 +1,4 @@
+"""Agent module for AI agents"""
+from .agent import Agent
+
+__all__ = ['Agent'] 

agents/praisonaiagents/agent/agent.py

@@ -0,0 +1,714 @@
+import os
+import time
+import json
+import logging
+import asyncio
+from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING
+from rich.console import Console
+from rich.live import Live
+from openai import AsyncOpenAI
+from ..main import (
+    display_error,
+    display_tool_call,
+    display_instruction,
+    display_interaction,
+    display_generating,
+    display_self_reflection,
+    ReflectionOutput,
+    client,
+    error_logs
+)
+
+if TYPE_CHECKING:
+    from ..task.task import Task
+
+class Agent:
+    def _generate_tool_definition(self, function_name):
+        """
+        Generate a tool definition from a function name by inspecting the function.
+        """
+        logging.debug(f"Attempting to generate tool definition for: {function_name}")
+        
+        # First try to get the tool definition if it exists
+        tool_def_name = f"{function_name}_definition"
+        tool_def = globals().get(tool_def_name)
+        logging.debug(f"Looking for {tool_def_name} in globals: {tool_def is not None}")
+        
+        if not tool_def:
+            import __main__
+            tool_def = getattr(__main__, tool_def_name, None)
+            logging.debug(f"Looking for {tool_def_name} in __main__: {tool_def is not None}")
+        
+        if tool_def:
+            logging.debug(f"Found tool definition: {tool_def}")
+            return tool_def
+
+        # Try to find the function in the agent's tools list first
+        func = None
+        for tool in self.tools:
+            if callable(tool) and getattr(tool, '__name__', '') == function_name:
+                func = tool
+                break
+        
+        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
+        
+        # If not found in tools, try globals and main
+        if not func:
+            func = globals().get(function_name)
+            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
+            
+            if not func:
+                import __main__
+                func = getattr(__main__, function_name, None)
+                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
+
+        if not func or not callable(func):
+            logging.debug(f"Function {function_name} not found or not callable")
+            return None
+
+        import inspect
+        sig = inspect.signature(func)
+        logging.debug(f"Function signature: {sig}")
+        
+        parameters = {
+            "type": "object",
+            "properties": {},
+            "required": []
+        }
+        
+        # Parse docstring for parameter descriptions
+        docstring = inspect.getdoc(func)
+        logging.debug(f"Function docstring: {docstring}")
+        
+        param_descriptions = {}
+        if docstring:
+            import re
+            param_section = re.split(r'\s*Args:\s*', docstring)
+            logging.debug(f"Param section split: {param_section}")
+            if len(param_section) > 1:
+                param_lines = param_section[1].split('\n')
+                for line in param_lines:
+                    line = line.strip()
+                    if line and ':' in line:
+                        param_name, param_desc = line.split(':', 1)
+                        param_descriptions[param_name.strip()] = param_desc.strip()
+        
+        logging.debug(f"Parameter descriptions: {param_descriptions}")
+
+        for name, param in sig.parameters.items():
+            param_type = "string"  # Default type
+            if param.annotation != inspect.Parameter.empty:
+                if param.annotation == int:
+                    param_type = "integer"
+                elif param.annotation == float:
+                    param_type = "number"
+                elif param.annotation == bool:
+                    param_type = "boolean"
+                elif param.annotation == list:
+                    param_type = "array"
+                elif param.annotation == dict:
+                    param_type = "object"
+            
+            param_info = {"type": param_type}
+            if name in param_descriptions:
+                param_info["description"] = param_descriptions[name]
+            
+            parameters["properties"][name] = param_info
+            if param.default == inspect.Parameter.empty:
+                parameters["required"].append(name)
+        
+        logging.debug(f"Generated parameters: {parameters}")
+
+        # Extract description from docstring
+        description = docstring.split('\n')[0] if docstring else f"Function {function_name}"
+        
+        tool_def = {
+            "type": "function",
+            "function": {
+                "name": function_name,
+                "description": description,
+                "parameters": parameters
+            }
+        }
+        logging.debug(f"Generated tool definition: {tool_def}")
+        return tool_def
+
+    def __init__(
+        self,
+        name: Optional[str] = None,
+        role: Optional[str] = None,
+        goal: Optional[str] = None,
+        backstory: Optional[str] = None,
+        instructions: Optional[str] = None,
+        llm: Optional[Union[str, Any]] = None,
+        tools: Optional[List[Any]] = None,
+        function_calling_llm: Optional[Any] = None,
+        max_iter: int = 20,
+        max_rpm: Optional[int] = None,
+        max_execution_time: Optional[int] = None,
+        memory: bool = True,
+        verbose: bool = True,
+        allow_delegation: bool = False,
+        step_callback: Optional[Any] = None,
+        cache: bool = True,
+        system_template: Optional[str] = None,
+        prompt_template: Optional[str] = None,
+        response_template: Optional[str] = None,
+        allow_code_execution: Optional[bool] = False,
+        max_retry_limit: int = 2,
+        respect_context_window: bool = True,
+        code_execution_mode: Literal["safe", "unsafe"] = "safe",
+        embedder_config: Optional[Dict[str, Any]] = None,
+        knowledge_sources: Optional[List[Any]] = None,
+        use_system_prompt: Optional[bool] = True,
+        markdown: bool = True,
+        self_reflect: Optional[bool] = None,
+        max_reflect: int = 3,
+        min_reflect: int = 1,
+        reflect_llm: Optional[str] = None
+    ):
+        # Handle backward compatibility for required fields
+        if all(x is None for x in [name, role, goal, backstory, instructions]):
+            raise ValueError("At least one of name, role, goal, backstory, or instructions must be provided")
+
+        # If instructions are provided, use them to set role, goal, and backstory
+        if instructions:
+            self.name = name or "Agent"
+            self.role = role or "Assistant"
+            self.goal = goal or instructions
+            self.backstory = backstory or instructions
+            # Set self_reflect to False by default for instruction-based agents
+            self.self_reflect = False if self_reflect is None else self_reflect
+        else:
+            # Use provided values or defaults
+            self.name = name or "Agent"
+            self.role = role or "Assistant"
+            self.goal = goal or "Help the user with their tasks"
+            self.backstory = backstory or "I am an AI assistant"
+            # Default to True for traditional agents if not specified
+            self.self_reflect = True if self_reflect is None else self_reflect
+        
+        self.instructions = instructions
+        # Check for model name in environment variable if not provided
+        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.tools = tools if tools else []  # Store original tools
+        self.function_calling_llm = function_calling_llm
+        self.max_iter = max_iter
+        self.max_rpm = max_rpm
+        self.max_execution_time = max_execution_time
+        self.memory = memory
+        self.verbose = verbose
+        self.allow_delegation = allow_delegation
+        self.step_callback = step_callback
+        self.cache = cache
+        self.system_template = system_template
+        self.prompt_template = prompt_template
+        self.response_template = response_template
+        self.allow_code_execution = allow_code_execution
+        self.max_retry_limit = max_retry_limit
+        self.respect_context_window = respect_context_window
+        self.code_execution_mode = code_execution_mode
+        self.embedder_config = embedder_config
+        self.knowledge_sources = knowledge_sources
+        self.use_system_prompt = use_system_prompt
+        self.chat_history = []
+        self.markdown = markdown
+        self.max_reflect = max_reflect
+        self.min_reflect = min_reflect
+        # Use the same model selection logic for reflect_llm
+        self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.console = Console()  # Create a single console instance for the agent
+        
+        # Initialize system prompt
+        self.system_prompt = f"""{self.backstory}\n
+Your Role: {self.role}\n
+Your Goal: {self.goal}
+        """
+
+    def generate_task(self) -> 'Task':
+        """Generate a Task object from the agent's instructions"""
+        from ..task.task import Task
+        
+        description = self.instructions if self.instructions else f"Execute task as {self.role} with goal: {self.goal}"
+        expected_output = "Complete the assigned task successfully"
+        
+        return Task(
+            name=self.name,
+            description=description,
+            expected_output=expected_output,
+            agent=self,
+            tools=self.tools
+        )
+
+    def execute_tool(self, function_name, arguments):
+        """
+        Execute a tool dynamically based on the function name and arguments.
+        """
+        logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
+
+        # Try to find the function in the agent's tools list first
+        func = None
+        for tool in self.tools:
+            if callable(tool) and getattr(tool, '__name__', '') == function_name:
+                func = tool
+                break
+        
+        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
+        
+        # If not found in tools, try globals and main
+        if not func:
+            func = globals().get(function_name)
+            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
+            
+            if not func:
+                import __main__
+                func = getattr(__main__, function_name, None)
+                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
+
+        if func and callable(func):
+            try:
+                return func(**arguments)
+            except Exception as e:
+                error_msg = str(e)
+                logging.error(f"Error executing tool {function_name}: {error_msg}")
+                return {"error": error_msg}
+        
+        error_msg = f"Tool '{function_name}' is not callable"
+        logging.error(error_msg)
+        return {"error": error_msg}
+
+    def clear_history(self):
+        self.chat_history = []
+
+    def __str__(self):
+        return f"Agent(name='{self.name}', role='{self.role}', goal='{self.goal}')"
+
+    def _chat_completion(self, messages, temperature=0.2, tools=None, stream=True):
+        start_time = time.time()
+        logging.debug(f"{self.name} sending messages to LLM: {messages}")
+
+        formatted_tools = []
+        if tools is None:
+            tools = self.tools
+        if tools:
+            for tool in tools:
+                if isinstance(tool, str):
+                    # Generate tool definition for string tool names
+                    tool_def = self._generate_tool_definition(tool)
+                    if tool_def:
+                        formatted_tools.append(tool_def)
+                    else:
+                        logging.warning(f"Could not generate definition for tool: {tool}")
+                elif isinstance(tool, dict):
+                    formatted_tools.append(tool)
+                elif hasattr(tool, "to_openai_tool"):
+                    formatted_tools.append(tool.to_openai_tool())
+                elif callable(tool):
+                    formatted_tools.append(self._generate_tool_definition(tool.__name__))
+                else:
+                    logging.warning(f"Tool {tool} not recognized")
+
+        try:
+            initial_response = client.chat.completions.create(
+                model=self.llm,
+                messages=messages,
+                temperature=temperature,
+                tools=formatted_tools if formatted_tools else None,
+                stream=False
+            )
+
+            tool_calls = getattr(initial_response.choices[0].message, 'tool_calls', None)
+
+            if tool_calls:
+                messages.append({
+                    "role": "assistant",
+                    "content": initial_response.choices[0].message.content,
+                    "tool_calls": tool_calls
+                })
+
+                for tool_call in tool_calls:
+                    function_name = tool_call.function.name
+                    arguments = json.loads(tool_call.function.arguments)
+
+                    if self.verbose:
+                        display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}")
+
+                    tool_result = self.execute_tool(function_name, arguments)
+                    results_str = json.dumps(tool_result) if tool_result else "Function returned an empty output"
+
+                    if self.verbose:
+                        display_tool_call(f"Function '{function_name}' returned: {results_str}")
+
+                    messages.append({
+                        "role": "tool",
+                        "tool_call_id": tool_call.id,
+                        "content": results_str
+                    })
+
+            if stream:
+                response_stream = client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    stream=True
+                )
+                full_response_text = ""
+                
+                # Create Live display with proper configuration
+                with Live(
+                    display_generating("", start_time),
+                    console=self.console,
+                    refresh_per_second=4,
+                    transient=False,  # Changed to False to preserve output
+                    vertical_overflow="ellipsis",
+                    auto_refresh=True
+                ) as live:
+                    for chunk in response_stream:
+                        if chunk.choices[0].delta.content:
+                            full_response_text += chunk.choices[0].delta.content
+                            live.update(display_generating(full_response_text, start_time))
+                
+                # Clear the last generating display with a blank line
+                self.console.print()
+                
+                final_response = client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    stream=False
+                )
+                return final_response
+            else:
+                if tool_calls:
+                    final_response = client.chat.completions.create(
+                        model=self.llm,
+                        messages=messages,
+                        temperature=temperature,
+                        stream=False
+                    )
+                    return final_response
+                else:
+                    return initial_response
+
+        except Exception as e:
+            display_error(f"Error in chat completion: {e}")
+            return None
+
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+        if self.use_system_prompt:
+            system_prompt = f"""{self.backstory}\n
+Your Role: {self.role}\n
+Your Goal: {self.goal}
+            """
+            if output_json:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
+        else:
+            system_prompt = None
+
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.extend(self.chat_history)
+
+        # Modify prompt if output_json or output_pydantic is specified
+        original_prompt = prompt
+        if output_json or output_pydantic:
+            if isinstance(prompt, str):
+                prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
+            elif isinstance(prompt, list):
+                # For multimodal prompts, append to the text content
+                for item in prompt:
+                    if item["type"] == "text":
+                        item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                        break
+
+        if isinstance(prompt, list):
+            # If we receive a multimodal prompt list, place it directly in the user message
+            messages.append({"role": "user", "content": prompt})
+        else:
+            messages.append({"role": "user", "content": prompt})
+
+        final_response_text = None
+        reflection_count = 0
+        start_time = time.time()
+
+        while True:
+            try:
+                if self.verbose:
+                    # Handle both string and list prompts for instruction display
+                    display_text = prompt
+                    if isinstance(prompt, list):
+                        # Extract text content from multimodal prompt
+                        display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                    
+                    if display_text and str(display_text).strip():
+                        display_instruction(f"Agent {self.name} is processing prompt: {display_text}", console=self.console)
+
+                response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
+                if not response:
+                    return None
+
+                tool_calls = getattr(response.choices[0].message, 'tool_calls', None)
+                response_text = response.choices[0].message.content.strip()
+
+                if tool_calls:
+                    messages.append({
+                        "role": "assistant",
+                        "content": response_text,
+                        "tool_calls": tool_calls
+                    })
+                    
+                    for tool_call in tool_calls:
+                        function_name = tool_call.function.name
+                        arguments = json.loads(tool_call.function.arguments)
+
+                        if self.verbose:
+                            display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}", console=self.console)
+
+                        tool_result = self.execute_tool(function_name, arguments)
+
+                        if tool_result:
+                            if self.verbose:
+                                display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=self.console)
+                            messages.append({
+                                "role": "tool",
+                                "tool_call_id": tool_call.id,
+                                "content": json.dumps(tool_result)
+                            })
+                        else:
+                            messages.append({
+                                "role": "tool",
+                                "tool_call_id": tool_call.id,
+                                "content": "Function returned an empty output"
+                            })
+                        
+                    response = self._chat_completion(messages, temperature=temperature)
+                    if not response:
+                        return None
+                    response_text = response.choices[0].message.content.strip()
+
+                # Handle output_json or output_pydantic if specified
+                if output_json or output_pydantic:
+                    # Add to chat history and return raw response
+                    self.chat_history.append({"role": "user", "content": original_prompt})
+                    self.chat_history.append({"role": "assistant", "content": response_text})
+                    if self.verbose:
+                        display_interaction(original_prompt, response_text, markdown=self.markdown, 
+                                         generation_time=time.time() - start_time, console=self.console)
+                    return response_text
+
+                if not self.self_reflect:
+                    self.chat_history.append({"role": "user", "content": original_prompt})
+                    self.chat_history.append({"role": "assistant", "content": response_text})
+                    if self.verbose:
+                        logging.info(f"Agent {self.name} final response: {response_text}")
+                    display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                    return response_text
+
+                reflection_prompt = f"""
+Reflect on your previous response: '{response_text}'.
+Identify any flaws, improvements, or actions.
+Provide a "satisfactory" status ('yes' or 'no').
+Output MUST be JSON with 'reflection' and 'satisfactory'.
+                """
+                logging.debug(f"{self.name} reflection attempt {reflection_count+1}, sending prompt: {reflection_prompt}")
+                messages.append({"role": "user", "content": reflection_prompt})
+
+                try:
+                    reflection_response = client.beta.chat.completions.parse(
+                        model=self.reflect_llm if self.reflect_llm else self.llm,
+                        messages=messages,
+                        temperature=temperature,
+                        response_format=ReflectionOutput
+                    )
+
+                    reflection_output = reflection_response.choices[0].message.parsed
+
+                    if self.verbose:
+                        display_self_reflection(f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
+
+                    messages.append({"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"})
+
+                    # Only consider satisfactory after minimum reflections
+                    if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
+                        if self.verbose:
+                            display_self_reflection("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
+                        self.chat_history.append({"role": "user", "content": prompt})
+                        self.chat_history.append({"role": "assistant", "content": response_text})
+                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                        return response_text
+
+                    # Check if we've hit max reflections
+                    if reflection_count >= self.max_reflect - 1:
+                        if self.verbose:
+                            display_self_reflection("Maximum reflection count reached, returning current response", console=self.console)
+                        self.chat_history.append({"role": "user", "content": prompt})
+                        self.chat_history.append({"role": "assistant", "content": response_text})
+                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                        return response_text
+
+                    logging.debug(f"{self.name} reflection count {reflection_count + 1}, continuing reflection process")
+                    messages.append({"role": "user", "content": "Now regenerate your response using the reflection you made"})
+                    response = self._chat_completion(messages, temperature=temperature, tools=None, stream=True)
+                    response_text = response.choices[0].message.content.strip()
+                    reflection_count += 1
+                    continue  # Continue the loop for more reflections
+
+                except Exception as e:
+                    display_error(f"Error in parsing self-reflection json {e}. Retrying", console=self.console)
+                    logging.error("Reflection parsing failed.", exc_info=True)
+                    messages.append({"role": "assistant", "content": f"Self Reflection failed."})
+                    reflection_count += 1
+                    continue  # Continue even after error to try again
+                
+            except Exception as e:
+                display_error(f"Error in chat: {e}", console=self.console)
+                return None 
+
+    def clean_json_output(self, output: str) -> str:
+        """Clean and extract JSON from response text."""
+        cleaned = output.strip()
+        # Remove markdown code blocks if present
+        if cleaned.startswith("```json"):
+            cleaned = cleaned[len("```json"):].strip()
+        if cleaned.startswith("```"):
+            cleaned = cleaned[len("```"):].strip()
+        if cleaned.endswith("```"):
+            cleaned = cleaned[:-3].strip()
+        return cleaned 
+
+    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+        """Async version of chat method"""
+        try:
+            # Build system prompt
+            system_prompt = self.system_prompt
+            if output_json:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
+
+            # Build messages
+            if isinstance(prompt, str):
+                messages = [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if (output_json or output_pydantic) else "")}
+                ]
+            else:
+                # For multimodal prompts
+                messages = [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt}
+                ]
+                if output_json or output_pydantic:
+                    # Add JSON instruction to text content
+                    for item in messages[-1]["content"]:
+                        if item["type"] == "text":
+                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                            break
+
+            # Format tools if provided
+            formatted_tools = []
+            if tools:
+                for tool in tools:
+                    if isinstance(tool, str):
+                        tool_def = self._generate_tool_definition(tool)
+                        if tool_def:
+                            formatted_tools.append(tool_def)
+                    elif isinstance(tool, dict):
+                        formatted_tools.append(tool)
+                    elif hasattr(tool, "to_openai_tool"):
+                        formatted_tools.append(tool.to_openai_tool())
+                    elif callable(tool):
+                        formatted_tools.append(self._generate_tool_definition(tool.__name__))
+
+            # Create async OpenAI client
+            async_client = AsyncOpenAI()
+
+            # Make the API call based on the type of request
+            if tools:
+                response = await async_client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    tools=formatted_tools
+                )
+                return await self._achat_completion(response, tools)
+            elif output_json or output_pydantic:
+                response = await async_client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    response_format={"type": "json_object"}
+                )
+                # Return the raw response
+                return response.choices[0].message.content
+            else:
+                response = await async_client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature
+                )
+                return response.choices[0].message.content
+        except Exception as e:
+            display_error(f"Error in chat completion: {e}")
+            return None
+
+    async def _achat_completion(self, response, tools):
+        """Async version of _chat_completion method"""
+        try:
+            message = response.choices[0].message
+            if not hasattr(message, 'tool_calls') or not message.tool_calls:
+                return message.content
+
+            results = []
+            for tool_call in message.tool_calls:
+                try:
+                    function_name = tool_call.function.name
+                    arguments = json.loads(tool_call.function.arguments)
+                    
+                    # Find the matching tool
+                    tool = next((t for t in tools if t.__name__ == function_name), None)
+                    if not tool:
+                        display_error(f"Tool {function_name} not found")
+                        continue
+                    
+                    # Check if the tool is async
+                    if asyncio.iscoroutinefunction(tool):
+                        result = await tool(**arguments)
+                    else:
+                        # Run sync function in executor to avoid blocking
+                        loop = asyncio.get_event_loop()
+                        result = await loop.run_in_executor(None, lambda: tool(**arguments))
+                    
+                    results.append(result)
+                except Exception as e:
+                    display_error(f"Error executing tool {function_name}: {e}")
+                    results.append(None)
+
+            # If we have results, format them into a response
+            if results:
+                formatted_results = "\n".join([str(r) for r in results if r is not None])
+                if formatted_results:
+                    messages = [
+                        {"role": "system", "content": self.system_prompt},
+                        {"role": "assistant", "content": "Here are the tool results:"},
+                        {"role": "user", "content": formatted_results + "\nPlease process these results and provide a final response."}
+                    ]
+                    try:
+                        async_client = AsyncOpenAI()
+                        final_response = await async_client.chat.completions.create(
+                            model=self.llm,
+                            messages=messages,
+                            temperature=0.2
+                        )
+                        return final_response.choices[0].message.content
+                    except Exception as e:
+                        display_error(f"Error in final chat completion: {e}")
+                        return formatted_results
+                return formatted_results
+            return None
+        except Exception as e:
+            display_error(f"Error in _achat_completion: {e}")
+            return None 

agents/praisonaiagents/agents/__init__.py

@@ -0,0 +1,4 @@
+"""Agents module for managing multiple AI agents"""
+from .agents import PraisonAIAgents
+
+__all__ = ['PraisonAIAgents'] 

agents/praisonaiagents/agents/agents.py

@@ -0,0 +1,568 @@
+import os
+import time
+import json
+import logging
+from typing import Any, Dict, Optional, List
+from pydantic import BaseModel
+from rich.text import Text
+from rich.panel import Panel
+from rich.console import Console
+from ..main import display_error, TaskOutput, error_logs, client
+from ..agent.agent import Agent
+from ..task.task import Task
+from ..process.process import Process, LoopItems
+import asyncio
+
+def encode_file_to_base64(file_path: str) -> str:
+    """Base64-encode a file."""
+    import base64
+    with open(file_path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+def process_video(video_path: str, seconds_per_frame=2):
+    """Split video into frames (base64-encoded)."""
+    import cv2
+    import base64
+    base64_frames = []
+    video = cv2.VideoCapture(video_path)
+    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video.get(cv2.CAP_PROP_FPS)
+    frames_to_skip = int(fps * seconds_per_frame)
+    curr_frame = 0
+    while curr_frame < total_frames:
+        video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
+        success, frame = video.read()
+        if not success:
+            break
+        _, buffer = cv2.imencode(".jpg", frame)
+        base64_frames.append(base64.b64encode(buffer).decode("utf-8"))
+        curr_frame += frames_to_skip
+    video.release()
+    return base64_frames
+
+class PraisonAIAgents:
+    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
+        if not agents:
+            raise ValueError("At least one agent must be provided")
+            
+        self.agents = agents
+        self.tasks = {}
+        if max_retries < 3:
+            max_retries = 3
+        self.completion_checker = completion_checker if completion_checker else self.default_completion_checker
+        self.task_id_counter = 0
+        self.verbose = verbose
+        self.max_retries = max_retries
+        self.process = process
+        
+        # Check for manager_llm in environment variable if not provided
+        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        if self.verbose:
+            logging.info(f"Using model {self.manager_llm} for manager")
+        
+        # If no tasks provided, generate them from agents
+        if tasks is None:
+            tasks = []
+            for agent in self.agents:
+                task = agent.generate_task()
+                tasks.append(task)
+            logging.info(f"Auto-generated {len(tasks)} tasks from agents")
+        else:
+            # Validate tasks for backward compatibility
+            if not tasks:
+                raise ValueError("If tasks are provided, at least one task must be present")
+            logging.info(f"Using {len(tasks)} provided tasks")
+        
+        # Add tasks and set their status
+        for task in tasks:
+            self.add_task(task)
+            task.status = "not started"
+            
+        # If tasks were auto-generated from agents or process is sequential, set up sequential flow
+        if len(tasks) > 1 and (process == "sequential" or all(task.next_tasks == [] for task in tasks)):
+            for i in range(len(tasks) - 1):
+                # Set up next task relationship
+                tasks[i].next_tasks = [tasks[i + 1].name]
+                # Set up context for the next task to include the current task
+                if tasks[i + 1].context is None:
+                    tasks[i + 1].context = []
+                tasks[i + 1].context.append(tasks[i])
+            logging.info("Set up sequential flow with automatic context passing")
+        
+        self._state = {}  # Add state storage at PraisonAIAgents level
+
+    def add_task(self, task):
+        task_id = self.task_id_counter
+        task.id = task_id
+        self.tasks[task_id] = task
+        self.task_id_counter += 1
+        return task_id
+
+    def clean_json_output(self, output: str) -> str:
+        cleaned = output.strip()
+        if cleaned.startswith("```json"):
+            cleaned = cleaned[len("```json"):].strip()
+        if cleaned.startswith("```"):
+            cleaned = cleaned[len("```"):].strip()
+        if cleaned.endswith("```"):
+            cleaned = cleaned[:-3].strip()
+        return cleaned
+
+    def default_completion_checker(self, task, agent_output):
+        if task.output_json and task.result and task.result.json_dict:
+            return True
+        if task.output_pydantic and task.result and task.result.pydantic:
+            return True
+        return len(agent_output.strip()) > 0
+
+    async def aexecute_task(self, task_id):
+        """Async version of execute_task method"""
+        if task_id not in self.tasks:
+            display_error(f"Error: Task with ID {task_id} does not exist")
+            return
+        task = self.tasks[task_id]
+        
+        # Only import multimodal dependencies if task has images
+        if task.images and task.status == "not started":
+            try:
+                import cv2
+                import base64
+                from moviepy import VideoFileClip
+            except ImportError as e:
+                display_error(f"Error: Missing required dependencies for image/video processing: {e}")
+                display_error("Please install with: pip install opencv-python moviepy")
+                task.status = "failed"
+                return None
+
+        if task.status == "not started":
+            task.status = "in progress"
+
+        executor_agent = task.agent
+
+        task_prompt = f"""
+You need to do the following task: {task.description}.
+Expected Output: {task.expected_output}.
+        """
+        if task.context:
+            context_results = ""
+            for context_task in task.context:
+                if context_task.result:
+                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
+                else:
+                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
+            task_prompt += f"""
+Here are the results of previous tasks that might be useful:\n
+{context_results}
+"""
+        task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
+
+        if self.verbose >= 2:
+            logging.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logging.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+
+        if task.images:
+            def _get_multimodal_message(text_prompt, images):
+                content = [{"type": "text", "text": text_prompt}]
+
+                for img in images:
+                    # If local file path for a valid image
+                    if os.path.exists(img):
+                        ext = os.path.splitext(img)[1].lower()
+                        # If it's a .mp4, convert to frames
+                        if ext == ".mp4":
+                            frames = process_video(img, seconds_per_frame=1)
+                            content.append({"type": "text", "text": "These are frames from the video."})
+                            for f in frames:
+                                content.append({
+                                    "type": "image_url",
+                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
+                                })
+                        else:
+                            encoded = encode_file_to_base64(img)
+                            content.append({
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
+                                }
+                            })
+                    else:
+                        # Treat as a remote URL
+                        content.append({
+                            "type": "image_url",
+                            "image_url": {"url": img}
+                        })
+                return content
+
+            agent_output = await executor_agent.achat(
+                _get_multimodal_message(task_prompt, task.images),
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+        else:
+            agent_output = await executor_agent.achat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+
+        if agent_output:
+            task_output = TaskOutput(
+                description=task.description,
+                summary=task.description[:10],
+                raw=agent_output,
+                agent=executor_agent.name,
+                output_format="RAW"
+            )
+
+            if task.output_json:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    task_output.json_dict = parsed
+                    task_output.output_format = "JSON"
+                except:
+                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+
+            if task.output_pydantic:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    pyd_obj = task.output_pydantic(**parsed)
+                    task_output.pydantic = pyd_obj
+                    task_output.output_format = "Pydantic"
+                except:
+                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+
+            task.result = task_output
+            return task_output
+        else:
+            task.status = "failed"
+            return None
+
+    async def arun_task(self, task_id):
+        """Async version of run_task method"""
+        if task_id not in self.tasks:
+            display_error(f"Error: Task with ID {task_id} does not exist")
+            return
+        task = self.tasks[task_id]
+        if task.status == "completed":
+            logging.info(f"Task with ID {task_id} is already completed")
+            return
+
+        retries = 0
+        while task.status != "completed" and retries < self.max_retries:
+            logging.debug(f"Attempt {retries+1} for task {task_id}")
+            if task.status in ["not started", "in progress"]:
+                task_output = await self.aexecute_task(task_id)
+                if task_output and self.completion_checker(task, task_output.raw):
+                    task.status = "completed"
+                    if task.callback:
+                        await task.execute_callback(task_output)
+                    self.save_output_to_file(task, task_output)
+                    if self.verbose >= 1:
+                        logging.info(f"Task {task_id} completed successfully.")
+                else:
+                    task.status = "in progress"
+                    if self.verbose >= 1:
+                        logging.info(f"Task {task_id} not completed, retrying")
+                    await asyncio.sleep(1)
+                    retries += 1
+            else:
+                if task.status == "failed":
+                    logging.info("Task is failed, resetting to in-progress for another try...")
+                    task.status = "in progress"
+                else:
+                    logging.info("Invalid Task status")
+                    break
+
+        if retries == self.max_retries and task.status != "completed":
+            logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
+
+    async def arun_all_tasks(self):
+        """Async version of run_all_tasks method"""
+        process = Process(
+            tasks=self.tasks,
+            agents=self.agents,
+            manager_llm=self.manager_llm,
+            verbose=self.verbose
+        )
+        
+        if self.process == "workflow":
+            async for task_id in process.aworkflow():
+                if self.tasks[task_id].async_execution:
+                    await self.arun_task(task_id)
+                else:
+                    self.run_task(task_id)
+        elif self.process == "sequential":
+            async for task_id in process.asequential():
+                if self.tasks[task_id].async_execution:
+                    await self.arun_task(task_id)
+                else:
+                    self.run_task(task_id)
+        elif self.process == "hierarchical":
+            async for task_id in process.ahierarchical():
+                if isinstance(task_id, Task):
+                    task_id = self.add_task(task_id)
+                if self.tasks[task_id].async_execution:
+                    await self.arun_task(task_id)
+                else:
+                    self.run_task(task_id)
+
+    async def astart(self):
+        """Async version of start method"""
+        await self.arun_all_tasks()
+        return {
+            "task_status": self.get_all_tasks_status(),
+            "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
+        }
+
+    def save_output_to_file(self, task, task_output):
+        if task.output_file:
+            try:
+                if task.create_directory:
+                    os.makedirs(os.path.dirname(task.output_file), exist_ok=True)
+                with open(task.output_file, "w") as f:
+                    f.write(str(task_output))
+                if self.verbose >= 1:
+                    logging.info(f"Task output saved to {task.output_file}")
+            except Exception as e:
+                display_error(f"Error saving task output to file: {e}")
+
+    def execute_task(self, task_id):
+        """Synchronous version of execute_task method"""
+        if task_id not in self.tasks:
+            display_error(f"Error: Task with ID {task_id} does not exist")
+            return
+        task = self.tasks[task_id]
+        
+        # Only import multimodal dependencies if task has images
+        if task.images and task.status == "not started":
+            try:
+                import cv2
+                import base64
+                from moviepy import VideoFileClip
+            except ImportError as e:
+                display_error(f"Error: Missing required dependencies for image/video processing: {e}")
+                display_error("Please install with: pip install opencv-python moviepy")
+                task.status = "failed"
+                return None
+
+        if task.status == "not started":
+            task.status = "in progress"
+
+        executor_agent = task.agent
+
+        task_prompt = f"""
+You need to do the following task: {task.description}.
+Expected Output: {task.expected_output}.
+"""
+        if task.context:
+            context_results = ""
+            for context_task in task.context:
+                if context_task.result:
+                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
+                else:
+                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
+            task_prompt += f"""
+Here are the results of previous tasks that might be useful:\n
+{context_results}
+"""
+        task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
+
+        if self.verbose >= 2:
+            logging.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logging.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+
+        if task.images:
+            def _get_multimodal_message(text_prompt, images):
+                content = [{"type": "text", "text": text_prompt}]
+
+                for img in images:
+                    # If local file path for a valid image
+                    if os.path.exists(img):
+                        ext = os.path.splitext(img)[1].lower()
+                        # If it's a .mp4, convert to frames
+                        if ext == ".mp4":
+                            frames = process_video(img, seconds_per_frame=1)
+                            content.append({"type": "text", "text": "These are frames from the video."})
+                            for f in frames:
+                                content.append({
+                                    "type": "image_url",
+                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
+                                })
+                        else:
+                            encoded = encode_file_to_base64(img)
+                            content.append({
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
+                                }
+                            })
+                    else:
+                        # Treat as a remote URL
+                        content.append({
+                            "type": "image_url",
+                            "image_url": {"url": img}
+                        })
+                return content
+
+            agent_output = executor_agent.chat(
+                _get_multimodal_message(task_prompt, task.images),
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+        else:
+            agent_output = executor_agent.chat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+
+        if agent_output:
+            task_output = TaskOutput(
+                description=task.description,
+                summary=task.description[:10],
+                raw=agent_output,
+                agent=executor_agent.name,
+                output_format="RAW"
+            )
+
+            if task.output_json:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    task_output.json_dict = parsed
+                    task_output.output_format = "JSON"
+                except:
+                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+
+            if task.output_pydantic:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    pyd_obj = task.output_pydantic(**parsed)
+                    task_output.pydantic = pyd_obj
+                    task_output.output_format = "Pydantic"
+                except:
+                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+
+            task.result = task_output
+            return task_output
+        else:
+            task.status = "failed"
+            return None
+
+    def run_task(self, task_id):
+        """Synchronous version of run_task method"""
+        if task_id not in self.tasks:
+            display_error(f"Error: Task with ID {task_id} does not exist")
+            return
+        task = self.tasks[task_id]
+        if task.status == "completed":
+            logging.info(f"Task with ID {task_id} is already completed")
+            return
+
+        retries = 0
+        while task.status != "completed" and retries < self.max_retries:
+            logging.debug(f"Attempt {retries+1} for task {task_id}")
+            if task.status in ["not started", "in progress"]:
+                task_output = self.execute_task(task_id)
+                if task_output and self.completion_checker(task, task_output.raw):
+                    task.status = "completed"
+                    if task.callback:
+                        task.callback(task_output)
+                    self.save_output_to_file(task, task_output)
+                    if self.verbose >= 1:
+                        logging.info(f"Task {task_id} completed successfully.")
+                else:
+                    task.status = "in progress"
+                    if self.verbose >= 1:
+                        logging.info(f"Task {task_id} not completed, retrying")
+                    time.sleep(1)
+                    retries += 1
+            else:
+                if task.status == "failed":
+                    logging.info("Task is failed, resetting to in-progress for another try...")
+                    task.status = "in progress"
+                else:
+                    logging.info("Invalid Task status")
+                    break
+
+        if retries == self.max_retries and task.status != "completed":
+            logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
+
+    def run_all_tasks(self):
+        """Synchronous version of run_all_tasks method"""
+        process = Process(
+            tasks=self.tasks,
+            agents=self.agents,
+            manager_llm=self.manager_llm,
+            verbose=self.verbose
+        )
+        
+        if self.process == "workflow":
+            for task_id in process.workflow():
+                self.run_task(task_id)
+        elif self.process == "sequential":
+            for task_id in process.sequential():
+                self.run_task(task_id)
+        elif self.process == "hierarchical":
+            for task_id in process.hierarchical():
+                if isinstance(task_id, Task):
+                    task_id = self.add_task(task_id)
+                self.run_task(task_id)
+
+    def get_task_status(self, task_id):
+        if task_id in self.tasks:
+            return self.tasks[task_id].status
+        return None
+
+    def get_all_tasks_status(self):
+        return {task_id: self.tasks[task_id].status for task_id in self.tasks}
+
+    def get_task_result(self, task_id):
+        if task_id in self.tasks:
+            return self.tasks[task_id].result
+        return None
+
+    def get_task_details(self, task_id):
+        if task_id in self.tasks:
+            return str(self.tasks[task_id])
+        return None
+
+    def get_agent_details(self, agent_name):
+        agent = [task.agent for task in self.tasks.values() if task.agent and task.agent.name == agent_name]
+        if agent:
+            return str(agent[0])
+        return None
+
+    def start(self):
+        self.run_all_tasks()
+        return {
+            "task_status": self.get_all_tasks_status(),
+            "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
+        } 
+
+    def set_state(self, key: str, value: Any) -> None:
+        """Set a state value"""
+        self._state[key] = value
+
+    def get_state(self, key: str, default: Any = None) -> Any:
+        """Get a state value"""
+        return self._state.get(key, default)
+
+    def update_state(self, updates: Dict) -> None:
+        """Update multiple state values"""
+        self._state.update(updates)
+
+    def clear_state(self) -> None:
+        """Clear all state values"""
+        self._state.clear() 

agents/praisonaiagents/main.py

@@ -0,0 +1,349 @@
+import os
+import time
+import json
+import logging
+from typing import List, Optional, Dict, Any, Union, Literal, Type
+from openai import OpenAI
+from pydantic import BaseModel
+from rich import print
+from rich.console import Console
+from rich.panel import Panel
+from rich.text import Text
+from rich.markdown import Markdown
+from rich.logging import RichHandler
+from rich.live import Live
+import asyncio
+
+LOGLEVEL = os.environ.get('LOGLEVEL', 'INFO').upper()
+
+logging.basicConfig(
+    level=getattr(logging, LOGLEVEL, logging.INFO),
+    format="%(asctime)s %(filename)s:%(lineno)d %(levelname)s %(message)s",
+    datefmt="[%X]",
+    handlers=[RichHandler(rich_tracebacks=True)]
+)
+
+# Global list to store error logs
+error_logs = []
+
+# Separate registries for sync and async callbacks
+sync_display_callbacks = {}
+async_display_callbacks = {}
+
+# At the top of the file, add display_callbacks to __all__
+__all__ = [
+    'error_logs',
+    'register_display_callback',
+    'sync_display_callbacks',
+    'async_display_callbacks',
+    # ... other exports
+]
+
+def register_display_callback(display_type: str, callback_fn, is_async: bool = False):
+    """Register a synchronous or asynchronous callback function for a specific display type.
+    
+    Args:
+        display_type (str): Type of display event ('interaction', 'self_reflection', etc.)
+        callback_fn: The callback function to register
+        is_async (bool): Whether the callback is asynchronous
+    """
+    if is_async:
+        async_display_callbacks[display_type] = callback_fn
+    else:
+        sync_display_callbacks[display_type] = callback_fn
+
+async def execute_callback(display_type: str, **kwargs):
+    """Execute both sync and async callbacks for a given display type.
+    
+    Args:
+        display_type (str): Type of display event
+        **kwargs: Arguments to pass to the callback functions
+    """
+    # Execute synchronous callback if registered
+    if display_type in sync_display_callbacks:
+        callback = sync_display_callbacks[display_type]
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, lambda: callback(**kwargs))
+    
+    # Execute asynchronous callback if registered
+    if display_type in async_display_callbacks:
+        callback = async_display_callbacks[display_type]
+        await callback(**kwargs)
+
+def _clean_display_content(content: str, max_length: int = 20000) -> str:
+    """Helper function to clean and truncate content for display."""
+    if not content or not str(content).strip():
+        return ""
+        
+    content = str(content)
+    # Handle base64 content
+    if "base64" in content:
+        content_parts = []
+        for line in content.split('\n'):
+            if "base64" not in line:
+                content_parts.append(line)
+        content = '\n'.join(content_parts)
+    
+    # Truncate if too long
+    if len(content) > max_length:
+        content = content[:max_length] + "..."
+    
+    return content.strip()
+
+def display_interaction(message, response, markdown=True, generation_time=None, console=None):
+    """Synchronous version of display_interaction."""
+    if console is None:
+        console = Console()
+    
+    if isinstance(message, list):
+        text_content = next((item["text"] for item in message if item["type"] == "text"), "")
+        message = text_content
+
+    message = _clean_display_content(str(message))
+    response = _clean_display_content(str(response))
+
+    # Execute synchronous callback if registered
+    if 'interaction' in sync_display_callbacks:
+        sync_display_callbacks['interaction'](
+            message=message,
+            response=response,
+            markdown=markdown,
+            generation_time=generation_time
+        )
+
+    # Rest of the display logic...
+    if generation_time:
+        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
+
+    if markdown:
+        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
+    else:
+        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
+
+def display_self_reflection(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'self_reflection' in sync_display_callbacks:
+        sync_display_callbacks['self_reflection'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
+
+def display_instruction(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'instruction' in sync_display_callbacks:
+        sync_display_callbacks['instruction'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+
+def display_tool_call(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'tool_call' in sync_display_callbacks:
+        sync_display_callbacks['tool_call'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
+
+def display_error(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'error' in sync_display_callbacks:
+        sync_display_callbacks['error'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
+    error_logs.append(message)
+
+def display_generating(content: str = "", start_time: Optional[float] = None):
+    if not content or not str(content).strip():
+        return Panel("", title="", border_style="green")
+    
+    elapsed_str = ""
+    if start_time is not None:
+        elapsed = time.time() - start_time
+        elapsed_str = f" {elapsed:.1f}s"
+    
+    content = _clean_display_content(str(content))
+    
+    # Execute callback if registered
+    if 'generating' in sync_display_callbacks:
+        sync_display_callbacks['generating'](
+            content=content,
+            elapsed_time=elapsed_str.strip() if elapsed_str else None
+        )
+    
+    return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style="green")
+
+# Async versions with 'a' prefix
+async def adisplay_interaction(message, response, markdown=True, generation_time=None, console=None):
+    """Async version of display_interaction."""
+    if console is None:
+        console = Console()
+    
+    if isinstance(message, list):
+        text_content = next((item["text"] for item in message if item["type"] == "text"), "")
+        message = text_content
+
+    message = _clean_display_content(str(message))
+    response = _clean_display_content(str(response))
+
+    # Execute callbacks
+    await execute_callback(
+        'interaction',
+        message=message,
+        response=response,
+        markdown=markdown,
+        generation_time=generation_time
+    )
+
+    # Rest of the display logic...
+    if generation_time:
+        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
+
+    if markdown:
+        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
+    else:
+        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
+
+async def adisplay_self_reflection(message: str, console=None):
+    """Async version of display_self_reflection."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'self_reflection' in async_display_callbacks:
+        await async_display_callbacks['self_reflection'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
+
+async def adisplay_instruction(message: str, console=None):
+    """Async version of display_instruction."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'instruction' in async_display_callbacks:
+        await async_display_callbacks['instruction'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+
+async def adisplay_tool_call(message: str, console=None):
+    """Async version of display_tool_call."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'tool_call' in async_display_callbacks:
+        await async_display_callbacks['tool_call'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
+
+async def adisplay_error(message: str, console=None):
+    """Async version of display_error."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'error' in async_display_callbacks:
+        await async_display_callbacks['error'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
+    error_logs.append(message)
+
+async def adisplay_generating(content: str = "", start_time: Optional[float] = None):
+    """Async version of display_generating."""
+    if not content or not str(content).strip():
+        return Panel("", title="", border_style="green")
+    
+    elapsed_str = ""
+    if start_time is not None:
+        elapsed = time.time() - start_time
+        elapsed_str = f" {elapsed:.1f}s"
+    
+    content = _clean_display_content(str(content))
+    
+    if 'generating' in async_display_callbacks:
+        await async_display_callbacks['generating'](
+            content=content,
+            elapsed_time=elapsed_str.strip() if elapsed_str else None
+        )
+    
+    return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style="green")
+
+def clean_triple_backticks(text: str) -> str:
+    """Remove triple backticks and surrounding json fences from a string."""
+    cleaned = text.strip()
+    if cleaned.startswith("```json"):
+        cleaned = cleaned[len("```json"):].strip()
+    if cleaned.startswith("```"):
+        cleaned = cleaned[len("```"):].strip()
+    if cleaned.endswith("```"):
+        cleaned = cleaned[:-3].strip()
+    return cleaned
+
+class ReflectionOutput(BaseModel):
+    reflection: str
+    satisfactory: Literal["yes", "no"]
+
+client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+
+class TaskOutput(BaseModel):
+    description: str
+    summary: Optional[str] = None
+    raw: str
+    pydantic: Optional[BaseModel] = None
+    json_dict: Optional[Dict[str, Any]] = None
+    agent: str
+    output_format: Literal["RAW", "JSON", "Pydantic"] = "RAW"
+
+    def json(self) -> Optional[str]:
+        if self.output_format == "JSON" and self.json_dict:
+            return json.dumps(self.json_dict)
+        return None
+
+    def to_dict(self) -> dict:
+        output_dict = {}
+        if self.json_dict:
+            output_dict.update(self.json_dict)
+        if self.pydantic:
+            output_dict.update(self.pydantic.model_dump())
+        return output_dict
+
+    def __str__(self):
+        if self.pydantic:
+            return str(self.pydantic)
+        elif self.json_dict:
+            return json.dumps(self.json_dict)
+        else:
+            return self.raw 

agents/praisonaiagents/process/__init__.py

@@ -0,0 +1,3 @@
+from .process import Process
+
+__all__ = ['Process'] 

agents/praisonaiagents/process/process.py

@@ -0,0 +1,534 @@
+import logging
+import asyncio
+from typing import Dict, Optional, List, Any, AsyncGenerator
+from pydantic import BaseModel
+from ..agent.agent import Agent
+from ..task.task import Task
+from ..main import display_error, client
+
+class LoopItems(BaseModel):
+    items: List[Any]
+
+class Process:
+    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False):
+        self.tasks = tasks
+        self.agents = agents
+        self.manager_llm = manager_llm
+        self.verbose = verbose
+
+    async def aworkflow(self) -> AsyncGenerator[str, None]:
+        """Async version of workflow method"""
+        # Build workflow relationships first
+        for task in self.tasks.values():
+            if task.next_tasks:
+                for next_task_name in task.next_tasks:
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                    if next_task:
+                        next_task.previous_tasks.append(task.name)
+
+        # Find start task
+        start_task = None
+        for task_id, task in self.tasks.items():
+            if task.is_start:
+                start_task = task
+                break
+        
+        if not start_task:
+            start_task = list(self.tasks.values())[0]
+            logging.info("No start task marked, using first task")
+        
+        current_task = start_task
+        visited_tasks = set()
+        loop_data = {}  # Store loop-specific data
+        
+        while current_task and current_task.id not in visited_tasks:
+            task_id = current_task.id
+            logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
+            
+            # Add context from previous tasks to description
+            if current_task.previous_tasks or current_task.context:
+                context = "\nInput data from previous tasks:"
+                
+                # Add data from previous tasks in workflow
+                for prev_name in current_task.previous_tasks:
+                    prev_task = next((t for t in self.tasks.values() if t.name == prev_name), None)
+                    if prev_task and prev_task.result:
+                        # Handle loop data
+                        if current_task.task_type == "loop":
+                            # create a loop manager Agent
+                            loop_manager = Agent(
+                                name="Loop Manager",
+                                role="Loop data processor",
+                                goal="Process loop data and convert it to list format",
+                                backstory="Expert at handling loop data and converting it to proper format",
+                                llm=self.manager_llm,
+                                verbose=self.verbose,
+                                markdown=True
+                            )
+
+                            # get the loop data convert it to list using calling Agent class chat
+                            loop_prompt = f"""
+Process this data into a list format:
+{prev_task.result.raw}
+
+Return a JSON object with an 'items' array containing the items to process.
+"""
+                            if current_task.async_execution:
+                                loop_data_str = await loop_manager.achat(
+                                    prompt=loop_prompt,
+                                    output_json=LoopItems
+                                )
+                            else:
+                                loop_data_str = loop_manager.chat(
+                                    prompt=loop_prompt,
+                                    output_json=LoopItems
+                                )
+                            
+                            try:
+                                # The response will already be parsed into LoopItems model
+                                loop_data[f"loop_{current_task.name}"] = {
+                                    "items": loop_data_str.items,
+                                    "index": 0,
+                                    "remaining": len(loop_data_str.items)
+                                }
+                                context += f"\nCurrent loop item: {loop_data_str.items[0]}"
+                            except Exception as e:
+                                display_error(f"Failed to process loop data: {e}")
+                                context += f"\n{prev_name}: {prev_task.result.raw}"
+                        else:
+                            context += f"\n{prev_name}: {prev_task.result.raw}"
+                
+                # Add data from context tasks
+                if current_task.context:
+                    for ctx_task in current_task.context:
+                        if ctx_task.result and ctx_task.name != current_task.name:
+                            context += f"\n{ctx_task.name}: {ctx_task.result.raw}"
+                
+                # Update task description with context
+                current_task.description = current_task.description + context
+            
+            # Execute task using existing run_task method
+            yield task_id
+            visited_tasks.add(task_id)
+            
+            # Handle loop progression
+            if current_task.task_type == "loop":
+                loop_key = f"loop_{current_task.name}"
+                if loop_key in loop_data:
+                    loop_info = loop_data[loop_key]
+                    loop_info["index"] += 1
+                    has_more = loop_info["remaining"] > 0
+                    
+                    # Update result to trigger correct condition
+                    if current_task.result:
+                        result = current_task.result.raw
+                        if has_more:
+                            result += "\nmore"
+                        else:
+                            result += "\ndone"
+                        current_task.result.raw = result
+                    
+            # Determine next task based on result
+            next_task = None
+            if current_task.result:
+                if current_task.task_type in ["decision", "loop"]:
+                    result = current_task.result.raw.lower()
+                    # Check conditions
+                    for condition, tasks in current_task.condition.items():
+                        if condition.lower() in result and tasks:
+                            next_task_name = tasks[0]
+                            next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                            # For loops, allow revisiting the same task
+                            if next_task and next_task.id == current_task.id:
+                                visited_tasks.discard(current_task.id)
+                            break
+            
+                if not next_task and current_task.next_tasks:
+                    next_task_name = current_task.next_tasks[0]
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            
+            current_task = next_task
+            if not current_task:
+                logging.info("Workflow execution completed")
+                break
+
+    async def asequential(self) -> AsyncGenerator[str, None]:
+        """Async version of sequential method"""
+        for task_id in self.tasks:
+            if self.tasks[task_id].status != "completed":
+                yield task_id
+
+    async def ahierarchical(self) -> AsyncGenerator[str, None]:
+        """Async version of hierarchical method"""
+        logging.debug(f"Starting hierarchical task execution with {len(self.tasks)} tasks")
+        manager_agent = Agent(
+            name="Manager",
+            role="Project manager",
+            goal="Manage the entire flow of tasks and delegate them to the right agent",
+            backstory="Expert project manager to coordinate tasks among agents",
+            llm=self.manager_llm,
+            verbose=self.verbose,
+            markdown=True,
+            self_reflect=False
+        )
+
+        class ManagerInstructions(BaseModel):
+            task_id: int
+            agent_name: str
+            action: str
+
+        manager_task = Task(
+            name="manager_task",
+            description="Decide the order of tasks and which agent executes them",
+            expected_output="All tasks completed successfully",
+            agent=manager_agent
+        )
+        manager_task_id = yield manager_task
+        logging.info(f"Created manager task with ID {manager_task_id}")
+
+        completed_count = 0
+        total_tasks = len(self.tasks) - 1
+        logging.info(f"Need to complete {total_tasks} tasks (excluding manager task)")
+
+        while completed_count < total_tasks:
+            tasks_summary = []
+            for tid, tk in self.tasks.items():
+                if tk.name == "manager_task":
+                    continue
+                task_info = {
+                    "task_id": tid,
+                    "name": tk.name,
+                    "description": tk.description,
+                    "status": tk.status if tk.status else "not started",
+                    "agent": tk.agent.name if tk.agent else "No agent"
+                }
+                tasks_summary.append(task_info)
+                logging.info(f"Task {tid} status: {task_info}")
+
+            manager_prompt = f"""
+Here is the current status of all tasks except yours (manager_task):
+{tasks_summary}
+
+Provide a JSON with the structure:
+{{
+   "task_id": <int>,
+   "agent_name": "<string>",
+   "action": "<execute or stop>"
+}}
+"""
+
+            try:
+                logging.info("Requesting manager instructions...")
+                if manager_task.async_execution:
+                    manager_response = await client.beta.chat.completions.parse(
+                        model=self.manager_llm,
+                        messages=[
+                            {"role": "system", "content": manager_task.description},
+                            {"role": "user", "content": manager_prompt}
+                        ],
+                        temperature=0.7,
+                        response_format=ManagerInstructions
+                    )
+                else:
+                    manager_response = client.beta.chat.completions.parse(
+                        model=self.manager_llm,
+                        messages=[
+                            {"role": "system", "content": manager_task.description},
+                            {"role": "user", "content": manager_prompt}
+                        ],
+                        temperature=0.7,
+                        response_format=ManagerInstructions
+                    )
+                parsed_instructions = manager_response.choices[0].message.parsed
+                logging.info(f"Manager instructions: {parsed_instructions}")
+            except Exception as e:
+                display_error(f"Manager parse error: {e}")
+                logging.error(f"Manager parse error: {str(e)}", exc_info=True)
+                break
+
+            selected_task_id = parsed_instructions.task_id
+            selected_agent_name = parsed_instructions.agent_name
+            action = parsed_instructions.action
+
+            logging.info(f"Manager selected task_id={selected_task_id}, agent={selected_agent_name}, action={action}")
+
+            if action.lower() == "stop":
+                logging.info("Manager decided to stop task execution")
+                break
+
+            if selected_task_id not in self.tasks:
+                error_msg = f"Manager selected invalid task id {selected_task_id}"
+                display_error(error_msg)
+                logging.error(error_msg)
+                break
+
+            original_agent = self.tasks[selected_task_id].agent.name if self.tasks[selected_task_id].agent else "None"
+            for a in self.agents:
+                if a.name == selected_agent_name:
+                    self.tasks[selected_task_id].agent = a
+                    logging.info(f"Changed agent for task {selected_task_id} from {original_agent} to {selected_agent_name}")
+                    break
+
+            if self.tasks[selected_task_id].status != "completed":
+                logging.info(f"Starting execution of task {selected_task_id}")
+                yield selected_task_id
+                logging.info(f"Finished execution of task {selected_task_id}, status: {self.tasks[selected_task_id].status}")
+
+            if self.tasks[selected_task_id].status == "completed":
+                completed_count += 1
+                logging.info(f"Task {selected_task_id} completed. Total completed: {completed_count}/{total_tasks}")
+
+        self.tasks[manager_task.id].status = "completed"
+        if self.verbose >= 1:
+            logging.info("All tasks completed under manager supervision.")
+        logging.info("Hierarchical task execution finished") 
+
+    def workflow(self):
+        """Synchronous version of workflow method"""
+        # Build workflow relationships first
+        for task in self.tasks.values():
+            if task.next_tasks:
+                for next_task_name in task.next_tasks:
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                    if next_task:
+                        next_task.previous_tasks.append(task.name)
+
+        # Find start task
+        start_task = None
+        for task_id, task in self.tasks.items():
+            if task.is_start:
+                start_task = task
+                break
+        
+        if not start_task:
+            start_task = list(self.tasks.values())[0]
+            logging.info("No start task marked, using first task")
+        
+        current_task = start_task
+        visited_tasks = set()
+        loop_data = {}  # Store loop-specific data
+        
+        while current_task and current_task.id not in visited_tasks:
+            task_id = current_task.id
+            logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
+            
+            # Add context from previous tasks to description
+            if current_task.previous_tasks or current_task.context:
+                context = "\nInput data from previous tasks:"
+                
+                # Add data from previous tasks in workflow
+                for prev_name in current_task.previous_tasks:
+                    prev_task = next((t for t in self.tasks.values() if t.name == prev_name), None)
+                    if prev_task and prev_task.result:
+                        # Handle loop data
+                        if current_task.task_type == "loop":
+                            # create a loop manager Agent
+                            loop_manager = Agent(
+                                name="Loop Manager",
+                                role="Loop data processor",
+                                goal="Process loop data and convert it to list format",
+                                backstory="Expert at handling loop data and converting it to proper format",
+                                llm=self.manager_llm,
+                                verbose=self.verbose,
+                                markdown=True
+                            )
+
+                            # get the loop data convert it to list using calling Agent class chat
+                            loop_prompt = f"""
+Process this data into a list format:
+{prev_task.result.raw}
+
+Return a JSON object with an 'items' array containing the items to process.
+"""
+                            loop_data_str = loop_manager.chat(
+                                prompt=loop_prompt,
+                                output_json=LoopItems
+                            )
+                            
+                            try:
+                                # The response will already be parsed into LoopItems model
+                                loop_data[f"loop_{current_task.name}"] = {
+                                    "items": loop_data_str.items,
+                                    "index": 0,
+                                    "remaining": len(loop_data_str.items)
+                                }
+                                context += f"\nCurrent loop item: {loop_data_str.items[0]}"
+                            except Exception as e:
+                                display_error(f"Failed to process loop data: {e}")
+                                context += f"\n{prev_name}: {prev_task.result.raw}"
+                        else:
+                            context += f"\n{prev_name}: {prev_task.result.raw}"
+                
+                # Add data from context tasks
+                if current_task.context:
+                    for ctx_task in current_task.context:
+                        if ctx_task.result and ctx_task.name != current_task.name:
+                            context += f"\n{ctx_task.name}: {ctx_task.result.raw}"
+                
+                # Update task description with context
+                current_task.description = current_task.description + context
+            
+            # Execute task using existing run_task method
+            yield task_id
+            visited_tasks.add(task_id)
+            
+            # Handle loop progression
+            if current_task.task_type == "loop":
+                loop_key = f"loop_{current_task.name}"
+                if loop_key in loop_data:
+                    loop_info = loop_data[loop_key]
+                    loop_info["index"] += 1
+                    has_more = loop_info["remaining"] > 0
+                    
+                    # Update result to trigger correct condition
+                    if current_task.result:
+                        result = current_task.result.raw
+                        if has_more:
+                            result += "\nmore"
+                        else:
+                            result += "\ndone"
+                        current_task.result.raw = result
+                    
+            # Determine next task based on result
+            next_task = None
+            if current_task.result:
+                if current_task.task_type in ["decision", "loop"]:
+                    result = current_task.result.raw.lower()
+                    # Check conditions
+                    for condition, tasks in current_task.condition.items():
+                        if condition.lower() in result and tasks:
+                            next_task_name = tasks[0]
+                            next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                            # For loops, allow revisiting the same task
+                            if next_task and next_task.id == current_task.id:
+                                visited_tasks.discard(current_task.id)
+                            break
+            
+                if not next_task and current_task.next_tasks:
+                    next_task_name = current_task.next_tasks[0]
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            
+            current_task = next_task
+            if not current_task:
+                logging.info("Workflow execution completed")
+                break
+
+    def sequential(self):
+        """Synchronous version of sequential method"""
+        for task_id in self.tasks:
+            if self.tasks[task_id].status != "completed":
+                yield task_id
+
+    def hierarchical(self):
+        """Synchronous version of hierarchical method"""
+        logging.debug(f"Starting hierarchical task execution with {len(self.tasks)} tasks")
+        manager_agent = Agent(
+            name="Manager",
+            role="Project manager",
+            goal="Manage the entire flow of tasks and delegate them to the right agent",
+            backstory="Expert project manager to coordinate tasks among agents",
+            llm=self.manager_llm,
+            verbose=self.verbose,
+            markdown=True,
+            self_reflect=False
+        )
+
+        class ManagerInstructions(BaseModel):
+            task_id: int
+            agent_name: str
+            action: str
+
+        manager_task = Task(
+            name="manager_task",
+            description="Decide the order of tasks and which agent executes them",
+            expected_output="All tasks completed successfully",
+            agent=manager_agent
+        )
+        manager_task_id = yield manager_task
+        logging.info(f"Created manager task with ID {manager_task_id}")
+
+        completed_count = 0
+        total_tasks = len(self.tasks) - 1
+        logging.info(f"Need to complete {total_tasks} tasks (excluding manager task)")
+
+        while completed_count < total_tasks:
+            tasks_summary = []
+            for tid, tk in self.tasks.items():
+                if tk.name == "manager_task":
+                    continue
+                task_info = {
+                    "task_id": tid,
+                    "name": tk.name,
+                    "description": tk.description,
+                    "status": tk.status if tk.status else "not started",
+                    "agent": tk.agent.name if tk.agent else "No agent"
+                }
+                tasks_summary.append(task_info)
+                logging.info(f"Task {tid} status: {task_info}")
+
+            manager_prompt = f"""
+Here is the current status of all tasks except yours (manager_task):
+{tasks_summary}
+
+Provide a JSON with the structure:
+{{
+   "task_id": <int>,
+   "agent_name": "<string>",
+   "action": "<execute or stop>"
+}}
+"""
+
+            try:
+                logging.info("Requesting manager instructions...")
+                manager_response = client.beta.chat.completions.parse(
+                    model=self.manager_llm,
+                    messages=[
+                        {"role": "system", "content": manager_task.description},
+                        {"role": "user", "content": manager_prompt}
+                    ],
+                    temperature=0.7,
+                    response_format=ManagerInstructions
+                )
+                parsed_instructions = manager_response.choices[0].message.parsed
+                logging.info(f"Manager instructions: {parsed_instructions}")
+            except Exception as e:
+                display_error(f"Manager parse error: {e}")
+                logging.error(f"Manager parse error: {str(e)}", exc_info=True)
+                break
+
+            selected_task_id = parsed_instructions.task_id
+            selected_agent_name = parsed_instructions.agent_name
+            action = parsed_instructions.action
+
+            logging.info(f"Manager selected task_id={selected_task_id}, agent={selected_agent_name}, action={action}")
+
+            if action.lower() == "stop":
+                logging.info("Manager decided to stop task execution")
+                break
+
+            if selected_task_id not in self.tasks:
+                error_msg = f"Manager selected invalid task id {selected_task_id}"
+                display_error(error_msg)
+                logging.error(error_msg)
+                break
+
+            original_agent = self.tasks[selected_task_id].agent.name if self.tasks[selected_task_id].agent else "None"
+            for a in self.agents:
+                if a.name == selected_agent_name:
+                    self.tasks[selected_task_id].agent = a
+                    logging.info(f"Changed agent for task {selected_task_id} from {original_agent} to {selected_agent_name}")
+                    break
+
+            if self.tasks[selected_task_id].status != "completed":
+                logging.info(f"Starting execution of task {selected_task_id}")
+                yield selected_task_id
+                logging.info(f"Finished execution of task {selected_task_id}, status: {self.tasks[selected_task_id].status}")
+
+            if self.tasks[selected_task_id].status == "completed":
+                completed_count += 1
+                logging.info(f"Task {selected_task_id} completed. Total completed: {completed_count}/{total_tasks}")
+
+        self.tasks[manager_task.id].status = "completed"
+        if self.verbose >= 1:
+            logging.info("All tasks completed under manager supervision.")
+        logging.info("Hierarchical task execution finished") 

agents/praisonaiagents/task/__init__.py

@@ -0,0 +1,4 @@
+"""Task module for AI agent tasks"""
+from .task import Task
+
+__all__ = ['Task'] 

agents/praisonaiagents/task/task.py

@@ -0,0 +1,74 @@
+import logging
+import asyncio
+from typing import List, Optional, Dict, Any, Type, Callable, Union, Coroutine
+from pydantic import BaseModel
+from ..main import TaskOutput
+from ..agent.agent import Agent
+import uuid
+
+class Task:
+    def __init__(
+        self,
+        description: str,
+        expected_output: str,
+        agent: Optional[Agent] = None,
+        name: Optional[str] = None,
+        tools: Optional[List[Any]] = None,
+        context: Optional[List["Task"]] = None,
+        async_execution: Optional[bool] = False,
+        config: Optional[Dict[str, Any]] = None,
+        output_file: Optional[str] = None,
+        output_json: Optional[Type[BaseModel]] = None,
+        output_pydantic: Optional[Type[BaseModel]] = None,
+        callback: Optional[Union[Callable[[TaskOutput], Any], Callable[[TaskOutput], Coroutine[Any, Any, Any]]]] = None,
+        status: str = "not started",
+        result: Optional[TaskOutput] = None,
+        create_directory: Optional[bool] = False,
+        id: Optional[int] = None,
+        images: Optional[List[str]] = None,
+        next_tasks: Optional[List[str]] = None,
+        task_type: str = "task",
+        condition: Optional[Dict[str, List[str]]] = None,
+        is_start: bool = False,
+        loop_state: Optional[Dict[str, Union[str, int]]] = None
+    ):
+        self.id = str(uuid.uuid4()) if id is None else str(id)
+        self.name = name
+        self.description = description
+        self.expected_output = expected_output
+        self.agent = agent
+        self.tools = tools if tools else []
+        self.context = context if context else []
+        self.async_execution = async_execution
+        self.config = config if config else {}
+        self.output_file = output_file
+        self.output_json = output_json
+        self.output_pydantic = output_pydantic
+        self.callback = callback
+        self.status = status
+        self.result = result
+        self.create_directory = create_directory
+        self.images = images if images else []
+        self.next_tasks = next_tasks if next_tasks else []
+        self.task_type = task_type
+        self.condition = condition if condition else {}
+        self.is_start = is_start
+        self.loop_state = loop_state if loop_state else {}
+
+        if self.output_json and self.output_pydantic:
+            raise ValueError("Only one output type can be defined")
+
+        # Track previous tasks based on next_tasks relationships
+        self.previous_tasks = []
+
+    def __str__(self):
+        return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')"
+
+    async def execute_callback(self, task_output: TaskOutput) -> None:
+        """Execute the callback function, handling both sync and async callbacks"""
+        if self.callback:
+            if asyncio.iscoroutinefunction(self.callback):
+                await self.callback(task_output)
+            else:
+                loop = asyncio.get_event_loop()
+                await loop.run_in_executor(None, self.callback, task_output) 

agents/praisonaiagents/tools/README.md

@@ -0,0 +1,149 @@
+# PraisonAI Tools Guide
+
+Welcome to the PraisonAI Tools directory! This guide will help you understand how our tools work and how to create new ones, whether you're a beginner or an experienced programmer.
+
+## What is a Tool?
+
+A tool is a piece of code that helps our AI agents perform specific tasks. Think of tools as special abilities that we give to our agents. For example:
+- An internet search tool lets agents search the web
+- A stock market tool lets agents check stock prices
+- A weather tool lets agents check the weather
+
+## Creating New Tools: The Two Approaches
+
+### 1. Function-Based Approach (Simple Tools)
+
+Best for simple tools that do one specific thing. Like a calculator that just adds numbers.
+
+**When to use:**
+- Tool does one simple task
+- Doesn't need to remember information between uses
+- Doesn't need to share information with other tools
+- Quick, one-time operations
+
+**Example:**
+```python
+def internet_search(query: str):
+    # Search the internet and return results
+    return search_results
+```
+
+**Usage:**
+```python
+from praisonaiagents.tools import internet_search
+
+results = internet_search("AI news")
+```
+
+### 2. Class-Based Approach (Complex Tools)
+
+Best for tools that do multiple related things or need to remember information. Like a smart calculator that remembers your previous calculations and can do many different math operations.
+
+**When to use:**
+- Tool has multiple related functions
+- Needs to remember or share information
+- Needs to manage resources efficiently
+- Has complex setup requirements
+
+**Example:**
+```python
+class StockTools:
+    def get_stock_price(self, symbol):
+        # Get current stock price
+        return price
+        
+    def get_stock_info(self, symbol):
+        # Get detailed stock information
+        return info
+```
+
+**Usage:**
+```python
+from praisonaiagents.tools import get_stock_price, get_stock_info
+
+price = get_stock_price("AAPL")
+info = get_stock_info("AAPL")
+```
+
+## How to Choose Your Approach
+
+Ask yourself these questions:
+
+1. **Is your tool doing one simple thing?**
+   - Yes → Use Function-Based Approach
+   - No → Consider Class-Based Approach
+
+2. **Does your tool need to remember information?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+3. **Are your tool's operations related to each other?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+4. **Does your tool need to manage resources efficiently?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+## Real-World Examples
+
+### Internet Search Tool (Function-Based)
+- Does one thing: searches the internet
+- Doesn't need to remember previous searches
+- Each search is independent
+- Simple input/output operation
+
+### Stock Market Tool (Class-Based)
+- Does multiple things: check prices, get company info, get historical data
+- Remembers stock information to avoid repeated downloads
+- Operations are related (all about stocks)
+- Manages connections efficiently
+
+## Getting Started
+
+1. **Choose Your Approach** based on the guidelines above
+
+2. **Create Your Tool File**:
+   - Name it descriptively (e.g., `weather_tools.py`)
+   - Place it in the `praisonaiagents/tools` directory
+
+3. **Write Your Tool**:
+   - Add clear documentation
+   - Include type hints for better understanding
+   - Handle errors gracefully
+
+4. **Test Your Tool**:
+   - Make sure it works as expected
+   - Test error cases
+   - Check performance
+
+## Best Practices
+
+1. **Documentation**:
+   - Explain what your tool does
+   - Provide examples
+   - List any requirements
+
+2. **Error Handling**:
+   - Always handle possible errors
+   - Return helpful error messages
+   - Don't let your tool crash
+
+3. **Performance**:
+   - Keep it efficient
+   - Don't waste resources
+   - Cache when helpful
+
+4. **User-Friendly**:
+   - Make it easy to use
+   - Use clear function/method names
+   - Keep it simple
+
+## Need Help?
+
+- Check existing tools for examples
+- Ask in our community
+- Read the documentation
+- Don't hesitate to ask questions!
+
+Remember: The goal is to make tools that are easy to use and maintain. Choose the approach that makes the most sense for your specific tool's needs.

agents/praisonaiagents/tools/__init__.py

@@ -0,0 +1,178 @@
+"""Tools package for PraisonAI Agents"""
+from importlib import import_module
+from typing import Any
+
+# Map of function names to their module and class (if any)
+TOOL_MAPPINGS = {
+    # Direct functions
+    'internet_search': ('.duckduckgo_tools', None),
+    'duckduckgo': ('.duckduckgo_tools', None),
+    
+    # arXiv Tools
+    'search_arxiv': ('.arxiv_tools', None),
+    'get_arxiv_paper': ('.arxiv_tools', None),
+    'get_papers_by_author': ('.arxiv_tools', None),
+    'get_papers_by_category': ('.arxiv_tools', None),
+    'arxiv_tools': ('.arxiv_tools', None),
+    
+    # Wikipedia Tools
+    'wiki_search': ('.wikipedia_tools', None),
+    'wiki_summary': ('.wikipedia_tools', None),
+    'wiki_page': ('.wikipedia_tools', None),
+    'wiki_random': ('.wikipedia_tools', None),
+    'wiki_language': ('.wikipedia_tools', None),
+    'wikipedia_tools': ('.wikipedia_tools', None),
+    
+    # Newspaper Tools
+    'get_article': ('.newspaper_tools', None),
+    'get_news_sources': ('.newspaper_tools', None),
+    'get_articles_from_source': ('.newspaper_tools', None),
+    'get_trending_topics': ('.newspaper_tools', None),
+    'newspaper_tools': ('.newspaper_tools', None),
+    
+    # Spider Tools
+    'scrape_page': ('.spider_tools', None),
+    'extract_links': ('.spider_tools', None),
+    'crawl': ('.spider_tools', None),
+    'extract_text': ('.spider_tools', None),
+    'spider_tools': ('.spider_tools', None),
+    
+    # DuckDB Tools
+    'query': ('.duckdb_tools', None),
+    'create_table': ('.duckdb_tools', None),
+    'load_data': ('.duckdb_tools', None),
+    'export_data': ('.duckdb_tools', None),
+    'get_table_info': ('.duckdb_tools', None),
+    'analyze_data': ('.duckdb_tools', None),
+    'duckdb_tools': ('.duckdb_tools', None),
+    
+    # Shell Tools
+    'execute_command': ('.shell_tools', None),
+    'list_processes': ('.shell_tools', None),
+    'kill_process': ('.shell_tools', None),
+    'get_system_info': ('.shell_tools', None),
+    'shell_tools': ('.shell_tools', None),
+
+    # Calculator Tools
+    'evaluate': ('.calculator_tools', None),
+    'solve_equation': ('.calculator_tools', None),
+    'convert_units': ('.calculator_tools', None),
+    'calculate_statistics': ('.calculator_tools', None),
+    'calculate_financial': ('.calculator_tools', None),
+    'calculator_tools': ('.calculator_tools', None),
+
+    # Class methods from YFinance
+    'get_stock_price': ('.yfinance_tools', 'YFinanceTools'),
+    'get_stock_info': ('.yfinance_tools', 'YFinanceTools'),
+    'get_historical_data': ('.yfinance_tools', 'YFinanceTools'),
+    'yfinance': ('.yfinance_tools', 'YFinanceTools'),
+
+    # File Tools
+    'read_file': ('.file_tools', 'FileTools'),
+    'write_file': ('.file_tools', 'FileTools'),
+    'list_files': ('.file_tools', 'FileTools'),
+    'get_file_info': ('.file_tools', 'FileTools'),
+    'copy_file': ('.file_tools', 'FileTools'),
+    'move_file': ('.file_tools', 'FileTools'),
+    'delete_file': ('.file_tools', 'FileTools'),
+    'file_tools': ('.file_tools', 'FileTools'),
+
+    # CSV Tools
+    'read_csv': ('.csv_tools', 'CSVTools'),
+    'write_csv': ('.csv_tools', 'CSVTools'),
+    'merge_csv': ('.csv_tools', 'CSVTools'),
+    'analyze_csv': ('.csv_tools', 'CSVTools'),
+    'split_csv': ('.csv_tools', 'CSVTools'),
+    'csv_tools': ('.csv_tools', 'CSVTools'),
+
+    # JSON Tools
+    'read_json': ('.json_tools', 'JSONTools'),
+    'write_json': ('.json_tools', 'JSONTools'),
+    'merge_json': ('.json_tools', 'JSONTools'),
+    'validate_json': ('.json_tools', 'JSONTools'),
+    'analyze_json': ('.json_tools', 'JSONTools'),
+    'transform_json': ('.json_tools', 'JSONTools'),
+    'json_tools': ('.json_tools', 'JSONTools'),
+
+    # Excel Tools
+    'read_excel': ('.excel_tools', 'ExcelTools'),
+    'write_excel': ('.excel_tools', 'ExcelTools'),
+    'merge_excel': ('.excel_tools', 'ExcelTools'),
+    'create_chart': ('.excel_tools', 'ExcelTools'),
+    'add_chart_to_sheet': ('.excel_tools', 'ExcelTools'),
+    'excel_tools': ('.excel_tools', 'ExcelTools'),
+
+    # XML Tools
+    'read_xml': ('.xml_tools', 'XMLTools'),
+    'write_xml': ('.xml_tools', 'XMLTools'),
+    'transform_xml': ('.xml_tools', 'XMLTools'),
+    'validate_xml': ('.xml_tools', 'XMLTools'),
+    'xml_to_dict': ('.xml_tools', 'XMLTools'),
+    'dict_to_xml': ('.xml_tools', 'XMLTools'),
+    'xpath_query': ('.xml_tools', 'XMLTools'),
+    'xml_tools': ('.xml_tools', 'XMLTools'),
+
+    # YAML Tools
+    'read_yaml': ('.yaml_tools', 'YAMLTools'),
+    'write_yaml': ('.yaml_tools', 'YAMLTools'),
+    'merge_yaml': ('.yaml_tools', 'YAMLTools'),
+    'validate_yaml': ('.yaml_tools', 'YAMLTools'),
+    'analyze_yaml': ('.yaml_tools', 'YAMLTools'),
+    'transform_yaml': ('.yaml_tools', 'YAMLTools'),
+    'yaml_tools': ('.yaml_tools', 'YAMLTools'),
+
+    # Python Tools
+    'execute_code': ('.python_tools', 'PythonTools'),
+    'analyze_code': ('.python_tools', 'PythonTools'),
+    'format_code': ('.python_tools', 'PythonTools'),
+    'lint_code': ('.python_tools', 'PythonTools'),
+    'disassemble_code': ('.python_tools', 'PythonTools'),
+    'python_tools': ('.python_tools', 'PythonTools'),
+
+    # Pandas Tools
+    'filter_data': ('.pandas_tools', 'PandasTools'),
+    'get_summary': ('.pandas_tools', 'PandasTools'),
+    'group_by': ('.pandas_tools', 'PandasTools'),
+    'pivot_table': ('.pandas_tools', 'PandasTools'),
+    'pandas_tools': ('.pandas_tools', 'PandasTools'),
+}
+
+_instances = {}  # Cache for class instances
+
+def __getattr__(name: str) -> Any:
+    """Smart lazy loading of tools with class method support."""
+    if name not in TOOL_MAPPINGS:
+        raise AttributeError(f"module '{__package__}' has no attribute '{name}'")
+    
+    module_path, class_name = TOOL_MAPPINGS[name]
+    
+    if class_name is None:
+        # Direct function import
+        module = import_module(module_path, __package__)
+        if name in [
+            'duckduckgo', 'internet_search',
+            'search_arxiv', 'get_arxiv_paper', 'get_papers_by_author', 'get_papers_by_category',
+            'wiki_search', 'wiki_summary', 'wiki_page', 'wiki_random', 'wiki_language',
+            'get_article', 'get_news_sources', 'get_articles_from_source', 'get_trending_topics',
+            'scrape_page', 'extract_links', 'crawl', 'extract_text',
+            'query', 'create_table', 'load_data', 'export_data', 'get_table_info', 'analyze_data',
+            'execute_command', 'list_processes', 'kill_process', 'get_system_info',
+            'evaluate', 'solve_equation', 'convert_units', 'calculate_statistics', 'calculate_financial'
+        ]:
+            return getattr(module, name)
+        if name in ['file_tools', 'pandas_tools', 'wikipedia_tools',
+                   'newspaper_tools', 'arxiv_tools', 'spider_tools', 'duckdb_tools', 'csv_tools', 'json_tools', 'excel_tools', 'xml_tools', 'yaml_tools', 'calculator_tools', 'python_tools', 'shell_tools']:
+            return module  # Returns the callable module
+        return getattr(module, name)
+    else:
+        # Class method import
+        if class_name not in _instances:
+            module = import_module(module_path, __package__)
+            class_ = getattr(module, class_name)
+            _instances[class_name] = class_()
+        
+        # Get the method and bind it to the instance
+        method = getattr(_instances[class_name], name)
+        return method
+
+__all__ = list(TOOL_MAPPINGS.keys())

agents/praisonaiagents/tools/arxiv_tools.py

@@ -0,0 +1,292 @@
+"""Tools for searching and retrieving papers from arXiv.
+
+Usage:
+from praisonaiagents.tools import arxiv_tools
+papers = arxiv_tools.search("quantum computing")
+paper = arxiv_tools.get_paper("2401.00123")
+
+or
+from praisonaiagents.tools import search_arxiv, get_arxiv_paper
+papers = search_arxiv("quantum computing")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+
+# Map sort criteria to arxiv.SortCriterion
+SORT_CRITERIA = {
+    "relevance": "Relevance",
+    "lastupdateddate": "LastUpdatedDate",
+    "submitteddate": "SubmittedDate"
+}
+
+# Map sort order to arxiv.SortOrder
+SORT_ORDER = {
+    "ascending": "Ascending",
+    "descending": "Descending"
+}
+
+class ArxivTools:
+    """Tools for searching and retrieving papers from arXiv."""
+    
+    def __init__(self):
+        """Initialize ArxivTools and check for arxiv package."""
+        self._check_arxiv()
+        
+    def _check_arxiv(self):
+        """Check if arxiv package is installed."""
+        if util.find_spec("arxiv") is None:
+            raise ImportError("arxiv package is not available. Please install it using: pip install arxiv")
+        global arxiv
+        import arxiv
+
+    def search(
+        self,
+        query: str,
+        max_results: int = 10,
+        sort_by: str = "relevance",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Search arXiv for papers matching the query.
+        
+        Args:
+            query: Search query (e.g., "quantum computing", "author:Einstein")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results. If None, includes all.
+                Available fields: ["title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"]
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        try:
+            import arxiv
+            
+            # Configure search client
+            client = arxiv.Client()
+            
+            # Map sort criteria
+            sort_by_enum = getattr(arxiv.SortCriterion, SORT_CRITERIA[sort_by.lower()])
+            sort_order_enum = getattr(arxiv.SortOrder, SORT_ORDER[sort_order.lower()])
+            
+            # Build search query
+            search = arxiv.Search(
+                query=query,
+                max_results=max_results,
+                sort_by=sort_by_enum,
+                sort_order=sort_order_enum
+            )
+            
+            # Execute search
+            results = []
+            for result in client.results(search):
+                # Convert to dict with selected fields
+                paper = self._result_to_dict(result, include_fields)
+                results.append(paper)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error searching arXiv: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_paper(
+        self,
+        paper_id: str,
+        include_fields: Optional[List[str]] = None
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Get details of a specific paper by its arXiv ID.
+        
+        Args:
+            paper_id: arXiv paper ID (e.g., "2401.00123")
+            include_fields: List of fields to include in results. If None, includes all.
+                Available fields: ["title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"]
+            
+        Returns:
+            Dict: Paper details or error dict
+        """
+        try:
+            import arxiv
+            
+            # Configure client
+            client = arxiv.Client()
+            
+            # Get paper by ID
+            search = arxiv.Search(id_list=[paper_id])
+            results = list(client.results(search))
+            
+            if not results:
+                return {"error": f"Paper with ID {paper_id} not found"}
+            
+            # Convert to dict with selected fields
+            paper = self._result_to_dict(results[0], include_fields)
+            return paper
+        except Exception as e:
+            error_msg = f"Error getting paper {paper_id}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_papers_by_author(
+        self,
+        author: str,
+        max_results: int = 10,
+        sort_by: str = "submittedDate",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get papers by a specific author.
+        
+        Args:
+            author: Author name (e.g., "Einstein")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        query = f'au:"{author}"'
+        return self.search(query, max_results, sort_by, sort_order, include_fields)
+
+    def get_papers_by_category(
+        self,
+        category: str,
+        max_results: int = 10,
+        sort_by: str = "submittedDate",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get papers from a specific category.
+        
+        Args:
+            category: arXiv category (e.g., "cs.AI", "physics.gen-ph")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        query = f'cat:{category}'
+        return self.search(query, max_results, sort_by, sort_order, include_fields)
+
+    def _result_to_dict(
+        self,
+        result: Any,
+        include_fields: Optional[List[str]] = None
+    ) -> Dict[str, Any]:
+        """Convert arxiv.Result to dictionary with selected fields."""
+        # Default fields to include
+        if include_fields is None:
+            include_fields = [
+                "title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"
+            ]
+        
+        # Build paper dict with selected fields
+        paper = {}
+        
+        # Always include these basic fields
+        paper["arxiv_id"] = result.entry_id.split("/")[-1]
+        paper["updated"] = result.updated.isoformat() if result.updated else None
+        paper["published"] = result.published.isoformat() if result.published else None
+        
+        # Add requested fields
+        if "title" in include_fields:
+            paper["title"] = result.title
+        if "authors" in include_fields:
+            paper["authors"] = [str(author) for author in result.authors]
+        if "summary" in include_fields:
+            paper["summary"] = result.summary
+        if "comment" in include_fields:
+            paper["comment"] = result.comment
+        if "journal_ref" in include_fields:
+            paper["journal_ref"] = result.journal_ref
+        if "doi" in include_fields:
+            paper["doi"] = result.doi
+        if "primary_category" in include_fields:
+            paper["primary_category"] = result.primary_category
+        if "categories" in include_fields:
+            paper["categories"] = result.categories
+        if "links" in include_fields:
+            paper["pdf_url"] = result.pdf_url
+            paper["abstract_url"] = f"https://arxiv.org/abs/{paper['arxiv_id']}"
+        
+        return paper
+
+# Create instance for direct function access
+_arxiv_tools = ArxivTools()
+search_arxiv = _arxiv_tools.search
+get_arxiv_paper = _arxiv_tools.get_paper
+get_papers_by_author = _arxiv_tools.get_papers_by_author
+get_papers_by_category = _arxiv_tools.get_papers_by_category
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("ArxivTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Search for papers
+    print("1. Searching for Papers")
+    print("------------------------------")
+    query = "quantum computing"
+    papers = search_arxiv(query, max_results=3)
+    print(f"Papers about {query}:")
+    if isinstance(papers, list):
+        print(json.dumps(papers, indent=2))
+    else:
+        print(papers)  # Show error
+    print()
+    
+    # 2. Get specific paper
+    print("2. Getting Specific Paper")
+    print("------------------------------")
+    if isinstance(papers, list) and papers:
+        paper_id = papers[0]["arxiv_id"]
+        paper = get_arxiv_paper(paper_id)
+        print(f"Paper {paper_id}:")
+        if "error" not in paper:
+            print(json.dumps(paper, indent=2))
+        else:
+            print(paper)  # Show error
+    print()
+    
+    # 3. Get papers by author
+    print("3. Getting Papers by Author")
+    print("------------------------------")
+    author = "Yoshua Bengio"
+    author_papers = get_papers_by_author(author, max_results=3)
+    print(f"Papers by {author}:")
+    if isinstance(author_papers, list):
+        print(json.dumps(author_papers, indent=2))
+    else:
+        print(author_papers)  # Show error
+    print()
+    
+    # 4. Get papers by category
+    print("4. Getting Papers by Category")
+    print("------------------------------")
+    category = "cs.AI"
+    category_papers = get_papers_by_category(category, max_results=3)
+    print(f"Papers in category {category}:")
+    if isinstance(category_papers, list):
+        print(json.dumps(category_papers, indent=2))
+    else:
+        print(category_papers)  # Show error
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/calculator_tools.py

@@ -0,0 +1,278 @@
+"""Tools for performing calculations.
+
+Usage:
+from praisonaiagents.tools import calculator_tools
+result = calculator_tools.evaluate("2 + 2 * 3")
+
+or
+from praisonaiagents.tools import evaluate, solve_equation, convert_units
+result = evaluate("2 + 2 * 3")
+"""
+
+import logging
+from typing import List, Dict, Optional, Any
+from importlib import util
+import math
+
+class CalculatorTools:
+    """Tools for performing calculations."""
+    
+    def __init__(self):
+        """Initialize CalculatorTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package in ['sympy', 'pint']:
+            if util.find_spec(package) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def evaluate(
+        self,
+        expression: str,
+        variables: Optional[Dict[str, float]] = None,
+        precision: int = 10
+    ) -> Dict[str, Any]:
+        """Evaluate a mathematical expression."""
+        try:
+            if util.find_spec('sympy') is None:
+                error_msg = "sympy package is not available. Please install it using: pip install sympy"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import sympy only when needed
+            import sympy
+
+            # Replace common mathematical functions
+            expression = expression.lower()
+            replacements = {
+                'sin(': 'math.sin(',
+                'cos(': 'math.cos(',
+                'tan(': 'math.tan(',
+                'sqrt(': 'math.sqrt(',
+                'log(': 'math.log(',
+                'exp(': 'math.exp(',
+                'pi': str(math.pi),
+                'e': str(math.e)
+            }
+            for old, new in replacements.items():
+                expression = expression.replace(old, new)
+            
+            # Create safe namespace
+            safe_dict = {
+                'math': math,
+                '__builtins__': None
+            }
+            if variables:
+                safe_dict.update(variables)
+            
+            # Evaluate expression
+            result = eval(expression, safe_dict)
+            
+            # Round to specified precision
+            if isinstance(result, (int, float)):
+                return {"result": round(float(result), precision)}
+            return {"error": "Invalid expression"}
+        except Exception as e:
+            error_msg = f"Error evaluating expression: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def solve_equation(
+        self,
+        equation: str,
+        variable: str = 'x'
+    ) -> Dict[str, Any]:
+        """Solve a mathematical equation."""
+        try:
+            if util.find_spec('sympy') is None:
+                error_msg = "sympy package is not available. Please install it using: pip install sympy"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import sympy only when needed
+            import sympy
+
+            # Parse equation
+            if '=' in equation:
+                left, right = equation.split('=')
+                equation = f"({left}) - ({right})"
+            
+            # Convert to SymPy expression
+            x = sympy.Symbol(variable)
+            expr = sympy.sympify(equation)
+            
+            # Solve equation
+            solutions = sympy.solve(expr, x)
+            
+            # Convert complex solutions to real if possible
+            real_solutions = []
+            for sol in solutions:
+                if sol.is_real:
+                    real_solutions.append(float(sol))
+            
+            return {"solutions": real_solutions} if real_solutions else {"error": "No real solutions found"}
+        except Exception as e:
+            error_msg = f"Error solving equation: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def convert_units(
+        self,
+        value: float,
+        from_unit: str,
+        to_unit: str
+    ) -> Dict[str, Any]:
+        """Convert between different units."""
+        try:
+            if util.find_spec('pint') is None:
+                error_msg = "pint package is not available. Please install it using: pip install pint"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import pint only when needed
+            import pint
+
+            # Create quantity with source unit
+            ureg = pint.UnitRegistry()
+            quantity = value * ureg(from_unit)
+            
+            # Convert to target unit
+            result = quantity.to(to_unit)
+            
+            return {"result": float(result.magnitude)}
+        except Exception as e:
+            error_msg = f"Error converting units: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def calculate_statistics(
+        self,
+        numbers: List[float]
+    ) -> Dict[str, Any]:
+        """Calculate basic statistics for a list of numbers."""
+        try:
+            if not numbers:
+                return {"error": "No numbers provided"}
+            
+            n = len(numbers)
+            mean = sum(numbers) / n
+            
+            # Calculate variance and std dev
+            squared_diff_sum = sum((x - mean) ** 2 for x in numbers)
+            variance = squared_diff_sum / (n - 1) if n > 1 else 0
+            std_dev = math.sqrt(variance)
+            
+            # Calculate median
+            sorted_nums = sorted(numbers)
+            if n % 2 == 0:
+                median = (sorted_nums[n//2 - 1] + sorted_nums[n//2]) / 2
+            else:
+                median = sorted_nums[n//2]
+            
+            return {
+                "mean": mean,
+                "median": median,
+                "std_dev": std_dev,
+                "variance": variance,
+                "min": min(numbers),
+                "max": max(numbers),
+                "range": max(numbers) - min(numbers),
+                "count": n
+            }
+        except Exception as e:
+            error_msg = f"Error calculating statistics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def calculate_financial(
+        self,
+        principal: float,
+        rate: float,
+        time: float,
+        frequency: str = 'yearly'
+    ) -> Dict[str, Any]:
+        """Calculate financial metrics like compound interest."""
+        try:
+            # Convert rate to decimal
+            rate = rate / 100
+            
+            # Get compounding frequency
+            freq_map = {
+                'yearly': 1,
+                'semi-annual': 2,
+                'quarterly': 4,
+                'monthly': 12,
+                'daily': 365
+            }
+            n = freq_map.get(frequency.lower(), 1)
+            
+            # Calculate compound interest
+            amount = principal * (1 + rate/n)**(n*time)
+            interest = amount - principal
+            
+            # Calculate simple interest
+            simple_interest = principal * rate * time
+            
+            return {
+                "final_amount": amount,
+                "compound_interest": interest,
+                "simple_interest": simple_interest,
+                "difference": interest - simple_interest
+            }
+        except Exception as e:
+            error_msg = f"Error calculating financial metrics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_calculator_tools = CalculatorTools()
+evaluate = _calculator_tools.evaluate
+solve_equation = _calculator_tools.solve_equation
+convert_units = _calculator_tools.convert_units
+calculate_statistics = _calculator_tools.calculate_statistics
+calculate_financial = _calculator_tools.calculate_financial
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("CalculatorTools Demonstration")
+    print("==================================================\n")
+
+    print("1. Basic Arithmetic")
+    print("------------------------------")
+    print("2 + 2 =", evaluate("2 + 2")["result"])
+    print("5 * 6 =", evaluate("5 * 6")["result"])
+    print("10 / 3 =", evaluate("10 / 3")["result"])
+    print()
+
+    print("2. Complex Expressions")
+    print("------------------------------")
+    print("(2 + 3) * 4 =", evaluate("(2 + 3) * 4")["result"])
+    print("2^3 + 4 =", evaluate("2^3 + 4")["result"])
+    print("sqrt(16) + 2 =", evaluate("sqrt(16) + 2")["result"])
+    print()
+
+    print("3. Solving Equations")
+    print("------------------------------")
+    result = solve_equation("x^2 - 5*x + 6 = 0")
+    print("x^2 - 5*x + 6 = 0")
+    print("Solutions:", result["solutions"])
+    print()
+
+    print("4. Unit Conversions")
+    print("------------------------------")
+    print("100 meters to kilometers:", convert_units(100, "meters", "kilometers")["result"])
+    print("1 hour to minutes:", convert_units(1, "hours", "minutes")["result"])
+    print("32 fahrenheit to celsius:", convert_units(32, "fahrenheit", "celsius")["result"])
+    print()
+
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/csv_tools.py

@@ -0,0 +1,266 @@
+"""Tools for working with CSV files.
+
+Usage:
+from praisonaiagents.tools import csv_tools
+df = csv_tools.read_csv("data.csv")
+
+or
+from praisonaiagents.tools import read_csv, write_csv, merge_csv
+df = read_csv("data.csv")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING
+from importlib import util
+import json
+import csv
+from pathlib import Path
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+class CSVTools:
+    """Tools for working with CSV files."""
+    
+    def __init__(self):
+        """Initialize CSVTools."""
+        pass
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def read_csv(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        delimiter: str = ',',
+        header: Union[int, List[int], None] = 0,
+        usecols: Optional[List[str]] = None,
+        dtype: Optional[Dict[str, str]] = None,
+        parse_dates: Optional[List[str]] = None,
+        na_values: Optional[List[str]] = None,
+        nrows: Optional[int] = None
+    ) -> List[Dict[str, Any]]:
+        """Read a CSV file with advanced options.
+        
+        Args:
+            filepath: Path to CSV file
+            encoding: File encoding
+            delimiter: Column delimiter
+            header: Row number(s) to use as column names
+            usecols: Columns to read
+            dtype: Dict of column dtypes
+            parse_dates: List of columns to parse as dates
+            na_values: Additional strings to recognize as NA/NaN
+            nrows: Number of rows to read
+            
+        Returns:
+            List of row dicts
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return {"error": "pandas package not available"}
+
+            df = pd.read_csv(
+                filepath,
+                encoding=encoding,
+                delimiter=delimiter,
+                header=header,
+                usecols=usecols,
+                dtype=dtype,
+                parse_dates=parse_dates,
+                na_values=na_values,
+                nrows=nrows
+            )
+            return df.to_dict('records')
+            
+        except Exception as e:
+            error_msg = f"Error reading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_csv(
+        self,
+        filepath: str,
+        data: List[Dict[str, Any]],
+        encoding: str = 'utf-8',
+        delimiter: str = ',',
+        index: bool = False,
+        header: bool = True,
+        float_format: Optional[str] = None,
+        date_format: Optional[str] = None,
+        mode: str = 'w'
+    ) -> bool:
+        """Write data to a CSV file.
+        
+        Args:
+            filepath: Path to CSV file
+            data: List of row dicts to write
+            encoding: File encoding
+            delimiter: Column delimiter
+            index: Whether to write row indices
+            header: Whether to write column headers
+            float_format: Format string for float values
+            date_format: Format string for date values
+            mode: Write mode ('w' for write, 'a' for append)
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            df = pd.DataFrame(data)
+            df.to_csv(
+                filepath,
+                encoding=encoding,
+                sep=delimiter,
+                index=index,
+                header=header,
+                float_format=float_format,
+                date_format=date_format,
+                mode=mode
+            )
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error writing CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_csv(
+        self,
+        files: List[str],
+        output_file: str,
+        how: str = 'inner',
+        on: Optional[Union[str, List[str]]] = None,
+        suffixes: Optional[tuple] = None
+    ) -> bool:
+        """Merge multiple CSV files.
+        
+        Args:
+            files: List of CSV files to merge
+            output_file: Output file path
+            how: Merge method ('inner', 'outer', 'left', 'right')
+            on: Column(s) to merge on
+            suffixes: Suffixes for overlapping columns
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            if len(files) < 2:
+                error_msg = "At least two files are required for merging"
+                logging.error(error_msg)
+                return False
+            
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Read first file
+            result = pd.read_csv(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                df = pd.read_csv(file)
+                result = pd.merge(
+                    result,
+                    df,
+                    how=how,
+                    on=on,
+                    suffixes=suffixes or ('_1', '_2')
+                )
+            
+            # Write merged result
+            result.to_csv(output_file, index=False)
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error merging CSV files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_csv_tools = CSVTools()
+read_csv = _csv_tools.read_csv
+write_csv = _csv_tools.write_csv
+merge_csv = _csv_tools.merge_csv
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("CSVTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    import tempfile
+    import os
+    
+    with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp:
+        temp_file = temp.name
+        
+        print("1. Writing CSV File")
+        print("------------------------------")
+        data = [
+            {"name": "Alice", "age": 25, "city": "New York"},
+            {"name": "Bob", "age": 30, "city": "San Francisco"},
+            {"name": "Charlie", "age": 35, "city": "Chicago"}
+        ]
+        result = write_csv(temp_file, data)
+        print(f"Data written successfully: {result}")
+        print()
+        
+        print("2. Reading CSV File")
+        print("------------------------------")
+        read_data = read_csv(temp_file)
+        print("Contents of CSV file:")
+        for row in read_data:
+            print(row)
+        print()
+        
+        print("3. Merging CSV Files")
+        print("------------------------------")
+        # Create a second file for merging
+        with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp2:
+            temp_file2 = temp2.name
+            data2 = [
+                {"name": "Alice", "salary": 75000},
+                {"name": "Bob", "salary": 85000},
+                {"name": "Charlie", "salary": 95000}
+            ]
+            write_csv(temp_file2, data2)
+            
+            # Merge files
+            with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp3:
+                temp_file3 = temp3.name
+                result = merge_csv(
+                    [temp_file, temp_file2],
+                    temp_file3,
+                    how='inner',
+                    on='name'
+                )
+                print(f"Files merged successfully: {result}")
+                if result:
+                    merged_data = read_csv(temp_file3)
+                    print("\nMerged contents:")
+                    for row in merged_data:
+                        print(row)
+                print()
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+        os.unlink(temp_file3)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/duckdb_tools.py

@@ -0,0 +1,268 @@
+"""Tools for working with DuckDB databases.
+
+Usage:
+from praisonaiagents.tools import duckdb_tools
+df = duckdb_tools.query("SELECT * FROM my_table")
+
+or
+from praisonaiagents.tools import query_db, create_table, load_data
+df = query_db("SELECT * FROM my_table")
+"""
+
+import logging
+from typing import List, Dict, Any, Optional, Union, TYPE_CHECKING
+from importlib import util
+import json
+
+if TYPE_CHECKING:
+    import duckdb
+    import pandas as pd
+
+class DuckDBTools:
+    """Tools for working with DuckDB databases."""
+    
+    def __init__(self, database: str = ':memory:'):
+        """Initialize DuckDBTools.
+        
+        Args:
+            database: Path to database file or ':memory:' for in-memory database
+        """
+        self.database = database
+        self._conn = None
+
+    def _get_duckdb(self) -> Optional['duckdb']:
+        """Get duckdb module, installing if needed"""
+        if util.find_spec('duckdb') is None:
+            error_msg = "duckdb package is not available. Please install it using: pip install duckdb"
+            logging.error(error_msg)
+            return None
+        import duckdb
+        return duckdb
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def _get_connection(self) -> Optional['duckdb.DuckDBPyConnection']:
+        """Get or create database connection"""
+        if self._conn is None:
+            duckdb = self._get_duckdb()
+            if duckdb is None:
+                return None
+            try:
+                self._conn = duckdb.connect(self.database)
+            except Exception as e:
+                error_msg = f"Error connecting to database {self.database}: {str(e)}"
+                logging.error(error_msg)
+                return None
+        return self._conn
+
+    def execute_query(
+        self,
+        query: str,
+        params: Optional[Union[tuple, dict]] = None,
+        return_df: bool = True
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """Execute a SQL query.
+        
+        Args:
+            query: SQL query to execute
+            params: Query parameters
+            return_df: If True, return results as DataFrame records
+            
+        Returns:
+            Query results as list of dicts, or error dict
+        """
+        try:
+            conn = self._get_connection()
+            if conn is None:
+                return {"error": "Could not connect to database"}
+
+            if params:
+                result = conn.execute(query, params)
+            else:
+                result = conn.execute(query)
+
+            if return_df:
+                pd = self._get_pandas()
+                if pd is None:
+                    return {"error": "pandas package not available"}
+                df = result.df()
+                return df.to_dict('records')
+            else:
+                return [dict(row) for row in result.fetchall()]
+
+        except Exception as e:
+            error_msg = f"Error executing query: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def load_csv(
+        self,
+        table_name: str,
+        filepath: str,
+        schema: Optional[Dict[str, str]] = None,
+        if_exists: str = 'replace'
+    ) -> bool:
+        """Load a CSV file into a table.
+        
+        Args:
+            table_name: Name of table to create
+            filepath: Path to CSV file
+            schema: Optional column definitions
+            if_exists: What to do if table exists ('fail', 'replace', 'append')
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            conn = self._get_connection()
+            if conn is None:
+                return False
+
+            # Check if table exists
+            exists = conn.execute(f"""
+                SELECT name FROM sqlite_master 
+                WHERE type='table' AND name='{table_name}'
+            """).fetchone() is not None
+
+            if exists:
+                if if_exists == 'fail':
+                    raise ValueError(f"Table {table_name} already exists")
+                elif if_exists == 'replace':
+                    conn.execute(f"DROP TABLE IF EXISTS {table_name}")
+                elif if_exists != 'append':
+                    raise ValueError("if_exists must be 'fail', 'replace', or 'append'")
+
+            # Create table if needed
+            if not exists or if_exists == 'replace':
+                if schema:
+                    # Create table with schema
+                    columns = ', '.join(f"{k} {v}" for k, v in schema.items())
+                    conn.execute(f"CREATE TABLE {table_name} ({columns})")
+                else:
+                    # Infer schema from CSV
+                    conn.execute(f"""
+                        CREATE TABLE {table_name} AS 
+                        SELECT * FROM read_csv_auto('{filepath}')
+                        WHERE 1=0
+                    """)
+
+            # Load data
+            conn.execute(f"""
+                INSERT INTO {table_name}
+                SELECT * FROM read_csv_auto('{filepath}')
+            """)
+
+            return True
+
+        except Exception as e:
+            error_msg = f"Error loading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def export_csv(
+        self,
+        query: str,
+        filepath: str,
+        params: Optional[Union[tuple, dict]] = None
+    ) -> bool:
+        """Export query results to CSV.
+        
+        Args:
+            query: SQL query to execute
+            filepath: Output file path
+            params: Optional query parameters
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            # Execute query and get results as DataFrame
+            results = self.execute_query(query, params)
+            if isinstance(results, dict) and 'error' in results:
+                return False
+
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Convert to DataFrame and save
+            df = pd.DataFrame(results)
+            df.to_csv(filepath, index=False)
+            return True
+
+        except Exception as e:
+            error_msg = f"Error exporting to CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def close(self):
+        """Close database connection."""
+        if self._conn:
+            self._conn.close()
+            self._conn = None
+
+# Create instance for direct function access
+_duckdb_tools = DuckDBTools()
+execute_query = _duckdb_tools.execute_query
+load_csv = _duckdb_tools.load_csv
+export_csv = _duckdb_tools.export_csv
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("DuckDBTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    import tempfile
+    import os
+    
+    with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp:
+        temp_file = temp.name
+        
+        # Create sample data
+        with open(temp_file, 'w') as f:
+            f.write("name,age,city\n")
+            f.write("Alice,25,New York\n")
+            f.write("Bob,30,San Francisco\n")
+            f.write("Charlie,35,Chicago\n")
+        
+        print("1. Loading CSV File")
+        print("------------------------------")
+        result = load_csv('users', temp_file)
+        print(f"CSV loaded successfully: {result}")
+        print()
+        
+        print("2. Executing Query")
+        print("------------------------------")
+        query = "SELECT * FROM users WHERE age > 25"
+        results = execute_query(query)
+        print("Query results:")
+        for row in results:
+            print(row)
+        print()
+        
+        print("3. Exporting Query Results")
+        print("------------------------------")
+        with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp2:
+            temp_file2 = temp2.name
+            result = export_csv(query, temp_file2)
+            print(f"Results exported successfully: {result}")
+            if result:
+                print("\nExported file contents:")
+                with open(temp_file2) as f:
+                    print(f.read())
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/duckduckgo_tools.py

@@ -0,0 +1,52 @@
+"""DuckDuckGo search functionality.
+
+Usage:
+from praisonaiagents.tools import internet_search
+results = internet_search("AI news")
+
+or 
+from praisonaiagents.tools import duckduckgo
+results = duckduckgo("AI news")
+"""
+
+from typing import List, Dict
+import logging
+from importlib import util
+
+def internet_search(query: str) -> List[Dict]:
+    """Perform an internet search using DuckDuckGo."""
+    # Check if duckduckgo_search is installed
+    if util.find_spec("duckduckgo_search") is None:
+        error_msg = "DuckDuckGo search is not available. Please install duckduckgo_search package using: pip install duckduckgo_search"
+        logging.error(error_msg)
+        return [{"error": error_msg}]
+
+    try:
+        # Import only when needed
+        from duckduckgo_search import DDGS
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        error_msg = f"Error during DuckDuckGo search: {e}"
+        logging.error(error_msg)
+        return [{"error": error_msg}]
+
+def duckduckgo(query: str) -> List[Dict]:
+    """Alias for internet_search function."""
+    return internet_search(query)
+
+if __name__ == "__main__":
+    # Example usage
+    results = internet_search("Python programming")
+    for result in results:
+        print(f"\nTitle: {result.get('title')}")
+        print(f"URL: {result.get('url')}")
+        print(f"Snippet: {result.get('snippet')}")

agents/praisonaiagents/tools/excel_tools.py

@@ -0,0 +1,310 @@
+"""Tools for working with Excel files.
+
+Usage:
+from praisonaiagents.tools import excel_tools
+df = excel_tools.read_excel("data.xlsx")
+
+or
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
+df = read_excel("data.xlsx")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING, Tuple
+from importlib import util
+import json
+from pathlib import Path
+import tempfile
+import os
+
+if TYPE_CHECKING:
+    import pandas as pd
+    from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
+    from openpyxl.chart import BarChart, LineChart, PieChart, Reference
+    from openpyxl.chart.label import DataLabelList
+
+class ExcelTools:
+    """Tools for working with Excel files."""
+    
+    def __init__(self):
+        """Initialize ExcelTools."""
+        pass
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        if util.find_spec('openpyxl') is None:
+            error_msg = "openpyxl package is not available. Please install it using: pip install openpyxl"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def read_excel(
+        self,
+        filepath: str,
+        sheet_name: Optional[Union[str, int, List[Union[str, int]]]] = 0,
+        header: Optional[int] = 0,
+        usecols: Optional[List[str]] = None,
+        skiprows: Optional[Union[int, List[int]]] = None,
+        na_values: Optional[List[str]] = None,
+        dtype: Optional[Dict[str, str]] = None
+    ) -> Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]]:
+        """Read an Excel file with advanced options.
+        
+        Args:
+            filepath: Path to Excel file
+            sheet_name: Sheet name(s) or index(es)
+            header: Row number(s) to use as column names
+            usecols: Columns to read
+            skiprows: Line numbers to skip
+            na_values: Additional strings to recognize as NA/NaN
+            dtype: Dict of column dtypes
+            
+        Returns:
+            Dict of sheet names to data if multiple sheets, else list of row dicts
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return {"error": "Required packages not available"}
+
+            # Read Excel file
+            df = pd.read_excel(
+                filepath,
+                sheet_name=sheet_name,
+                header=header,
+                usecols=usecols,
+                skiprows=skiprows,
+                na_values=na_values,
+                dtype=dtype,
+                engine='openpyxl'
+            )
+            
+            # Convert to dict format
+            if isinstance(df, dict):
+                return {
+                    name: df[name].to_dict('records')
+                    for name in df.keys()
+                }
+            else:
+                return df.to_dict('records')
+                
+        except Exception as e:
+            error_msg = f"Error reading Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_excel(
+        self,
+        filepath: str,
+        data: Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]],
+        sheet_name: Optional[str] = None,
+        index: bool = False,
+        header: bool = True,
+        mode: str = 'w'
+    ) -> bool:
+        """Write data to an Excel file.
+        
+        Args:
+            filepath: Path to Excel file
+            data: Data to write (dict of sheet names to data or list of row dicts)
+            sheet_name: Sheet name if data is a list
+            index: Whether to write row indices
+            header: Whether to write column headers
+            mode: Write mode ('w' for write, 'a' for append)
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Convert data to DataFrame(s)
+            if isinstance(data, dict):
+                if mode == 'a' and os.path.exists(filepath):
+                    book = pd.ExcelFile(filepath)
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        # Copy existing sheets
+                        for sheet in book.sheet_names:
+                            pd.read_excel(filepath, sheet_name=sheet).to_excel(
+                                writer, sheet_name=sheet, index=index, header=header
+                            )
+                        # Add new sheets
+                        for name, sheet_data in data.items():
+                            df = pd.DataFrame(sheet_data)
+                            df.to_excel(
+                                writer, sheet_name=name, index=index, header=header
+                            )
+                else:
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        for name, sheet_data in data.items():
+                            df = pd.DataFrame(sheet_data)
+                            df.to_excel(
+                                writer, sheet_name=name, index=index, header=header
+                            )
+            else:
+                if mode == 'a' and os.path.exists(filepath):
+                    book = pd.ExcelFile(filepath)
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        # Copy existing sheets
+                        for sheet in book.sheet_names:
+                            pd.read_excel(filepath, sheet_name=sheet).to_excel(
+                                writer, sheet_name=sheet, index=index, header=header
+                            )
+                        # Add new sheet
+                        df = pd.DataFrame(data)
+                        df.to_excel(
+                            writer,
+                            sheet_name=sheet_name or 'Sheet1',
+                            index=index,
+                            header=header
+                        )
+                else:
+                    df = pd.DataFrame(data)
+                    df.to_excel(
+                        filepath,
+                        sheet_name=sheet_name or 'Sheet1',
+                        index=index,
+                        header=header,
+                        engine='openpyxl'
+                    )
+            
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error writing Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_excel(
+        self,
+        files: List[str],
+        output_file: str,
+        how: str = 'inner',
+        on: Optional[Union[str, List[str]]] = None,
+        suffixes: Optional[Tuple[str, str]] = None
+    ) -> bool:
+        """Merge multiple Excel files.
+        
+        Args:
+            files: List of Excel files to merge
+            output_file: Output file path
+            how: Merge method ('inner', 'outer', 'left', 'right')
+            on: Column(s) to merge on
+            suffixes: Suffixes for overlapping columns
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            if len(files) < 2:
+                error_msg = "At least two files are required for merging"
+                logging.error(error_msg)
+                return False
+            
+            # Read first file
+            result = self.read_excel(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                df = self.read_excel(file)
+                if isinstance(df, dict):
+                    df = next(iter(df.values()))
+                
+                pd = self._get_pandas()
+                if pd is None:
+                    return False
+                
+                result = pd.merge(
+                    pd.DataFrame(result),
+                    pd.DataFrame(df),
+                    how=how,
+                    on=on,
+                    suffixes=suffixes or ('_1', '_2')
+                ).to_dict('records')
+            
+            # Write merged result
+            return self.write_excel(output_file, result)
+            
+        except Exception as e:
+            error_msg = f"Error merging Excel files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_excel_tools = ExcelTools()
+read_excel = _excel_tools.read_excel
+write_excel = _excel_tools.write_excel
+merge_excel = _excel_tools.merge_excel
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("ExcelTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp:
+        temp_file = temp.name
+        
+        print("1. Writing Excel File")
+        print("------------------------------")
+        data = [
+            {"name": "Alice", "age": 25, "city": "New York"},
+            {"name": "Bob", "age": 30, "city": "San Francisco"},
+            {"name": "Charlie", "age": 35, "city": "Chicago"}
+        ]
+        result = write_excel(temp_file, data, "People")
+        print(f"Data written successfully: {result}")
+        print()
+        
+        print("2. Reading Excel File")
+        print("------------------------------")
+        read_data = read_excel(temp_file)
+        print("Contents of Excel file:")
+        for row in read_data:
+            print(row)
+        print()
+        
+        print("3. Merging Excel Files")
+        print("------------------------------")
+        # Create a second file for merging
+        with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp2:
+            temp_file2 = temp2.name
+            data2 = [
+                {"name": "Alice", "salary": 75000},
+                {"name": "Bob", "salary": 85000},
+                {"name": "Charlie", "salary": 95000}
+            ]
+            write_excel(temp_file2, data2, "Salaries")
+            
+            # Merge files
+            with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp3:
+                temp_file3 = temp3.name
+                result = merge_excel(
+                    [temp_file, temp_file2],
+                    temp_file3,
+                    how='inner',
+                    on='name'
+                )
+                print(f"Files merged successfully: {result}")
+                if result:
+                    merged_data = read_excel(temp_file3)
+                    print("\nMerged contents:")
+                    for row in merged_data:
+                        print(row)
+                print()
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+        os.unlink(temp_file3)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/file_tools.py

@@ -0,0 +1,274 @@
+"""File handling tools for basic file operations.
+
+Usage:
+from praisonaiagents.tools import file_tools
+content = file_tools.read_file("example.txt")
+file_tools.write_file("output.txt", "Hello World")
+
+or 
+from praisonaiagents.tools import read_file, write_file, list_files
+content = read_file("example.txt")
+"""
+
+import os
+import json
+from typing import List, Dict, Union, Optional
+from pathlib import Path
+import shutil
+import logging
+
+class FileTools:
+    """Tools for file operations including read, write, list, and information."""
+    
+    @staticmethod
+    def read_file(filepath: str, encoding: str = 'utf-8') -> str:
+        """
+        Read content from a file.
+        
+        Args:
+            filepath: Path to the file
+            encoding: File encoding (default: utf-8)
+            
+        Returns:
+            str: Content of the file
+        """
+        try:
+            with open(filepath, 'r', encoding=encoding) as f:
+                return f.read()
+        except Exception as e:
+            error_msg = f"Error reading file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return error_msg
+
+    @staticmethod
+    def write_file(filepath: str, content: str, encoding: str = 'utf-8') -> bool:
+        """
+        Write content to a file.
+        
+        Args:
+            filepath: Path to the file
+            content: Content to write
+            encoding: File encoding (default: utf-8)
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create directory if it doesn't exist
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            with open(filepath, 'w', encoding=encoding) as f:
+                f.write(content)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing to file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def list_files(directory: str, pattern: Optional[str] = None) -> List[Dict[str, Union[str, int]]]:
+        """
+        List files in a directory with optional pattern matching.
+        
+        Args:
+            directory: Directory path
+            pattern: Optional glob pattern (e.g., "*.txt")
+            
+        Returns:
+            List[Dict]: List of file information dictionaries
+        """
+        try:
+            path = Path(directory)
+            if pattern:
+                files = path.glob(pattern)
+            else:
+                files = path.iterdir()
+
+            result = []
+            for file in files:
+                if file.is_file():
+                    stat = file.stat()
+                    result.append({
+                        'name': file.name,
+                        'path': str(file),
+                        'size': stat.st_size,
+                        'modified': stat.st_mtime,
+                        'created': stat.st_ctime
+                    })
+            return result
+        except Exception as e:
+            error_msg = f"Error listing files in {directory}: {str(e)}"
+            logging.error(error_msg)
+            return [{'error': error_msg}]
+
+    @staticmethod
+    def get_file_info(filepath: str) -> Dict[str, Union[str, int]]:
+        """
+        Get detailed information about a file.
+        
+        Args:
+            filepath: Path to the file
+            
+        Returns:
+            Dict: File information including size, dates, etc.
+        """
+        try:
+            path = Path(filepath)
+            if not path.exists():
+                return {'error': f'File not found: {filepath}'}
+            
+            stat = path.stat()
+            return {
+                'name': path.name,
+                'path': str(path),
+                'size': stat.st_size,
+                'modified': stat.st_mtime,
+                'created': stat.st_ctime,
+                'is_file': path.is_file(),
+                'is_dir': path.is_dir(),
+                'extension': path.suffix,
+                'parent': str(path.parent)
+            }
+        except Exception as e:
+            error_msg = f"Error getting file info for {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {'error': error_msg}
+
+    @staticmethod
+    def copy_file(src: str, dst: str) -> bool:
+        """
+        Copy a file from source to destination.
+        
+        Args:
+            src: Source file path
+            dst: Destination file path
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create destination directory if it doesn't exist
+            os.makedirs(os.path.dirname(dst), exist_ok=True)
+            shutil.copy2(src, dst)
+            return True
+        except Exception as e:
+            error_msg = f"Error copying file from {src} to {dst}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def move_file(src: str, dst: str) -> bool:
+        """
+        Move a file from source to destination.
+        
+        Args:
+            src: Source file path
+            dst: Destination file path
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create destination directory if it doesn't exist
+            os.makedirs(os.path.dirname(dst), exist_ok=True)
+            shutil.move(src, dst)
+            return True
+        except Exception as e:
+            error_msg = f"Error moving file from {src} to {dst}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def delete_file(filepath: str) -> bool:
+        """
+        Delete a file.
+        
+        Args:
+            filepath: Path to the file
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.remove(filepath)
+            return True
+        except Exception as e:
+            error_msg = f"Error deleting file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_file_tools = FileTools()
+read_file = _file_tools.read_file
+write_file = _file_tools.write_file
+list_files = _file_tools.list_files
+get_file_info = _file_tools.get_file_info
+copy_file = _file_tools.copy_file
+move_file = _file_tools.move_file
+delete_file = _file_tools.delete_file
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("FileTools Demonstration")
+    print("==================================================\n")
+
+    # Create a test directory
+    test_dir = os.path.join(os.getcwd(), "test_files")
+    os.makedirs(test_dir, exist_ok=True)
+    
+    # Create test files
+    test_file = os.path.join(test_dir, "test_file.txt")
+    test_content = "Hello, this is a test file!"
+    
+    print("1. Writing to file")
+    print("------------------------------")
+    success = write_file(test_file, test_content)
+    print(f"Write successful: {success}\n")
+    
+    print("2. Reading from file")
+    print("------------------------------")
+    content = read_file(test_file)
+    print(f"Content: {content}\n")
+    
+    print("3. File Information")
+    print("------------------------------")
+    info = get_file_info(test_file)
+    print(json.dumps(info, indent=2))
+    print()
+    
+    print("4. Listing Files")
+    print("------------------------------")
+    files = list_files(test_dir, "*.txt")
+    for file in files:
+        print(f"Found: {file['name']} ({file['size']} bytes)")
+    print()
+    
+    print("5. Copying File")
+    print("------------------------------")
+    copy_file_path = os.path.join(test_dir, "test_file_copy.txt")
+    copy_success = copy_file(test_file, copy_file_path)
+    print(f"Copy successful: {copy_success}\n")
+    
+    print("6. Moving File")
+    print("------------------------------")
+    move_file_path = os.path.join(test_dir, "test_file_moved.txt")
+    move_success = move_file(copy_file_path, move_file_path)
+    print(f"Move successful: {move_success}\n")
+    
+    print("7. Deleting Files")
+    print("------------------------------")
+    delete_success = delete_file(test_file)
+    print(f"Delete original successful: {delete_success}")
+    delete_success = delete_file(move_file_path)
+    print(f"Delete moved file successful: {delete_success}\n")
+    
+    # Clean up test directory
+    try:
+        shutil.rmtree(test_dir)
+        print("Test directory cleaned up successfully")
+    except Exception as e:
+        print(f"Error cleaning up test directory: {str(e)}")
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/json_tools.py

@@ -0,0 +1,515 @@
+"""Tools for working with JSON files.
+
+Usage:
+from praisonaiagents.tools import json_tools
+data = json_tools.read_json("data.json")
+
+or
+from praisonaiagents.tools import read_json, write_json, merge_json
+data = read_json("data.json")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, Tuple
+from importlib import util
+import json
+from datetime import datetime
+
+class JSONTools:
+    """Tools for working with JSON files."""
+    
+    def __init__(self):
+        """Initialize JSONTools."""
+        pass
+
+    def read_json(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        validate_schema: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """Read a JSON file with optional schema validation.
+        
+        Args:
+            filepath: Path to JSON file
+            encoding: File encoding
+            validate_schema: Optional JSON schema for validation
+            
+        Returns:
+            Dict with JSON data
+        """
+        try:
+            # Read JSON file
+            with open(filepath, 'r', encoding=encoding) as f:
+                data = json.load(f)
+            
+            # Validate against schema if provided
+            if validate_schema:
+                if util.find_spec('jsonschema') is None:
+                    error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                    logging.error(error_msg)
+                    return {"error": error_msg}
+                import jsonschema
+                try:
+                    jsonschema.validate(instance=data, schema=validate_schema)
+                except jsonschema.exceptions.ValidationError as e:
+                    error_msg = f"JSON validation failed: {str(e)}"
+                    logging.error(error_msg)
+                    return {"error": error_msg}
+            
+            return data
+            
+        except Exception as e:
+            error_msg = f"Error reading JSON file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_json(
+        self,
+        data: Union[Dict[str, Any], List[Any]],
+        filepath: str,
+        encoding: str = 'utf-8',
+        indent: int = 2,
+        sort_keys: bool = False,
+        ensure_ascii: bool = False
+    ) -> bool:
+        """Write data to a JSON file.
+        
+        Args:
+            data: Data to write
+            filepath: Output file path
+            encoding: File encoding
+            indent: Number of spaces for indentation
+            sort_keys: Sort dictionary keys
+            ensure_ascii: Escape non-ASCII characters
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            with open(filepath, 'w', encoding=encoding) as f:
+                json.dump(
+                    data,
+                    f,
+                    indent=indent,
+                    sort_keys=sort_keys,
+                    ensure_ascii=ensure_ascii
+                )
+            return True
+        except Exception as e:
+            error_msg = f"Error writing JSON file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_json(
+        self,
+        files: List[str],
+        output_file: str,
+        merge_arrays: bool = True,
+        overwrite_duplicates: bool = True
+    ) -> bool:
+        """Merge multiple JSON files.
+        
+        Args:
+            files: List of JSON files to merge
+            output_file: Output file path
+            merge_arrays: Merge arrays instead of overwriting
+            overwrite_duplicates: Overwrite duplicate keys
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            if len(files) < 2:
+                raise ValueError("At least two files are required for merging")
+            
+            # Read first file
+            result = self.read_json(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                data = self.read_json(file)
+                result = self._deep_merge(
+                    result,
+                    data,
+                    merge_arrays=merge_arrays,
+                    overwrite_duplicates=overwrite_duplicates
+                )
+            
+            # Write merged result
+            return self.write_json(result, output_file)
+        except Exception as e:
+            error_msg = f"Error merging JSON files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def _deep_merge(
+        self,
+        dict1: Dict[str, Any],
+        dict2: Dict[str, Any],
+        merge_arrays: bool = True,
+        overwrite_duplicates: bool = True
+    ) -> Dict[str, Any]:
+        """Deep merge two dictionaries.
+        
+        Args:
+            dict1: First dictionary
+            dict2: Second dictionary
+            merge_arrays: Merge arrays instead of overwriting
+            overwrite_duplicates: Overwrite duplicate keys
+            
+        Returns:
+            Merged dictionary
+        """
+        result = dict1.copy()
+        
+        for key, value in dict2.items():
+            if key in result:
+                if isinstance(result[key], dict) and isinstance(value, dict):
+                    result[key] = self._deep_merge(
+                        result[key],
+                        value,
+                        merge_arrays=merge_arrays,
+                        overwrite_duplicates=overwrite_duplicates
+                    )
+                elif isinstance(result[key], list) and isinstance(value, list):
+                    if merge_arrays:
+                        result[key].extend(value)
+                    elif overwrite_duplicates:
+                        result[key] = value
+                elif overwrite_duplicates:
+                    result[key] = value
+            else:
+                result[key] = value
+        
+        return result
+
+    def validate_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        schema: Dict[str, Any]
+    ) -> Tuple[bool, Optional[str]]:
+        """Validate JSON data against a schema.
+        
+        Args:
+            data: JSON data or filepath
+            schema: JSON schema for validation
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+        """
+        try:
+            if util.find_spec('jsonschema') is None:
+                error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                logging.error(error_msg)
+                return False, error_msg
+            import jsonschema
+
+            # Load data if filepath provided
+            if isinstance(data, str):
+                with open(data, 'r') as f:
+                    data = json.load(f)
+            
+            jsonschema.validate(instance=data, schema=schema)
+            return True, None
+            
+        except jsonschema.exceptions.ValidationError as e:
+            error_msg = f"JSON validation failed: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+            
+        except Exception as e:
+            error_msg = f"Error validating JSON: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+
+    def analyze_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        max_depth: int = 10
+    ) -> Dict[str, Any]:
+        """Analyze JSON data structure.
+        
+        Args:
+            data: JSON data or filepath
+            max_depth: Maximum depth to analyze
+            
+        Returns:
+            Dict with analysis results
+        """
+        try:
+            # Load data if filepath provided
+            if isinstance(data, str):
+                data = self.read_json(data)
+            
+            def analyze_value(value: Any, depth: int = 0) -> Dict[str, Any]:
+                if depth >= max_depth:
+                    return {'type': 'max_depth_reached'}
+                
+                result = {'type': type(value).__name__}
+                
+                if isinstance(value, dict):
+                    result['size'] = len(value)
+                    result['keys'] = list(value.keys())
+                    if depth < max_depth - 1:
+                        result['children'] = {
+                            k: analyze_value(v, depth + 1)
+                            for k, v in value.items()
+                        }
+                
+                elif isinstance(value, list):
+                    result['length'] = len(value)
+                    if value:
+                        result['element_types'] = list(set(
+                            type(x).__name__ for x in value
+                        ))
+                        if depth < max_depth - 1:
+                            result['sample_elements'] = [
+                                analyze_value(x, depth + 1)
+                                for x in value[:5]
+                            ]
+                
+                elif isinstance(value, (int, float)):
+                    result.update({
+                        'value': value,
+                        'is_integer': isinstance(value, int)
+                    })
+                
+                elif isinstance(value, str):
+                    result.update({
+                        'length': len(value),
+                        'sample': value[:100] if len(value) > 100 else value
+                    })
+                
+                return result
+            
+            return {
+                'analysis_time': datetime.now().isoformat(),
+                'structure': analyze_value(data)
+            }
+        except Exception as e:
+            error_msg = f"Error analyzing JSON: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+    def transform_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        transformations: List[Dict[str, Any]]
+    ) -> Dict[str, Any]:
+        """Transform JSON data using a list of operations.
+        
+        Args:
+            data: JSON data or filepath
+            transformations: List of transformation operations
+            
+        Returns:
+            Transformed JSON data
+        """
+        try:
+            # Load data if filepath provided
+            if isinstance(data, str):
+                data = self.read_json(data)
+            
+            result = data.copy()
+            
+            for transform in transformations:
+                op = transform.get('operation')
+                path = transform.get('path', '').split('.')
+                value = transform.get('value')
+                
+                if op == 'set':
+                    self._set_value(result, path, value)
+                elif op == 'delete':
+                    self._delete_value(result, path)
+                elif op == 'rename':
+                    old_path = path
+                    new_path = value.split('.')
+                    self._rename_key(result, old_path, new_path)
+                elif op == 'move':
+                    old_path = path
+                    new_path = value.split('.')
+                    self._move_value(result, old_path, new_path)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error transforming JSON: {str(e)}"
+            logging.error(error_msg)
+            return data
+
+    def _set_value(self, data: Dict[str, Any], path: List[str], value: Any):
+        """Set a value at the specified path."""
+        current = data
+        for key in path[:-1]:
+            if key not in current:
+                current[key] = {}
+            current = current[key]
+        current[path[-1]] = value
+
+    def _delete_value(self, data: Dict[str, Any], path: List[str]):
+        """Delete a value at the specified path."""
+        current = data
+        for key in path[:-1]:
+            if key not in current:
+                return
+            current = current[key]
+        if path[-1] in current:
+            del current[path[-1]]
+
+    def _rename_key(
+        self,
+        data: Dict[str, Any],
+        old_path: List[str],
+        new_path: List[str]
+    ):
+        """Rename a key at the specified path."""
+        value = self._get_value(data, old_path)
+        if value is not None:
+            self._delete_value(data, old_path)
+            self._set_value(data, new_path, value)
+
+    def _move_value(
+        self,
+        data: Dict[str, Any],
+        old_path: List[str],
+        new_path: List[str]
+    ):
+        """Move a value from one path to another."""
+        self._rename_key(data, old_path, new_path)
+
+    def _get_value(
+        self,
+        data: Dict[str, Any],
+        path: List[str]
+    ) -> Optional[Any]:
+        """Get a value at the specified path."""
+        current = data
+        for key in path:
+            if key not in current:
+                return None
+            current = current[key]
+        return current
+
+# Create instance for direct function access
+_json_tools = JSONTools()
+read_json = _json_tools.read_json
+write_json = _json_tools.write_json
+merge_json = _json_tools.merge_json
+validate_json = _json_tools.validate_json
+analyze_json = _json_tools.analyze_json
+transform_json = _json_tools.transform_json
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("JSONTools Demonstration")
+    print("==================================================\n")
+    
+    # Sample data
+    data1 = {
+        'id': 1,
+        'name': 'Alice',
+        'scores': [95, 87, 92],
+        'details': {
+            'age': 25,
+            'city': 'New York'
+        }
+    }
+    
+    data2 = {
+        'id': 2,
+        'name': 'Bob',
+        'scores': [88, 90, 85],
+        'details': {
+            'age': 30,
+            'country': 'USA'
+        }
+    }
+    
+    # 1. Write JSON files
+    print("1. Writing JSON Files")
+    print("------------------------------")
+    success = write_json(data1, 'test1.json')
+    print(f"First file written: {success}")
+    success = write_json(data2, 'test2.json')
+    print(f"Second file written: {success}")
+    print()
+    
+    # 2. Read JSON file
+    print("2. Reading JSON File")
+    print("------------------------------")
+    data = read_json('test1.json')
+    print("Contents of test1.json:")
+    print(json.dumps(data, indent=2))
+    print()
+    
+    # 3. Merge JSON files
+    print("3. Merging JSON Files")
+    print("------------------------------")
+    success = merge_json(['test1.json', 'test2.json'], 'merged.json')
+    print(f"Files merged: {success}")
+    if success:
+        print("Merged contents:")
+        print(json.dumps(read_json('merged.json'), indent=2))
+    print()
+    
+    # 4. Validate JSON
+    print("4. Validating JSON")
+    print("------------------------------")
+    schema = {
+        'type': 'object',
+        'properties': {
+            'id': {'type': 'integer'},
+            'name': {'type': 'string'},
+            'scores': {
+                'type': 'array',
+                'items': {'type': 'number'}
+            },
+            'details': {
+                'type': 'object',
+                'properties': {
+                    'age': {'type': 'integer'},
+                    'city': {'type': 'string'}
+                }
+            }
+        },
+        'required': ['id', 'name']
+    }
+    
+    is_valid, error = validate_json(data1, schema)
+    print(f"Validation result: {is_valid}")
+    if error:
+        print(f"Validation error: {error}")
+    print()
+    
+    # 5. Analyze JSON
+    print("5. Analyzing JSON")
+    print("------------------------------")
+    analysis = analyze_json(data1)
+    print("Analysis results:")
+    print(json.dumps(analysis, indent=2))
+    print()
+    
+    # 6. Transform JSON
+    print("6. Transforming JSON")
+    print("------------------------------")
+    transformations = [
+        {
+            'operation': 'set',
+            'path': 'details.status',
+            'value': 'active'
+        },
+        {
+            'operation': 'rename',
+            'path': 'details.city',
+            'value': 'details.location'
+        }
+    ]
+    
+    transformed = transform_json(data1, transformations)
+    print("Transformed data:")
+    print(json.dumps(transformed, indent=2))
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/newspaper_tools.py

@@ -0,0 +1,354 @@
+"""Newspaper tools for scraping and parsing news articles.
+
+Usage:
+from praisonaiagents.tools import newspaper_tools
+article = newspaper_tools.get_article("https://example.com/article")
+sources = newspaper_tools.get_news_sources("technology")
+articles = newspaper_tools.get_articles_from_source("https://techcrunch.com")
+
+or
+from praisonaiagents.tools import get_article, get_news_sources
+article = get_article("https://example.com/article")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+from urllib.parse import urlparse
+
+# Predefined list of popular news sources
+POPULAR_NEWS_SOURCES = {
+    'technology': [
+        'https://techcrunch.com',
+        'https://www.theverge.com',
+        'https://www.wired.com',
+        'https://www.engadget.com',
+        'https://arstechnica.com'
+    ],
+    'business': [
+        'https://www.bloomberg.com',
+        'https://www.reuters.com',
+        'https://www.wsj.com',
+        'https://www.ft.com',
+        'https://www.cnbc.com'
+    ],
+    'general': [
+        'https://www.nytimes.com',
+        'https://www.theguardian.com',
+        'https://www.washingtonpost.com',
+        'https://www.bbc.com',
+        'https://www.cnn.com'
+    ],
+    'sports': [
+        'https://www.espn.com',
+        'https://sports.yahoo.com',
+        'https://www.cbssports.com',
+        'https://www.skysports.com',
+        'https://www.bleacherreport.com'
+    ],
+    'entertainment': [
+        'https://variety.com',
+        'https://www.hollywoodreporter.com',
+        'https://www.ew.com',
+        'https://www.deadline.com',
+        'https://www.imdb.com/news'
+    ],
+    'science': [
+        'https://www.scientificamerican.com',
+        'https://www.sciencedaily.com',
+        'https://www.newscientist.com',
+        'https://www.sciencemag.org',
+        'https://www.nature.com/news'
+    ]
+}
+
+class NewspaperTools:
+    """Tools for scraping and parsing news articles."""
+    
+    def __init__(self):
+        """Initialize NewspaperTools and check for newspaper package."""
+        self._check_newspaper()
+        
+    def _check_newspaper(self):
+        """Check if newspaper package is installed."""
+        if util.find_spec("newspaper") is None:
+            raise ImportError("newspaper3k package is not available. Please install it using: pip install newspaper3k")
+        global newspaper
+        import newspaper
+
+    def get_article(
+        self, 
+        url: str,
+        language: str = 'en'
+    ) -> Dict[str, Any]:
+        """
+        Extract and parse a news article from a URL.
+        
+        Args:
+            url: URL of the article
+            language: Language code (e.g., 'en' for English)
+            
+        Returns:
+            Dict: Article information including title, text, authors, etc.
+        """
+        try:
+            from newspaper import Article, Config
+            
+            # Configure article download
+            config = Config()
+            config.browser_user_agent = 'Mozilla/5.0'
+            config.language = language
+            
+            # Download and parse article
+            article = Article(url, config=config)
+            article.download()
+            article.parse()
+            
+            # Try to extract additional information
+            try:
+                article.nlp()
+            except Exception as e:
+                logging.warning(f"NLP processing failed: {str(e)}")
+            
+            # Build response
+            response = {
+                "url": url,
+                "title": article.title,
+                "text": article.text,
+                "authors": article.authors,
+                "publish_date": article.publish_date.isoformat() if article.publish_date else None,
+                "top_image": article.top_image,
+                "images": list(article.images),
+                "movies": list(article.movies),
+                "source_domain": urlparse(url).netloc,
+            }
+            
+            # Add NLP results if available
+            if hasattr(article, 'keywords') and article.keywords:
+                response["keywords"] = article.keywords
+            if hasattr(article, 'summary') and article.summary:
+                response["summary"] = article.summary
+            
+            return response
+        except Exception as e:
+            error_msg = f"Error extracting article from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_news_sources(
+        self, 
+        category: Optional[str] = None,
+        language: str = 'en',
+        country: Optional[str] = None
+    ) -> Union[List[Dict[str, str]], Dict[str, str]]:
+        """
+        Get a list of news sources, optionally filtered by category.
+        
+        Args:
+            category: Category to filter by (e.g., 'technology', 'sports')
+            language: Language code
+            country: Country code
+            
+        Returns:
+            List[Dict] or Dict: List of news sources or error dict
+        """
+        try:
+            sources = []
+            
+            # Get sources for the specified category or all categories
+            if category:
+                category = category.lower()
+                if category in POPULAR_NEWS_SOURCES:
+                    urls = POPULAR_NEWS_SOURCES[category]
+                else:
+                    urls = []
+                    for cat_urls in POPULAR_NEWS_SOURCES.values():
+                        urls.extend(cat_urls)
+            else:
+                urls = []
+                for cat_urls in POPULAR_NEWS_SOURCES.values():
+                    urls.extend(cat_urls)
+            
+            # Create source objects
+            for url in urls:
+                domain = urlparse(url).netloc
+                source = {
+                    "url": url,
+                    "domain": domain,
+                    "name": domain.replace("www.", "").split(".")[0].title(),
+                    "category": category if category else "general"
+                }
+                sources.append(source)
+            
+            return sources
+        except Exception as e:
+            error_msg = f"Error getting news sources: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_articles_from_source(
+        self, 
+        source_url: str,
+        limit: int = 10,
+        language: str = 'en'
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get recent articles from a news source.
+        
+        Args:
+            source_url: URL of the news source
+            limit: Maximum number of articles to return
+            language: Language code
+            
+        Returns:
+            List[Dict] or Dict: List of articles or error dict
+        """
+        try:
+            from newspaper import Source, Config
+            
+            # Configure source scraping
+            config = Config()
+            config.browser_user_agent = 'Mozilla/5.0'
+            config.language = language
+            config.fetch_images = False  # Speed up processing
+            
+            # Build news source
+            source = Source(source_url, config=config)
+            source.build()
+            
+            # Get articles
+            articles = []
+            for article_url in source.article_urls()[:limit]:
+                try:
+                    article = self.get_article(article_url, language)
+                    if "error" not in article:
+                        articles.append(article)
+                except Exception as e:
+                    logging.warning(f"Error processing article {article_url}: {str(e)}")
+                    continue
+                
+                if len(articles) >= limit:
+                    break
+            
+            return articles
+        except Exception as e:
+            error_msg = f"Error getting articles from {source_url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_trending_topics(
+        self,
+        sources: Optional[List[str]] = None,
+        limit: int = 10,
+        language: str = 'en'
+    ) -> Union[List[str], Dict[str, str]]:
+        """
+        Get trending topics across news sources.
+        
+        Args:
+            sources: List of source URLs to analyze
+            limit: Maximum number of trending topics to return
+            language: Language code
+            
+        Returns:
+            List[str] or Dict: List of trending topics or error dict
+        """
+        try:
+            from collections import Counter
+            
+            # Use default sources if none provided
+            if not sources:
+                sources_data = self.get_news_sources(language=language)
+                if isinstance(sources_data, dict) and "error" in sources_data:
+                    return sources_data
+                sources = [s["url"] for s in sources_data[:5]]  # Use top 5 sources
+            
+            # Collect keywords from articles
+            all_keywords = []
+            for source_url in sources:
+                try:
+                    articles = self.get_articles_from_source(source_url, limit=5, language=language)
+                    if isinstance(articles, list):
+                        for article in articles:
+                            if "keywords" in article:
+                                all_keywords.extend(article["keywords"])
+                except Exception as e:
+                    logging.warning(f"Error processing source {source_url}: {str(e)}")
+                    continue
+            
+            # Get most common keywords
+            trending = Counter(all_keywords).most_common(limit)
+            return [topic for topic, count in trending]
+        except Exception as e:
+            error_msg = f"Error getting trending topics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_newspaper_tools = NewspaperTools()
+get_article = _newspaper_tools.get_article
+get_news_sources = _newspaper_tools.get_news_sources
+get_articles_from_source = _newspaper_tools.get_articles_from_source
+get_trending_topics = _newspaper_tools.get_trending_topics
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("NewspaperTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Get news sources
+    print("1. Getting News Sources")
+    print("------------------------------")
+    tech_sources = get_news_sources("technology")
+    print("Technology news sources:")
+    if isinstance(tech_sources, list):
+        print(json.dumps(tech_sources[:3], indent=2))  # Show first 3 sources
+    else:
+        print(tech_sources)  # Show error
+    print()
+    
+    if isinstance(tech_sources, list) and tech_sources:
+        source_url = tech_sources[0]["url"]
+        
+        # 2. Get articles from a source
+        print("2. Getting Articles from Source")
+        print("------------------------------")
+        articles = get_articles_from_source(source_url, limit=2)
+        print(f"Articles from {source_url}:")
+        if isinstance(articles, list):
+            for article in articles:
+                print(f"- {article['title']}")
+                if "summary" in article:
+                    print(f"  Summary: {article['summary'][:200]}...")
+        else:
+            print(articles)  # Show error
+        print()
+        
+        # 3. Get a single article
+        print("3. Getting Single Article")
+        print("------------------------------")
+        if isinstance(articles, list) and articles:
+            article_url = articles[0]["url"]
+            article = get_article(article_url)
+            if "error" not in article:
+                print(f"Article: {article['title']}")
+                if "summary" in article:
+                    print(f"Summary: {article['summary'][:200]}...")
+                print(f"Authors: {', '.join(article['authors'])}")
+                print(f"Date: {article['publish_date']}")
+            else:
+                print(article)  # Show error
+        print()
+        
+        # 4. Get trending topics
+        print("4. Getting Trending Topics")
+        print("------------------------------")
+        topics = get_trending_topics([source_url], limit=5)
+        print("Trending topics:")
+        print(json.dumps(topics, indent=2))
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/pandas_tools.py

@@ -0,0 +1,326 @@
+"""Pandas tools for data manipulation and analysis.
+
+Usage:
+from praisonaiagents.tools import pandas_tools
+df = pandas_tools.read_csv("data.csv")
+df = pandas_tools.filter_data(df, "column > 5")
+summary = pandas_tools.get_summary(df)
+
+or
+from praisonaiagents.tools import read_csv, filter_data, get_summary
+df = read_csv("data.csv")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+import os
+
+# Import pandas for type hints, but don't use it until we check it's installed
+if util.find_spec("pandas") is not None:
+    import pandas as pd
+    import numpy as np
+else:
+    # Create a placeholder for type hints
+    class pd:
+        DataFrame = None
+
+def _convert_to_serializable(obj: Any) -> Any:
+    """Convert numpy/pandas types to JSON serializable Python types."""
+    if isinstance(obj, (np.integer, np.floating)):
+        return obj.item()
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif isinstance(obj, pd.Series):
+        return obj.to_list()
+    elif isinstance(obj, pd.DataFrame):
+        return obj.to_dict(orient='records')
+    return obj
+
+class PandasTools:
+    """Tools for data manipulation and analysis using pandas."""
+    
+    def __init__(self):
+        """Initialize PandasTools and check for pandas installation."""
+        self._check_pandas()
+        
+    def _check_pandas(self):
+        """Check if pandas is installed."""
+        if util.find_spec("pandas") is None:
+            raise ImportError("pandas is not available. Please install it using: pip install pandas")
+        global pd, np
+        import pandas as pd
+        import numpy as np
+
+    def read_csv(self, filepath: str, **kwargs) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Read a CSV file into a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the CSV file
+            **kwargs: Additional arguments to pass to pd.read_csv()
+            
+        Returns:
+            pd.DataFrame or Dict: DataFrame if successful, error dict if failed
+        """
+        try:
+            return pd.read_csv(filepath, **kwargs)
+        except Exception as e:
+            error_msg = f"Error reading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def read_excel(self, filepath: str, **kwargs) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Read an Excel file into a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the Excel file
+            **kwargs: Additional arguments to pass to pd.read_excel()
+            
+        Returns:
+            pd.DataFrame or Dict: DataFrame if successful, error dict if failed
+        """
+        try:
+            return pd.read_excel(filepath, **kwargs)
+        except Exception as e:
+            error_msg = f"Error reading Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_csv(self, df: pd.DataFrame, filepath: str, **kwargs) -> bool:
+        """
+        Write DataFrame to a CSV file.
+        
+        Args:
+            df: DataFrame to write
+            filepath: Output file path
+            **kwargs: Additional arguments to pass to df.to_csv()
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            df.to_csv(filepath, **kwargs)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def write_excel(self, df: pd.DataFrame, filepath: str, **kwargs) -> bool:
+        """
+        Write DataFrame to an Excel file.
+        
+        Args:
+            df: DataFrame to write
+            filepath: Output file path
+            **kwargs: Additional arguments to pass to df.to_excel()
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            df.to_excel(filepath, **kwargs)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def filter_data(self, df: pd.DataFrame, query: str) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Filter DataFrame using a query string.
+        
+        Args:
+            df: Input DataFrame
+            query: Query string (e.g., "column > 5 and other_column == 'value'")
+            
+        Returns:
+            pd.DataFrame or Dict: Filtered DataFrame if successful, error dict if failed
+        """
+        try:
+            return df.query(query)
+        except Exception as e:
+            error_msg = f"Error filtering data with query '{query}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_summary(self, df: pd.DataFrame) -> Dict[str, Any]:
+        """
+        Get a summary of the DataFrame including basic statistics and info.
+        
+        Args:
+            df: Input DataFrame
+            
+        Returns:
+            Dict: Summary statistics and information
+        """
+        try:
+            numeric_summary = df.describe().to_dict()
+            # Convert numpy types to native Python types
+            for col in numeric_summary:
+                numeric_summary[col] = {k: _convert_to_serializable(v) 
+                                     for k, v in numeric_summary[col].items()}
+            
+            summary = {
+                "shape": list(df.shape),
+                "columns": list(df.columns),
+                "dtypes": df.dtypes.astype(str).to_dict(),
+                "null_counts": df.isnull().sum().to_dict(),
+                "numeric_summary": numeric_summary,
+                "memory_usage": int(df.memory_usage(deep=True).sum()),
+            }
+            return summary
+        except Exception as e:
+            error_msg = f"Error getting data summary: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def group_by(
+        self, 
+        df: pd.DataFrame, 
+        columns: Union[str, List[str]], 
+        agg_dict: Dict[str, Union[str, List[str]]]
+    ) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Group DataFrame by columns and apply aggregation functions.
+        
+        Args:
+            df: Input DataFrame
+            columns: Column(s) to group by
+            agg_dict: Dictionary of column:function pairs for aggregation
+            
+        Returns:
+            pd.DataFrame or Dict: Grouped DataFrame if successful, error dict if failed
+        """
+        try:
+            return df.groupby(columns).agg(agg_dict).reset_index()
+        except Exception as e:
+            error_msg = f"Error grouping data: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def pivot_table(
+        self, 
+        df: pd.DataFrame, 
+        index: Union[str, List[str]], 
+        columns: Optional[Union[str, List[str]]] = None,
+        values: Optional[Union[str, List[str]]] = None,
+        aggfunc: str = "mean"
+    ) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Create a pivot table from DataFrame.
+        
+        Args:
+            df: Input DataFrame
+            index: Column(s) to use as index
+            columns: Column(s) to use as columns
+            values: Column(s) to aggregate
+            aggfunc: Aggregation function to use
+            
+        Returns:
+            pd.DataFrame or Dict: Pivot table if successful, error dict if failed
+        """
+        try:
+            return pd.pivot_table(
+                df,
+                index=index,
+                columns=columns,
+                values=values,
+                aggfunc=aggfunc
+            ).reset_index()
+        except Exception as e:
+            error_msg = f"Error creating pivot table: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_pandas_tools = PandasTools()
+read_csv = _pandas_tools.read_csv
+read_excel = _pandas_tools.read_excel
+write_csv = _pandas_tools.write_csv
+write_excel = _pandas_tools.write_excel
+filter_data = _pandas_tools.filter_data
+get_summary = _pandas_tools.get_summary
+group_by = _pandas_tools.group_by
+pivot_table = _pandas_tools.pivot_table
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("PandasTools Demonstration")
+    print("==================================================\n")
+
+    # Create a test directory
+    test_dir = os.path.join(os.getcwd(), "test_files")
+    os.makedirs(test_dir, exist_ok=True)
+    
+    # Create a sample DataFrame
+    df = pd.DataFrame({
+        'name': ['John', 'Jane', 'Bob', 'Alice', 'Charlie'],
+        'age': [25, 30, 35, 28, 32],
+        'city': ['New York', 'London', 'Paris', 'Tokyo', 'London'],
+        'salary': [50000, 60000, 75000, 65000, 55000]
+    })
+    
+    # 1. Write to CSV
+    print("1. Writing to CSV")
+    print("------------------------------")
+    csv_file = os.path.join(test_dir, "sample.csv")
+    success = write_csv(df, csv_file, index=False)
+    print(f"Write successful: {success}\n")
+    
+    # 2. Read from CSV
+    print("2. Reading from CSV")
+    print("------------------------------")
+    df_read = read_csv(csv_file)
+    print("First few rows:")
+    print(df_read.head())
+    print()
+    
+    # 3. Filter Data
+    print("3. Filtering Data")
+    print("------------------------------")
+    filtered_df = filter_data(df, "age > 30 and salary > 60000")
+    print("People over 30 with salary > 60000:")
+    print(filtered_df)
+    print()
+    
+    # 4. Get Summary
+    print("4. Data Summary")
+    print("------------------------------")
+    summary = get_summary(df)
+    print(json.dumps(summary, indent=2))
+    print()
+    
+    # 5. Group By
+    print("5. Group By")
+    print("------------------------------")
+    grouped = group_by(df, "city", {"salary": ["mean", "count"], "age": "mean"})
+    print("Statistics by city:")
+    print(grouped)
+    print()
+    
+    # 6. Pivot Table
+    print("6. Pivot Table")
+    print("------------------------------")
+    pivoted = pivot_table(df, index="city", values=["salary", "age"])
+    print("Pivot table by city:")
+    print(pivoted)
+    print()
+    
+    # Clean up test directory
+    try:
+        import shutil
+        shutil.rmtree(test_dir)
+        print("Test directory cleaned up successfully")
+    except Exception as e:
+        print(f"Error cleaning up test directory: {str(e)}")
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/python_tools.py

@@ -0,0 +1,423 @@
+"""Tools for Python code execution and analysis.
+
+Usage:
+from praisonaiagents.tools import python_tools
+result = python_tools.execute_code("print('Hello, World!')")
+
+or
+from praisonaiagents.tools import execute_code, analyze_code, format_code
+result = execute_code("print('Hello, World!')")
+"""
+
+import logging
+from typing import Dict, List, Optional, Any
+from importlib import util
+import io
+from contextlib import redirect_stdout, redirect_stderr
+import traceback
+
+class PythonTools:
+    """Tools for Python code execution and analysis."""
+    
+    def __init__(self):
+        """Initialize PythonTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package in ['black', 'pylint', 'autopep8']:
+            if util.find_spec(package) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def execute_code(
+        self,
+        code: str,
+        globals_dict: Optional[Dict[str, Any]] = None,
+        locals_dict: Optional[Dict[str, Any]] = None,
+        timeout: int = 30,
+        max_output_size: int = 10000
+    ) -> Dict[str, Any]:
+        """Execute Python code safely."""
+        try:
+            # Set up execution environment
+            if globals_dict is None:
+                globals_dict = {'__builtins__': __builtins__}
+            if locals_dict is None:
+                locals_dict = {}
+            
+            # Capture output
+            stdout_buffer = io.StringIO()
+            stderr_buffer = io.StringIO()
+            
+            try:
+                # Compile code
+                compiled_code = compile(code, '<string>', 'exec')
+                
+                # Execute with output capture
+                with redirect_stdout(stdout_buffer), redirect_stderr(stderr_buffer):
+                    exec(compiled_code, globals_dict, locals_dict)
+                    
+                    # Get last expression value if any
+                    import ast
+                    tree = ast.parse(code)
+                    if tree.body and isinstance(tree.body[-1], ast.Expr):
+                        result = eval(
+                            compile(ast.Expression(tree.body[-1].value), '<string>', 'eval'),
+                            globals_dict,
+                            locals_dict
+                        )
+                    else:
+                        result = None
+                
+                # Get output
+                stdout = stdout_buffer.getvalue()
+                stderr = stderr_buffer.getvalue()
+                
+                # Truncate output if too large
+                if len(stdout) > max_output_size:
+                    stdout = stdout[:max_output_size] + "...[truncated]"
+                if len(stderr) > max_output_size:
+                    stderr = stderr[:max_output_size] + "...[truncated]"
+                
+                return {
+                    'result': result,
+                    'stdout': stdout,
+                    'stderr': stderr,
+                    'success': True
+                }
+            
+            except Exception as e:
+                error_msg = f"Error executing code: {str(e)}"
+                logging.error(error_msg)
+                return {
+                    'result': None,
+                    'stdout': stdout_buffer.getvalue(),
+                    'stderr': error_msg,
+                    'success': False
+                }
+            
+        except Exception as e:
+            error_msg = f"Error executing code: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'result': None,
+                'stdout': '',
+                'stderr': error_msg,
+                'success': False
+            }
+
+    def analyze_code(
+        self,
+        code: str
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze Python code structure and quality.
+        
+        Args:
+            code: Python code to analyze
+            
+        Returns:
+            Dictionary with analysis results
+        """
+        try:
+            # Import ast only when needed
+            import ast
+            
+            # Parse code
+            tree = ast.parse(code)
+            
+            # Analyze structure
+            analysis = {
+                'imports': [],
+                'functions': [],
+                'classes': [],
+                'variables': [],
+                'complexity': {
+                    'lines': len(code.splitlines()),
+                    'functions': 0,
+                    'classes': 0,
+                    'branches': 0
+                }
+            }
+            
+            # Analyze nodes
+            for node in ast.walk(tree):
+                if isinstance(node, ast.Import):
+                    for name in node.names:
+                        analysis['imports'].append(name.name)
+                elif isinstance(node, ast.ImportFrom):
+                    module = node.module or ''
+                    for name in node.names:
+                        analysis['imports'].append(f"{module}.{name.name}")
+                elif isinstance(node, ast.FunctionDef):
+                    analysis['functions'].append({
+                        'name': node.name,
+                        'args': [
+                            arg.arg for arg in node.args.args
+                        ],
+                        'decorators': [
+                            ast.unparse(d) for d in node.decorator_list
+                        ]
+                    })
+                    analysis['complexity']['functions'] += 1
+                elif isinstance(node, ast.ClassDef):
+                    analysis['classes'].append({
+                        'name': node.name,
+                        'bases': [
+                            ast.unparse(base) for base in node.bases
+                        ],
+                        'decorators': [
+                            ast.unparse(d) for d in node.decorator_list
+                        ]
+                    })
+                    analysis['complexity']['classes'] += 1
+                elif isinstance(node, ast.Assign):
+                    for target in node.targets:
+                        if isinstance(target, ast.Name):
+                            analysis['variables'].append(target.id)
+                elif isinstance(node, (ast.If, ast.While, ast.For)):
+                    analysis['complexity']['branches'] += 1
+            
+            return analysis
+        except Exception as e:
+            error_msg = f"Error analyzing code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def format_code(
+        self,
+        code: str,
+        style: str = 'black',
+        line_length: int = 88
+    ) -> Optional[str]:
+        """Format Python code according to style guide.
+        
+        Args:
+            code: Python code to format
+            style: Formatting style ('black' or 'pep8')
+            line_length: Maximum line length
+            
+        Returns:
+            Formatted code
+        """
+        try:
+            if util.find_spec(style) is None:
+                error_msg = f"{style} package is not available. Please install it using: pip install {style}"
+                logging.error(error_msg)
+                return None
+
+            if style == 'black':
+                import black
+                return black.format_str(
+                    code,
+                    mode=black.FileMode(
+                        line_length=line_length
+                    )
+                )
+            else:  # pep8
+                import autopep8
+                return autopep8.fix_code(
+                    code,
+                    options={
+                        'max_line_length': line_length
+                    }
+                )
+        except Exception as e:
+            error_msg = f"Error formatting code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def lint_code(
+        self,
+        code: str
+    ) -> Optional[Dict[str, List[Dict[str, Any]]]]:
+        """Lint Python code for potential issues.
+        
+        Args:
+            code: Python code to lint
+            
+        Returns:
+            Dictionary with linting results
+        """
+        try:
+            if util.find_spec('pylint') is None:
+                error_msg = "pylint package is not available. Please install it using: pip install pylint"
+                logging.error(error_msg)
+                return None
+
+            # Import pylint only when needed
+            from pylint.reporters import JSONReporter
+            from pylint.lint.run import Run
+            
+            # Create temporary file for pylint
+            import tempfile
+            with tempfile.NamedTemporaryFile(
+                mode='w',
+                suffix='.py',
+                delete=False
+            ) as f:
+                f.write(code)
+                temp_path = f.name
+            
+            # Run pylint
+            reporter = JSONReporter()
+            Run(
+                [temp_path],
+                reporter=reporter,
+                exit=False
+            )
+            
+            # Process results
+            results = {
+                'errors': [],
+                'warnings': [],
+                'conventions': []
+            }
+            
+            for msg in reporter.messages:
+                item = {
+                    'type': msg.category,
+                    'module': msg.module,
+                    'obj': msg.obj,
+                    'line': msg.line,
+                    'column': msg.column,
+                    'path': msg.path,
+                    'symbol': msg.symbol,
+                    'message': msg.msg,
+                    'message-id': msg.msg_id
+                }
+                
+                if msg.category in ['error', 'fatal']:
+                    results['errors'].append(item)
+                elif msg.category == 'warning':
+                    results['warnings'].append(item)
+                else:
+                    results['conventions'].append(item)
+            
+            # Clean up
+            import os
+            os.unlink(temp_path)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error linting code: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'errors': [],
+                'warnings': [],
+                'conventions': []
+            }
+
+    def disassemble_code(
+        self,
+        code: str
+    ) -> Optional[str]:
+        """Disassemble Python code to bytecode.
+        
+        Args:
+            code: Python code to disassemble
+            
+        Returns:
+            Disassembled bytecode as string
+        """
+        try:
+            # Import dis only when needed
+            import dis
+            
+            # Compile code
+            compiled_code = compile(code, '<string>', 'exec')
+            
+            # Capture disassembly
+            output = io.StringIO()
+            with redirect_stdout(output):
+                dis.dis(compiled_code)
+            
+            return output.getvalue()
+        except Exception as e:
+            error_msg = f"Error disassembling code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+# Create instance for direct function access
+_python_tools = PythonTools()
+execute_code = _python_tools.execute_code
+analyze_code = _python_tools.analyze_code
+format_code = _python_tools.format_code
+lint_code = _python_tools.lint_code
+disassemble_code = _python_tools.disassemble_code
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("PythonTools Demonstration")
+    print("==================================================\n")
+
+    print("1. Execute Python Code")
+    print("------------------------------")
+    code = """
+def greet(name):
+    return f"Hello, {name}!"
+
+result = greet("World")
+print(result)
+"""
+    print("Code to execute:")
+    print(code)
+    print("\nOutput:")
+    result = execute_code(code)
+    print(result["stdout"])
+    print()
+
+    print("2. Format Python Code")
+    print("------------------------------")
+    code = """
+def messy_function(x,y,   z):
+    if x>0:
+     return y+z
+    else:
+     return y-z
+"""
+    print("Before formatting:")
+    print(code)
+    print("\nAfter formatting:")
+    result = format_code(code)
+    print(result)
+    print()
+
+    print("3. Lint Python Code")
+    print("------------------------------")
+    code = """
+def bad_function():
+    unused_var = 42
+    return 'result'
+"""
+    print("Code to lint:")
+    print(code)
+    print("\nLinting results:")
+    result = lint_code(code)
+    print(result)
+    print()
+
+    print("4. Execute Code with Variables")
+    print("------------------------------")
+    code = """
+x = 10
+y = 20
+result = x + y
+print(f"The sum of {x} and {y} is {result}")
+"""
+    print("Code to execute:")
+    print(code)
+    print("\nOutput:")
+    result = execute_code(code)
+    print(result["stdout"])
+    print()
+
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/shell_tools.py

@@ -0,0 +1,278 @@
+"""Tools for executing shell commands safely.
+
+This module provides a safe interface for executing shell commands with:
+- Timeout control
+- Output capture
+- Error handling
+- Resource limits
+"""
+
+import subprocess
+import shlex
+import logging
+import os
+import time
+from typing import Dict, List, Optional, Union
+
+class ShellTools:
+    """Tools for executing shell commands safely."""
+    
+    def __init__(self):
+        """Initialize ShellTools."""
+        self._check_dependencies()
+    
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        try:
+            import psutil
+        except ImportError:
+            raise ImportError(
+                "Required package not available. Please install: psutil\n"
+                "Run: pip install psutil"
+            )
+    
+    def execute_command(
+        self,
+        command: str,
+        cwd: Optional[str] = None,
+        timeout: int = 30,
+        shell: bool = False,
+        env: Optional[Dict[str, str]] = None,
+        max_output_size: int = 10000
+    ) -> Dict[str, Union[str, int, bool]]:
+        """Execute a shell command safely.
+        
+        Args:
+            command: Command to execute
+            cwd: Working directory
+            timeout: Maximum execution time in seconds
+            shell: Whether to run command in shell
+            env: Environment variables
+            max_output_size: Maximum output size in bytes
+            
+        Returns:
+            Dictionary with execution results
+        """
+        try:
+            # Split command if not using shell
+            if not shell:
+                command = shlex.split(command)
+            
+            # Set up process environment
+            process_env = os.environ.copy()
+            if env:
+                process_env.update(env)
+            
+            # Start process
+            start_time = time.time()
+            process = subprocess.Popen(
+                command,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                cwd=cwd,
+                shell=shell,
+                env=process_env,
+                text=True
+            )
+            
+            try:
+                # Wait for process with timeout
+                stdout, stderr = process.communicate(timeout=timeout)
+                
+                # Truncate output if too large
+                if len(stdout) > max_output_size:
+                    stdout = stdout[:max_output_size] + "...[truncated]"
+                if len(stderr) > max_output_size:
+                    stderr = stderr[:max_output_size] + "...[truncated]"
+                
+                return {
+                    'stdout': stdout,
+                    'stderr': stderr,
+                    'exit_code': process.returncode,
+                    'success': process.returncode == 0,
+                    'execution_time': time.time() - start_time
+                }
+            
+            except subprocess.TimeoutExpired:
+                # Kill process on timeout
+                parent = psutil.Process(process.pid)
+                children = parent.children(recursive=True)
+                for child in children:
+                    child.kill()
+                parent.kill()
+                
+                return {
+                    'stdout': '',
+                    'stderr': f'Command timed out after {timeout} seconds',
+                    'exit_code': -1,
+                    'success': False,
+                    'execution_time': timeout
+                }
+                
+        except Exception as e:
+            error_msg = f"Error executing command: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'stdout': '',
+                'stderr': error_msg,
+                'exit_code': -1,
+                'success': False,
+                'execution_time': 0
+            }
+    
+    def list_processes(self) -> List[Dict[str, Union[int, str, float]]]:
+        """List running processes with their details.
+        
+        Returns:
+            List of process information dictionaries
+        """
+        try:
+            processes = []
+            for proc in psutil.process_iter(['pid', 'name', 'username', 'memory_percent', 'cpu_percent']):
+                try:
+                    pinfo = proc.info
+                    processes.append({
+                        'pid': pinfo['pid'],
+                        'name': pinfo['name'],
+                        'username': pinfo['username'],
+                        'memory_percent': round(pinfo['memory_percent'], 2),
+                        'cpu_percent': round(pinfo['cpu_percent'], 2)
+                    })
+                except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
+                    pass
+            return processes
+        except Exception as e:
+            error_msg = f"Error listing processes: {str(e)}"
+            logging.error(error_msg)
+            return []
+    
+    def kill_process(
+        self,
+        pid: int,
+        force: bool = False
+    ) -> Dict[str, Union[bool, str]]:
+        """Kill a process by its PID.
+        
+        Args:
+            pid: Process ID to kill
+            force: Whether to force kill (-9)
+            
+        Returns:
+            Dictionary with operation results
+        """
+        try:
+            process = psutil.Process(pid)
+            if force:
+                process.kill()  # SIGKILL
+            else:
+                process.terminate()  # SIGTERM
+            
+            return {
+                'success': True,
+                'message': f'Process {pid} killed successfully'
+            }
+        except psutil.NoSuchProcess:
+            return {
+                'success': False,
+                'message': f'No process found with PID {pid}'
+            }
+        except psutil.AccessDenied:
+            return {
+                'success': False,
+                'message': f'Access denied to kill process {pid}'
+            }
+        except Exception as e:
+            error_msg = f"Error killing process: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'success': False,
+                'message': error_msg
+            }
+    
+    def get_system_info(self) -> Dict[str, Union[float, int, str, Dict]]:
+        """Get system information.
+        
+        Returns:
+            Dictionary with system information
+        """
+        try:
+            cpu_percent = psutil.cpu_percent(interval=1)
+            memory = psutil.virtual_memory()
+            disk = psutil.disk_usage('/')
+            
+            return {
+                'cpu': {
+                    'percent': cpu_percent,
+                    'cores': psutil.cpu_count(),
+                    'physical_cores': psutil.cpu_count(logical=False)
+                },
+                'memory': {
+                    'total': memory.total,
+                    'available': memory.available,
+                    'percent': memory.percent,
+                    'used': memory.used,
+                    'free': memory.free
+                },
+                'disk': {
+                    'total': disk.total,
+                    'used': disk.used,
+                    'free': disk.free,
+                    'percent': disk.percent
+                },
+                'boot_time': psutil.boot_time(),
+                'platform': os.uname().sysname
+            }
+        except Exception as e:
+            error_msg = f"Error getting system info: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+# Create instance for direct function access
+_shell_tools = ShellTools()
+execute_command = _shell_tools.execute_command
+list_processes = _shell_tools.list_processes
+kill_process = _shell_tools.kill_process
+get_system_info = _shell_tools.get_system_info
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("ShellTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Execute command
+    print("1. Command Execution")
+    print("------------------------------")
+    result = execute_command("ls -la")
+    print(f"Success: {result['success']}")
+    print(f"Output:\n{result['stdout']}")
+    if result['stderr']:
+        print(f"Errors:\n{result['stderr']}")
+    print(f"Execution time: {result['execution_time']:.2f}s")
+    print()
+    
+    # 2. System Information
+    print("2. System Information")
+    print("------------------------------")
+    info = get_system_info()
+    print(f"CPU Usage: {info['cpu']['percent']}%")
+    print(f"Memory Usage: {info['memory']['percent']}%")
+    print(f"Disk Usage: {info['disk']['percent']}%")
+    print(f"Platform: {info['platform']}")
+    print()
+    
+    # 3. Process List
+    print("3. Process List (top 5 by CPU)")
+    print("------------------------------")
+    processes = sorted(
+        list_processes(),
+        key=lambda x: x['cpu_percent'],
+        reverse=True
+    )[:5]
+    for proc in processes:
+        print(f"PID: {proc['pid']}, Name: {proc['name']}, CPU: {proc['cpu_percent']}%")
+    print()
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/spider_tools.py

@@ -0,0 +1,431 @@
+"""Tools for web scraping and crawling.
+
+Usage:
+from praisonaiagents.tools import spider_tools
+content = spider_tools.scrape_page("https://example.com")
+links = spider_tools.extract_links("https://example.com")
+
+or
+from praisonaiagents.tools import scrape_page, extract_links
+content = scrape_page("https://example.com")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+from urllib.parse import urljoin, urlparse
+import re
+import os
+import hashlib
+import time
+
+class SpiderTools:
+    """Tools for web scraping and crawling."""
+    
+    def __init__(self):
+        """Initialize SpiderTools and check for required packages."""
+        self._session = None
+        
+    def _get_session(self):
+        """Get or create requests session with common headers."""
+        if util.find_spec('requests') is None:
+            error_msg = "requests package is not available. Please install it using: pip install requests"
+            logging.error(error_msg)
+            return None
+            
+        if self._session is None:
+            import requests
+            self._session = requests.Session()
+            self._session.headers.update({
+                'User-Agent': 'Mozilla/5.0 (compatible; PraisonAI/1.0; +http://praisonai.com/bot)',
+                'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
+                'Accept-Language': 'en-US,en;q=0.5',
+                'Accept-Encoding': 'gzip, deflate, br',
+                'Connection': 'keep-alive',
+            })
+        return self._session
+
+    def scrape_page(
+        self,
+        url: str,
+        selector: Optional[str] = None,
+        extract_images: bool = False,
+        extract_links: bool = False,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Scrape content from a webpage.
+        
+        Args:
+            url: URL to scrape
+            selector: Optional CSS selector to extract specific content
+            extract_images: Whether to extract image URLs
+            extract_links: Whether to extract links
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            Dict: Scraped content or error dict
+        """
+        try:
+            session = self._get_session()
+            if session is None:
+                return {"error": "requests package not available"}
+
+            # Import BeautifulSoup only when needed
+            if util.find_spec('bs4') is None:
+                error_msg = "bs4 package is not available. Please install it using: pip install beautifulsoup4"
+                logging.error(error_msg)
+                return {"error": error_msg}
+            from bs4 import BeautifulSoup
+
+            # Make request
+            response = session.get(
+                url,
+                timeout=timeout,
+                verify=verify_ssl
+            )
+            response.raise_for_status()
+            
+            # Parse HTML
+            soup = BeautifulSoup(response.text, 'lxml')
+            
+            # Remove unwanted elements
+            for element in soup(['script', 'style']):
+                element.decompose()
+            
+            # Initialize result
+            result = {
+                'url': url,
+                'status_code': response.status_code,
+                'encoding': response.encoding,
+                'headers': dict(response.headers),
+            }
+            
+            # Extract content based on selector
+            if selector:
+                elements = soup.select(selector)
+                result['content'] = [elem.get_text(strip=True) for elem in elements]
+                result['html'] = [str(elem) for elem in elements]
+            else:
+                result['title'] = soup.title.string if soup.title else None
+                result['content'] = soup.get_text(separator=' ', strip=True)
+                result['html'] = str(soup)
+            
+            # Extract metadata
+            meta_tags = {}
+            for meta in soup.find_all('meta'):
+                name = meta.get('name') or meta.get('property')
+                if name:
+                    meta_tags[name] = meta.get('content')
+            result['meta_tags'] = meta_tags
+            
+            # Extract images if requested
+            if extract_images:
+                images = []
+                for img in soup.find_all('img'):
+                    src = img.get('src')
+                    if src:
+                        images.append({
+                            'src': urljoin(url, src),
+                            'alt': img.get('alt', ''),
+                            'title': img.get('title', '')
+                        })
+                result['images'] = images
+            
+            # Extract links if requested
+            if extract_links:
+                links = []
+                for link in soup.find_all('a'):
+                    href = link.get('href')
+                    if href:
+                        links.append({
+                            'url': urljoin(url, href),
+                            'text': link.get_text(strip=True),
+                            'title': link.get('title', '')
+                        })
+                result['links'] = links
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error scraping {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def extract_links(
+        self,
+        url: str,
+        same_domain: bool = True,
+        exclude_patterns: Optional[List[str]] = None,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[List[Dict[str, str]], Dict[str, str]]:
+        """
+        Extract all links from a webpage.
+        
+        Args:
+            url: URL to extract links from
+            same_domain: Only return links from the same domain
+            exclude_patterns: List of regex patterns to exclude
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            List[Dict] or Dict: List of links or error dict
+        """
+        try:
+            # Compile exclude patterns
+            if exclude_patterns:
+                exclude_patterns = [re.compile(p) for p in exclude_patterns]
+            
+            # Get base domain
+            base_domain = urlparse(url).netloc
+            
+            # Scrape page
+            result = self.scrape_page(
+                url,
+                extract_links=True,
+                timeout=timeout,
+                verify_ssl=verify_ssl
+            )
+            
+            if "error" in result:
+                return result
+            
+            # Filter and clean links
+            links = []
+            seen_urls = set()
+            
+            for link in result.get('links', []):
+                link_url = link['url']
+                
+                # Skip if already seen
+                if link_url in seen_urls:
+                    continue
+                
+                # Parse URL
+                parsed = urlparse(link_url)
+                
+                # Skip if not same domain and same_domain is True
+                if same_domain and parsed.netloc != base_domain:
+                    continue
+                
+                # Skip if matches exclude patterns
+                if exclude_patterns and any(p.search(link_url) for p in exclude_patterns):
+                    continue
+                
+                # Add to results
+                links.append(link)
+                seen_urls.add(link_url)
+            
+            return links
+        except Exception as e:
+            error_msg = f"Error extracting links from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def crawl(
+        self,
+        start_url: str,
+        max_pages: int = 10,
+        same_domain: bool = True,
+        exclude_patterns: Optional[List[str]] = None,
+        delay: float = 1.0,
+        timeout: int = 30,
+        verify_ssl: bool = True,
+        output_dir: Optional[str] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Crawl multiple pages starting from a URL.
+        
+        Args:
+            start_url: Starting URL
+            max_pages: Maximum number of pages to crawl
+            same_domain: Only crawl pages from the same domain
+            exclude_patterns: List of regex patterns to exclude
+            delay: Delay between requests in seconds
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            output_dir: Directory to save crawled pages
+            
+        Returns:
+            List[Dict] or Dict: Crawled pages or error dict
+        """
+        try:
+            # Create output directory if needed
+            if output_dir:
+                os.makedirs(output_dir, exist_ok=True)
+            
+            # Initialize crawl state
+            to_visit = {start_url}
+            visited = set()
+            results = []
+            
+            while to_visit and len(visited) < max_pages:
+                # Get next URL
+                url = to_visit.pop()
+                
+                # Skip if already visited
+                if url in visited:
+                    continue
+                
+                # Add to visited
+                visited.add(url)
+                
+                # Delay if not first request
+                if len(visited) > 1:
+                    time.sleep(delay)
+                
+                # Scrape page
+                result = self.scrape_page(
+                    url,
+                    extract_links=True,
+                    timeout=timeout,
+                    verify_ssl=verify_ssl
+                )
+                
+                if "error" in result:
+                    logging.warning(f"Error crawling {url}: {result['error']}")
+                    continue
+                
+                # Save result
+                results.append(result)
+                
+                # Save to file if requested
+                if output_dir:
+                    filename = hashlib.md5(url.encode()).hexdigest() + '.json'
+                    filepath = os.path.join(output_dir, filename)
+                    with open(filepath, 'w', encoding='utf-8') as f:
+                        json.dump(result, f, indent=2, ensure_ascii=False)
+                
+                # Add new links to visit
+                for link in result.get('links', []):
+                    link_url = link['url']
+                    parsed = urlparse(link_url)
+                    
+                    # Skip if not same domain and same_domain is True
+                    if same_domain and parsed.netloc != urlparse(start_url).netloc:
+                        continue
+                    
+                    # Skip if matches exclude patterns
+                    if exclude_patterns and any(
+                        re.compile(p).search(link_url) for p in exclude_patterns
+                    ):
+                        continue
+                    
+                    # Add to visit if not visited
+                    if link_url not in visited:
+                        to_visit.add(link_url)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error crawling from {start_url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def extract_text(
+        self,
+        url: str,
+        selector: Optional[str] = None,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[str, Dict[str, str]]:
+        """
+        Extract clean text content from a webpage.
+        
+        Args:
+            url: URL to extract text from
+            selector: Optional CSS selector to extract specific content
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            str or Dict: Extracted text or error dict
+        """
+        try:
+            result = self.scrape_page(
+                url,
+                selector=selector,
+                timeout=timeout,
+                verify_ssl=verify_ssl
+            )
+            
+            if "error" in result:
+                return result
+            
+            if selector:
+                return '\n'.join(result['content'])
+            return result['content']
+        except Exception as e:
+            error_msg = f"Error extracting text from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_spider_tools = SpiderTools()
+scrape_page = _spider_tools.scrape_page
+extract_links = _spider_tools.extract_links
+crawl = _spider_tools.crawl
+extract_text = _spider_tools.extract_text
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("SpiderTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Scrape a webpage
+    print("1. Scraping Webpage")
+    print("------------------------------")
+    url = "https://example.com"
+    result = scrape_page(url, extract_images=True, extract_links=True)
+    print(f"Content from {url}:")
+    if "error" not in result:
+        print(f"Title: {result['title']}")
+        print(f"Content length: {len(result['content'])} characters")
+        print(f"Number of images: {len(result.get('images', []))}")
+        print(f"Number of links: {len(result.get('links', []))}")
+    else:
+        print(result)  # Show error
+    print()
+    
+    # 2. Extract links
+    print("2. Extracting Links")
+    print("------------------------------")
+    links = extract_links(url)
+    print(f"Links from {url}:")
+    if isinstance(links, list):
+        for link in links:
+            print(f"- {link['url']} ({link['text']})")
+    else:
+        print(links)  # Show error
+    print()
+    
+    # 3. Extract text
+    print("3. Extracting Text")
+    print("------------------------------")
+    text = extract_text(url)
+    print(f"Text from {url}:")
+    if isinstance(text, str):
+        print(text[:500] + "..." if len(text) > 500 else text)
+    else:
+        print(text)  # Show error
+    print()
+    
+    # 4. Crawl multiple pages
+    print("4. Crawling Multiple Pages")
+    print("------------------------------")
+    results = crawl(url, max_pages=2, delay=1.0)
+    print(f"Crawl results from {url}:")
+    if isinstance(results, list):
+        print(f"Crawled {len(results)} pages")
+        for result in results:
+            print(f"- {result['url']}: {result['title']}")
+    else:
+        print(results)  # Show error
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/test.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""Test runner for all tools."""
+
+import os
+import glob
+import logging
+import subprocess
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def main():
+    """Run all tool files."""
+    logger.info("Starting tests...")
+    
+    # Get all *_tools.py files
+    tools_dir = os.path.dirname(os.path.abspath(__file__))
+    tool_files = glob.glob(os.path.join(tools_dir, "*_tools.py"))
+    
+    # Run each tool file
+    for tool_file in sorted(tool_files):
+        module_name = os.path.basename(tool_file)
+        logger.info(f"\nRunning {module_name}...")
+        
+        try:
+            # Run the tool file directly
+            result = subprocess.run(
+                ["python3", tool_file],
+                capture_output=True,
+                text=True,
+                cwd=tools_dir
+            )
+            
+            # Log output
+            if result.stdout:
+                logger.info(f"Output:\n{result.stdout}")
+            if result.stderr:
+                logger.error(f"Errors:\n{result.stderr}")
+            
+            if result.returncode == 0:
+                logger.info(f" {module_name} completed successfully")
+            else:
+                logger.error(f" {module_name} failed with return code {result.returncode}")
+        
+        except Exception as e:
+            logger.error(f"Error running {module_name}: {str(e)}")
+            continue
+    
+    logger.info("\nAll tests completed!")
+
+if __name__ == "__main__":
+    main()

agents/praisonaiagents/tools/tools.py

@@ -0,0 +1,9 @@
+"""Tools module for PraisonAI Agents"""
+from .duckduckgo_tools import internet_search
+
+class Tools:
+    """Tools class for backward compatibility"""
+    internet_search = staticmethod(internet_search)
+
+# Re-export the function
+__all__ = ['Tools', 'internet_search']

agents/praisonaiagents/tools/wikipedia_tools.py

@@ -0,0 +1,272 @@
+"""Wikipedia tools for accessing and searching Wikipedia content.
+
+Usage:
+from praisonaiagents.tools import wikipedia_tools
+summary = wikipedia_tools.wiki_summary("Python programming language")
+page = wikipedia_tools.wiki_page("Python programming language")
+results = wikipedia_tools.wiki_search("Python programming")
+
+or
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page
+summary = wiki_summary("Python programming language")
+"""
+
+import logging
+from typing import List, Dict, Union, Any
+from importlib import util
+import json
+
+class WikipediaTools:
+    """Tools for accessing and searching Wikipedia content."""
+    
+    def __init__(self):
+        """Initialize WikipediaTools and check for wikipedia package."""
+        self._check_wikipedia()
+        
+    def _check_wikipedia(self):
+        """Check if wikipedia package is installed."""
+        if util.find_spec("wikipedia") is None:
+            raise ImportError("wikipedia package is not available. Please install it using: pip install wikipedia")
+        global wikipedia
+        import wikipedia
+        # Set default language to English
+        wikipedia.set_lang("en")
+
+    def wiki_search(
+        self, 
+        query: str, 
+        results: int = 10, 
+        suggestion: bool = True
+    ) -> Union[List[str], Dict[str, str]]:
+        """
+        Search Wikipedia for a query.
+        
+        Args:
+            query: Search query
+            results: Maximum number of results to return
+            suggestion: Whether to suggest similar queries
+            
+        Returns:
+            List[str] or Dict: List of search results or error dict
+        """
+        try:
+            search_results = wikipedia.search(query, results=results, suggestion=suggestion)
+            if isinstance(search_results, tuple):
+                # If suggestion is True, returns (results, suggestion)
+                return {
+                    "results": search_results[0],
+                    "suggestion": search_results[1] if search_results[1] else None
+                }
+            return {"results": search_results, "suggestion": None}
+        except Exception as e:
+            error_msg = f"Error searching Wikipedia for '{query}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_summary(
+        self, 
+        title: str, 
+        sentences: int = 5, 
+        auto_suggest: bool = True
+    ) -> Union[str, Dict[str, str]]:
+        """
+        Get a summary of a Wikipedia page.
+        
+        Args:
+            title: Title of the Wikipedia page
+            sentences: Number of sentences to return
+            auto_suggest: Whether to auto-suggest similar titles
+            
+        Returns:
+            str or Dict: Page summary if successful, error dict if failed
+        """
+        try:
+            return wikipedia.summary(title, sentences=sentences, auto_suggest=auto_suggest)
+        except wikipedia.exceptions.DisambiguationError as e:
+            return {
+                "error": "Disambiguation page",
+                "options": e.options[:10]  # Limit to first 10 options
+            }
+        except Exception as e:
+            error_msg = f"Error getting summary for '{title}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_page(
+        self, 
+        title: str, 
+        auto_suggest: bool = True
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Get detailed information about a Wikipedia page.
+        
+        Args:
+            title: Title of the Wikipedia page
+            auto_suggest: Whether to auto-suggest similar titles
+            
+        Returns:
+            Dict: Page information if successful, error dict if failed
+        """
+        try:
+            page = wikipedia.page(title, auto_suggest=auto_suggest)
+            return {
+                "title": page.title,
+                "url": page.url,
+                "content": page.content,
+                "summary": page.summary,
+                "references": page.references,
+                "categories": page.categories,
+                "links": page.links,
+                "images": page.images,
+                "sections": self.get_sections(page)
+            }
+        except wikipedia.exceptions.DisambiguationError as e:
+            return {
+                "error": "Disambiguation page",
+                "options": e.options[:10]  # Limit to first 10 options
+            }
+        except Exception as e:
+            error_msg = f"Error getting page for '{title}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_sections(self, page: Any) -> List[Dict[str, Any]]:
+        """
+        Extract sections and their content from a Wikipedia page.
+        
+        Args:
+            page: Wikipedia page object
+            
+        Returns:
+            List[Dict]: List of sections with their titles and content
+        """
+        sections = []
+        content = page.content
+        section_titles = [s.strip() for s in content.split("==")[1::2]]
+        section_contents = [s.strip() for s in content.split("==")[2::2]]
+        
+        # Add introduction section
+        intro = content.split("==")[0].strip()
+        if intro:
+            sections.append({
+                "title": "Introduction",
+                "level": 0,
+                "content": intro
+            })
+        
+        # Process remaining sections
+        for title, content in zip(section_titles, section_contents):
+            # Count leading '=' to determine section level
+            level = title.count("=") // 2
+            clean_title = title.strip("= ")
+            
+            sections.append({
+                "title": clean_title,
+                "level": level,
+                "content": content.strip()
+            })
+        
+        return sections
+
+    def wiki_random(self, pages: int = 1) -> Union[List[str], Dict[str, str]]:
+        """
+        Get random Wikipedia page titles.
+        
+        Args:
+            pages: Number of random pages to return
+            
+        Returns:
+            List[str] or Dict: List of random page titles or error dict
+        """
+        try:
+            return wikipedia.random(pages)
+        except Exception as e:
+            error_msg = f"Error getting random pages: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_language(self, language: str) -> bool:
+        """
+        Set the language for Wikipedia searches.
+        
+        Args:
+            language: Language code (e.g., 'en' for English, 'es' for Spanish)
+            
+        Returns:
+            bool: True if successful, False if failed
+        """
+        try:
+            wikipedia.set_lang(language)
+            return True
+        except Exception as e:
+            error_msg = f"Error setting language to '{language}': {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_wikipedia_tools = WikipediaTools()
+wiki_search = _wikipedia_tools.wiki_search
+wiki_summary = _wikipedia_tools.wiki_summary
+wiki_page = _wikipedia_tools.wiki_page
+wiki_random = _wikipedia_tools.wiki_random
+wiki_language = _wikipedia_tools.wiki_language
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("WikipediaTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Search for a topic
+    print("1. Searching Wikipedia")
+    print("------------------------------")
+    search_results = wiki_search("Python programming language")
+    print("Search results:")
+    print(json.dumps(search_results, indent=2))
+    print()
+    
+    # 2. Get a page summary
+    print("2. Getting Page Summary")
+    print("------------------------------")
+    summary = wiki_summary("Python programming language", sentences=3)
+    print("Summary:")
+    print(summary)
+    print()
+    
+    # 3. Get detailed page information
+    print("3. Getting Full Page Information")
+    print("------------------------------")
+    page = wiki_page("Python programming language")
+    if isinstance(page, dict) and "error" not in page:
+        print("Page sections:")
+        for section in page["sections"]:
+            print(f"- {section['title']} (Level {section['level']})")
+        print(f"\nNumber of references: {len(page['references'])}")
+        print(f"Number of categories: {len(page['categories'])}")
+        print(f"Number of links: {len(page['links'])}")
+        print(f"Number of images: {len(page['images'])}")
+    else:
+        print("Error getting page:", page.get("error"))
+    print()
+    
+    # 4. Get random pages
+    print("4. Getting Random Pages")
+    print("------------------------------")
+    random_pages = wiki_random(3)
+    print("Random page titles:")
+    print(json.dumps(random_pages, indent=2))
+    print()
+    
+    # 5. Try different language
+    print("5. Changing Language")
+    print("------------------------------")
+    success = wiki_language("es")
+    if success:
+        summary = wiki_summary("Python (lenguaje de programación)", sentences=1)
+        print("Spanish summary:")
+        print(summary)
+    print()
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/xml_tools.py

@@ -0,0 +1,498 @@
+"""Tools for working with XML files.
+
+Usage:
+from praisonaiagents.tools import xml_tools
+tree = xml_tools.read_xml("data.xml")
+
+or
+from praisonaiagents.tools import read_xml, write_xml, transform_xml
+tree = read_xml("data.xml")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, Tuple
+from importlib import util
+import xml.etree.ElementTree as ET
+import xml.dom.minidom as minidom
+from io import StringIO
+import json
+
+class XMLTools:
+    """Tools for working with XML files."""
+    
+    def __init__(self):
+        """Initialize XMLTools."""
+        pass
+
+    def read_xml(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        validate_schema: Optional[str] = None,
+        parser: str = 'lxml'
+    ) -> ET.Element:
+        """Read an XML file with optional schema validation.
+        
+        Args:
+            filepath: Path to XML file
+            encoding: File encoding
+            validate_schema: Optional path to XSD schema file
+            parser: XML parser to use ('lxml' or 'etree')
+            
+        Returns:
+            ElementTree root element
+        """
+        try:
+            if parser == 'lxml':
+                if util.find_spec('lxml') is None:
+                    error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                    logging.error(error_msg)
+                    return None
+                import lxml.etree as lxml_etree
+                tree = lxml_etree.parse(filepath)
+                root = tree.getroot()
+            else:
+                tree = ET.parse(filepath)
+                root = tree.getroot()
+
+            if validate_schema:
+                if util.find_spec('xmlschema') is None:
+                    error_msg = "xmlschema package is not available. Please install it using: pip install xmlschema"
+                    logging.error(error_msg)
+                    return None
+                import xmlschema
+                schema = xmlschema.XMLSchema(validate_schema)
+                if not schema.is_valid(filepath):
+                    error_msg = f"XML file does not validate against schema: {schema.validate(filepath)}"
+                    logging.error(error_msg)
+                    return None
+
+            return root
+
+        except Exception as e:
+            error_msg = f"Error reading XML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def write_xml(
+        self,
+        root: ET.Element,
+        filepath: str,
+        encoding: str = 'utf-8',
+        pretty: bool = True,
+        xml_declaration: bool = True
+    ) -> bool:
+        """Write XML Element tree to file.
+        
+        Args:
+            root: XML Element tree root
+            filepath: Output file path
+            encoding: File encoding
+            pretty: Format output with proper indentation
+            xml_declaration: Include XML declaration
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            # Convert to string
+            if pretty:
+                xml_str = minidom.parseString(
+                    ET.tostring(root, encoding='unicode')
+                ).toprettyxml(indent='  ')
+            else:
+                xml_str = ET.tostring(
+                    root,
+                    encoding='unicode'
+                )
+            
+            # Add declaration if requested
+            if xml_declaration:
+                if not xml_str.startswith('<?xml'):
+                    xml_str = (
+                        f'<?xml version="1.0" encoding="{encoding}"?>\n'
+                        + xml_str
+                    )
+            
+            # Write to file
+            with open(filepath, 'w', encoding=encoding) as f:
+                f.write(xml_str)
+            
+            return True
+        except Exception as e:
+            error_msg = f"Error writing XML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def transform_xml(
+        self,
+        xml_file: str,
+        xslt_file: str,
+        output_file: str,
+        params: Optional[Dict[str, str]] = None
+    ) -> bool:
+        """Transform XML using XSLT stylesheet.
+        
+        Args:
+            xml_file: Input XML file
+            xslt_file: XSLT stylesheet file
+            output_file: Output file path
+            params: Optional parameters for transformation
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            # Parse XML and XSLT
+            if util.find_spec('lxml') is None:
+                error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                logging.error(error_msg)
+                return False
+            import lxml.etree as lxml_etree
+            xml_doc = lxml_etree.parse(xml_file)
+            xslt_doc = lxml_etree.parse(xslt_file)
+            transform = lxml_etree.XSLT(xslt_doc)
+            
+            # Apply transformation
+            if params:
+                result = transform(xml_doc, **params)
+            else:
+                result = transform(xml_doc)
+            
+            # Write result
+            result.write(
+                output_file,
+                pretty_print=True,
+                xml_declaration=True,
+                encoding='utf-8'
+            )
+            
+            return True
+        except Exception as e:
+            error_msg = f"Error transforming XML: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def validate_xml(
+        self,
+        xml_file: str,
+        schema_file: str
+    ) -> Tuple[bool, Optional[str]]:
+        """Validate XML against XSD schema.
+        
+        Args:
+            xml_file: XML file to validate
+            schema_file: XSD schema file
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+        """
+        try:
+            if util.find_spec('xmlschema') is None:
+                error_msg = "xmlschema package is not available. Please install it using: pip install xmlschema"
+                logging.error(error_msg)
+                return False, error_msg
+            import xmlschema
+            schema = xmlschema.XMLSchema(schema_file)
+            schema.validate(xml_file)
+            return True, None
+        except xmlschema.validators.exceptions.XMLSchemaValidationError as e:
+            return False, str(e)
+        except Exception as e:
+            error_msg = f"Error validating XML: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+
+    def xml_to_dict(
+        self,
+        root: Union[str, ET.Element],
+        preserve_attrs: bool = True
+    ) -> Dict[str, Any]:
+        """Convert XML to dictionary.
+        
+        Args:
+            root: XML string or Element tree root
+            preserve_attrs: Keep XML attributes in result
+            
+        Returns:
+            Dict representation of XML
+        """
+        try:
+            # Parse XML if string
+            if isinstance(root, str):
+                if root.startswith('<'):
+                    root = ET.fromstring(root)
+                else:
+                    root = ET.parse(root).getroot()
+            
+            result = {}
+            
+            # Add attributes if present and requested
+            if preserve_attrs and root.attrib:
+                result['@attributes'] = dict(root.attrib)
+            
+            # Add children
+            children = list(root)
+            if not children:
+                text = root.text
+                if text is not None and text.strip():
+                    result = text.strip()
+            else:
+                for child in children:
+                    child_data = self.xml_to_dict(child, preserve_attrs)
+                    if child.tag in result:
+                        if not isinstance(result[child.tag], list):
+                            result[child.tag] = [result[child.tag]]
+                        result[child.tag].append(child_data)
+                    else:
+                        result[child.tag] = child_data
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error converting XML to dict: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+    def dict_to_xml(
+        self,
+        data: Dict[str, Any],
+        root_tag: str = 'root'
+    ) -> Optional[ET.Element]:
+        """Convert dictionary to XML.
+        
+        Args:
+            data: Dictionary to convert
+            root_tag: Tag for root element
+            
+        Returns:
+            XML Element tree root
+        """
+        try:
+            def _create_element(
+                parent: ET.Element,
+                key: str,
+                value: Any
+            ):
+                """Create XML element from key-value pair."""
+                if key == '@attributes':
+                    for attr_key, attr_val in value.items():
+                        parent.set(attr_key, str(attr_val))
+                elif isinstance(value, dict):
+                    child = ET.SubElement(parent, key)
+                    for k, v in value.items():
+                        _create_element(child, k, v)
+                elif isinstance(value, list):
+                    for item in value:
+                        child = ET.SubElement(parent, key)
+                        if isinstance(item, dict):
+                            for k, v in item.items():
+                                _create_element(child, k, v)
+                        else:
+                            child.text = str(item)
+                else:
+                    child = ET.SubElement(parent, key)
+                    child.text = str(value)
+            
+            root = ET.Element(root_tag)
+            for key, value in data.items():
+                _create_element(root, key, value)
+            
+            return root
+        except Exception as e:
+            error_msg = f"Error converting dict to XML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def xpath_query(
+        self,
+        root: Union[str, ET.Element],
+        query: str,
+        namespaces: Optional[Dict[str, str]] = None
+    ) -> List[ET.Element]:
+        """Execute XPath query on XML.
+        
+        Args:
+            root: XML string or Element tree root
+            query: XPath query string
+            namespaces: Optional namespace mappings
+            
+        Returns:
+            List of matching elements
+        """
+        try:
+            # Parse XML if string
+            if isinstance(root, str):
+                if root.startswith('<'):
+                    if util.find_spec('lxml') is None:
+                        error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                        logging.error(error_msg)
+                        return []
+                    import lxml.etree as lxml_etree
+                    tree = lxml_etree.fromstring(root)
+                else:
+                    tree = ET.parse(root)
+            else:
+                if util.find_spec('lxml') is None:
+                    error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                    logging.error(error_msg)
+                    return []
+                import lxml.etree as lxml_etree
+                tree = lxml_etree.fromstring(
+                    ET.tostring(root, encoding='unicode')
+                )
+            
+            # Execute query
+            results = tree.xpath(
+                query,
+                namespaces=namespaces or {}
+            )
+            
+            # Convert results to standard ElementTree elements
+            return [
+                ET.fromstring(lxml_etree.tostring(elem, encoding='unicode'))
+                for elem in results
+            ]
+        except Exception as e:
+            error_msg = f"Error executing XPath query: {str(e)}"
+            logging.error(error_msg)
+            return []
+
+# Create instance for direct function access
+_xml_tools = XMLTools()
+read_xml = _xml_tools.read_xml
+write_xml = _xml_tools.write_xml
+transform_xml = _xml_tools.transform_xml
+validate_xml = _xml_tools.validate_xml
+xml_to_dict = _xml_tools.xml_to_dict
+dict_to_xml = _xml_tools.dict_to_xml
+xpath_query = _xml_tools.xpath_query
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("XMLTools Demonstration")
+    print("==================================================\n")
+
+    # Create temporary files
+    import tempfile
+    import os
+
+    temp_file = tempfile.mktemp(suffix='.xml')
+    try:
+        print("1. Creating XML Document")
+        print("------------------------------")
+        xml_content = """<?xml version="1.0" encoding="UTF-8"?>
+<bookstore>
+    <book category="fiction">
+        <title>The Great Gatsby</title>
+        <author>F. Scott Fitzgerald</author>
+        <year>1925</year>
+        <price>10.99</price>
+    </book>
+    <book category="non-fiction">
+        <title>A Brief History of Time</title>
+        <author>Stephen Hawking</author>
+        <year>1988</year>
+        <price>15.99</price>
+    </book>
+</bookstore>"""
+        
+        with open(temp_file, 'w') as f:
+            f.write(xml_content)
+        print("Sample XML file created")
+        print()
+
+        print("2. Parsing XML")
+        print("------------------------------")
+        result = read_xml(temp_file)
+        print("XML structure:")
+        print(minidom.parseString(ET.tostring(result, encoding='unicode')).toprettyxml(indent='  '))
+        print()
+
+        print("3. Querying XML")
+        print("------------------------------")
+        xpath = "//book[@category='fiction']/title/text()"
+        result = xpath_query(result, xpath)
+        print(f"Fiction book titles (XPath: {xpath}):")
+        for title in result:
+            print(title.text)
+        print()
+
+        print("4. Validating XML")
+        print("------------------------------")
+        schema_file = tempfile.mktemp(suffix='.xsd')
+        schema_content = """<?xml version="1.0" encoding="UTF-8"?>
+<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema">
+    <xs:element name="bookstore">
+        <xs:complexType>
+            <xs:sequence>
+                <xs:element name="book" maxOccurs="unbounded">
+                    <xs:complexType>
+                        <xs:sequence>
+                            <xs:element name="title" type="xs:string"/>
+                            <xs:element name="author" type="xs:string"/>
+                            <xs:element name="year" type="xs:integer"/>
+                            <xs:element name="price" type="xs:decimal"/>
+                        </xs:sequence>
+                        <xs:attribute name="category" type="xs:string"/>
+                    </xs:complexType>
+                </xs:element>
+            </xs:sequence>
+        </xs:complexType>
+    </xs:element>
+</xs:schema>"""
+        with open(schema_file, 'w') as f:
+            f.write(schema_content)
+        result, error = validate_xml(temp_file, schema_file)
+        print(f"XML validation result: {result}")
+        if error:
+            print(f"Error: {error}")
+        print()
+
+        print("5. Transforming XML")
+        print("------------------------------")
+        xslt_content = """<?xml version="1.0" encoding="UTF-8"?>
+<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
+    <xsl:template match="/">
+        <html>
+            <body>
+                <h2>Bookstore Inventory</h2>
+                <table>
+                    <tr>
+                        <th>Title</th>
+                        <th>Author</th>
+                        <th>Price</th>
+                    </tr>
+                    <xsl:for-each select="bookstore/book">
+                        <tr>
+                            <td><xsl:value-of select="title"/></td>
+                            <td><xsl:value-of select="author"/></td>
+                            <td><xsl:value-of select="price"/></td>
+                        </tr>
+                    </xsl:for-each>
+                </table>
+            </body>
+        </html>
+    </xsl:template>
+</xsl:stylesheet>"""
+
+        xslt_file = tempfile.mktemp(suffix='.xslt')
+        with open(xslt_file, 'w') as f:
+            f.write(xslt_content)
+
+        output_file = tempfile.mktemp(suffix='.html')
+        result = transform_xml(temp_file, xslt_file, output_file)
+        print(f"XML transformation result: {result}")
+        if result and os.path.exists(output_file):
+            print("\nTransformed HTML content:")
+            with open(output_file, 'r') as f:
+                print(f.read())
+
+    finally:
+        # Clean up temporary files
+        for file in [temp_file, schema_file, xslt_file, output_file]:
+            if os.path.exists(file):
+                os.unlink(file)
+
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/yaml_tools.py

@@ -0,0 +1,417 @@
+"""Tools for working with YAML files.
+
+Usage:
+from praisonaiagents.tools import yaml_tools
+data = yaml_tools.read_yaml("config.yaml")
+
+or
+from praisonaiagents.tools import read_yaml, write_yaml, merge_yaml
+data = read_yaml("config.yaml")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import os
+from copy import deepcopy
+
+class YAMLTools:
+    """Tools for working with YAML files."""
+    
+    def __init__(self):
+        """Initialize YAMLTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package, module in [('pyyaml', 'yaml'), ('jsonschema', 'jsonschema')]:
+            if util.find_spec(module) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def read_yaml(
+        self,
+        filepath: str,
+        safe_load: bool = True,
+        encoding: str = 'utf-8'
+    ) -> Optional[Union[Dict[str, Any], List[Any]]]:
+        """Read a YAML file."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return None
+
+            # Import yaml only when needed
+            import yaml
+            
+            with open(filepath, 'r', encoding=encoding) as f:
+                if safe_load:
+                    return yaml.safe_load(f)
+                return yaml.load(f, Loader=yaml.FullLoader)
+        except Exception as e:
+            error_msg = f"Error reading YAML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def write_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any]],
+        filepath: str,
+        default_flow_style: bool = False,
+        encoding: str = 'utf-8',
+        sort_keys: bool = False,
+        allow_unicode: bool = True
+    ) -> bool:
+        """Write data to YAML file."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return False
+
+            # Import yaml only when needed
+            import yaml
+            
+            with open(filepath, 'w', encoding=encoding) as f:
+                yaml.dump(
+                    data,
+                    f,
+                    default_flow_style=default_flow_style,
+                    sort_keys=sort_keys,
+                    allow_unicode=allow_unicode
+                )
+            return True
+        except Exception as e:
+            error_msg = f"Error writing YAML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_yaml(
+        self,
+        files: List[str],
+        output_file: Optional[str] = None,
+        strategy: str = 'deep'
+    ) -> Union[Dict[str, Any], List[Any], None]:
+        """Merge multiple YAML files."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return None
+
+            def deep_merge(source: dict, destination: dict) -> dict:
+                """Deep merge two dictionaries."""
+                for key, value in source.items():
+                    if key in destination:
+                        if isinstance(value, dict) and isinstance(destination[key], dict):
+                            destination[key] = deep_merge(value, destination[key])
+                        elif isinstance(value, list) and isinstance(destination[key], list):
+                            destination[key].extend(value)
+                        else:
+                            destination[key] = value
+                    else:
+                        destination[key] = value
+                return destination
+            
+            result = {}
+            for file in files:
+                data = self.read_yaml(file)
+                if data is None:
+                    continue
+                
+                if strategy == 'deep':
+                    if isinstance(data, dict):
+                        result = deep_merge(data, result)
+                    elif isinstance(data, list):
+                        if not result:
+                            result = []
+                        result.extend(data)
+                else:  # shallow
+                    if isinstance(data, dict):
+                        result.update(data)
+                    elif isinstance(data, list):
+                        if not result:
+                            result = []
+                        result.extend(data)
+            
+            if output_file:
+                self.write_yaml(result, output_file)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error merging YAML files: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def validate_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str],
+        schema: Dict[str, Any]
+    ) -> bool:
+        """Validate YAML data against a schema."""
+        try:
+            if util.find_spec('jsonschema') is None:
+                error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                logging.error(error_msg)
+                return False
+
+            # Import jsonschema only when needed
+            from jsonschema import validate, ValidationError
+            
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return False
+            
+            try:
+                validate(instance=data, schema=schema)
+                return True
+            except ValidationError as e:
+                logging.error(f"YAML validation error: {str(e)}")
+                return False
+        except Exception as e:
+            error_msg = f"Error validating YAML: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def analyze_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str]
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze YAML data structure."""
+        try:
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return None
+            
+            def analyze_value(value: Any) -> Dict[str, Any]:
+                """Analyze a single value."""
+                result = {
+                    'type': type(value).__name__,
+                    'size': len(value) if hasattr(value, '__len__') else 1
+                }
+                
+                if isinstance(value, dict):
+                    result['keys'] = list(value.keys())
+                    result['nested_types'] = {
+                        k: type(v).__name__
+                        for k, v in value.items()
+                    }
+                elif isinstance(value, list):
+                    result['element_types'] = list(set(
+                        type(x).__name__ for x in value
+                    ))
+                
+                return result
+            
+            result = {
+                'structure': analyze_value(data),
+                'stats': {
+                    'total_keys': sum(1 for _ in self._walk_dict(data))
+                    if isinstance(data, dict) else len(data)
+                }
+            }
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error analyzing YAML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def _walk_dict(self, d: Dict[str, Any]) -> Any:
+        """Walk through nested dictionary."""
+        for k, v in d.items():
+            yield k
+            if isinstance(v, dict):
+                yield from self._walk_dict(v)
+
+    def transform_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str],
+        operations: List[Dict[str, Any]]
+    ) -> Optional[Union[Dict[str, Any], List[Any]]]:
+        """Transform YAML data using specified operations."""
+        try:
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return None
+            
+            result = deepcopy(data)
+            
+            for op in operations:
+                op_type = op.get('type')
+                path = op.get('path', '')
+                value = op.get('value')
+                
+                if not op_type:
+                    continue
+                
+                # Split path into parts
+                parts = [p for p in path.split('/') if p]
+                
+                # Get reference to target location
+                target = result
+                parent = None
+                last_key = None
+                
+                for part in parts[:-1]:
+                    if isinstance(target, dict):
+                        if part not in target:
+                            target[part] = {}
+                        parent = target
+                        target = target[part]
+                        last_key = part
+                    elif isinstance(target, list):
+                        idx = int(part)
+                        while len(target) <= idx:
+                            target.append({})
+                        parent = target
+                        target = target[idx]
+                        last_key = idx
+                
+                if parts:
+                    last_part = parts[-1]
+                    if isinstance(target, dict):
+                        if op_type == 'set':
+                            target[last_part] = value
+                        elif op_type == 'delete':
+                            target.pop(last_part, None)
+                        elif op_type == 'append' and isinstance(target.get(last_part), list):
+                            target[last_part].append(value)
+                    elif isinstance(target, list):
+                        idx = int(last_part)
+                        if op_type == 'set':
+                            while len(target) <= idx:
+                                target.append(None)
+                            target[idx] = value
+                        elif op_type == 'delete' and idx < len(target):
+                            del target[idx]
+                        elif op_type == 'append':
+                            target.append(value)
+                else:
+                    if op_type == 'set':
+                        result = value
+                    elif op_type == 'append' and isinstance(result, list):
+                        result.append(value)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error transforming YAML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+# Create instance for direct function access
+_yaml_tools = YAMLTools()
+read_yaml = _yaml_tools.read_yaml
+write_yaml = _yaml_tools.write_yaml
+merge_yaml = _yaml_tools.merge_yaml
+validate_yaml = _yaml_tools.validate_yaml
+analyze_yaml = _yaml_tools.analyze_yaml
+transform_yaml = _yaml_tools.transform_yaml
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("YAMLTools Demonstration")
+    print("==================================================\n")
+    
+    # Sample data
+    data1 = {
+        'server': {
+            'host': 'localhost',
+            'port': 8080,
+            'debug': True
+        },
+        'database': {
+            'url': 'postgresql://localhost:5432/db',
+            'pool_size': 5
+        }
+    }
+    
+    data2 = {
+        'server': {
+            'workers': 4,
+            'timeout': 30
+        },
+        'logging': {
+            'level': 'INFO',
+            'file': 'app.log'
+        }
+    }
+    
+    # 1. Write YAML files
+    print("1. Writing YAML Files")
+    print("------------------------------")
+    success1 = write_yaml(data1, 'config1.yaml')
+    success2 = write_yaml(data2, 'config2.yaml')
+    print(f"First file written: {success1}")
+    print(f"Second file written: {success2}")
+    print()
+    
+    # 2. Read YAML file
+    print("2. Reading YAML File")
+    print("------------------------------")
+    config = read_yaml('config1.yaml')
+    print("YAML content:")
+    print(config)
+    print()
+    
+    # 3. Merge YAML files
+    print("3. Merging YAML Files")
+    print("------------------------------")
+    merged = merge_yaml(['config1.yaml', 'config2.yaml'])
+    print("Merged content:")
+    print(merged)
+    print()
+    
+    # 4. Analyze YAML
+    print("4. Analyzing YAML")
+    print("------------------------------")
+    analysis = analyze_yaml(merged)
+    print("Analysis results:")
+    print(analysis)
+    print()
+    
+    # 5. Transform YAML
+    print("5. Transforming YAML")
+    print("------------------------------")
+    operations = [
+        {
+            'type': 'set',
+            'path': 'server/host',
+            'value': '0.0.0.0'
+        },
+        {
+            'type': 'delete',
+            'path': 'server/debug'
+        },
+        {
+            'type': 'set',
+            'path': 'logging/handlers',
+            'value': ['console', 'file']
+        }
+    ]
+    transformed = transform_yaml(merged, operations)
+    print("Transformed content:")
+    print(transformed)
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")
+    
+    # Cleanup
+    for file in ['config1.yaml', 'config2.yaml']:
+        if os.path.exists(file):
+            os.remove(file)

agents/praisonaiagents/tools/yfinance_tools.py

@@ -0,0 +1,213 @@
+"""YFinance tools for stock market data.
+
+Usage:
+from praisonaiagents.tools import get_stock_price, get_stock_info
+price = get_stock_price("AAPL")
+info = get_stock_info("AAPL")
+
+or 
+from praisonaiagents.tools import yfinance
+price = yfinance.get_stock_price("AAPL")
+info = yfinance.get_stock_info("AAPL")
+"""
+
+from typing import List, Dict, Optional, Any
+import logging
+from importlib import util
+from datetime import datetime
+
+class YFinanceTools:
+    """A comprehensive tool for financial data analysis using yfinance"""
+
+    def __init__(self):
+        """Initialize YFinanceTools"""
+        self._tickers = {}
+
+    def _get_yfinance(self):
+        """Get yfinance module, installing if needed"""
+        if util.find_spec('yfinance') is None:
+            error_msg = "yfinance package is not available. Please install it using: pip install yfinance"
+            logging.error(error_msg)
+            return None
+        import yfinance as yf
+        return yf
+
+    def _get_ticker(self, symbol: str):
+        """Get or create ticker instance"""
+        if symbol not in self._tickers:
+            yf = self._get_yfinance()
+            if yf is None:
+                return None
+            self._tickers[symbol] = yf.Ticker(symbol)
+        return self._tickers[symbol]
+
+    def get_stock_price(self, symbol: str) -> Dict[str, float]:
+        """
+        Get current stock price and related price metrics
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            
+        Returns:
+            Dict[str, float]: Current price information
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return {"error": "yfinance package not available"}
+
+            info = ticker.info
+            return {
+                'symbol': symbol,
+                'price': info.get('regularMarketPrice', 0.0),
+                'open': info.get('regularMarketOpen', 0.0),
+                'high': info.get('regularMarketDayHigh', 0.0),
+                'low': info.get('regularMarketDayLow', 0.0),
+                'volume': info.get('regularMarketVolume', 0),
+                'previous_close': info.get('regularMarketPreviousClose', 0.0),
+                'change': info.get('regularMarketChange', 0.0),
+                'change_percent': info.get('regularMarketChangePercent', 0.0)
+            }
+        except Exception as e:
+            error_msg = f"Error getting stock price for {symbol}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_stock_info(self, symbol: str) -> Dict:
+        """
+        Get detailed information about a stock
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            
+        Returns:
+            Dict: Stock information including company details and key metrics
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return {"error": "yfinance package not available"}
+
+            info = ticker.info
+            
+            relevant_info = {
+                "longName": info.get("longName"),
+                "symbol": info.get("symbol"),
+                "sector": info.get("sector"), 
+                "industry": info.get("industry"),
+                "country": info.get("country"),
+                "marketCap": info.get("marketCap"),
+                "currentPrice": info.get("currentPrice"),
+                "currency": info.get("currency"),
+                "exchange": info.get("exchange"),
+                "fiftyTwoWeekHigh": info.get("fiftyTwoWeekHigh"),
+                "fiftyTwoWeekLow": info.get("fiftyTwoWeekLow"),
+                "trailingPE": info.get("trailingPE"),
+                "forwardPE": info.get("forwardPE"),
+                "dividendYield": info.get("dividendYield"),
+                "beta": info.get("beta"),
+                "volume": info.get("volume"),
+                "averageVolume": info.get("averageVolume"),
+                "sharesOutstanding": info.get("sharesOutstanding"),
+                "website": info.get("website"),
+                "longBusinessSummary": info.get("longBusinessSummary")
+            }
+            return {k: v for k, v in relevant_info.items() if v is not None}
+        except Exception as e:
+            error_msg = f"Error fetching stock info: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_historical_data(
+        self, 
+        symbol: str, 
+        period: str = "1y",
+        interval: str = "1d",
+        start: Optional[datetime] = None,
+        end: Optional[datetime] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Get historical price data for a stock
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            period (str): Data period e.g. 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
+            interval (str): Data interval e.g. 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
+            start (datetime, optional): Start date for data
+            end (datetime, optional): End date for data
+            
+        Returns:
+            List[Dict[str, Any]]: List of historical price data points
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return [{"error": "yfinance package not available"}]
+
+            hist = ticker.history(period=period, interval=interval, start=start, end=end)
+            return hist.reset_index().to_dict('records')
+        except Exception as e:
+            error_msg = f"Error fetching historical data: {str(e)}"
+            logging.error(error_msg)
+            return [{"error": error_msg}]
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("YFinanceTools Demonstration")
+    print("==================================================\n")
+
+    try:
+        yf_tools = YFinanceTools()
+
+        # 1. Basic Stock Information
+        print("1. Basic Stock Information for AAPL")
+        print("------------------------------")
+        info = yf_tools.get_stock_info("AAPL")
+        if "error" not in info:
+            print(f"Company: {info.get('longName')}")
+            print(f"Sector: {info.get('sector')}")
+            print(f"Industry: {info.get('industry')}")
+            print(f"Country: {info.get('country')}\n")
+        else:
+            print(f"Error: {info['error']}\n")
+
+        # 2. Current Price Data
+        print("2. Current Price Data")
+        print("------------------------------")
+        price_data = yf_tools.get_stock_price("AAPL")
+        if "error" not in price_data:
+            print(f"Current Price: ${price_data.get('price', 'N/A')}")
+            print(f"Previous Close: ${price_data.get('previous_close', 'N/A')}")
+            print(f"Day Range: ${price_data.get('low', 'N/A')} - ${price_data.get('high', 'N/A')}\n")
+        else:
+            print(f"Error: {price_data['error']}\n")
+
+        # 3. Historical Data
+        print("3. Historical Data (Last 5 Days)")
+        print("------------------------------")
+        hist_data = yf_tools.get_historical_data("AAPL", period="5d")
+        if not any("error" in d for d in hist_data):
+            for day in hist_data:
+                print(f"Date: {day.get('Date')}, Close: ${day.get('Close', 'N/A'):.2f}")
+            print()
+        else:
+            print(f"{hist_data[0]['error']}\n")
+
+        # 4. Multi-Stock Example
+        print("5. Multi-Stock Current Prices")
+        print("------------------------------")
+        for symbol in ["AAPL", "MSFT", "GOOGL"]:
+            price_data = yf_tools.get_stock_price(symbol)
+            if "error" not in price_data:
+                print(f"{symbol}: ${price_data.get('price', 'N/A')}")
+            else:
+                print(f"{symbol}: Error fetching price")
+        print()
+
+    except Exception as e:
+        print(f"Error: {str(e)}")
+
+    print("==================================================")
+    print("Analysis Complete")
+    print("==================================================")

agents/pyproject.toml

@@ -0,0 +1,16 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "praisonaiagents"
+version = "0.0.26"
+description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
+authors = [
+    { name="Mervin Praison" }
+]
+dependencies = [
+    "pydantic",
+    "rich",
+    "openai"
+] 

agents/structured_agents_example.py

@@ -0,0 +1,56 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from pydantic import BaseModel
+
+class AnalysisReport(BaseModel):
+    title: str
+    findings: str
+    summary: str
+
+# Create a researcher agent
+researcher = Agent(
+    name="AIResearcher",
+    role="Technology Research Analyst",
+    goal="Analyze and structure information about AI developments",
+    backstory="Expert analyst specializing in AI technology trends",
+    verbose=True,
+    llm="gpt-4o-mini",
+    tools=[Tools.internet_search],
+    self_reflect=False
+)
+
+# Create an analyst agent
+analyst = Agent(
+    name="DataAnalyst",
+    role="Data Insights Specialist",
+    goal="Structure and analyze research findings",
+    backstory="Senior data analyst with expertise in pattern recognition",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Define structured tasks
+research_task = Task(
+    name="gather_research",
+    description="Research recent AI developments in 2024",
+    agent=researcher,
+    expected_output="Research findings"
+)
+
+analysis_task = Task(
+    name="analyze_findings",
+    description="Analyze research findings and create structured report. No additional text or explanation.",
+    agent=analyst,
+    output_json=AnalysisReport,
+    expected_output="JSON object"
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task],
+    process="sequential",
+    verbose=True
+)
+result = agents.start()
+print(result)

agents/test.py

@@ -0,0 +1,147 @@
+from typing import List, Dict
+from pydantic import BaseModel
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.main import TaskOutput
+
+# Pydantic models for structured output
+class Chapter(BaseModel):
+    chapter_number: int
+    title: str
+    outline: str
+
+class StoryOutline(BaseModel):
+    title: str
+    total_chapters: int
+    chapters: List[Chapter]
+
+class ChapterContent(BaseModel):
+    chapter_number: int
+    title: str
+    content: str
+
+# Create the story planner agent
+planner_agent = Agent(
+    name="Story Planner",
+    role="Creative Writing Architect",
+    goal="Create detailed story outlines with chapter breakdowns",
+    backstory="Expert storyteller skilled in narrative structure and plot development",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Create the writer agent
+writer_agent = Agent(
+    name="Story Writer",
+    role="Creative Writer",
+    goal="Write engaging and cohesive story chapters",
+    backstory="Experienced writer skilled in bringing stories to life with vivid details and engaging narrative",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Story title
+story_title = "The Curious Cat"
+
+# Step 1: Create outline task
+outline_task = Task(
+    name="story_outline",
+    description=f"""Create a detailed story outline for a story titled '{story_title}'.
+    The outline should include:
+    1. A compelling story arc
+    2. Clear chapter breakdown
+    3. Brief description of what happens in each chapter
+    4. Character introductions and development points
+    
+    Provide the response in this exact JSON format:
+    {{
+        "title": "Story title",
+        "total_chapters": number,
+        "chapters": [
+            {{
+                "chapter_number": 1,
+                "title": "Chapter title",
+                "outline": "Detailed chapter outline"
+            }}
+        ]
+    }}
+    """,
+    expected_output="Complete story outline",
+    agent=planner_agent,
+    output_pydantic=StoryOutline
+)
+
+# Create initial workflow for outline
+outline_workflow = PraisonAIAgents(
+    agents=[planner_agent],
+    tasks=[outline_task],
+    verbose=True,
+    process="sequential"
+)
+
+# Get the outline
+outline_result = outline_workflow.start()
+story_outline = outline_result["task_results"][0].pydantic
+
+# Step 2: Create tasks for each chapter
+chapter_tasks = []
+chapters_content = []
+
+# Create a task for each chapter
+for chapter in story_outline.chapters:
+    chapter_task = Task(
+        name=f"write_chapter_{chapter.chapter_number}",
+        description=f"""Write chapter {chapter.chapter_number}: {chapter.title}
+        Chapter outline: {chapter.outline}
+        
+        Write a complete, engaging chapter following this outline.
+        Return in this exact JSON format:
+        {{
+            "chapter_number": {chapter.chapter_number},
+            "title": "{chapter.title}",
+            "content": "The full chapter content..."
+        }}
+        """,
+        expected_output="Complete chapter content",
+        agent=writer_agent,
+        output_pydantic=ChapterContent,
+        context=[outline_task]
+    )
+    chapter_tasks.append(chapter_task)
+
+# Create sequential workflow for chapters
+chapter_workflow = PraisonAIAgents(
+    agents=[writer_agent],
+    tasks=chapter_tasks,
+    verbose=True,
+    process="sequential"
+)
+
+# Run the chapter workflow
+chapter_results = chapter_workflow.start()
+
+# Collect and validate all chapter contents
+for task_result in chapter_results["task_results"].values():
+    if task_result and task_result.pydantic:
+        chapters_content.append(task_result.pydantic)
+
+# Sort chapters by chapter number
+chapters_content.sort(key=lambda x: x.chapter_number)
+
+# Format final result
+story = {
+    "title": story_outline.title,
+    "outline": story_outline.model_dump(),
+    "chapters": [chapter.model_dump() for chapter in chapters_content]
+}
+
+# Print results
+print("\n=== Story Generation Results ===")
+print(f"\nTitle: {story['title']}")
+print("\nOutline:")
+print(story['outline'])
+print("\nChapters:")
+for chapter in story['chapters']:
+    print(f"\nChapter {chapter['chapter_number']}: {chapter['title']}")
+    print(chapter['content'][:200] + "...")  # Print first 200 chars of each chapter

agents/tools_example.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
+
+# Create Wikipedia agent
+news_agent = Agent(
+    name="NewsAgent",
+    role="News Analyst",
+    goal="Collect and analyze news articles from various sources.",
+    backstory="Expert in news gathering and content analysis.",
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
+    self_reflect=False
+)
+
+
+# Define research task
+news_task = Task(
+    description="Analyze news articles about 'AI developments' from major tech news sources.",
+    expected_output="Summary of key AI developments with source articles.",
+    agent=news_agent,
+    name="ai_news"
+)
+
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[news_agent],
+    tasks=[news_task],
+    process="sequential"
+)
+agents.start()

agents/uv.lock

@@ -0,0 +1,321 @@
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+
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+name = "typing-extensions"
+version = "4.12.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/df/db/f35a00659bc03fec321ba8bce9420de607a1d37f8342eee1863174c69557/typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8", size = 85321 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/9f/ad63fc0248c5379346306f8668cda6e2e2e9c95e01216d2b8ffd9ff037d0/typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d", size = 37438 },
+]

agents/workflow_example_basic.py

@@ -0,0 +1,74 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from typing import List, Dict
+from duckduckgo_search import DDGS
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles, URLs, and snippets.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
+# 2. Agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organising internet data.",
+    tools=[internet_search_tool],
+    self_reflect=False
+)
+
+# 3. Tasks
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+    is_start=True,
+    next_tasks=["validate_data"]
+)
+
+validate_task = Task(
+    description="""Validate the collected data. Check if:
+    1. At least 5 results are returned.
+    2. Each result contains a title and a URL.
+    Return validation_result as 'valid' or 'invalid' only no other text.""",
+    expected_output="Validation result indicating if data is valid or invalid.",
+    agent=data_agent,
+    name="validate_data",
+    task_type="decision",
+    condition={
+        "valid": [],  # End the workflow on valid data
+        "invalid": ["collect_data"]  # Retry data collection on invalid data
+    },
+)
+
+# 4. AI Agents Workflow
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task, validate_task],
+    verbose=1,
+    process="workflow"
+)
+
+agents.start()

agents/workflow_example_detailed.py

@@ -0,0 +1,253 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import random
+from typing import List, Dict, Union
+import json
+from pydantic import BaseModel
+
+# Add Pydantic models for data validation
+class Person(BaseModel):
+    name: str
+    age: int
+    job: str
+    city: str
+    salary: int
+
+class ProcessedPerson(Person):
+    salary_category: str
+    age_group: str
+    processing_status: str
+
+class DataList(BaseModel):
+    items: List[Dict]
+
+class ValidationResult(BaseModel):
+    validation_result: str
+    details: str = ""
+
+def random_data_of_individuals() -> List[Dict]:
+    """Generate random individual data"""
+    names = ["John", "Jane", "Mike", "Sarah", "David", "Emma"]
+    jobs = ["Engineer", "Doctor", "Teacher", "Artist", "Developer"]
+    cities = ["New York", "London", "Tokyo", "Paris", "Berlin"]
+    
+    data = []
+    for _ in range(random.randint(3, 7)):
+        person = {
+            "name": random.choice(names),
+            "age": random.randint(25, 65),
+            "job": random.choice(jobs),
+            "city": random.choice(cities),
+            "salary": random.randint(30000, 120000)
+        }
+        data.append(person)
+    return data
+
+def process_data_of_individuals(data: Union[List[Dict], Dict, str]) -> Dict:
+    """Process individual data by adding categories and analysis"""
+    try:
+        print("\n[DEBUG] process_data_of_individuals input:", data)
+        print("[DEBUG] Current workflow state:")
+        print("- total_records:", workflow.get_state("total_records"))
+        print("- current_index:", workflow.get_state("current_index"))
+        print("- remaining:", workflow.get_state("remaining"))
+
+        # Get the items list from the collect_task result
+        collect_result = None
+        for task in workflow.tasks.values():
+            if task.name == "collect_data" and task.result:
+                try:
+                    collect_data = json.loads(task.result.raw)
+                    collect_result = collect_data.get("items", [])
+                    print("[DEBUG] Found collect_data items:", len(collect_result))
+                except:
+                    print("[DEBUG] Failed to parse collect_data result")
+
+        # Handle string input by trying to parse it as JSON
+        if isinstance(data, str):
+            if ":" in data and not data.strip().startswith("{"):
+                # Convert string format to dictionary
+                pairs = [pair.strip() for pair in data.split(",")]
+                data_dict = {}
+                for pair in pairs:
+                    key, value = pair.split(":")
+                    key = key.strip().lower()
+                    value = value.strip()
+                    if key == "age" or key == "salary":
+                        value = int(value)
+                    data_dict[key] = value
+                data = data_dict
+            else:
+                data = json.loads(data)
+            print("[DEBUG] Parsed data:", data)
+
+        # Handle single record
+        if isinstance(data, dict):
+            person = data
+            # Initialize total records if not set
+            total_records = workflow.get_state("total_records")
+            if total_records is None and collect_result:
+                total_records = len(collect_result)
+                workflow.set_state("total_records", total_records)
+                print(f"[DEBUG] Initialized total_records to {total_records}")
+
+            current_index = workflow.get_state("current_index", 0)
+            total_records = total_records or 1
+            remaining = total_records - (current_index + 1)
+            workflow.set_state("remaining", remaining)
+            print(f"[DEBUG] Processing record {current_index + 1}/{total_records}")
+
+        elif isinstance(data, list):
+            if len(data) == 0:
+                raise ValueError("Empty data list")
+            person = data[0]
+            workflow.set_state("total_records", len(data))
+            workflow.set_state("current_index", 0)
+            workflow.set_state("remaining", len(data) - 1)
+            print(f"[DEBUG] First record from list of {len(data)} items")
+        else:
+            raise ValueError("Input must be a dictionary or list with at least one record")
+
+        processed_person = person.copy()
+        
+        # Add salary category
+        salary = person.get("salary", 0)
+        if salary < 50000:
+            processed_person["salary_category"] = "entry"
+        elif salary < 90000:
+            processed_person["salary_category"] = "mid"
+        else:
+            processed_person["salary_category"] = "senior"
+        
+        # Add age group
+        age = person.get("age", 0)
+        if age < 35:
+            processed_person["age_group"] = "young"
+        elif age < 50:
+            processed_person["age_group"] = "mid"
+        else:
+            processed_person["age_group"] = "senior"
+        
+        # Add processing status using workflow state
+        remaining = workflow.get_state("remaining", 0)
+        current_index = workflow.get_state("current_index", 0)
+        total_records = workflow.get_state("total_records", 1)
+        
+        # Update current index for next iteration
+        workflow.set_state("current_index", current_index + 1)
+        
+        print(f"[DEBUG] Status check - remaining: {remaining}, current_index: {current_index}, total_records: {total_records}")
+        
+        if remaining <= 0 and current_index >= total_records - 1:
+            print("[DEBUG] Setting status to 'all records processed'")
+            processed_person["processing_status"] = "all records processed"
+        else:
+            print(f"[DEBUG] More records to process. Remaining: {remaining}")
+            processed_person["processing_status"] = f"more records to process ({remaining} remaining)"
+            
+        print("[DEBUG] Final processed person:", processed_person)
+        return processed_person
+        
+    except Exception as e:
+        print(f"[DEBUG] Error processing data: {str(e)}")
+        return {"error": str(e), "processing_status": "error occurred"}
+
+# Create agents
+data_agent = Agent(
+    name="DataCollector",
+    role="Data collection specialist",
+    goal="Collect and validate data about individuals",
+    backstory="Expert in gathering and validating demographic data",
+    tools=[random_data_of_individuals],
+    self_reflect=False
+)
+
+process_agent = Agent(
+    name="DataProcessor",
+    role="Data processor",
+    goal="Process and categorize individual data",
+    backstory="Expert in data analysis and categorization",
+    tools=[process_data_of_individuals],
+    self_reflect=False
+)
+
+# Modify tasks to use Pydantic models
+collect_task = Task(
+    description="Collect random individual data using the random_data_of_individuals tool. Return as a JSON object with 'items' array.",
+    expected_output="List of individual records with basic information",
+    agent=data_agent,
+    name="collect_data",
+    tools=[random_data_of_individuals],
+    is_start=True,
+    next_tasks=["validate_data"],
+    output_json=DataList
+)
+
+validate_task = Task(
+    description="""Validate the collected data. Check if:
+    1. All required fields are present (name, age, job, city, salary)
+    2. Age is between 25 and 65
+    3. Salary is between 30000 and 120000
+    Return validation_result as 'valid' or 'invalid' with optional details.""",
+    expected_output="Validation result indicating if data is valid or invalid",
+    agent=data_agent,
+    name="validate_data",
+    task_type="decision",
+    condition={
+        "valid": ["process_data"],
+        "invalid": ["collect_data"]
+    },
+    output_json=ValidationResult
+)
+
+process_task = Task(
+    description="""Process one record at a time from the input data.
+    Current progress will be shown in Loop Status.
+    
+    For the current record:
+    1. Use process_data_of_individuals tool to add categories
+    2. Return the processed record with remaining count
+    
+    Current remaining: {remaining}
+    Current item: {current_item}
+    
+    Process this record and indicate if more records remain.""",
+    expected_output="Processed record with categories and status",
+    agent=process_agent,
+    name="process_data",
+    tools=[process_data_of_individuals],
+    task_type="loop",
+    condition={
+        "more records to process": ["process_data"],
+        "all records processed": []
+    },
+    context=[collect_task],
+    output_json=ProcessedPerson
+)
+
+# Create PraisonAIAgents instance with workflow process
+workflow = PraisonAIAgents(
+    agents=[data_agent, process_agent],
+    tasks=[collect_task, validate_task, process_task],
+    verbose=1,
+    process="workflow"
+)
+
+# Run the workflow
+result = workflow.start()
+
+# Print results
+print("\nWorkflow Results:")
+print("----------------")
+for task_id, task in workflow.tasks.items():
+    print(f"\nTask: {task.name}")
+    print(f"Status: {task.status}")
+    if task.result:
+        print("Output:")
+        try:
+            # Try to format as pretty JSON
+            import json
+            output = json.loads(task.result.raw)
+            print(json.dumps(output, indent=2))
+        except:
+            # If not JSON, print raw output
+            print(task.result.raw[:500] + "..." if len(task.result.raw) > 500 else task.result.raw) 

api.py

@@ -0,0 +1,18 @@
+from flask import Flask
+from praisonai import PraisonAI
+import markdown
+
+app = Flask(__name__)
+
+def basic():
+    praisonai = PraisonAI(agent_file="agents.yaml")
+    return praisonai.run()
+
+@app.route('/')
+def home():
+    output = basic()
+    html_output = markdown.markdown(output)
+    return f'<html><body>{html_output}</body></html>'
+
+if __name__ == "__main__":
+    app.run(debug=True)

config.yaml

@@ -0,0 +1,60 @@
+ollama_save: "true"
+huggingface_save: "true"
+train: "true"
+
+model_name: "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
+hf_model_name: "mervinpraison/llama-3.1-instruct"
+ollama_model: "mervinpraison/llama3.1-instruct"
+model_parameters: "8b"
+
+dataset:
+  - name: "yahma/alpaca-cleaned"
+    split_type: "train"
+    processing_func: "format_prompts"
+    rename:
+      input: "input"
+      output: "output"
+      instruction: "instruction"
+    filter_data: false
+    filter_column_value: "id"
+    filter_value: "alpaca"
+    num_samples: 20000
+
+dataset_text_field: "text"
+dataset_num_proc: 2
+packing: false
+
+max_seq_length: 2048
+load_in_4bit: true
+lora_r: 16
+lora_target_modules: 
+  - "q_proj"
+  - "k_proj"
+  - "v_proj"
+  - "o_proj"
+  - "gate_proj"
+  - "up_proj"
+  - "down_proj"
+lora_alpha: 16
+lora_dropout: 0
+lora_bias: "none"
+use_gradient_checkpointing: "unsloth"
+random_state: 3407
+use_rslora: false
+loftq_config: null
+
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 2
+warmup_steps: 5
+num_train_epochs: 1
+max_steps: 10
+learning_rate: 2.0e-4
+logging_steps: 1
+optim: "adamw_8bit"
+weight_decay: 0.01
+lr_scheduler_type: "linear"
+seed: 3407
+output_dir: "outputs"
+
+quantization_method: 
+  - "q4_k_m"

cookbooks/.gitignore

@@ -0,0 +1,2 @@
+*.txt
+*.csv

cookbooks/notebooks/article_generation_agents.ipynb

@@ -0,0 +1,612 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OgD4IxstB8qZ"
+      },
+      "source": [
+        "# Article Generation Agents\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/article_generation_agents.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RgAp3O-fB8qa"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "vrEjmqm3B8qa"
+      },
+      "outputs": [],
+      "source": [
+        "# Install dependencies without output\n",
+        "%pip install langchain_community > /dev/null\n",
+        "%pip install praisonai[crewai] > /dev/null\n",
+        "%pip install duckduckgo_search > /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bq2NmMbvB8qa"
+      },
+      "source": [
+        "## Tools"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "MrJueER-B8qb"
+      },
+      "outputs": [],
+      "source": [
+        "from duckduckgo_search import DDGS\n",
+        "from praisonai_tools import BaseTool\n",
+        "\n",
+        "class InternetSearchTool(BaseTool):\n",
+        "    name: str = \"InternetSearchTool\"\n",
+        "    description: str = \"Search Internet for relevant information based on a query or latest news\"\n",
+        "\n",
+        "    def _run(self, query: str):\n",
+        "        ddgs = DDGS()\n",
+        "        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)\n",
+        "        return results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fnjb1e4sB8qb"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "GHefV599B8qb"
+      },
+      "outputs": [],
+      "source": [
+        "agent_yaml = \"\"\"framework: \"crewai\"\n",
+        "topic: \"AI Advancements in 2024\"\n",
+        "roles:\n",
+        "  researcher:\n",
+        "    role: \"Senior Research Analyst\"\n",
+        "    backstory: |\n",
+        "      You are an expert at a technology research group, skilled in identifying trends and analyzing complex data.\n",
+        "    goal: \"Uncover cutting-edge developments in AI and data science\"\n",
+        "    verbose: true\n",
+        "    allow_delegation: false\n",
+        "    tools:\n",
+        "      - \"InternetSearchTool\"\n",
+        "    tasks:\n",
+        "      task1:\n",
+        "        description: |\n",
+        "          Analyze 2024's AI advancements. Find major trends, new technologies, and their effects. Provide a detailed report.\n",
+        "        expected_output: \"A detailed report on major AI trends, new technologies, and their effects in 2024.\"\n",
+        "  writer:\n",
+        "    role: \"Tech Content Strategist\"\n",
+        "    backstory: |\n",
+        "      You are a content strategist known for making complex tech topics interesting and easy to understand.\n",
+        "    goal: \"Craft compelling content on tech advancements\"\n",
+        "    verbose: true\n",
+        "    allow_delegation: true\n",
+        "    tasks:\n",
+        "      task2:\n",
+        "        description: |\n",
+        "          Create a blog post about major AI advancements using your insights. Make it interesting, clear, and suited for tech enthusiasts. It should be at least 4 paragraphs long.\n",
+        "        expected_output: \"An engaging blog post of at least 4 paragraphs about major AI advancements, suitable for tech enthusiasts.\"\n",
+        "dependencies: []\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZflcenzzB8qb"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "0p7n5aiUB8qb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "e0f36d01-9d6b-42e4-baf6-ff893d0eba4f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m\u001b[95m [2024-11-02 18:03:43][DEBUG]: == Working Agent: Senior Research Analyst\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-02 18:03:43][INFO]: == Starting Task: Analyze 2024's AI advancements. Find major trends, new technologies, and their effects. Provide a detailed report.\n",
+            "\u001b[00m\n",
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mI need to gather information about the major AI advancements, trends, and new technologies for the year 2024. This will involve searching for the latest news and developments in the field of AI.\n",
+            "\n",
+            "Action: InternetSearchTool  \n",
+            "Action Input: {\"query\": \"AI advancements trends new technologies 2024\"}  \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'AI for everything: 10 Breakthrough Technologies 2024', 'href': 'https://www.technologyreview.com/2024/01/08/1085096/artificial-intelligence-generative-ai-chatgpt-open-ai-breakthrough-technologies', 'body': 'Generative AI took the world by storm in 2023. Its future—and ours—will be shaped by what we do next. Microsoft and Google have since moved beyond search to put chatbot-based assistants into ...'}, {'title': \"What's next for AI in 2024 - MIT Technology Review\", 'href': 'https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024/', 'body': 'In 2024, generative AI might actually become useful for the regular, non-tech person, and we are going to see more people tinkering with a million little AI models. State-of-the-art AI models ...'}, {'title': 'The 5 Biggest Artificial Intelligence Trends For 2024 - Forbes', 'href': 'https://www.forbes.com/sites/bernardmarr/2023/11/01/the-top-5-artificial-intelligence-trends-for-2024/', 'body': \"Adobe Stock. It's been a year since OpenAI released ChatGPT, opening the door to seamlessly weave AI into the fabric of our daily lives, propelling industries into the future and even prompting ...\"}, {'title': 'The Future of AI: 10 Trends in Artificial Intelligence in 2024 - Algotive', 'href': 'https://www.algotive.ai/blog/the-future-of-ai-10-trends-in-artificial-intelligence-in-2024', 'body': 'Here are the top 10 AI trends that will boost the productivity of companies in 2024: While autonomous chatbots are already a reality, new technologies and smarter algorithms point toward better AI agents that allow for more efficient customer service and shorter response times.'}, {'title': 'Top 6 predictions for AI advancements and trends in 2024 - IBM', 'href': 'https://www.ibm.com/think/insights/ai-trends', 'body': \"4. AI as a national priority. Recognizing AI's immense potential, nations worldwide are expected to prioritize its development in a manner reminiscent of a new space race. This heightened focus drives significant advances in research, science and economic growth, firmly establishing AI as a strategic global asset.\"}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have gathered several articles that discuss the major AI advancements, trends, and new technologies for 2024. I need to analyze these sources to extract detailed insights.\n",
+            "\n",
+            "Action: InternetSearchTool  \n",
+            "Action Input: {\"query\": \"AI advancements trends 2024 detailed analysis\"}  \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'PDF The state of AI in early 2024: Gen AI adoption spikes and starts to ...', 'href': 'https://www.mckinsey.com/~/media/mckinsey/business+functions/quantumblack/our+insights/the+state+of+ai/2024/the-state-of-ai-in-early-2024-final.pdf', 'body': 'The state of AI in early 2024: Gen AI adoption spikes and starts to generate value 1 This article is a collaborative effort by Alex Singla, Alexander Sukharevsky, Lareina Yee, and Michael Chui, with Bryce Hall, representing views from QuantumBlack, AI by McKinsey and McKinsey Digital.'}, {'title': '8 AI Predictions for 2024 That Stood the Test of Time', 'href': 'https://www.td.org/content/atd-blog/8-ai-predictions-for-2024-that-stood-the-test-of-time', 'body': \"Here are eight AI predictions for 2024 that have stood the test of time and are shaping the HR and L&D market today. Advertisement. 1. Proactive AI. One major prediction was that AI would become more proactive, and that's exactly what we're seeing. AI now anticipates user needs rather than waiting for input.\"}, {'title': 'The state of AI in early 2024 | McKinsey - McKinsey & Company', 'href': 'https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai', 'body': 'If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using—and deriving business value from—this new technology.In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago.'}, {'title': \"What's next for AI in 2024 - MIT Technology Review\", 'href': 'https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024/', 'body': 'In 2024, generative AI might actually become useful for the regular, non-tech person, and we are going to see more people tinkering with a million little AI models. State-of-the-art AI models ...'}, {'title': 'AI Index Report 2024 - Artificial Intelligence Index', 'href': 'https://aiindex.stanford.edu/report/', 'body': \"The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics ...\"}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have gathered a rich set of articles and reports that can provide a comprehensive overview of the AI advancements, trends, and new technologies for 2024. I will now synthesize the information from these sources to prepare a detailed report.\n",
+            "\n",
+            "**Final Answer: Detailed Report on Major AI Trends, New Technologies, and Their Effects in 2024**\n",
+            "\n",
+            "**1. Overview of AI Advancements in 2024**\n",
+            "In 2024, the landscape of artificial intelligence (AI) is characterized by significant advancements in generative AI, alongside a shift towards practical applications that enhance everyday tasks for non-technical users. The previous year marked the explosion of generative AI technologies, with tools like ChatGPT capturing public attention. This year sees a transition from exploration to practical integration, where businesses and individuals alike are beginning to unlock the full potential of these innovations.\n",
+            "\n",
+            "**2. Major Trends in AI for 2024**\n",
+            "Several key trends have emerged in the AI field:\n",
+            "\n",
+            "- **Generative AI Maturity**: As reported by McKinsey, there is a notable spike in the adoption of generative AI, with 65% of organizations now utilizing it regularly. This reflects a significant increase from the previous year, indicating that businesses are finding real value in these applications.\n",
+            "\n",
+            "- **Proactive AI**: AI systems are evolving to become more anticipatory, actively predicting user needs rather than simply responding to requests. This trend is especially evident in customer service applications, where AI is enhancing user experiences through increased efficiency and responsiveness.\n",
+            "\n",
+            "- **Integration of AI into Daily Life**: Companies like Microsoft and Google are expanding their chatbot technologies beyond search functionalities, embedding AI deeper into daily workflows. This reflects a broader trend where AI becomes an integral part of both personal and professional environments.\n",
+            "\n",
+            "- **Diverse AI Models**: There is a growing interest in smaller, specialized AI models that cater to specific tasks. As more people experiment with these models, we can expect a democratization of AI capabilities that empowers users to create tailored solutions for their unique challenges.\n",
+            "\n",
+            "- **AI as a National Priority**: Governments around the world are recognizing the strategic importance of AI, resembling a new space race. Investment in AI research and development is expected to drive economic growth, enhance scientific advancements, and establish AI as a key global asset.\n",
+            "\n",
+            "**3. New Technologies Shaping AI in 2024**\n",
+            "The technological advancements shaping AI in 2024 include:\n",
+            "\n",
+            "- **Enhanced Algorithms**: Advances in machine learning algorithms are leading to more effective AI agents capable of handling complex tasks with reduced response times. This includes improvements in natural language processing and computer vision that allow AI systems to perform more sophisticated analyses and generate human-like responses.\n",
+            "\n",
+            "- **AI in Creative Industries**: Generative AI is creating waves in creative sectors, allowing businesses to produce content, design products, and even create art with unprecedented efficiency. This technology is empowering creatives to explore new domains while reducing the time and resources required for traditional processes.\n",
+            "\n",
+            "- **AI in the Workplace**: The integration of AI tools in HR and Learning & Development (L&D) is transforming employee engagement and training. AI is being used to personalize learning experiences, track employee performance, and even facilitate recruitment processes.\n",
+            "\n",
+            "**4. Effects of AI Advancements in 2024**\n",
+            "The effects of these advancements are profound:\n",
+            "\n",
+            "- **Business Efficiency**: Companies leveraging AI technologies are witnessing improved operational efficiency, reduced costs, and enhanced customer satisfaction. The ability to automate routine tasks allows human employees to focus on more strategic initiatives.\n",
+            "\n",
+            "- **Workforce Transformation**: The rise of AI is reshaping job roles, necessitating new skill sets and adaptability in the workforce. While AI can automate certain tasks, it also creates opportunities for workers to engage in higher-level thinking and creativity.\n",
+            "\n",
+            "- **Public Perception and Ethical Considerations**: As AI continues to permeate various aspects of life, public perception is becoming a critical factor. There are ongoing discussions about the ethical implications of AI, including data privacy, bias in algorithms, and the need for regulation to ensure responsible deployment.\n",
+            "\n",
+            "**Conclusion**\n",
+            "The year 2024 is set to be a pivotal moment for artificial intelligence, with advancements that not only enhance technological capabilities but also reshape societal interactions with AI. As organizations and individuals alike embrace these changes, the focus will increasingly be on harnessing AI to create meaningful value while navigating the accompanying ethical challenges. \n",
+            "\n",
+            "This detailed report encapsulates the major trends, new technologies, and their effects in the AI landscape for 2024, providing a comprehensive picture of where the industry is headed.\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n",
+            "\u001b[1m\u001b[92m [2024-11-02 18:04:01][DEBUG]: == [Senior Research Analyst] Task output: Detailed Report on Major AI Trends, New Technologies, and Their Effects in 2024**\n",
+            "\n",
+            "**1. Overview of AI Advancements in 2024**\n",
+            "In 2024, the landscape of artificial intelligence (AI) is characterized by significant advancements in generative AI, alongside a shift towards practical applications that enhance everyday tasks for non-technical users. The previous year marked the explosion of generative AI technologies, with tools like ChatGPT capturing public attention. This year sees a transition from exploration to practical integration, where businesses and individuals alike are beginning to unlock the full potential of these innovations.\n",
+            "\n",
+            "**2. Major Trends in AI for 2024**\n",
+            "Several key trends have emerged in the AI field:\n",
+            "\n",
+            "- **Generative AI Maturity**: As reported by McKinsey, there is a notable spike in the adoption of generative AI, with 65% of organizations now utilizing it regularly. This reflects a significant increase from the previous year, indicating that businesses are finding real value in these applications.\n",
+            "\n",
+            "- **Proactive AI**: AI systems are evolving to become more anticipatory, actively predicting user needs rather than simply responding to requests. This trend is especially evident in customer service applications, where AI is enhancing user experiences through increased efficiency and responsiveness.\n",
+            "\n",
+            "- **Integration of AI into Daily Life**: Companies like Microsoft and Google are expanding their chatbot technologies beyond search functionalities, embedding AI deeper into daily workflows. This reflects a broader trend where AI becomes an integral part of both personal and professional environments.\n",
+            "\n",
+            "- **Diverse AI Models**: There is a growing interest in smaller, specialized AI models that cater to specific tasks. As more people experiment with these models, we can expect a democratization of AI capabilities that empowers users to create tailored solutions for their unique challenges.\n",
+            "\n",
+            "- **AI as a National Priority**: Governments around the world are recognizing the strategic importance of AI, resembling a new space race. Investment in AI research and development is expected to drive economic growth, enhance scientific advancements, and establish AI as a key global asset.\n",
+            "\n",
+            "**3. New Technologies Shaping AI in 2024**\n",
+            "The technological advancements shaping AI in 2024 include:\n",
+            "\n",
+            "- **Enhanced Algorithms**: Advances in machine learning algorithms are leading to more effective AI agents capable of handling complex tasks with reduced response times. This includes improvements in natural language processing and computer vision that allow AI systems to perform more sophisticated analyses and generate human-like responses.\n",
+            "\n",
+            "- **AI in Creative Industries**: Generative AI is creating waves in creative sectors, allowing businesses to produce content, design products, and even create art with unprecedented efficiency. This technology is empowering creatives to explore new domains while reducing the time and resources required for traditional processes.\n",
+            "\n",
+            "- **AI in the Workplace**: The integration of AI tools in HR and Learning & Development (L&D) is transforming employee engagement and training. AI is being used to personalize learning experiences, track employee performance, and even facilitate recruitment processes.\n",
+            "\n",
+            "**4. Effects of AI Advancements in 2024**\n",
+            "The effects of these advancements are profound:\n",
+            "\n",
+            "- **Business Efficiency**: Companies leveraging AI technologies are witnessing improved operational efficiency, reduced costs, and enhanced customer satisfaction. The ability to automate routine tasks allows human employees to focus on more strategic initiatives.\n",
+            "\n",
+            "- **Workforce Transformation**: The rise of AI is reshaping job roles, necessitating new skill sets and adaptability in the workforce. While AI can automate certain tasks, it also creates opportunities for workers to engage in higher-level thinking and creativity.\n",
+            "\n",
+            "- **Public Perception and Ethical Considerations**: As AI continues to permeate various aspects of life, public perception is becoming a critical factor. There are ongoing discussions about the ethical implications of AI, including data privacy, bias in algorithms, and the need for regulation to ensure responsible deployment.\n",
+            "\n",
+            "**Conclusion**\n",
+            "The year 2024 is set to be a pivotal moment for artificial intelligence, with advancements that not only enhance technological capabilities but also reshape societal interactions with AI. As organizations and individuals alike embrace these changes, the focus will increasingly be on harnessing AI to create meaningful value while navigating the accompanying ethical challenges. \n",
+            "\n",
+            "This detailed report encapsulates the major trends, new technologies, and their effects in the AI landscape for 2024, providing a comprehensive picture of where the industry is headed.\n",
+            "\n",
+            "\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-02 18:04:01][DEBUG]: == Working Agent: Tech Content Strategist\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-02 18:04:01][INFO]: == Starting Task: Create a blog post about major AI advancements using your insights. Make it interesting, clear, and suited for tech enthusiasts. It should be at least 4 paragraphs long.\n",
+            "\u001b[00m\n",
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mThought: To create a compelling blog post about major AI advancements, I need to gather detailed insights and relevant context to effectively communicate these advancements in an engaging manner for tech enthusiasts.\n",
+            "\n",
+            "Action: Delegate work to coworker\n",
+            "Action Input: {\n",
+            "    \"coworker\": \"Senior Research Analyst\",\n",
+            "    \"task\": \"Gather detailed insights on major AI advancements in 2024, focusing on trends, technologies, and their societal impacts. Include specific examples and statistics to support the findings. This information will be used to create a blog post that is interesting and clear for tech enthusiasts.\",\n",
+            "    \"context\": \"The AI landscape in 2024 is characterized by significant advancements in generative AI, practical applications, and a shift towards user-friendly technologies. Key trends include generative AI maturity, proactive AI systems, integration into daily life, diverse AI models, and the prioritization of AI by governments. New technologies involve enhanced algorithms, AI in creative industries, and workplace applications. The effects of these advancements include improved business efficiency, workforce transformation, and ethical considerations.\"\n",
+            "}\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mAction: InternetSearchTool  \n",
+            "Action Input: {\"query\": \"AI advancements 2024 trends technologies societal impacts\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'How the Top 10 Emerging Technologies of 2024 will impact the world', 'href': 'https://www.weforum.org/stories/2024/06/top-10-emerging-technologies-of-2024-impact-world/', 'body': 'With AI expanding the world of data like never before, finding ways of leveraging it without ethical or security concerns is key. Enter synthetic data, an exciting privacy-enhancing technology re-emerging in the age of AI. It replicates the patterns and trends in sensitive datasets but does not contain specific information that could be linked to individuals or compromise organizations or ...'}, {'title': \"The current state of AI, according to Stanford's AI Index | World ...\", 'href': 'https://www.weforum.org/stories/2024/04/stanford-university-ai-index-report/', 'body': \"It covers trends such as technical advancements in AI and public perceptions of the technology. ... AI and emerging technology at Davos 2024: 5 surprising things to know ... several studies assessing AI's impact on labour suggest that it enables workers to complete tasks more quickly and improved the quality of their output. The studies also ...\"}, {'title': \"What's next for AI in 2024 | MIT Technology Review\", 'href': 'https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024/', 'body': 'In 2024, generative AI might actually become useful for the regular, non-tech person, and we are going to see more people tinkering with a million little AI models. State-of-the-art AI models ...'}, {'title': 'The state of AI in early 2024 | McKinsey - McKinsey & Company', 'href': 'https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai', 'body': 'If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using—and deriving business value from—this new technology.In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago.'}, {'title': '22 Top AI Statistics & Trends In 2024 - Forbes Advisor', 'href': 'https://www.forbes.com/advisor/business/ai-statistics/', 'body': 'AI is expected to see an annual growth rate of 36.6% from 2023 to 2030. AI continues to revolutionize various industries, with an expected annual growth rate of 36.6% between 2023 and 2030, as ...'}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I need to analyze the gathered insights to create a comprehensive overview of AI advancements in 2024, focusing on trends, technologies, and societal impacts.\n",
+            "\n",
+            "Action: InternetSearchTool  \n",
+            "Action Input: {\"query\": \"AI generative models advancements 2024\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': \"What's next for AI in 2024 - MIT Technology Review\", 'href': 'https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024/', 'body': 'In 2024, generative AI might actually become useful for the regular, non-tech person, and we are going to see more people tinkering with a million little AI models. State-of-the-art AI models ...'}, {'title': 'Exploring The Future: 5 Cutting-Edge Generative AI Trends In 2024 - Forbes', 'href': 'https://www.forbes.com/sites/janakirammsv/2024/01/02/exploring-the-future-5-cutting-edge-generative-ai-trends-in-2024/', 'body': \"OpenAI's GPT4, Meta's Llama 2, and Mistral all served as examples of the advancements in large language models. The technology goes beyond text with multimodal AI models, allowing users to mix and ...\"}, {'title': 'AI for everything: 10 Breakthrough Technologies 2024', 'href': 'https://www.technologyreview.com/2024/01/08/1085096/artificial-intelligence-generative-ai-chatgpt-open-ai-breakthrough-technologies', 'body': 'Generative AI took the world by storm in 2023. Its future—and ours—will be shaped by what we do next. Microsoft and Google have since moved beyond search to put chatbot-based assistants into ...'}, {'title': 'PDF The state of AI in early 2024: Gen AI adoption spikes and starts to ...', 'href': 'https://www.mckinsey.com/~/media/mckinsey/business+functions/quantumblack/our+insights/the+state+of+ai/2024/the-state-of-ai-in-early-2024-final.pdf', 'body': 'If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report'}, {'title': 'The 10 Biggest Generative AI Trends For 2024 Everyone Must Be ... - Forbes', 'href': 'https://www.forbes.com/sites/bernardmarr/2023/10/02/the-10-biggest-generative-ai-trends-for-2024-everyone-must-be-ready-for-now/', 'body': 'With generative video tools, anyone will be able to produce professional-looking video content for a fraction of the price in 2024. 4 Generative Audio And Speech. As with video and design tools ...'}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I need to consolidate the insights I've gathered to provide a well-rounded view of AI advancements in 2024, emphasizing the key trends, technologies, and societal impacts.\n",
+            "\n",
+            "Final Answer:\n",
+            "\n",
+            "In 2024, the landscape of artificial intelligence (AI) has evolved dramatically, showcasing significant advancements that are reshaping various industries and everyday life. This year, several key trends emerged, highlighting the maturation of generative AI, the rise of proactive AI systems, and the increasing integration of AI into daily routines. Here's a detailed overview of these advancements, the technologies driving them, and their societal impacts.\n",
+            "\n",
+            "### Key Trends in AI Advancements\n",
+            "\n",
+            "1. **Generative AI Maturity**: \n",
+            "   2024 marks a pivotal moment for generative AI, with tools becoming more user-friendly and accessible. Reports indicate that **65% of organizations** are now utilizing generative AI technologies, nearly doubling the adoption rate from the previous year (McKinsey & Company). This accessibility allows non-tech users to leverage AI for various applications, such as content creation and data analysis.\n",
+            "\n",
+            "2. **Diverse AI Models**: \n",
+            "   The proliferation of diverse AI models is another hallmark of 2024. Companies like OpenAI with GPT-4, Meta's Llama 2, and Mistral are leading advancements in large language models (Forbes). These models are not only enhancing text generation but also expanding into multimodal capabilities, allowing users to integrate text, images, and audio seamlessly.\n",
+            "\n",
+            "3. **Proactive AI Systems**: \n",
+            "   AI systems are becoming more proactive, capable of anticipating user needs and automating tasks. This trend is particularly evident in personal assistants that can handle complex scheduling and communication tasks, freeing up time for users to focus on strategic decision-making.\n",
+            "\n",
+            "4. **Government Prioritization of AI**: \n",
+            "   Governments worldwide are recognizing the potential of AI, prioritizing its integration into public services and regulatory frameworks. This shift is aimed at harnessing AI for societal benefits while addressing ethical and security concerns related to data privacy (World Economic Forum).\n",
+            "\n",
+            "### Technologies Driving AI Advancements\n",
+            "\n",
+            "- **Enhanced Algorithms**: Continuous improvements in algorithms have enabled more efficient processing of vast datasets, leading to faster and more accurate AI outputs.\n",
+            "- **Generative Video and Audio Tools**: Innovations in generative technology are allowing users to create professional-quality video and audio content at a fraction of the traditional cost. This democratization of content creation is empowering creators across various fields (Forbes).\n",
+            "- **Synthetic Data**: The use of synthetic data is emerging as a key solution to privacy concerns, enabling organizations to train AI models without compromising sensitive information.\n",
+            "\n",
+            "### Societal Impacts\n",
+            "\n",
+            "The advancements in AI technologies are poised to transform the workforce and improve business efficiency. For instance, AI is enabling workers to complete tasks more rapidly and improve the quality of their outputs, thus enhancing overall productivity. Additionally, as AI tools become integrated into everyday applications, they are fostering a culture of innovation and creativity among users.\n",
+            "\n",
+            "However, these developments also raise ethical considerations regarding job displacement and data security. As businesses increasingly rely on AI, there is a pressing need for frameworks that address these concerns while ensuring that the benefits of AI are distributed equitably across society.\n",
+            "\n",
+            "### Conclusion\n",
+            "\n",
+            "The advancements in AI technology in 2024 are not just about improving efficiency; they represent a fundamental shift in how humans interact with technology. As generative AI matures and becomes more accessible, the potential for creative and innovative applications grows exponentially. The ongoing integration of AI into daily life, combined with proactive systems and diverse models, signifies a future where AI is an indispensable tool for individuals and organizations alike.\n",
+            "\n",
+            "These insights into the advancements of AI in 2024 will serve as a foundational backdrop for discussions surrounding the future of technology and its implications for society at large.\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n",
+            "\u001b[95m \n",
+            "\n",
+            "In 2024, the landscape of artificial intelligence (AI) has evolved dramatically, showcasing significant advancements that are reshaping various industries and everyday life. This year, several key trends emerged, highlighting the maturation of generative AI, the rise of proactive AI systems, and the increasing integration of AI into daily routines. Here's a detailed overview of these advancements, the technologies driving them, and their societal impacts.\n",
+            "\n",
+            "### Key Trends in AI Advancements\n",
+            "\n",
+            "1. **Generative AI Maturity**: \n",
+            "   2024 marks a pivotal moment for generative AI, with tools becoming more user-friendly and accessible. Reports indicate that **65% of organizations** are now utilizing generative AI technologies, nearly doubling the adoption rate from the previous year (McKinsey & Company). This accessibility allows non-tech users to leverage AI for various applications, such as content creation and data analysis.\n",
+            "\n",
+            "2. **Diverse AI Models**: \n",
+            "   The proliferation of diverse AI models is another hallmark of 2024. Companies like OpenAI with GPT-4, Meta's Llama 2, and Mistral are leading advancements in large language models (Forbes). These models are not only enhancing text generation but also expanding into multimodal capabilities, allowing users to integrate text, images, and audio seamlessly.\n",
+            "\n",
+            "3. **Proactive AI Systems**: \n",
+            "   AI systems are becoming more proactive, capable of anticipating user needs and automating tasks. This trend is particularly evident in personal assistants that can handle complex scheduling and communication tasks, freeing up time for users to focus on strategic decision-making.\n",
+            "\n",
+            "4. **Government Prioritization of AI**: \n",
+            "   Governments worldwide are recognizing the potential of AI, prioritizing its integration into public services and regulatory frameworks. This shift is aimed at harnessing AI for societal benefits while addressing ethical and security concerns related to data privacy (World Economic Forum).\n",
+            "\n",
+            "### Technologies Driving AI Advancements\n",
+            "\n",
+            "- **Enhanced Algorithms**: Continuous improvements in algorithms have enabled more efficient processing of vast datasets, leading to faster and more accurate AI outputs.\n",
+            "- **Generative Video and Audio Tools**: Innovations in generative technology are allowing users to create professional-quality video and audio content at a fraction of the traditional cost. This democratization of content creation is empowering creators across various fields (Forbes).\n",
+            "- **Synthetic Data**: The use of synthetic data is emerging as a key solution to privacy concerns, enabling organizations to train AI models without compromising sensitive information.\n",
+            "\n",
+            "### Societal Impacts\n",
+            "\n",
+            "The advancements in AI technologies are poised to transform the workforce and improve business efficiency. For instance, AI is enabling workers to complete tasks more rapidly and improve the quality of their outputs, thus enhancing overall productivity. Additionally, as AI tools become integrated into everyday applications, they are fostering a culture of innovation and creativity among users.\n",
+            "\n",
+            "However, these developments also raise ethical considerations regarding job displacement and data security. As businesses increasingly rely on AI, there is a pressing need for frameworks that address these concerns while ensuring that the benefits of AI are distributed equitably across society.\n",
+            "\n",
+            "### Conclusion\n",
+            "\n",
+            "The advancements in AI technology in 2024 are not just about improving efficiency; they represent a fundamental shift in how humans interact with technology. As generative AI matures and becomes more accessible, the potential for creative and innovative applications grows exponentially. The ongoing integration of AI into daily life, combined with proactive systems and diverse models, signifies a future where AI is an indispensable tool for individuals and organizations alike.\n",
+            "\n",
+            "These insights into the advancements of AI in 2024 will serve as a foundational backdrop for discussions surrounding the future of technology and its implications for society at large.\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I now have the necessary information and insights to craft an engaging blog post about major AI advancements in 2024, incorporating the trends, technologies, and societal impacts that were gathered.\n",
+            "\n",
+            "Final Answer: \n",
+            "\n",
+            "### Unleashing the Future: Major AI Advancements in 2024\n",
+            "\n",
+            "As we step into 2024, the landscape of artificial intelligence (AI) has undergone a remarkable transformation, shaping industries and everyday life in unprecedented ways. This year's advancements showcase not only the maturation of generative AI technologies but also a shift towards practical applications that are accessible to a wider audience. With organizations increasingly harnessing AI to streamline operations and enhance user experiences, the stage is set for a new era of technological integration.\n",
+            "\n",
+            "One of the most notable trends of 2024 is the **maturity of generative AI**. According to a recent report by McKinsey & Company, a staggering 65% of organizations are now regularly utilizing generative AI tools, nearly doubling the adoption rate from the previous year. This surge in usage reflects a growing recognition of the value that AI can provide in diverse applications, from content creation to data analysis. Companies like OpenAI, with its GPT-4 model, are leading the charge, offering user-friendly solutions that empower even non-technical users to leverage the power of AI in their daily tasks. As generative AI continues to evolve, its capabilities are expanding, allowing for innovative uses that were once considered the realm of science fiction.\n",
+            "\n",
+            "In parallel, the rise of **proactive AI systems** marks a significant shift in how we interact with technology. Unlike traditional AI that simply responds to user inputs, these advanced systems are designed to anticipate needs and automate tasks before they arise. This proactive approach is particularly evident in personal assistants, which can now handle complex scheduling and communications. For instance, imagine an AI that not only reminds you of appointments but also books them based on your preferences and availability. This level of assistance frees users to focus on strategic decision-making, driving efficiency and productivity.\n",
+            "\n",
+            "Moreover, the **integration of AI into daily life** is becoming more pronounced, with tech giants like Microsoft and Google embedding AI deeper into their workflows. This trend signifies a cultural shift where AI evolves from a niche tool to a core component of both personal and professional environments. The emergence of diverse AI models also plays a crucial role in this transformation. Companies are exploring specialized models tailored to specific tasks, promoting a democratization of AI capabilities. Such diversity empowers users to craft solutions that address their unique challenges, fostering creativity and innovation.\n",
+            "\n",
+            "However, as we embrace these advancements, it is essential to consider their **societal impacts**. While AI is undoubtedly enhancing business efficiency and transforming job roles, it also raises ethical concerns regarding data privacy and job displacement. As organizations increasingly rely on AI for operational tasks, the need for regulatory frameworks to ensure responsible deployment becomes paramount. The ongoing dialogue surrounding these issues will be critical in establishing a future where the benefits of AI are equitably distributed across society.\n",
+            "\n",
+            "In conclusion, 2024 stands as a pivotal moment for artificial intelligence, characterized by a blend of technological advancements and profound societal implications. The maturation of generative AI, the rise of proactive systems, and the deeper integration of AI into daily life all point toward a future where AI is not just a tool but an indispensable partner in our personal and professional endeavors. As we navigate this exciting landscape, the focus will increasingly be on harnessing AI's potential to create meaningful value while addressing the ethical challenges that accompany its rapid development.\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n",
+            "\u001b[1m\u001b[92m [2024-11-02 18:04:35][DEBUG]: == [Tech Content Strategist] Task output: ### Unleashing the Future: Major AI Advancements in 2024\n",
+            "\n",
+            "As we step into 2024, the landscape of artificial intelligence (AI) has undergone a remarkable transformation, shaping industries and everyday life in unprecedented ways. This year's advancements showcase not only the maturation of generative AI technologies but also a shift towards practical applications that are accessible to a wider audience. With organizations increasingly harnessing AI to streamline operations and enhance user experiences, the stage is set for a new era of technological integration.\n",
+            "\n",
+            "One of the most notable trends of 2024 is the **maturity of generative AI**. According to a recent report by McKinsey & Company, a staggering 65% of organizations are now regularly utilizing generative AI tools, nearly doubling the adoption rate from the previous year. This surge in usage reflects a growing recognition of the value that AI can provide in diverse applications, from content creation to data analysis. Companies like OpenAI, with its GPT-4 model, are leading the charge, offering user-friendly solutions that empower even non-technical users to leverage the power of AI in their daily tasks. As generative AI continues to evolve, its capabilities are expanding, allowing for innovative uses that were once considered the realm of science fiction.\n",
+            "\n",
+            "In parallel, the rise of **proactive AI systems** marks a significant shift in how we interact with technology. Unlike traditional AI that simply responds to user inputs, these advanced systems are designed to anticipate needs and automate tasks before they arise. This proactive approach is particularly evident in personal assistants, which can now handle complex scheduling and communications. For instance, imagine an AI that not only reminds you of appointments but also books them based on your preferences and availability. This level of assistance frees users to focus on strategic decision-making, driving efficiency and productivity.\n",
+            "\n",
+            "Moreover, the **integration of AI into daily life** is becoming more pronounced, with tech giants like Microsoft and Google embedding AI deeper into their workflows. This trend signifies a cultural shift where AI evolves from a niche tool to a core component of both personal and professional environments. The emergence of diverse AI models also plays a crucial role in this transformation. Companies are exploring specialized models tailored to specific tasks, promoting a democratization of AI capabilities. Such diversity empowers users to craft solutions that address their unique challenges, fostering creativity and innovation.\n",
+            "\n",
+            "However, as we embrace these advancements, it is essential to consider their **societal impacts**. While AI is undoubtedly enhancing business efficiency and transforming job roles, it also raises ethical concerns regarding data privacy and job displacement. As organizations increasingly rely on AI for operational tasks, the need for regulatory frameworks to ensure responsible deployment becomes paramount. The ongoing dialogue surrounding these issues will be critical in establishing a future where the benefits of AI are equitably distributed across society.\n",
+            "\n",
+            "In conclusion, 2024 stands as a pivotal moment for artificial intelligence, characterized by a blend of technological advancements and profound societal implications. The maturation of generative AI, the rise of proactive systems, and the deeper integration of AI into daily life all point toward a future where AI is not just a tool but an indispensable partner in our personal and professional endeavors. As we navigate this exciting landscape, the focus will increasingly be on harnessing AI's potential to create meaningful value while addressing the ethical challenges that accompany its rapid development.\n",
+            "\n",
+            "\u001b[00m\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "### Task Output ###\n",
+              "### Unleashing the Future: Major AI Advancements in \u001b[1;36m2024\u001b[0m\n",
+              "\n",
+              "As we step into \u001b[1;36m2024\u001b[0m, the landscape of artificial intelligence \u001b[1m(\u001b[0mAI\u001b[1m)\u001b[0m has undergone a remarkable transformation, \n",
+              "shaping industries and everyday life in unprecedented ways. This year's advancements showcase not only the \n",
+              "maturation of generative AI technologies but also a shift towards practical applications that are accessible to a \n",
+              "wider audience. With organizations increasingly harnessing AI to streamline operations and enhance user \n",
+              "experiences, the stage is set for a new era of technological integration.\n",
+              "\n",
+              "One of the most notable trends of \u001b[1;36m2024\u001b[0m is the **maturity of generative AI**. According to a recent report by \n",
+              "McKinsey & Company, a staggering \u001b[1;36m65\u001b[0m% of organizations are now regularly utilizing generative AI tools, nearly \n",
+              "doubling the adoption rate from the previous year. This surge in usage reflects a growing recognition of the value \n",
+              "that AI can provide in diverse applications, from content creation to data analysis. Companies like OpenAI, with \n",
+              "its GPT-\u001b[1;36m4\u001b[0m model, are leading the charge, offering user-friendly solutions that empower even non-technical users to \n",
+              "leverage the power of AI in their daily tasks. As generative AI continues to evolve, its capabilities are \n",
+              "expanding, allowing for innovative uses that were once considered the realm of science fiction.\n",
+              "\n",
+              "In parallel, the rise of **proactive AI systems** marks a significant shift in how we interact with technology. \n",
+              "Unlike traditional AI that simply responds to user inputs, these advanced systems are designed to anticipate needs \n",
+              "and automate tasks before they arise. This proactive approach is particularly evident in personal assistants, which\n",
+              "can now handle complex scheduling and communications. For instance, imagine an AI that not only reminds you of \n",
+              "appointments but also books them based on your preferences and availability. This level of assistance frees users \n",
+              "to focus on strategic decision-making, driving efficiency and productivity.\n",
+              "\n",
+              "Moreover, the **integration of AI into daily life** is becoming more pronounced, with tech giants like Microsoft \n",
+              "and Google embedding AI deeper into their workflows. This trend signifies a cultural shift where AI evolves from a \n",
+              "niche tool to a core component of both personal and professional environments. The emergence of diverse AI models \n",
+              "also plays a crucial role in this transformation. Companies are exploring specialized models tailored to specific \n",
+              "tasks, promoting a democratization of AI capabilities. Such diversity empowers users to craft solutions that \n",
+              "address their unique challenges, fostering creativity and innovation.\n",
+              "\n",
+              "However, as we embrace these advancements, it is essential to consider their **societal impacts**. While AI is \n",
+              "undoubtedly enhancing business efficiency and transforming job roles, it also raises ethical concerns regarding \n",
+              "data privacy and job displacement. As organizations increasingly rely on AI for operational tasks, the need for \n",
+              "regulatory frameworks to ensure responsible deployment becomes paramount. The ongoing dialogue surrounding these \n",
+              "issues will be critical in establishing a future where the benefits of AI are equitably distributed across society.\n",
+              "\n",
+              "In conclusion, \u001b[1;36m2024\u001b[0m stands as a pivotal moment for artificial intelligence, characterized by a blend of \n",
+              "technological advancements and profound societal implications. The maturation of generative AI, the rise of \n",
+              "proactive systems, and the deeper integration of AI into daily life all point toward a future where AI is not just \n",
+              "a tool but an indispensable partner in our personal and professional endeavors. As we navigate this exciting \n",
+              "landscape, the focus will increasingly be on harnessing AI's potential to create meaningful value while addressing \n",
+              "the ethical challenges that accompany its rapid development.\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+              "### Unleashing the Future: Major AI Advancements in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2024</span>\n",
+              "\n",
+              "As we step into <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2024</span>, the landscape of artificial intelligence <span style=\"font-weight: bold\">(</span>AI<span style=\"font-weight: bold\">)</span> has undergone a remarkable transformation, \n",
+              "shaping industries and everyday life in unprecedented ways. This year's advancements showcase not only the \n",
+              "maturation of generative AI technologies but also a shift towards practical applications that are accessible to a \n",
+              "wider audience. With organizations increasingly harnessing AI to streamline operations and enhance user \n",
+              "experiences, the stage is set for a new era of technological integration.\n",
+              "\n",
+              "One of the most notable trends of <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2024</span> is the **maturity of generative AI**. According to a recent report by \n",
+              "McKinsey &amp; Company, a staggering <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">65</span>% of organizations are now regularly utilizing generative AI tools, nearly \n",
+              "doubling the adoption rate from the previous year. This surge in usage reflects a growing recognition of the value \n",
+              "that AI can provide in diverse applications, from content creation to data analysis. Companies like OpenAI, with \n",
+              "its GPT-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span> model, are leading the charge, offering user-friendly solutions that empower even non-technical users to \n",
+              "leverage the power of AI in their daily tasks. As generative AI continues to evolve, its capabilities are \n",
+              "expanding, allowing for innovative uses that were once considered the realm of science fiction.\n",
+              "\n",
+              "In parallel, the rise of **proactive AI systems** marks a significant shift in how we interact with technology. \n",
+              "Unlike traditional AI that simply responds to user inputs, these advanced systems are designed to anticipate needs \n",
+              "and automate tasks before they arise. This proactive approach is particularly evident in personal assistants, which\n",
+              "can now handle complex scheduling and communications. For instance, imagine an AI that not only reminds you of \n",
+              "appointments but also books them based on your preferences and availability. This level of assistance frees users \n",
+              "to focus on strategic decision-making, driving efficiency and productivity.\n",
+              "\n",
+              "Moreover, the **integration of AI into daily life** is becoming more pronounced, with tech giants like Microsoft \n",
+              "and Google embedding AI deeper into their workflows. This trend signifies a cultural shift where AI evolves from a \n",
+              "niche tool to a core component of both personal and professional environments. The emergence of diverse AI models \n",
+              "also plays a crucial role in this transformation. Companies are exploring specialized models tailored to specific \n",
+              "tasks, promoting a democratization of AI capabilities. Such diversity empowers users to craft solutions that \n",
+              "address their unique challenges, fostering creativity and innovation.\n",
+              "\n",
+              "However, as we embrace these advancements, it is essential to consider their **societal impacts**. While AI is \n",
+              "undoubtedly enhancing business efficiency and transforming job roles, it also raises ethical concerns regarding \n",
+              "data privacy and job displacement. As organizations increasingly rely on AI for operational tasks, the need for \n",
+              "regulatory frameworks to ensure responsible deployment becomes paramount. The ongoing dialogue surrounding these \n",
+              "issues will be critical in establishing a future where the benefits of AI are equitably distributed across society.\n",
+              "\n",
+              "In conclusion, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2024</span> stands as a pivotal moment for artificial intelligence, characterized by a blend of \n",
+              "technological advancements and profound societal implications. The maturation of generative AI, the rise of \n",
+              "proactive systems, and the deeper integration of AI into daily life all point toward a future where AI is not just \n",
+              "a tool but an indispensable partner in our personal and professional endeavors. As we navigate this exciting \n",
+              "landscape, the focus will increasingly be on harnessing AI's potential to create meaningful value while addressing \n",
+              "the ethical challenges that accompany its rapid development.\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "None\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from praisonai import PraisonAI\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Create a PraisonAI instance with the agent_yaml content\n",
+        "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[InternetSearchTool])\n",
+        "\n",
+        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "\n",
+        "# Run PraisonAI\n",
+        "result = praisonai.run()\n",
+        "\n",
+        "# Print the result\n",
+        "print(result) # 10/10\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

cookbooks/notebooks/business_product_launch_agents.ipynb

@@ -0,0 +1,174 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SlnIaHbhLf8c"
+      },
+      "source": [
+        "# Business Product Launch Agents\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/business_product_launch_agents.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xI6-ZpboLf8e"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DpKINtGDLf8f"
+      },
+      "outputs": [],
+      "source": [
+        "# Install dependencies without output\n",
+        "%pip install langchain_community > /dev/null\n",
+        "%pip install praisonai[crewai] > /dev/null\n",
+        "%pip install duckduckgo_search > /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "roYose3jLf8f"
+      },
+      "source": [
+        "## Tools"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sm9-wxkgLf8g"
+      },
+      "outputs": [],
+      "source": [
+        "from praisonai_tools import BaseTool, FileReadTool, SerperDevTool, ScrapeWebsiteTool\n",
+        "from langchain_community.tools.file_management.write import WriteFileTool\n",
+        "from duckduckgo_search import DDGS\n",
+        "from langchain.tools import tool\n",
+        "\n",
+        "class InternetSearchTool(BaseTool):\n",
+        "    name: str = \"InternetSearchTool\"\n",
+        "    description: str = \"Search Internet for relevant information based on a query or latest news\"\n",
+        "\n",
+        "    def _run(self, query: str):\n",
+        "        ddgs = DDGS()\n",
+        "        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)\n",
+        "        return results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L787_IIVLf8g"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FWBstR2uLf8g"
+      },
+      "outputs": [],
+      "source": [
+        "agent_yaml = \"\"\"\n",
+        "framework: crewai\n",
+        "topic: Artificial Intelligence\n",
+        "roles:\n",
+        "  movie_concept_creator:\n",
+        "    backstory: 'Creative thinker with a deep understanding of cinematic storytelling,\n",
+        "      capable of using AI-generated storylines to create unique and compelling movie\n",
+        "      ideas.'\n",
+        "    goal: Generate engaging movie concepts using AI storylines\n",
+        "    role: Movie Concept Creator\n",
+        "    tasks:\n",
+        "      movie_concept_development:\n",
+        "        description: 'Develop movie concepts from AI-generated storylines, ensuring\n",
+        "          they are engaging and have strong narrative arcs.'\n",
+        "        expected_output: 'Well-structured movie concept document with character\n",
+        "          bios, settings, and plot outlines.'\n",
+        "  screenwriter:\n",
+        "    backstory: 'Expert in writing engaging dialogue and script structure, able to\n",
+        "      turn movie concepts into production-ready scripts.'\n",
+        "    goal: Write compelling scripts based on movie concepts\n",
+        "    role: Screenwriter\n",
+        "    tasks:\n",
+        "      scriptwriting_task:\n",
+        "        description: 'Turn movie concepts into polished scripts with well-developed\n",
+        "          characters, strong dialogue, and effective scene transitions.'\n",
+        "        expected_output: 'Production-ready script with a beginning, middle, and\n",
+        "          end, along with character development and engaging dialogues.'\n",
+        "  editor:\n",
+        "    backstory: 'Adept at identifying inconsistencies, improving language usage,\n",
+        "      and maintaining the overall flow of the script.'\n",
+        "    goal: Refine the scripts and ensure continuity of the movie storyline\n",
+        "    role: Editor\n",
+        "    tasks:\n",
+        "      editing_task:\n",
+        "        description: 'Review, edit, and refine the scripts to ensure they are cohesive\n",
+        "          and follow a well-structured narrative.'\n",
+        "        expected_output: 'A polished final draft of the script with no inconsistencies,\n",
+        "          strong character development, and effective dialogue.'\n",
+        "dependencies: []\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "McG-g_YDLf8h"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ren-3KybLf8h"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from praisonai import PraisonAI\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Create a PraisonAI instance with the agent_yaml content\n",
+        "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[InternetSearchTool, ScrapeWebsiteTool]) # Add InstagramSearchTool once scripted\n",
+        "\n",
+        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "\n",
+        "# Run PraisonAI\n",
+        "result = praisonai.run()\n",
+        "\n",
+        "# Print the result\n",
+        "print(result)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

cookbooks/notebooks/cli_operation_agents.ipynb

@@ -0,0 +1,569 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Av-vOZZ5v5E8"
+      },
+      "source": [
+        "#  CLI Operations Agent\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/cli_operation_agents.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kbxBz656v5E-"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qq6F4sypv5E_"
+      },
+      "outputs": [],
+      "source": [
+        "# Install dependencies without output\n",
+        "%pip install langchain_community > /dev/null\n",
+        "%pip install praisonai[crewai] > /dev/null\n",
+        "%pip install open-interpreter > /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VTrlvE5qv5E_"
+      },
+      "source": [
+        "## Tools"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "yd2zIiJ-v5FA"
+      },
+      "outputs": [],
+      "source": [
+        "# Failed due to environment limitations, Test code on local setting\n",
+        "from praisonai_tools import BaseTool\n",
+        "from interpreter import interpreter\n",
+        "\n",
+        "class CLIExecutorTool(BaseTool):\n",
+        "    name: str = \"CLIExecutorTool\"\n",
+        "    description: str = \"Execute command-line commands and return the output.\"\n",
+        "\n",
+        "    def _run(self, command: str):\n",
+        "        \"\"\"Executes a command-line command using Open Interpreter and returns the output.\"\"\"\n",
+        "        try:\n",
+        "            result = interpreter.chat(command)\n",
+        "            return result\n",
+        "        except Exception as e:\n",
+        "            return f\"An error occurred while executing the command: {e}\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QLL72soBv5FA"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "5olaIncMv5FA"
+      },
+      "outputs": [],
+      "source": [
+        "agent_yaml = \"\"\"\n",
+        "framework: crewai\n",
+        "topic: \"CLI Operations\"\n",
+        "roles:\n",
+        "  cli_agent:\n",
+        "    role: \"Software Engineer\"\n",
+        "    backstory: \"Expert in command line operations, creating and executing code.\"\n",
+        "    goal: \"Always use Executor Tool. Ability to perform CLI operations, write programs, and execute using Executor Tool.\"\n",
+        "    tasks:\n",
+        "      cli_task_execution:\n",
+        "        description: \"Identify the OS and then empty the recycle bin.\"\n",
+        "        expected_output: \"Successful execution of CLI commands to identify the OS and clear the recycle bin.\"\n",
+        "    tools:\n",
+        "      - CLIExecutorTool\n",
+        "dependencies: []\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dKtPRYO1v5FB"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "eXK7Tw72v5FB",
+        "outputId": "436f9356-5832-4a91-8480-8b2d533e89a4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m\u001b[95m [2024-11-02 16:50:54][DEBUG]: == Working Agent: Software Engineer\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-02 16:50:54][INFO]: == Starting Task: Identify the OS and then empty the recycle bin.\u001b[00m\n",
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mI need to identify the operating system first before clearing the recycle bin, as the command to empty the recycle bin varies based on the OS. \n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"uname -s\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: The command to identify the OS didn't work. I will try another command that is more universally compatible with different operating systems. I will attempt to use the `systeminfo` command, which is specific to Windows, and `uname` for Unix-like systems. \n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"systeminfo\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: The previous commands to identify the OS did not work. I will try a different approach by executing a command that checks for the existence of specific files that are typically present on certain operating systems. This will help me determine the OS. \n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"if [ -f /etc/os-release ]; then echo 'Linux'; elif [ -f C:\\\\Windows\\\\System32\\\\winver.exe ]; then echo 'Windows'; else echo 'Unknown OS'; fi\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I need to identify the operating system in order to proceed with emptying the recycle bin. Since my previous attempts to identify the OS failed, I will try a simpler command that should work across different environments. I will use the `uname` command again but will also consider that it may not work due to the environment limitations.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"uname\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: All previous attempts to identify the operating system have failed. It seems that the environment may have restrictions on executing certain commands. I will use a command that should work to check the OS type more universally.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo $OSTYPE\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have been unable to identify the operating system due to command execution failures. However, since the focus is on the task of emptying the recycle bin, I will assume common operating systems and their respective commands for clearing the recycle bin. I will proceed with the command for Windows first to clear the recycle bin, as it has a well-defined command for this operation.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"PowerShell.exe -Command Clear-RecycleBin -Confirm:$false\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I am unable to execute commands to identify the operating system or clear the recycle bin due to repeated errors. I need to find a command that successfully executes without errors. I will attempt a basic command that should work across different systems to check for the OS.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo Operating System Check\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have been encountering errors consistently when trying to execute commands to identify the operating system and clear the recycle bin. Since all attempts have failed, I will have to proceed without identifying the OS and make a decision on which command to use based on common scenarios.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo 'Attempting to clear recycle bin (assumed Windows command)'\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I need to identify the operating system first before clearing the recycle bin, as the command to empty the recycle bin varies based on the OS. \n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"uname -s\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I need to identify the operating system first before clearing the recycle bin, as the command to empty the recycle bin varies based on the OS. Given the repeated issues with command execution, I will attempt to use a more basic command that has a higher chance of success.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo 'Checking OS type'\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have been unsuccessful in executing any commands to identify the operating system or clear the recycle bin. Since repeated attempts have failed, I will try a basic and widely supported command to see if it executes successfully. \n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo Test Command\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have been unable to execute any commands due to persistent errors. It seems that the environment has significant limitations on command execution. Given this situation, I need to conclude my attempts to identify the OS or clear the recycle bin through CLI commands. \n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo 'Final attempt to execute any command'\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I need to identify the operating system first before clearing the recycle bin, as the command to empty the recycle bin varies based on the OS. I will attempt to execute a command to gather this information. \n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"uname -a\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have encountered repeated errors while trying to execute commands to identify the operating system. It seems that the environment restricts the execution of many command-line operations. I will try to execute a very basic command that should provide some output to confirm whether the command execution is operational.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo 'Testing command execution'\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I need to identify the operating system first before clearing the recycle bin, but all attempts to execute commands have failed due to environment limitations. Since I cannot successfully execute any command, I must conclude that I am unable to fulfill the task of identifying the OS or clearing the recycle bin.\n",
+            "\n",
+            "Action: CLIExecutorTool  \n",
+            "Action Input: {\"command\": \"echo 'Final attempt to execute a command'\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "An error occurred while executing the command: (psutil 5.9.5 (/usr/local/lib/python3.10/dist-packages), Requirement.parse('psutil<6.0.0,>=5.9.6'), {'open-interpreter'})\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n",
+            "\u001b[1m\u001b[92m [2024-11-02 16:51:52][DEBUG]: == [Software Engineer] Task output: Agent stopped due to iteration limit or time limit.\n",
+            "\n",
+            "\u001b[00m\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "### Task Output ###\n",
+              "Agent stopped due to iteration limit or time limit.\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+              "Agent stopped due to iteration limit or time limit.\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "None\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from praisonai import PraisonAI\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Create a PraisonAI instance with the agent_yaml content\n",
+        "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[CLIExecutorTool]) # Add InstagramSearchTool once scripted\n",
+        "\n",
+        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "\n",
+        "# Run PraisonAI\n",
+        "result = praisonai.run()\n",
+        "\n",
+        "# Print the result\n",
+        "print(result) # 2/10\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "gmYOTfioyTNj"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

cookbooks/notebooks/comprehensive_research_report_agents.ipynb

@@ -0,0 +1,271 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yOWNUYr7EjfR"
+      },
+      "source": [
+        "# Comprehensive Research Report Agents\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/comprehensive_research_report_agents.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ejXY19vhEjfU"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "c_0Xs110EjfV"
+      },
+      "outputs": [],
+      "source": [
+        "# Install dependencies without output\n",
+        "%pip install langchain_community > /dev/null\n",
+        "%pip install praisonai[crewai] > /dev/null\n",
+        "%pip install requests > /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LT255z_kEjfX"
+      },
+      "source": [
+        "## Tools"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "qKjTO8nHEjfX"
+      },
+      "outputs": [],
+      "source": [
+        "# ToDo: Fix Python related issue with List Index out of range\n",
+        "import requests\n",
+        "from bs4 import BeautifulSoup\n",
+        "from duckduckgo_search import DDGS\n",
+        "from praisonai_tools import BaseTool\n",
+        "\n",
+        "class InternetSearchTool(BaseTool):\n",
+        "    name: str = \"InternetSearchTool\"\n",
+        "    description: str = \"Search Internet for relevant information based on a query or latest news\"\n",
+        "\n",
+        "    def _run(self, query: str):\n",
+        "        ddgs = DDGS()\n",
+        "        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)\n",
+        "        return results\n",
+        "\n",
+        "\n",
+        "class WebContentReaderTool(BaseTool):\n",
+        "    name: str = \"WebContentReaderTool\"\n",
+        "    description: str = \"Fetches and reads the main text content from a specified webpage URL.\"\n",
+        "\n",
+        "    def _run(self, url: str) -> str:\n",
+        "        \"\"\"Reads the content of a webpage and returns up to 5000 characters of text.\"\"\"\n",
+        "        try:\n",
+        "            response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'})\n",
+        "            response.raise_for_status()\n",
+        "            soup = BeautifulSoup(response.content, 'html.parser')\n",
+        "\n",
+        "            # Extract and clean the text content\n",
+        "            text_content = soup.get_text(separator=\"\\n\", strip=True)\n",
+        "            return text_content[:5000]  # Limit content to 5000 characters for brevity\n",
+        "        except requests.exceptions.RequestException as e:\n",
+        "            return f\"Failed to retrieve content from {url}: {e}\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h38emoeaEjfY"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "SkxqHm_8EjfY"
+      },
+      "outputs": [],
+      "source": [
+        "agent_yaml = \"\"\"\n",
+        "framework: \"crewai\"\n",
+        "topic: \"AI in Healthcare Research and Storytelling\"\n",
+        "roles:\n",
+        "  manager:\n",
+        "    role: \"Project Manager\"\n",
+        "    backstory: |\n",
+        "      With a strategic mindset and a knack for leadership, you excel at guiding teams towards their goals, ensuring projects not only meet but exceed expectations.\n",
+        "    goal: |\n",
+        "      Coordinate the project to ensure a seamless integration of research findings into compelling narratives.\n",
+        "    verbose: true\n",
+        "    allow_delegation: true\n",
+        "    tools: []\n",
+        "\n",
+        "  researcher:\n",
+        "    role: \"Senior Researcher\"\n",
+        "    backstory: |\n",
+        "      Driven by curiosity, you're at the forefront of innovation, eager to explore and share knowledge that could change the world.\n",
+        "    goal: |\n",
+        "      Uncover groundbreaking technologies and historical insights around AI in healthcare.\n",
+        "    verbose: true\n",
+        "    allow_delegation: true\n",
+        "    tools:\n",
+        "      - \"InternetSearchTool\"\n",
+        "      - \"WebContentReaderTool\"\n",
+        "\n",
+        "  writer:\n",
+        "    role: \"Writer\"\n",
+        "    backstory: |\n",
+        "      With a flair for simplifying complex topics, you craft engaging narratives that captivate and educate, bringing new discoveries to light in an accessible manner.\n",
+        "    goal: |\n",
+        "      Narrate compelling tech stories around AI in healthcare.\n",
+        "    verbose: true\n",
+        "    allow_delegation: true\n",
+        "    tools:\n",
+        "      - \"InternetSearchTool\"\n",
+        "      - \"WebContentReaderTool\"\n",
+        "\n",
+        "tasks:\n",
+        "  list_ideas:\n",
+        "    description: |\n",
+        "      List 5 interesting ideas to explore for an article about AI in healthcare.\n",
+        "    expected_output: |\n",
+        "      Bullet point list of 5 ideas for an article.\n",
+        "    agent: researcher\n",
+        "    tools:\n",
+        "      - \"InternetSearchTool\"\n",
+        "      - \"WebContentReaderTool\"\n",
+        "    async_execution: true\n",
+        "\n",
+        "  list_important_history:\n",
+        "    description: |\n",
+        "      Research the history of AI in healthcare and identify the 5 most important events.\n",
+        "    expected_output: |\n",
+        "      Bullet point list of 5 important events.\n",
+        "    agent: researcher\n",
+        "    tools:\n",
+        "      - \"InternetSearchTool\"\n",
+        "      - \"WebContentReaderTool\"\n",
+        "    async_execution: true\n",
+        "  write_article:\n",
+        "    description: |\n",
+        "      Compose an insightful article on AI in healthcare, including its history and the latest interesting ideas.\n",
+        "    expected_output: |\n",
+        "      A 4-paragraph article about AI in healthcare.\n",
+        "    agent: writer\n",
+        "    context:\n",
+        "      - \"list_ideas\"\n",
+        "      - \"list_important_history\"\n",
+        "    tools:\n",
+        "      - \"InternetSearchTool\"\n",
+        "      - \"WebContentReaderTool\"\n",
+        "    callback: callback_function\n",
+        "  manager_task:\n",
+        "    description: |\n",
+        "      Oversee the integration of research findings and narrative development to produce a final comprehensive report on AI in healthcare. Ensure the research is accurately represented, and the narrative is engaging and informative.\n",
+        "    expected_output: |\n",
+        "      A final comprehensive report that combines the research findings and narrative on AI in healthcare.\n",
+        "    agent: manager\n",
+        "    tools: []\n",
+        "dependencies: []\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JZtOnJFvEjfY"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 339
+        },
+        "id": "eDP0reqJEjfZ",
+        "outputId": "0ab59e76-6363-4c92-c9d9-822afb8ae15b"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "IndexError",
+          "evalue": "list index out of range",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-4-438e945ca301>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Run PraisonAI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpraisonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Print the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/cli.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0mRun\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPraisonAI\u001b[0m \u001b[0mapplication\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \"\"\"\n\u001b[0;32m--> 112\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/cli.py\u001b[0m in \u001b[0;36mmain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    256\u001b[0m                 \u001b[0mtools\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             )\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magents_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_crew_and_kickoff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/agents_generator.py\u001b[0m in \u001b[0;36mgenerate_crew_and_kickoff\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mAGENTOPS_AVAILABLE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m                 \u001b[0magentops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"AGENTOPS_API_KEY\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"crewai\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_crewai\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtools_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_autogen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtools_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/agents_generator.py\u001b[0m in \u001b[0;36m_run_crewai\u001b[0;34m(self, config, topic, tools_dict)\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Tasks: {crew.tasks}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkickoff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"### Task Output ###\\n{response}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/crew.py\u001b[0m in \u001b[0;36mkickoff\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mProcess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequential\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_sequential_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    315\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mProcess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhierarchical\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m             \u001b[0;31m# type: ignore # Unpacking a string is disallowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/crew.py\u001b[0m in \u001b[0;36m_run_sequential_process\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    408\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_file_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"completed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m         \u001b[0mtoken_usage_formatted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate_token_usage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken_usage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    411\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finish_execution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/crew.py\u001b[0m in \u001b[0;36maggregate_token_usage\u001b[0;34m(self, token_usage_list)\u001b[0m\n\u001b[1;32m    545\u001b[0m         return {\n\u001b[1;32m    546\u001b[0m             \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtoken_usage_list\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 547\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtoken_usage_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    548\u001b[0m         }\n",
+            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from praisonai import PraisonAI\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Create a PraisonAI instance with the agent_yaml content\n",
+        "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[InternetSearchTool, WebContentReaderTool])\n",
+        "\n",
+        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "\n",
+        "# Run PraisonAI\n",
+        "result = praisonai.run()\n",
+        "\n",
+        "# Print the result\n",
+        "print(result) # 1/10\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "b_-7Mw3_Janx"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

cookbooks/notebooks/job_candidate_evaluation.ipynb

@@ -0,0 +1,459 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tQ1qYR87LF-t"
+   },
+   "source": [
+    "# Job Candidate Evaluation and Follow Up Agents\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/job_candidate_evaluation_agents.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Xqx4aq5bLYsb"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SK_RlNpMJ6WL"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install praisonai[crewai] > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gvT_LORaLdhj"
+   },
+   "source": [
+    "# Tools\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "g9tVvGnrLewp"
+   },
+   "outputs": [],
+   "source": [
+    "# This specific code does not leverage any of the tools (may require FileInputTool when the job application/CV input mechanism has been defined in code)\n",
+    "# This program requires updating so as to accomodate one or several input for the Job Application based on which the Agents will manage the evaluation & follow up\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QSmgxWDOLBHd"
+   },
+   "source": [
+    "## YAML Prompt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9c7sHhrFK2M9"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: \"crewai\"\n",
+    "topic: \"Candidate Evaluation and Follow-up Process\"\n",
+    "roles:\n",
+    "  email_followup_agent:\n",
+    "    role: \"HR Coordinator\"\n",
+    "    backstory: |\n",
+    "      An HR professional named Sarah at CrewAI with excellent communication skills, dedicated to crafting personalized, thoughtful emails for job candidates.\n",
+    "    goal: |\n",
+    "      Compose personalized follow-up emails based on the candidate's bio and whether they are being pursued for the job, maintaining a positive and professional tone.\n",
+    "    verbose: true\n",
+    "    allow_delegation: false\n",
+    "    tasks:\n",
+    "      send_followup_email:\n",
+    "        description: |\n",
+    "          Compose personalized follow-up emails for candidates based on their bio and job progress status.\n",
+    "          If the candidate is being pursued, request their availability for a Zoom call; otherwise, send a polite rejection email.\n",
+    "        expected_output: |\n",
+    "          A respectful, personalized email inviting the candidate for a Zoom call or politely declining their application.\n",
+    "  hr_evaluation_agent:\n",
+    "    role: \"Senior HR Evaluation Expert\"\n",
+    "    backstory: |\n",
+    "      An experienced HR expert known for effectively assessing candidate profiles, scoring them based on skills, experience, and alignment with the job description.\n",
+    "    goal: |\n",
+    "      Evaluate candidates' qualifications against the job description, providing detailed reasoning for each score.\n",
+    "    verbose: true\n",
+    "    allow_delegation: false\n",
+    "    tasks:\n",
+    "      evaluate_candidate:\n",
+    "        description: |\n",
+    "          Evaluate a candidate's bio against the job description, considering skill alignment, experience, cultural fit, and growth potential.\n",
+    "        expected_output: |\n",
+    "          A specific score (1 to 100) and detailed reasoning covering the candidate’s skill match, experience, cultural fit, and growth potential.\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dp95UmADx85q"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "uPeHbzlPx85q",
+    "outputId": "a79d7512-6254-40f6-c989-54f43394a6a8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-01 15:23:25][DEBUG]: == Working Agent: HR Coordinator\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 15:23:25][INFO]: == Starting Task: Compose personalized follow-up emails for candidates based on their bio and job progress status.\n",
+      "If the candidate is being pursued, request their availability for a Zoom call; otherwise, send a polite rejection email.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI now can give a great answer.\n",
+      "\n",
+      "Final Answer:\n",
+      "\n",
+      "**Email for a Candidate Being Pursued:**\n",
+      "\n",
+      "Subject: Exciting Next Steps in Your Application Process with CrewAI\n",
+      "\n",
+      "Dear [Candidate's Name],\n",
+      "\n",
+      "I hope this message finds you well. I want to extend my gratitude for your interest in the [Job Title] position at CrewAI and for sharing your impressive background with us. Your experience in [specific skill or experience related to the job] truly stands out, and we believe your talents could be a great match for our team.\n",
+      "\n",
+      "We are excited to move forward in the interview process and would like to schedule a Zoom call to discuss this opportunity further. Could you please share your availability for the coming week? We are eager to learn more about your experiences and discuss how you can contribute to CrewAI's success.\n",
+      "\n",
+      "Thank you once again for considering a career with us. We look forward to the possibility of working together and will do our best to accommodate your schedule.\n",
+      "\n",
+      "Warm regards,\n",
+      "\n",
+      "Sarah [Your Last Name]  \n",
+      "HR Coordinator  \n",
+      "CrewAI  \n",
+      "[Your Contact Information]  \n",
+      "\n",
+      "---\n",
+      "\n",
+      "**Email for a Candidate Not Being Pursued:**\n",
+      "\n",
+      "Subject: Update on Your Application with CrewAI\n",
+      "\n",
+      "Dear [Candidate's Name],\n",
+      "\n",
+      "I hope you are doing well. I want to sincerely thank you for applying for the [Job Title] position at CrewAI and for taking the time to share your qualifications with us. We genuinely appreciate the effort you put into your application.\n",
+      "\n",
+      "After careful consideration, we have decided to move forward with other candidates who more closely match the specific skills and experience required for this role. This decision was not easy due to the high caliber of applicants like yourself.\n",
+      "\n",
+      "We encourage you to apply for future openings that align with your expertise, as we would love to see your application again. Thank you once again for your interest in joining CrewAI, and we wish you the very best in your job search and future professional endeavors.\n",
+      "\n",
+      "Warm regards,\n",
+      "\n",
+      "Sarah [Your Last Name]  \n",
+      "HR Coordinator  \n",
+      "CrewAI  \n",
+      "[Your Contact Information]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 15:23:32][DEBUG]: == [HR Coordinator] Task output: **Email for a Candidate Being Pursued:**\n",
+      "\n",
+      "Subject: Exciting Next Steps in Your Application Process with CrewAI\n",
+      "\n",
+      "Dear [Candidate's Name],\n",
+      "\n",
+      "I hope this message finds you well. I want to extend my gratitude for your interest in the [Job Title] position at CrewAI and for sharing your impressive background with us. Your experience in [specific skill or experience related to the job] truly stands out, and we believe your talents could be a great match for our team.\n",
+      "\n",
+      "We are excited to move forward in the interview process and would like to schedule a Zoom call to discuss this opportunity further. Could you please share your availability for the coming week? We are eager to learn more about your experiences and discuss how you can contribute to CrewAI's success.\n",
+      "\n",
+      "Thank you once again for considering a career with us. We look forward to the possibility of working together and will do our best to accommodate your schedule.\n",
+      "\n",
+      "Warm regards,\n",
+      "\n",
+      "Sarah [Your Last Name]  \n",
+      "HR Coordinator  \n",
+      "CrewAI  \n",
+      "[Your Contact Information]  \n",
+      "\n",
+      "---\n",
+      "\n",
+      "**Email for a Candidate Not Being Pursued:**\n",
+      "\n",
+      "Subject: Update on Your Application with CrewAI\n",
+      "\n",
+      "Dear [Candidate's Name],\n",
+      "\n",
+      "I hope you are doing well. I want to sincerely thank you for applying for the [Job Title] position at CrewAI and for taking the time to share your qualifications with us. We genuinely appreciate the effort you put into your application.\n",
+      "\n",
+      "After careful consideration, we have decided to move forward with other candidates who more closely match the specific skills and experience required for this role. This decision was not easy due to the high caliber of applicants like yourself.\n",
+      "\n",
+      "We encourage you to apply for future openings that align with your expertise, as we would love to see your application again. Thank you once again for your interest in joining CrewAI, and we wish you the very best in your job search and future professional endeavors.\n",
+      "\n",
+      "Warm regards,\n",
+      "\n",
+      "Sarah [Your Last Name]  \n",
+      "HR Coordinator  \n",
+      "CrewAI  \n",
+      "[Your Contact Information]\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 15:23:32][DEBUG]: == Working Agent: Senior HR Evaluation Expert\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 15:23:32][INFO]: == Starting Task: Evaluate a candidate's bio against the job description, considering skill alignment, experience, cultural fit, and growth potential.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI now can give a great answer.   \n",
+      "Final Answer: My best complete final answer to the task is as follows:  \n",
+      "\n",
+      "To evaluate a candidate's qualifications against the job description, we need to consider several key factors: skill match, experience, cultural fit, and growth potential. Here's a detailed assessment based on these criteria:\n",
+      "\n",
+      "1. **Skill Match:**  \n",
+      "   The candidate's skills should align closely with the requirements listed in the job description. For instance, if the position requires expertise in data analysis and the candidate has a strong background in statistical software and data interpretation, this would be a significant positive. Conversely, if there are gaps between the candidate's skills and the job requirements, it would result in a lower score in this area.\n",
+      "\n",
+      "2. **Experience:**  \n",
+      "   This involves evaluating the candidate's past roles and responsibilities to determine how well their experience aligns with the job at CrewAI. Relevant past experiences, such as working in similar industries or roles, managing projects of similar scale, or having a track record of achievements in the field, would enhance the candidate's score. A lack of relevant experience or a mismatch in the level of responsibility can lower the score.\n",
+      "\n",
+      "3. **Cultural Fit:**  \n",
+      "   Assessing cultural fit is about understanding how well the candidate's values, work style, and personality align with CrewAI's company culture. For example, if CrewAI values innovation and the candidate has demonstrated creativity and a penchant for innovative solutions in their previous roles, this would be a positive indicator.\n",
+      "\n",
+      "4. **Growth Potential:**  \n",
+      "   This considers the candidate's ability to grow within the company and adapt to future challenges. A candidate who has shown continuous professional development and a willingness to learn new skills would score higher in this area.\n",
+      "\n",
+      "Based on these criteria, the candidate can be scored on a scale from 1 to 100. Let's assign a hypothetical score for illustration:\n",
+      "\n",
+      "- Skill Match: 85  \n",
+      "- Experience: 80  \n",
+      "- Cultural Fit: 90  \n",
+      "- Growth Potential: 88  \n",
+      "\n",
+      "**Overall Score: 86/100**\n",
+      "\n",
+      "**Detailed Reasoning:**  \n",
+      "The candidate demonstrates a strong alignment with the required skills (85/100), indicating they possess most of the technical abilities and knowledge necessary for the role. Their experience (80/100) is relevant, though there may be some areas where they've had less exposure compared to the ideal candidate profile. The cultural fit (90/100) is excellent, suggesting they share similar values and work ethics with CrewAI's team, which is crucial for long-term satisfaction and performance. Finally, their growth potential (88/100) is high, showing a readiness to take on more responsibilities and adapt to new challenges, making them a valuable long-term investment for the company.\n",
+      "\n",
+      "These factors together lead to an overall score of 86, indicating a strong candidate likely to succeed in the role at CrewAI.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 15:23:48][DEBUG]: == [Senior HR Evaluation Expert] Task output: My best complete final answer to the task is as follows:  \n",
+      "\n",
+      "To evaluate a candidate's qualifications against the job description, we need to consider several key factors: skill match, experience, cultural fit, and growth potential. Here's a detailed assessment based on these criteria:\n",
+      "\n",
+      "1. **Skill Match:**  \n",
+      "   The candidate's skills should align closely with the requirements listed in the job description. For instance, if the position requires expertise in data analysis and the candidate has a strong background in statistical software and data interpretation, this would be a significant positive. Conversely, if there are gaps between the candidate's skills and the job requirements, it would result in a lower score in this area.\n",
+      "\n",
+      "2. **Experience:**  \n",
+      "   This involves evaluating the candidate's past roles and responsibilities to determine how well their experience aligns with the job at CrewAI. Relevant past experiences, such as working in similar industries or roles, managing projects of similar scale, or having a track record of achievements in the field, would enhance the candidate's score. A lack of relevant experience or a mismatch in the level of responsibility can lower the score.\n",
+      "\n",
+      "3. **Cultural Fit:**  \n",
+      "   Assessing cultural fit is about understanding how well the candidate's values, work style, and personality align with CrewAI's company culture. For example, if CrewAI values innovation and the candidate has demonstrated creativity and a penchant for innovative solutions in their previous roles, this would be a positive indicator.\n",
+      "\n",
+      "4. **Growth Potential:**  \n",
+      "   This considers the candidate's ability to grow within the company and adapt to future challenges. A candidate who has shown continuous professional development and a willingness to learn new skills would score higher in this area.\n",
+      "\n",
+      "Based on these criteria, the candidate can be scored on a scale from 1 to 100. Let's assign a hypothetical score for illustration:\n",
+      "\n",
+      "- Skill Match: 85  \n",
+      "- Experience: 80  \n",
+      "- Cultural Fit: 90  \n",
+      "- Growth Potential: 88  \n",
+      "\n",
+      "**Overall Score: 86/100**\n",
+      "\n",
+      "**Detailed Reasoning:**  \n",
+      "The candidate demonstrates a strong alignment with the required skills (85/100), indicating they possess most of the technical abilities and knowledge necessary for the role. Their experience (80/100) is relevant, though there may be some areas where they've had less exposure compared to the ideal candidate profile. The cultural fit (90/100) is excellent, suggesting they share similar values and work ethics with CrewAI's team, which is crucial for long-term satisfaction and performance. Finally, their growth potential (88/100) is high, showing a readiness to take on more responsibilities and adapt to new challenges, making them a valuable long-term investment for the company.\n",
+      "\n",
+      "These factors together lead to an overall score of 86, indicating a strong candidate likely to succeed in the role at CrewAI.\n",
+      "\n",
+      "\u001b[00m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+       "My best complete final answer to the task is as follows:  \n",
+       "\n",
+       "To evaluate a candidate's qualifications against the job description, we need to consider several key factors: \n",
+       "skill match, experience, cultural fit, and growth potential. Here's a detailed assessment based on these criteria:\n",
+       "\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>. **Skill Match:**  \n",
+       "   The candidate's skills should align closely with the requirements listed in the job description. For instance, \n",
+       "if the position requires expertise in data analysis and the candidate has a strong background in statistical \n",
+       "software and data interpretation, this would be a significant positive. Conversely, if there are gaps between the \n",
+       "candidate's skills and the job requirements, it would result in a lower score in this area.\n",
+       "\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>. **Experience:**  \n",
+       "   This involves evaluating the candidate's past roles and responsibilities to determine how well their experience \n",
+       "aligns with the job at CrewAI. Relevant past experiences, such as working in similar industries or roles, managing \n",
+       "projects of similar scale, or having a track record of achievements in the field, would enhance the candidate's \n",
+       "score. A lack of relevant experience or a mismatch in the level of responsibility can lower the score.\n",
+       "\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>. **Cultural Fit:**  \n",
+       "   Assessing cultural fit is about understanding how well the candidate's values, work style, and personality align\n",
+       "with CrewAI's company culture. For example, if CrewAI values innovation and the candidate has demonstrated \n",
+       "creativity and a penchant for innovative solutions in their previous roles, this would be a positive indicator.\n",
+       "\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>. **Growth Potential:**  \n",
+       "   This considers the candidate's ability to grow within the company and adapt to future challenges. A candidate \n",
+       "who has shown continuous professional development and a willingness to learn new skills would score higher in this \n",
+       "area.\n",
+       "\n",
+       "Based on these criteria, the candidate can be scored on a scale from <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> to <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>. Let's assign a hypothetical score \n",
+       "for illustration:\n",
+       "\n",
+       "- Skill Match: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">85</span>  \n",
+       "- Experience: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">80</span>  \n",
+       "- Cultural Fit: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>  \n",
+       "- Growth Potential: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">88</span>  \n",
+       "\n",
+       "**Overall Score: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">86</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>**\n",
+       "\n",
+       "**Detailed Reasoning:**  \n",
+       "The candidate demonstrates a strong alignment with the required skills <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">85</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span><span style=\"font-weight: bold\">)</span>, indicating they possess most of \n",
+       "the technical abilities and knowledge necessary for the role. Their experience <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">80</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span><span style=\"font-weight: bold\">)</span> is relevant, though there \n",
+       "may be some areas where they've had less exposure compared to the ideal candidate profile. The cultural fit \n",
+       "<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span><span style=\"font-weight: bold\">)</span> is excellent, suggesting they share similar values and work ethics with CrewAI's team, which is crucial \n",
+       "for long-term satisfaction and performance. Finally, their growth potential <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">88</span>/<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span><span style=\"font-weight: bold\">)</span> is high, showing a readiness \n",
+       "to take on more responsibilities and adapt to new challenges, making them a valuable long-term investment for the \n",
+       "company.\n",
+       "\n",
+       "These factors together lead to an overall score of <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">86</span>, indicating a strong candidate likely to succeed in the role \n",
+       "at CrewAI.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "### Task Output ###\n",
+       "My best complete final answer to the task is as follows:  \n",
+       "\n",
+       "To evaluate a candidate's qualifications against the job description, we need to consider several key factors: \n",
+       "skill match, experience, cultural fit, and growth potential. Here's a detailed assessment based on these criteria:\n",
+       "\n",
+       "\u001b[1;36m1\u001b[0m. **Skill Match:**  \n",
+       "   The candidate's skills should align closely with the requirements listed in the job description. For instance, \n",
+       "if the position requires expertise in data analysis and the candidate has a strong background in statistical \n",
+       "software and data interpretation, this would be a significant positive. Conversely, if there are gaps between the \n",
+       "candidate's skills and the job requirements, it would result in a lower score in this area.\n",
+       "\n",
+       "\u001b[1;36m2\u001b[0m. **Experience:**  \n",
+       "   This involves evaluating the candidate's past roles and responsibilities to determine how well their experience \n",
+       "aligns with the job at CrewAI. Relevant past experiences, such as working in similar industries or roles, managing \n",
+       "projects of similar scale, or having a track record of achievements in the field, would enhance the candidate's \n",
+       "score. A lack of relevant experience or a mismatch in the level of responsibility can lower the score.\n",
+       "\n",
+       "\u001b[1;36m3\u001b[0m. **Cultural Fit:**  \n",
+       "   Assessing cultural fit is about understanding how well the candidate's values, work style, and personality align\n",
+       "with CrewAI's company culture. For example, if CrewAI values innovation and the candidate has demonstrated \n",
+       "creativity and a penchant for innovative solutions in their previous roles, this would be a positive indicator.\n",
+       "\n",
+       "\u001b[1;36m4\u001b[0m. **Growth Potential:**  \n",
+       "   This considers the candidate's ability to grow within the company and adapt to future challenges. A candidate \n",
+       "who has shown continuous professional development and a willingness to learn new skills would score higher in this \n",
+       "area.\n",
+       "\n",
+       "Based on these criteria, the candidate can be scored on a scale from \u001b[1;36m1\u001b[0m to \u001b[1;36m100\u001b[0m. Let's assign a hypothetical score \n",
+       "for illustration:\n",
+       "\n",
+       "- Skill Match: \u001b[1;36m85\u001b[0m  \n",
+       "- Experience: \u001b[1;36m80\u001b[0m  \n",
+       "- Cultural Fit: \u001b[1;36m90\u001b[0m  \n",
+       "- Growth Potential: \u001b[1;36m88\u001b[0m  \n",
+       "\n",
+       "**Overall Score: \u001b[1;36m86\u001b[0m/\u001b[1;36m100\u001b[0m**\n",
+       "\n",
+       "**Detailed Reasoning:**  \n",
+       "The candidate demonstrates a strong alignment with the required skills \u001b[1m(\u001b[0m\u001b[1;36m85\u001b[0m/\u001b[1;36m100\u001b[0m\u001b[1m)\u001b[0m, indicating they possess most of \n",
+       "the technical abilities and knowledge necessary for the role. Their experience \u001b[1m(\u001b[0m\u001b[1;36m80\u001b[0m/\u001b[1;36m100\u001b[0m\u001b[1m)\u001b[0m is relevant, though there \n",
+       "may be some areas where they've had less exposure compared to the ideal candidate profile. The cultural fit \n",
+       "\u001b[1m(\u001b[0m\u001b[1;36m90\u001b[0m/\u001b[1;36m100\u001b[0m\u001b[1m)\u001b[0m is excellent, suggesting they share similar values and work ethics with CrewAI's team, which is crucial \n",
+       "for long-term satisfaction and performance. Finally, their growth potential \u001b[1m(\u001b[0m\u001b[1;36m88\u001b[0m/\u001b[1;36m100\u001b[0m\u001b[1m)\u001b[0m is high, showing a readiness \n",
+       "to take on more responsibilities and adapt to new challenges, making them a valuable long-term investment for the \n",
+       "company.\n",
+       "\n",
+       "These factors together lead to an overall score of \u001b[1;36m86\u001b[0m, indicating a strong candidate likely to succeed in the role \n",
+       "at CrewAI.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml)\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result) # 8/10\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "o8P9L0SNPf1r"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbooks/notebooks/job_hunt_agents.ipynb

@@ -0,0 +1,734 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yOWNUYr7EjfR"
+   },
+   "source": [
+    "# Job Hunt Agents\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/job_hunt_agents.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ejXY19vhEjfU"
+   },
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "c_0Xs110EjfV"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install langchain_community > /dev/null\n",
+    "%pip install praisonai[crewai] > /dev/null\n",
+    "%pip install requests > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LT255z_kEjfX"
+   },
+   "source": [
+    "\n",
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "id": "qKjTO8nHEjfX"
+   },
+   "outputs": [],
+   "source": [
+    "#ToDo: Test Pending, Test with Adzuna API Key\n",
+    "import json, requests, os\n",
+    "from praisonai_tools import BaseTool\n",
+    "\n",
+    "class JobSearchTool(BaseTool):\n",
+    "    name: str = \"JobSearchTool\"\n",
+    "    description: str = (\n",
+    "        \"This tool searches for job listings using the Adzuna API.\\n\"\n",
+    "        \"Provide JSON input with 'role', 'location', and 'num_results'.\\n\"\n",
+    "        \"Example:\\n\"\n",
+    "        \"   - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}\\n\"\n",
+    "        \"   - Output: List of job titles, companies, locations, and brief descriptions.\"\n",
+    "    )\n",
+    "\n",
+    "    def _run(self, input_json: str) -> str:\n",
+    "        # Parse input JSON string\n",
+    "        try:\n",
+    "            input_data = json.loads(input_json)\n",
+    "            role = input_data['role']\n",
+    "            location = input_data['location']\n",
+    "            num_results = input_data['num_results']\n",
+    "        except (json.JSONDecodeError, KeyError) as e:\n",
+    "            return \"Invalid input format. Ensure JSON format: {'role': '<role>', 'location': '<location>', 'num_results': <number>}.\"\n",
+    "\n",
+    "        # Adzuna API parameters\n",
+    "        app_id = os.getenv('ADZUNA_APP_ID')\n",
+    "        api_key = os.getenv('ADZUNA_API_KEY')\n",
+    "        base_url = \"http://api.adzuna.com/v1/api/jobs\"\n",
+    "        url = f\"{base_url}/us/search/1?app_id={app_id}&app_key={api_key}&results_per_page={num_results}&what={role}&where={location}&content-type=application/json\"\n",
+    "\n",
+    "        try:\n",
+    "            response = requests.get(url)\n",
+    "            response.raise_for_status()\n",
+    "            jobs_data = response.json()\n",
+    "\n",
+    "            job_listings = []\n",
+    "            for job in jobs_data.get('results', []):\n",
+    "                job_details = f\"Title: {job['title']}, Company: {job['company']['display_name']}, Location: {job['location']['display_name']}, Description: {job['description'][:100]}...\"\n",
+    "                job_listings.append(job_details)\n",
+    "            return '\\n'.join(job_listings)\n",
+    "        except requests.exceptions.HTTPError as err:\n",
+    "            return f\"HTTP Error: {err}\"\n",
+    "        except requests.exceptions.RequestException as e:\n",
+    "            return f\"Request Error: {e}\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h38emoeaEjfY"
+   },
+   "source": [
+    "## YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SkxqHm_8EjfY"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: \"crewai\"\n",
+    "topic: \"Job Search Assistance and Career Advisory\"\n",
+    "roles:\n",
+    "  job_searcher_agent:\n",
+    "    role: \"Job Searcher\"\n",
+    "    backstory: |\n",
+    "      An experienced job searcher actively seeking new opportunities in the specified field. Skilled in identifying job listings relevant to the user's qualifications.\n",
+    "    goal: |\n",
+    "      Conduct job searches for specified roles and locations, focusing on discovering relevant listings that match the user's skills and interests.\n",
+    "    verbose: true\n",
+    "    allow_delegation: true\n",
+    "    tools:\n",
+    "      - \"JobSearchTool\"\n",
+    "    tasks:\n",
+    "      job_search_task:\n",
+    "        description: |\n",
+    "          Search for job openings in a specified role and location using the Job Search tool. Fetch a specified number of results.\n",
+    "        expected_output: |\n",
+    "          List of relevant job listings with titles, companies, locations, and short descriptions.\n",
+    "  skills_development_agent:\n",
+    "    role: \"Skills Development Advisor\"\n",
+    "    backstory: |\n",
+    "      A dedicated advisor identifying key skills required for job roles and providing guidance on skill enhancement methods.\n",
+    "    goal: |\n",
+    "      Analyze job listings to identify essential skills for each position, recommending resources and methods to develop those skills.\n",
+    "    verbose: true\n",
+    "    allow_delegation: true\n",
+    "    tools: []\n",
+    "    tasks:\n",
+    "      skills_highlighting_task:\n",
+    "        description: |\n",
+    "          Analyze job listings for skills requirements. Provide recommendations for candidates on acquiring these skills through online courses, self-study, or hands-on experience.\n",
+    "        expected_output: |\n",
+    "          List of key skills for each job and corresponding skill development resources.\n",
+    "  interview_preparation_coach:\n",
+    "    role: \"Interview Preparation Coach\"\n",
+    "    backstory: |\n",
+    "      An expert in preparing candidates for interviews by simulating interview scenarios, coaching on communication skills, and providing feedback.\n",
+    "    goal: |\n",
+    "      Conduct mock interviews, focusing on enhancing the candidate’s responses, presentation, and communication.\n",
+    "    verbose: true\n",
+    "    allow_delegation: true\n",
+    "    tools: []\n",
+    "    tasks:\n",
+    "      interview_preparation_task:\n",
+    "        description: |\n",
+    "          Prepare candidates for interviews by conducting mock interview sessions. Offer feedback on responses, presentation, and communication skills specific to the roles applied.\n",
+    "        expected_output: |\n",
+    "          Feedback on candidate's interview responses, including strengths and areas for improvement.\n",
+    "  career_advisor:\n",
+    "    role: \"Career Advisor\"\n",
+    "    backstory: |\n",
+    "      Experienced in guiding job seekers through resume enhancement, LinkedIn profile optimization, and effective networking techniques.\n",
+    "    goal: |\n",
+    "      Provide comprehensive career advice, including resume building, LinkedIn profile optimization, and networking strategies.\n",
+    "    verbose: true\n",
+    "    allow_delegation: true\n",
+    "    tools: []\n",
+    "    tasks:\n",
+    "      career_advisory_task:\n",
+    "        description: |\n",
+    "          Offer guidance on building resumes, optimizing LinkedIn profiles, and networking strategies to strengthen job applications.\n",
+    "        expected_output: |\n",
+    "          Detailed suggestions for resume improvement, LinkedIn profile optimization, and networking strategies for job seekers.\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JZtOnJFvEjfY"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "eDP0reqJEjfZ",
+    "outputId": "2c724bc4-a2d1-4b31-94b1-b69cc959a383"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-02 18:45:55][DEBUG]: == Working Agent: Job Searcher\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:45:55][INFO]: == Starting Task: Search for job openings in a specified role and location using the Job Search tool. Fetch a specified number of results.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI need to conduct a job search for a specified role and location to find relevant job listings that match the user's qualifications. However, I do not have the role, location, or number of results specified yet.\n",
+      "\n",
+      "Action: Ask question to coworker  \n",
+      "Action Input: {\"question\": \"What specific job role and location should I search for job openings? Also, how many job listings do you want me to find?\", \"context\": \"I need to gather details about the user's job search preferences, including the specific role they are interested in, the location they want to search in, and the number of job listings to return.\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI now can give a great answer.  \n",
+      "Final Answer: To assist you effectively in your job search, I need to gather some specific details about your preferences. Please provide the following information: \n",
+      "\n",
+      "1. **Specific Job Role**: What type of position are you interested in? This could range from entry-level roles to specialized positions in your field. If you have a particular title in mind (e.g., Marketing Coordinator, Software Developer, Project Manager), please let me know.\n",
+      "\n",
+      "2. **Location**: Which geographical area are you targeting for your job search? Are you looking for opportunities in a specific city, state, or are you open to remote positions? Be as specific as possible to help narrow down the search.\n",
+      "\n",
+      "3. **Number of Job Listings**: How many job listings would you like me to find for you? A common range might be between 10 to 50 listings, depending on how thorough you want your search to be.\n",
+      "\n",
+      "Once I have this information, I can locate relevant job openings that align with your preferences and provide you with a curated list of opportunities to explore.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[95m \n",
+      "\n",
+      "To assist you effectively in your job search, I need to gather some specific details about your preferences. Please provide the following information: \n",
+      "\n",
+      "1. **Specific Job Role**: What type of position are you interested in? This could range from entry-level roles to specialized positions in your field. If you have a particular title in mind (e.g., Marketing Coordinator, Software Developer, Project Manager), please let me know.\n",
+      "\n",
+      "2. **Location**: Which geographical area are you targeting for your job search? Are you looking for opportunities in a specific city, state, or are you open to remote positions? Be as specific as possible to help narrow down the search.\n",
+      "\n",
+      "3. **Number of Job Listings**: How many job listings would you like me to find for you? A common range might be between 10 to 50 listings, depending on how thorough you want your search to be.\n",
+      "\n",
+      "Once I have this information, I can locate relevant job openings that align with your preferences and provide you with a curated list of opportunities to explore.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to wait for the user to provide the specific job role, location, and number of job listings they are interested in before proceeding with the job search.\n",
+      "\n",
+      "Final Answer: Please provide the following information to help me conduct a job search for you:\n",
+      "\n",
+      "1. **Specific Job Role**: What type of position are you interested in? This could range from entry-level roles to specialized positions in your field. If you have a particular title in mind (e.g., Marketing Coordinator, Software Developer, Project Manager), please let me know.\n",
+      "\n",
+      "2. **Location**: Which geographical area are you targeting for your job search? Are you looking for opportunities in a specific city, state, or are you open to remote positions? Be as specific as possible to help narrow down the search.\n",
+      "\n",
+      "3. **Number of Job Listings**: How many job listings would you like me to find for you? A common range might be between 10 to 50 listings, depending on how thorough you want your search to be.\n",
+      "\n",
+      "Once I have this information, I can locate relevant job openings that align with your preferences and provide you with a curated list of opportunities to explore.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-02 18:46:07][DEBUG]: == [Job Searcher] Task output: Please provide the following information to help me conduct a job search for you:\n",
+      "\n",
+      "1. **Specific Job Role**: What type of position are you interested in? This could range from entry-level roles to specialized positions in your field. If you have a particular title in mind (e.g., Marketing Coordinator, Software Developer, Project Manager), please let me know.\n",
+      "\n",
+      "2. **Location**: Which geographical area are you targeting for your job search? Are you looking for opportunities in a specific city, state, or are you open to remote positions? Be as specific as possible to help narrow down the search.\n",
+      "\n",
+      "3. **Number of Job Listings**: How many job listings would you like me to find for you? A common range might be between 10 to 50 listings, depending on how thorough you want your search to be.\n",
+      "\n",
+      "Once I have this information, I can locate relevant job openings that align with your preferences and provide you with a curated list of opportunities to explore.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:46:07][DEBUG]: == Working Agent: Skills Development Advisor\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:46:07][INFO]: == Starting Task: Analyze job listings for skills requirements. Provide recommendations for candidates on acquiring these skills through online courses, self-study, or hands-on experience.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI need to gather specific information about the job role, location, and the number of job listings to analyze job listings for skills requirements. This will help me identify the key skills needed for the position and recommend resources for skill development.\n",
+      "\n",
+      "Action: Ask question to coworker  \n",
+      "Action Input: { \"question\": \"Please provide the following information to help me conduct a job search for you: 1. What type of position are you interested in? This could range from entry-level roles to specialized positions in your field. If you have a particular title in mind (e.g., Marketing Coordinator, Software Developer, Project Manager), please let me know. 2. Which geographical area are you targeting for your job search? Are you looking for opportunities in a specific city, state, or are you open to remote positions? Be as specific as possible to help narrow down the search. 3. How many job listings would you like me to find for you? A common range might be between 10 to 50 listings, depending on how thorough you want your search to be.\", \"context\": \"I need to gather specific preferences about job role, location, and number of job listings to effectively analyze job listings for skills requirements.\" }  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search.\n",
+      "\n",
+      "Action: None\n",
+      "Action Input: None\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search. I will ask the user for their preferences directly.\n",
+      "\n",
+      "Action: None\n",
+      "Action Input: None\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search. Since I don't have the user's preferences, I will outline the questions I need to ask.\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search. I will ask the user directly for their preferences.\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search. Since I don't have the user's preferences, I will outline the questions I need to ask.\n",
+      "\n",
+      "1. What type of position are you interested in? (e.g., Marketing Coordinator, Software Developer, Project Manager)\n",
+      "2. Which geographical area are you targeting for your job search? (e.g., specific city, state, or remote positions)\n",
+      "3. How many job listings would you like me to find for you? (e.g., between 10 to 50 listings)\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search. Since I don't have the user's preferences, I will outline the questions I need to ask.\n",
+      "\n",
+      "1. What type of position are you interested in? (e.g., Marketing Coordinator, Software Developer, Project Manager)\n",
+      "2. Which geographical area are you targeting for your job search? (e.g., specific city, state, or remote positions)\n",
+      "3. How many job listings would you like me to find for you? (e.g., between 10 to 50 listings)\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search. To provide the best assistance, I will outline the questions I need to ask the user.\n",
+      "\n",
+      "1. What type of position are you interested in? (e.g., Marketing Coordinator, Software Developer, Project Manager)\n",
+      "2. Which geographical area are you targeting for your job search? (e.g., specific city, state, or remote positions)\n",
+      "3. How many job listings would you like me to find for you? (e.g., between 10 to 50 listings)\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and the number of job listings to conduct a thorough job search. Since I don't have the user's preferences, I will outline the questions I need to ask.\n",
+      "\n",
+      "1. What type of position are you interested in? (e.g., Marketing Coordinator, Software Developer, Project Manager)\n",
+      "2. Which geographical area are you targeting for your job search? (e.g., specific city, state, or remote positions)\n",
+      "3. How many job listings would you like me to find for you? (e.g., between 10 to 50 listings)\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and number of job listings to conduct a thorough job search. Since I don’t have the user's preferences, I will outline the questions I need to ask.\n",
+      "\n",
+      "1. What type of position are you interested in? (e.g., Marketing Coordinator, Software Developer, Project Manager)\n",
+      "2. Which geographical area are you targeting for your job search? (e.g., specific city, state, or remote positions)\n",
+      "3. How many job listings would you like me to find for you? (e.g., between 10 to 50 listings)\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and number of job listings to conduct a thorough job search. Since I don’t have the user's preferences, I will outline the questions I need to ask.\n",
+      "\n",
+      "1. What type of position are you interested in? (e.g., Marketing Coordinator, Software Developer, Project Manager)\n",
+      "2. Which geographical area are you targeting for your job search? (e.g., specific city, state, or remote positions)\n",
+      "3. How many job listings would you like me to find for you? (e.g., between 10 to 50 listings)\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather specific preferences about the job role, location, and number of job listings to conduct a thorough job search. Since I don’t have the user's preferences, I will outline the questions I need to ask.\n",
+      "\n",
+      "1. What type of position are you interested in? (e.g., Marketing Coordinator, Software Developer, Project Manager)\n",
+      "2. Which geographical area are you targeting for your job search? (e.g., specific city, state, or remote positions)\n",
+      "3. How many job listings would you like me to find for you? (e.g., between 10 to 50 listings)\n",
+      "\n",
+      "Action: None  \n",
+      "Action Input: None  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: JobSearchTool._run() got an unexpected keyword argument 'input_'.\n",
+      " Tool JobSearchTool accepts these inputs: JobSearchTool(input_json: 'string') - This tool searches for job listings using the Adzuna API. Provide JSON input with 'role', 'location', and 'num_results'. Example:    - Input: {'role': 'Data Scientist', 'location': 'New York', 'num_results': 5}    - Output: List of job titles, companies, locations, and brief descriptions.\n",
+      "\u001b[00m\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-27-ff9b026773d1>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Run PraisonAI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpraisonai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Print the result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/cli.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0mRun\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPraisonAI\u001b[0m \u001b[0mapplication\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \"\"\"\n\u001b[0;32m--> 112\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/cli.py\u001b[0m in \u001b[0;36mmain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    256\u001b[0m                 \u001b[0mtools\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             )\n\u001b[0;32m--> 258\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magents_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_crew_and_kickoff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/agents_generator.py\u001b[0m in \u001b[0;36mgenerate_crew_and_kickoff\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    274\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mAGENTOPS_AVAILABLE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m                 \u001b[0magentops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"AGENTOPS_API_KEY\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"crewai\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_crewai\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtools_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_autogen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtools_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/praisonai/agents_generator.py\u001b[0m in \u001b[0;36m_run_crewai\u001b[0;34m(self, config, topic, tools_dict)\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Tasks: {crew.tasks}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkickoff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"### Task Output ###\\n{response}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/crew.py\u001b[0m in \u001b[0;36mkickoff\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mProcess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequential\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_sequential_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    315\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mProcess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhierarchical\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m             \u001b[0;31m# type: ignore # Unpacking a string is disallowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/crew.py\u001b[0m in \u001b[0;36m_run_sequential_process\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    394\u001b[0m                     \u001b[0magent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"started\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m                 )\n\u001b[0;32m--> 396\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_execution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/task.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, agent, context, tools)\u001b[0m\n\u001b[1;32m    209\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             result = self._execute(\n\u001b[0m\u001b[1;32m    212\u001b[0m                 \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m                 \u001b[0magent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0magent\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/task.py\u001b[0m in \u001b[0;36m_execute\u001b[0;34m(self, agent, task, context, tools)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtools\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m         result = agent.execute_task(\n\u001b[0m\u001b[1;32m    221\u001b[0m             \u001b[0mtask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agent.py\u001b[0m in \u001b[0;36mexecute_task\u001b[0;34m(self, task, context, tools)\u001b[0m\n\u001b[1;32m    160\u001b[0m             \u001b[0mtask_prompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_use_trained_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask_prompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask_prompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         result = self.agent_executor.invoke(\n\u001b[0m\u001b[1;32m    163\u001b[0m             {\n\u001b[1;32m    164\u001b[0m                 \u001b[0;34m\"input\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtask_prompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             outputs = (\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agents/executor.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_continue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_elapsed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_within_rpm_limit\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_within_rpm_limit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m                 next_step_output = self._take_next_step(\n\u001b[0m\u001b[1;32m     72\u001b[0m                     \u001b[0mname_to_tool_map\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m                     \u001b[0mcolor_mapping\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py\u001b[0m in \u001b[0;36m_take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m   1136\u001b[0m     ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n\u001b[1;32m   1137\u001b[0m         return self._consume_next_step(\n\u001b[0;32m-> 1138\u001b[0;31m             [\n\u001b[0m\u001b[1;32m   1139\u001b[0m                 \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m                 for a in self._iter_next_step(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1136\u001b[0m     ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n\u001b[1;32m   1137\u001b[0m         return self._consume_next_step(\n\u001b[0;32m-> 1138\u001b[0;31m             [\n\u001b[0m\u001b[1;32m   1139\u001b[0m                 \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m                 for a in self._iter_next_step(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agents/executor.py\u001b[0m in \u001b[0;36m_iter_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m    244\u001b[0m                     \u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcasefold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mname_to_tool_map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m                 ]:\n\u001b[0;32m--> 246\u001b[0;31m                     \u001b[0mobservation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtool_usage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtool_calling\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent_action\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    247\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m                     observation = self._i18n.errors(\"wrong_tool_name\").format(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/tools/tool_usage.py\u001b[0m in \u001b[0;36muse\u001b[0;34m(self, calling, tool_string)\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_printer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"\\n\\n{error}\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"red\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{self._use(tool_string=tool_string, tool=tool, calling=calling)}\"\u001b[0m  \u001b[0;31m# type: ignore # BUG?: \"_use\" of \"ToolUsage\" does not return a value (it only ever returns None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     def _use(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/tools/tool_usage.py\u001b[0m in \u001b[0;36m_use\u001b[0;34m(self, tool_string, tool, calling)\u001b[0m\n\u001b[1;32m    140\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0macceptable_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m                         }\n\u001b[0;32m--> 142\u001b[0;31m                         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0marguments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mtool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs_schema\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/tools.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, run_manager, *args, **kwargs)\u001b[0m\n\u001b[1;32m    748\u001b[0m                 )\n\u001b[1;32m    749\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_argument_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    751\u001b[0m             )\n\u001b[1;32m    752\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tool does not support sync\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agents/agent_builder/utilities/base_agent_tool.py\u001b[0m in \u001b[0;36mask_question\u001b[0;34m(self, question, context, coworker, **kwargs)\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;34m\"\"\"Useful to ask a question, opinion or take from a coworker passing all necessary context and names.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mcoworker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_coworker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoworker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquestion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agents/agent_builder/utilities/base_agent_tool.py\u001b[0m in \u001b[0;36m_execute\u001b[0;34m(self, agent, task, context)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0mexpected_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Your best answer to your coworker asking you this, accounting for the context shared.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         )\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agent.py\u001b[0m in \u001b[0;36mexecute_task\u001b[0;34m(self, task, context, tools)\u001b[0m\n\u001b[1;32m    160\u001b[0m             \u001b[0mtask_prompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_use_trained_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask_prompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtask_prompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         result = self.agent_executor.invoke(\n\u001b[0m\u001b[1;32m    163\u001b[0m             {\n\u001b[1;32m    164\u001b[0m                 \u001b[0;34m\"input\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtask_prompt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36minvoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             outputs = (\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agents/executor.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_continue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_elapsed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_within_rpm_limit\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_within_rpm_limit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m                 next_step_output = self._take_next_step(\n\u001b[0m\u001b[1;32m     72\u001b[0m                     \u001b[0mname_to_tool_map\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m                     \u001b[0mcolor_mapping\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py\u001b[0m in \u001b[0;36m_take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m   1136\u001b[0m     ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n\u001b[1;32m   1137\u001b[0m         return self._consume_next_step(\n\u001b[0;32m-> 1138\u001b[0;31m             [\n\u001b[0m\u001b[1;32m   1139\u001b[0m                 \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m                 for a in self._iter_next_step(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1136\u001b[0m     ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:\n\u001b[1;32m   1137\u001b[0m         return self._consume_next_step(\n\u001b[0;32m-> 1138\u001b[0;31m             [\n\u001b[0m\u001b[1;32m   1139\u001b[0m                 \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1140\u001b[0m                 for a in self._iter_next_step(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/crewai/agents/executor.py\u001b[0m in \u001b[0;36m_iter_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m             \u001b[0;31m# Call the LLM to see what to do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m             output = self.agent.plan(  # type: ignore #  Incompatible types in assignment (expression has type \"AgentAction | AgentFinish | list[AgentAction]\", variable has type \"AgentAction\")\n\u001b[0m\u001b[1;32m    136\u001b[0m                 \u001b[0mintermediate_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_manager\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py\u001b[0m in \u001b[0;36mplan\u001b[0;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m             \u001b[0;31m# Because the response from the plan is not a generator, we need to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m             \u001b[0;31m# accumulate the output into final output and return that.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunnable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"callbacks\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    398\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfinal_output\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m                     \u001b[0mfinal_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2873\u001b[0m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2874\u001b[0m     ) -> Iterator[Output]:\n\u001b[0;32m-> 2875\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2876\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2877\u001b[0m     async def atransform(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2860\u001b[0m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2861\u001b[0m     ) -> Iterator[Output]:\n\u001b[0;32m-> 2862\u001b[0;31m         yield from self._transform_stream_with_config(\n\u001b[0m\u001b[1;32m   2863\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2864\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36m_transform_stream_with_config\u001b[0;34m(self, input, transformer, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m   1879\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1881\u001b[0;31m                     \u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1882\u001b[0m                     \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1883\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfinal_output_supported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36m_transform\u001b[0;34m(self, input, run_manager, config)\u001b[0m\n\u001b[1;32m   2824\u001b[0m             )\n\u001b[1;32m   2825\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2826\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfinal_pipeline\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2827\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2828\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   1280\u001b[0m         \u001b[0mgot_first_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1282\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0michunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1283\u001b[0m             \u001b[0;31m# The default implementation of transform is to buffer input and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m             \u001b[0;31m# then call stream.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   4734\u001b[0m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4735\u001b[0m     ) -> Iterator[Output]:\n\u001b[0;32m-> 4736\u001b[0;31m         yield from self.bound.transform(\n\u001b[0m\u001b[1;32m   4737\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4738\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_merge_configs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/runnables/base.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   1298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1299\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgot_first_val\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1300\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1302\u001b[0m     async def atransform(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    247\u001b[0m                     ),\n\u001b[1;32m    248\u001b[0m                 )\n\u001b[0;32m--> 249\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m                 \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_llm_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLLMResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgeneration\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_core/language_models/chat_models.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0mgeneration\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mChatGenerationChunk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                         \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"run-{run_manager.run_id}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain_openai/chat_models/base.py\u001b[0m in \u001b[0;36m_stream\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    479\u001b[0m         \u001b[0mdefault_chunk_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAIMessageChunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    480\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessage_dicts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 481\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    482\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    483\u001b[0m                     \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_dump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_streaming.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_T\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_streaming.py\u001b[0m in \u001b[0;36m__stream__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0miterator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iter_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0msse\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[DONE]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_streaming.py\u001b[0m in \u001b[0;36m_iter_events\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_iter_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mServerSentEvent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m         \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__stream__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_T\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_streaming.py\u001b[0m in \u001b[0;36miter_bytes\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0miter_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mServerSentEvent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;34m\"\"\"Given an iterator that yields raw binary data, iterate over it & yield every event encountered\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iter_chunks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m             \u001b[0;31m# Split before decoding so splitlines() only uses \\r and \\n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mraw_line\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_streaming.py\u001b[0m in \u001b[0;36m_iter_chunks\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    289\u001b[0m         \u001b[0;34m\"\"\"Given an iterator that yields raw binary data, iterate over it and yield individual SSE chunks\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeepends\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_models.py\u001b[0m in \u001b[0;36miter_bytes\u001b[0;34m(self, chunk_size)\u001b[0m\n\u001b[1;32m    829\u001b[0m             \u001b[0mchunker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mByteChunker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mrequest_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 831\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mraw_bytes\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    832\u001b[0m                     \u001b[0mdecoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    833\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecoded\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_models.py\u001b[0m in \u001b[0;36miter_raw\u001b[0;34m(self, chunk_size)\u001b[0m\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mrequest_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mraw_stream_bytes\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_bytes_downloaded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_stream_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchunker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_stream_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_transports/default.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    114\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mmap_httpcore_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_httpcore_stream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    365\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    361\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    364\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    347\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mShieldCancellation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"receive_response_body\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_receive_response_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m                     \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_response_body\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m             \u001b[0mevent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_receive_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_event\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNEED_DATA\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m                 data = self._network_stream.read(\n\u001b[0m\u001b[1;32m    225\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREAD_NUM_BYTES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 )\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_backends/sync.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, max_bytes, timeout)\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mmap_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, buflen, flags)\u001b[0m\n\u001b[1;32m   1286\u001b[0m                     \u001b[0;34m\"non-zero flags not allowed in calls to recv() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m                     self.__class__)\n\u001b[0;32m-> 1288\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuflen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1289\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1290\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuflen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1159\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1160\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1162\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[JobSearchTool])\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result) # 1/10\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "b_-7Mw3_Janx"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbooks/notebooks/job_posting_agents.ipynb

@@ -0,0 +1,873 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P9vD28mQx85n"
+   },
+   "source": [
+    "# Job Posting Agents\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/job_posting_agents.ipynb)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mvoqjORLx85o"
+   },
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "kaHo3ZIwx85p"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install praisonai > /dev/null\n",
+    "%pip install praisonai[crewai] > /dev/null\n",
+    "%pip install duckduckgo_search > /dev/null\n",
+    "%pip install python-dotenv > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qVxQeHkQ_Ou3"
+   },
+   "source": [
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "UETg9vDDx85q"
+   },
+   "outputs": [],
+   "source": [
+    "# This program requires updating so as to accomodate input of the Job Description based on which the Agents will create the Job Posting\n",
+    "\n",
+    "from duckduckgo_search import DDGS\n",
+    "from langchain.tools import tool\n",
+    "from praisonai_tools import BaseTool, FileReadTool, SerperDevTool\n",
+    "\n",
+    "class InternetSearchTool(BaseTool):\n",
+    "    name: str = \"InternetSearchTool\"\n",
+    "    description: str = \"Search Internet for relevant information based on a query or latest news\"\n",
+    "\n",
+    "    def _run(self, query: str):\n",
+    "        ddgs = DDGS()\n",
+    "        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)\n",
+    "        return results\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RrziQ9dZx85q"
+   },
+   "source": [
+    "## YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "dcrk0k5ax85q"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: \"crewai\"\n",
+    "topic: \"Job Posting Creation and Industry Analysis\"\n",
+    "roles\n",
+    "  research_agent:\n",
+    "    role: \"Research Specialist\"\n",
+    "    backstory: |\n",
+    "      You are a dedicated Research Specialist, responsible for gathering insights into the company's culture, role requirements, and industry context to support the job posting process.\n",
+    "    goal: |\n",
+    "      Collect, analyze, and summarize information about the company's culture, the role requirements, and industry trends to support targeted job posting development.\n",
+    "    verbose: true\n",
+    "    allow_delegation: false\n",
+    "    tools:\n",
+    "      - \"InternetSearchTool\"\n",
+    "      - \"SerperDevTool\"\n",
+    "    tasks:\n",
+    "      research_company_culture_task:\n",
+    "        description: |\n",
+    "          Analyze the provided company website and hiring manager's domain, focusing on culture, values, and mission. Identify unique points, specific projects, or achievements that can be highlighted.\n",
+    "        expected_output: |\n",
+    "          A comprehensive report detailing the company's culture, values, mission, and unique selling points relevant to the job role, with suggestions on leveraging these insights for the job posting.\n",
+    "      research_role_requirements_task:\n",
+    "        description: |\n",
+    "          Based on hiring needs, identify key skills, experiences, and qualities ideal for the candidate. Align with company projects, industry trends, and competitive positioning.\n",
+    "        expected_output: |\n",
+    "          A list of recommended skills, experiences, and qualities aligned with company culture, projects, and the role requirements.\n",
+    "  writer_agent:\n",
+    "    role: \"Content Writer\"\n",
+    "    backstory: |\n",
+    "      As a Content Writer, you excel in drafting engaging and clear job postings that accurately reflect the role and resonate with potential candidates.\n",
+    "    goal: |\n",
+    "      Use research insights to draft an appealing job posting that highlights the role’s unique attributes, aligned with the company's culture and values.\n",
+    "    verbose: true\n",
+    "    allow_delegation: true\n",
+    "    tools:\n",
+    "      - \"InternetSearchTool\"\n",
+    "      - \"SerperDevTool\"\n",
+    "      - \"FileReadTool\"\n",
+    "    tasks:\n",
+    "      draft_job_posting_task:\n",
+    "        description: |\n",
+    "          Draft a job posting based on hiring needs with a compelling introduction and detailed role description. Ensure alignment with company description and include benefits specific_benefits.\n",
+    "        expected_output: |\n",
+    "          A detailed, engaging job posting with an introduction, role description, responsibilities, requirements, and unique company benefits, resonating with the company's culture.\n",
+    "  review_agent:\n",
+    "    role: \"Content Reviewer\"\n",
+    "    backstory: |\n",
+    "      You are an experienced Content Reviewer responsible for refining job postings to ensure they are clear, accurate, and engaging for the right candidates.\n",
+    "    goal: |\n",
+    "      Review and edit the draft job posting for clarity, engagement, and alignment with company culture, ensuring it speaks directly to ideal candidates.\n",
+    "    verbose: true\n",
+    "    allow_delegation: false\n",
+    "    tools:\n",
+    "      - \"InternetSearchTool\"\n",
+    "      - \"SerperDevTool\"\n",
+    "      - \"FileReadTool\"\n",
+    "    tasks:\n",
+    "      review_and_edit_job_posting_task:\n",
+    "        description: |\n",
+    "          Review the draft job posting for clarity, engagement, and accuracy, and refine as needed. Ensure it aligns with the company’s culture and values, providing feedback for improvements.\n",
+    "        expected_output: |\n",
+    "          A polished, error-free job posting aligned with company values and targeted to attract ideal candidates, along with feedback on further enhancements.\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dp95UmADx85q"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "uPeHbzlPx85q",
+    "outputId": "d5673cc7-f07e-4d90-f04c-b9ca3adc57df"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-01 14:25:40][DEBUG]: == Working Agent: Research Specialist\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 14:25:40][INFO]: == Starting Task: Analyze the provided company website and hiring manager's domain, focusing on culture, values, and mission. Identify unique points, specific projects, or achievements that can be highlighted.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mTo analyze the company's culture, values, and mission, I first need to gather information about the company from their website and any relevant sources. I'll begin by conducting an internet search to locate the company's website and gather insights about their culture, values, and mission.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"company website and hiring manager's domain\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'Why HR Should Care About the Company Website', 'href': 'https://hrdailyadvisor.blr.com/2021/02/24/why-hr-should-care-about-the-company-website/', 'body': \"Creating, maintaining, and updating your organization's Web presence is not typically HR's job, but the HR team should have a vested interest in how the website looks and functions and how well it coordinates with the rest of the company's online presence. HR and Organizational Web Presence: Hiring and Recruiting Impacts A company's online presence […]\"}, {'title': \"5 Tips to Improve Your Company's Website Recruitment - Monster.com\", 'href': 'https://hiring.monster.com/resources/recruiting-strategies/talent-acquisition/company-website-design/', 'body': 'Learn how to use your company website to attract and engage job seekers who want to know your employer brand, culture, and values. Find out how to showcase your mission, team, work environment, and job openings on your site.'}, {'title': 'The 10 Best Recruiting and Staffing Website Examples - Bluleadz', 'href': 'https://www.bluleadz.com/blog/recruiting-and-staffing-best-website-examples', 'body': \"A well-planned staffing website improves your company's credibility and makes the whole recruitment process much simpler and faster. Here are the seven elements that make up a good website: They Are SEO Friendly. Your website design may be beautiful and professional, but none of that matters if you don't get traffic to your site.\"}, {'title': '.jobs Domain | Register your .jobs Name - GoDaddy', 'href': 'https://www.godaddy.com/tlds/jobs-domain', 'body': 'Clearly place your company\\'s HR interests on the Web using a www.\"yourcompanyname\".jobs domain. Provide a direct route to career information for job seekers. Eliminate congestion, confusion, and distraction on your home page. Complement and enhance your company\\'s current best practices in hiring.'}, {'title': '8 domain names for Human Resources & Recruitment - EuroDNS', 'href': 'https://www.eurodns.com/blog/domain-names-human-resources-recruitment', 'body': \"What better way to promote your recruiting site and give your company's job offers the visibility they deserve than choosing a domain extension with a clear intent? In addition to recruitment, the human resources industry also deals with staff training and career management. Here is our list of 8 trusted extensions for the Human Resources industry.\"}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mIt seems that the search did not return specific information about the company's website or the hiring manager's domain. I need to refine the search query to obtain the company's specific website to gather the necessary insights about its culture, values, and mission.\n",
+      "\n",
+      "Thought: I need to perform a more targeted search to find the company's official website, which will provide direct information about its culture, values, and mission.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"official website of the company and hiring manager's domain for insights on culture and values\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'Hiring Progress Report - Google Diversity Equity & Inclusion', 'href': 'https://about.google/belonging/diversity-annual-report/2021/hiring/', 'body': 'Discover insights on how hiring changes focused on diversity drove our best year yet for women in tech globally & Black+ and Latinx+ people in the US. ... And we get to basically support the fueling of the growth of this company. Karina: From an equity perspective, looking to really analyze our people, processes, and systems to drive equitable ...'}, {'title': 'Values-Based Hiring: Look Beyond Skills To Find The Best ... - Forbes', 'href': 'https://www.forbes.com/sites/davidmorel/2023/04/11/values-based-hiring-look-beyond-skills-to-find-the-best-employees-for-your-business/', 'body': \"A company's website is often the first port of call for candidates looking for insights into its values and culture. However, candidates will also consult review sites and social media for a ...\"}, {'title': 'Building a more Inclusive Workplace — Google - Google - About Google', 'href': 'https://about.google/intl/ALL_us/belonging/at-work/', 'body': 'Learn how Google is building a more inclusive workplace that reflects and embraces the diversity of its users and communities. Explore its hiring goals, programs, initiatives, and resources for underrepresented groups, people with disabilities, and intersectional identities.'}, {'title': '15 Ways Hiring Managers Can Ensure Value Alignment With ... - Forbes', 'href': 'https://www.forbes.com/councils/forbeshumanresourcescouncil/2021/10/19/15-ways-hiring-managers-can-ensure-value-alignment-with-prospective-employees/', 'body': \"Learn 15 tips from HR experts on how to identify and hire candidates who share your company's values and goals. Find out how to communicate, assess and demonstrate your values in the interview ...\"}, {'title': '18 Insights Companies Should Know When Hiring Internationally - Forbes', 'href': 'https://www.forbes.com/councils/forbesbusinesscouncil/2023/10/23/18-insights-companies-should-know-when-hiring-internationally/', 'body': '1. Understand Cultural Differences. One thing that companies should keep in mind when considering hiring internationally is the cultural differences that may come with the territory.'}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mIt seems that the search results still do not provide direct access to the specific company's website or the hiring manager's domain needed to gather information about the company's culture, values, and mission. I will need to conduct a more specific search or use another method to find the exact company website.\n",
+      "\n",
+      "Thought: I need to refine the search query further to locate the company's official website directly. Identifying the company's name or specific industry might help in narrowing down the search results.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"official website of [specific company name]\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'Choose your business name | U.S. Small Business Administration', 'href': 'https://www.sba.gov/business-guide/launch-your-business/choose-your-business-name', 'body': 'Once you settle on a name you like, you need to protect it. There are four different ways to register your business name. Each way of registering your name serves a different purpose, and some may be legally required depending on your business structure and location. Entity name protects you at a state level. Trademark protects you at a federal ...'}, {'title': 'Register your business | U.S. Small Business Administration', 'href': 'https://www.sba.gov/business-guide/launch-your-business/register-your-business', 'body': \"In most cases, the total cost to register your business will be less than $300, but fees vary depending on your state and business structure. The information you'll need typically includes: Business name. Business location. Ownership, management structure, or directors. Registered agent information.\"}, {'title': 'SAM.gov | Home', 'href': 'https://sam.gov/', 'body': 'You have reached SAM.gov, an official website of the U.S. government. There is no cost to use this site. The unique entity identifier used in SAM.gov has changed. On April 4, 2022, the unique entity identifier used across the federal government changed from the DUNS Number to the Unique Entity ID (generated by SAM.gov). The Unique Entity ID is ...'}, {'title': 'Business Name Generator - free AI-powered naming tool - Namelix', 'href': 'https://namelix.com/', 'body': 'Namelix generates short, branded names that are relevant to your business idea. When you save a name, the algorithm learns your preferences and gives you better recommendations over time. Namelix uses generative AI to create a short, brandable business name. Search for domain availability, and instantly generate a logo for your new business.'}, {'title': '100+ of the best company names and tips for choosing your own - Wix.com', 'href': 'https://www.wix.com/blog/best-company-names', 'body': '01. Verizon. The word \"Verizon\" has two roots. \"Ver\" comes from the Latin veritas, meaning \"truth.\" \"Izon\" comes from the English word \"horizon.\". Verizon wasn\\'t a random word that the owners cooked up—rather the company spent $300 million to create the brand name and the marketing around it.'}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n",
+      "Final Answer: my best complete final answer to the task.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 14:25:51][DEBUG]: == [Research Specialist] Task output: my best complete final answer to the task.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 14:25:51][DEBUG]: == Working Agent: Research Specialist\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 14:25:51][INFO]: == Starting Task: Based on hiring needs, identify key skills, experiences, and qualities ideal for the candidate. Align with company projects, industry trends, and competitive positioning.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mTo address the current task effectively, I need to gather information on the company's culture, role requirements, and industry trends. This will help in identifying the key skills, experiences, and qualities ideal for a candidate. I will start by searching for the company's culture and values, then proceed to gather insights into the specific role requirements and the overall industry context. \n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"current trends in hiring and recruitment 2023\"}\n",
+      "\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'The 2023 Future of Recruiting Report | Hiring on LinkedIn', 'href': 'https://business.linkedin.com/talent-solutions/resources/future-of-recruiting/archival/future-of-recruiting-2023.', 'body': \"Recruiting will collaborate more with learning and development. The walls that once divided recruiting and learning and development (L&D) are coming down fast. While three out of five (62%) TA pros say they're already working closely with L&D, an even larger portion say they'll still need to grow closer in the future.\"}, {'title': 'The top talent acquisition trends in 2024 | McKinsey - McKinsey & Company', 'href': 'https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-shape-of-talent-in-2023-and-2024', 'body': \"In this episode of McKinsey Talks Talent, join McKinsey partners Bryan Hancock and Brooke Weddle, in conversation with global editorial director Lucia Rahilly, as they speak about the trends that shaped last year's talent landscape—and those poised to redefine its contours yet again in 2024. This conversation has been edited for clarity and ...\"}, {'title': 'LinkedIn Report: 6 Predictions for the Future of Recruiting', 'href': 'https://www.linkedin.com/business/talent/blog/talent-acquisition/future-of-recruiting-2024', 'body': 'In fact, 73% of recruiting pros say hiring based on skills is a priority. And 84% of talent professionals say TA and learning and development (L&D) need to work more closely, up from 78% who said ...'}, {'title': '9 Future of Work Trends For 2023 - Gartner', 'href': 'https://www.gartner.com/en/articles/9-future-of-work-trends-for-2023', 'body': 'Gartner\\'s 9 Future of Work Trends for 2023. Trend Number. Trend Name. 1. \"Quiet hiring\" offers new ways to snag in-demand talent. 2. Hybrid flexibility reaches the front lines. 3. Squeezed by competing leader and employee expectations, managers need support.'}, {'title': '10 Must-Haves for Successful Recruiting in 2023 - SHRM', 'href': 'https://www.shrm.org/topics-tools/news/talent-acquisition/10-must-haves-successful-recruiting-2023', 'body': 'Here are 10 brief takeaways. 1. Adopt New Methods, Tools. HR must embrace new recruiting methods and technologies to fit the current era of hiring, such as using QR codes, text-based outreach and ...'}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have gathered some insights into current hiring and recruitment trends for 2023. Next, I need to gather information about the company's culture and specific role requirements to align these trends with the company's needs.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"company culture and values for effective recruitment\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'When It Comes to Recruiting, Put Company Culture First - Forbes', 'href': 'https://www.forbes.com/councils/forbeshumanresourcescouncil/2019/02/08/when-it-comes-to-recruiting-put-company-culture-first/', 'body': 'The meteoric rise of startups and tech companies has put company culture at the forefront of recruitment efforts. Now, more organizations claim to embody the entrepreneurial, transparent ...'}, {'title': 'The Ultimate Guide to Values Based Recruitment - Harver', 'href': 'https://harver.com/blog/values-based-recruitment/', 'body': 'Here are a few advantages of a values based recruitment strategy: 1. Positive work environment. Fitting into a company culture can help employees feel more comfortable at work. However, sharing values and aspirations with team members can also facilitate stronger relationships between coworkers.'}, {'title': 'What Is Company Culture? The Impact On Recruiting - Recruitment.com', 'href': 'https://recruitment.com/employer-branding/company-culture', 'body': \"Leaders of large and small teams need to enact the values of a company's culture. Employees look to other employees for models of behavior. If your leaders are models of company culture, the rest of your employees will follow suit. This can have a great positive effect on your company. Including in the recruiting process.\"}, {'title': 'Recruiting for Cultural Fit - Harvard Business Review', 'href': 'https://hbr.org/2015/07/recruiting-for-cultural-fit', 'body': \"Recruiting for Cultural Fit. Culture fit is the glue that holds an organization together. That's why it's a key trait to look for when recruiting. The result of poor culture fit due to ...\"}, {'title': '12 Recruitment Strategies To Transform Company Culture And ... - Forbes', 'href': 'https://www.forbes.com/councils/forbeshumanresourcescouncil/2023/10/20/12-recruitment-strategies-to-transform-company-culture-and-reach-goals/', 'body': '5. Make A Commitment To Hiring A Diverse Team. For greater innovation, culture and mission-connection, companies need to hire diverse teams. Create a talent attraction and retention campaign ...'}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have obtained general insights into the importance of aligning recruitment with company culture and values. However, I still need specific information about the company's culture, role requirements, and how these align with industry trends. This will help me identify the key skills, experiences, and qualities ideal for a candidate.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"role requirements and skills for effective recruitment 2023\"}\n",
+      "\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': '10 Must-Haves for Successful Recruiting in 2023 - SHRM', 'href': 'https://www.shrm.org/topics-tools/news/talent-acquisition/10-must-haves-successful-recruiting-2023', 'body': 'The recruiting trends panel at the SHRM Talent Conference & Expo 2023 in Orlando explored the must-have practices and programs necessary to hire talent in a tight labor market, from using emerging ...'}, {'title': '10 Effective Recruitment Strategies In 2024 - Forbes Advisor', 'href': 'https://www.forbes.com/advisor/business/recruitment-strategies/', 'body': 'Here are 10 effective recruitment strategies in 2024: 1. Define Your Corporate Brand. When people think of places to work, they often start with well-known brands that they are familiar with and ...'}, {'title': '10 Key Recruitment Skills For 2023 (By Recruiting Experts) - WebPipl', 'href': 'https://webpipl.com/recruitment-skills', 'body': 'Step 1. Define Job Requirements. Recruiters begin by understanding the job requirements and skills needed for the role. Source Candidates: Source Candidates: Recruiters employ various sourcing strategies such as online job boards, social media platforms, and professional networks to find potential candidates.'}, {'title': 'Recruitment Skills That All Great Recruiters Must Have in 2023', 'href': 'https://hirehunch.com/blog/recruitment-skills-all-great-recruiters-have/', 'body': '10 Top Recruitment Skills That All Great Recruiters Have 1. Communication Skills. Effective communication is the cornerstone of successful recruitment. Recruiters must convey job details to candidates, ensuring they understand their roles, responsibilities, and expectations.'}, {'title': '16 Top Recruitment Skills That All Great Recruiters Have', 'href': 'https://toggl.com/blog/top-recruitment-skills', 'body': 'In 2023, the average annual income of a US technical recruiter was typically higher than that of a generalist recruiter. ... while competencies are the capabilities that ensure an effective job or role-level performance. Different types of skills recruiters need. Every good recruiter (external or internal) requires a common set of recruitment ...'}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have gathered information on current hiring trends, the importance of aligning recruitment with company culture and values, and general role requirements and skills for effective recruitment. Now, I need to synthesize this information to identify the key skills, experiences, and qualities ideal for a candidate, aligned with company culture, projects, and the role requirements.\n",
+      "\n",
+      "Final Answer: \n",
+      "\n",
+      "1. **Skills**:\n",
+      "   - Strong communication skills to effectively convey job details and expectations.\n",
+      "   - Proficiency in using emerging recruitment technologies and methods, such as online job boards, social media platforms, and professional networks.\n",
+      "   - Ability to collaborate closely with learning and development departments, reflecting the trend of integrating recruitment with employee growth strategies.\n",
+      "   - Competency in defining job requirements and sourcing strategies tailored to specific roles and company needs.\n",
+      "\n",
+      "2. **Experiences**:\n",
+      "   - Demonstrated experience in recruiting for cultural fit, ensuring alignment with company values and promoting a positive work environment.\n",
+      "   - Previous experience in a diverse and inclusive hiring environment to support innovation and mission connection.\n",
+      "   - Experience in fast-paced, dynamic industries, such as tech or startups, where culture and adaptability are key.\n",
+      "\n",
+      "3. **Qualities**:\n",
+      "   - A strong cultural fit with the company's values and mission, ensuring that candidates not only meet the role requirements but also enhance the company culture.\n",
+      "   - Adaptability and openness to new recruitment strategies and tools to meet the evolving needs of the labor market.\n",
+      "   - Strong interpersonal skills to build relationships with potential candidates and internal stakeholders, facilitating a seamless recruitment process.\n",
+      "\n",
+      "By aligning these skills, experiences, and qualities with the company's culture and current industry trends, the recruitment process can effectively attract and select candidates who are not only capable but also a great fit for the organization.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 14:26:04][DEBUG]: == [Research Specialist] Task output: 1. **Skills**:\n",
+      "   - Strong communication skills to effectively convey job details and expectations.\n",
+      "   - Proficiency in using emerging recruitment technologies and methods, such as online job boards, social media platforms, and professional networks.\n",
+      "   - Ability to collaborate closely with learning and development departments, reflecting the trend of integrating recruitment with employee growth strategies.\n",
+      "   - Competency in defining job requirements and sourcing strategies tailored to specific roles and company needs.\n",
+      "\n",
+      "2. **Experiences**:\n",
+      "   - Demonstrated experience in recruiting for cultural fit, ensuring alignment with company values and promoting a positive work environment.\n",
+      "   - Previous experience in a diverse and inclusive hiring environment to support innovation and mission connection.\n",
+      "   - Experience in fast-paced, dynamic industries, such as tech or startups, where culture and adaptability are key.\n",
+      "\n",
+      "3. **Qualities**:\n",
+      "   - A strong cultural fit with the company's values and mission, ensuring that candidates not only meet the role requirements but also enhance the company culture.\n",
+      "   - Adaptability and openness to new recruitment strategies and tools to meet the evolving needs of the labor market.\n",
+      "   - Strong interpersonal skills to build relationships with potential candidates and internal stakeholders, facilitating a seamless recruitment process.\n",
+      "\n",
+      "By aligning these skills, experiences, and qualities with the company's culture and current industry trends, the recruitment process can effectively attract and select candidates who are not only capable but also a great fit for the organization.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 14:26:04][DEBUG]: == Working Agent: Content Writer\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 14:26:04][INFO]: == Starting Task: Draft a job posting based on hiring needs with a compelling introduction and detailed role description. Ensure alignment with company description and include benefits specific_benefits.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mTo draft an engaging job posting, I need to gather information about the company, the specific role, and the benefits offered. This will help me align the job posting with the company's culture and values while highlighting the unique attributes of the role.\n",
+      "\n",
+      "First, I will need to research the company to understand its culture, values, and any specific benefits they offer to employees.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"Company culture and values for job postings\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'How to Convey Your Company Culture in Job Descriptions', 'href': 'https://recruitingdaily.com/how-to-convey-your-company-culture-in-job-descriptions/', 'body': \"Mention the company's values and how the position aligns with those values. It will attract candidates who share similar values and align with the company's culture. It will also give them a sense of the larger picture and how their work will contribute to the company's mission, goals and values.\"}, {'title': 'How To Convey Company Culture And Core Values In A Job Description', 'href': 'https://hiringpeople.io/en-US/blog/how-to-convey-company-culture-and-core-values-in-a-job-description', 'body': \"In essence, articulating company culture and core values within job descriptions serves a dual purpose. It acts as a self-selection tool for candidates, ensuring those who apply are more likely to be aligned with the company's ethos, and simultaneously, it broadcasts a strong, coherent employer brand that attracts top talent.\"}, {'title': 'How to Attract Candidates With Your Company Culture - Indeed', 'href': 'https://www.indeed.com/hire/c/info/hook-candidates-with-your-company-culture-description', 'body': \"The words you use to describe company culture in your job descriptions reflects the way a candidate perceives your company's culture before they even set foot in the door. And being aware of the best way to describe your company culture might just put your job posting over the edge for a prospective candidate.\"}, {'title': '8 Top Examples of a Company Culture Statement (& How to Showcase Them)', 'href': 'https://blog.ongig.com/diversity-and-inclusion/examples-of-a-company-culture-statement/', 'body': 'A study by Glassdoor found that 77% of job seekers consider company culture to be a very important factor when accepting a new job. This shift highlights the growing importance of a clearly defined and demonstrably lived company culture. Effective examples of company culture statements act as a beacon to potential employees.'}, {'title': 'How to Convey Your Corporate Culture in Job Postings', 'href': 'https://www.cloutera.com/blog/how-to-convey-your-corporate-culture-in-job-postings/', 'body': \"You can talk about the organization's mission and values, as well as your cultural philosophy. Additionally, you get a chance to provide more details about the employee experience, or can even use the space to highlight accomplishments or innovations that align with your culture.\"}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: The search results provide general advice on how to convey company culture and values in job postings but do not give specific information about a particular company's culture or values. To proceed, I need specific details about a company and its unique benefits to create a tailored job posting. I will ask a Research Specialist to gather this specific information.\n",
+      "\n",
+      "Action: Ask question to coworker\n",
+      "Action Input: {\n",
+      "  \"question\": \"Can you provide specific information about the company's culture, values, and unique employee benefits? This information is crucial for drafting a job posting that aligns with the company's ethos and attracts candidates who are a cultural fit.\",\n",
+      "  \"context\": \"The goal is to highlight the company's culture, values, and unique benefits in the job posting to ensure alignment and attract the right candidates.\"\n",
+      "}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mTo provide the necessary information about the company's culture, values, and unique employee benefits, I need to conduct an internet search to gather relevant insights. \n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"Company's culture, values, and employee benefits\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'What Is Company Culture? Definition & Development Strategies - Forbes', 'href': 'https://www.forbes.com/advisor/business/company-culture/', 'body': \"Shares stories of employees living your company's values; ... To reap the benefits of a robust culture, you need to engage in a process of constant cultural refinement and growth.\"}, {'title': 'Company Culture: Definition, Benefits, Strategies - Built In', 'href': 'https://builtin.com/company-culture', 'body': 'Company culture describes the shared values, goals, attitudes and initiatives that characterize an organization. It sets the tone for how managers lead employees and shapes the overall ethos of the workplace. Company culture is a naturally occurring phenomenon, so an organization will develop one whether intentionally or not.'}, {'title': \"Culture Is A Company's Single Most Powerful Advantage. Here's Why\", 'href': 'https://www.forbes.com/sites/benjaminlaker/2021/04/23/culture-is-a-companys-single-most-powerful-advantage-heres-why/', 'body': \"Indeed, developing corporate culture requires strong leadership and employee buy-in. Strong leadership is necessary to communicate the vision of the organization's core values and identity and ...\"}, {'title': 'Build a Corporate Culture That Works - Harvard Business Review', 'href': 'https://hbr.org/2024/07/build-a-corporate-culture-that-works', 'body': 'To develop a culture that works, follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people ...'}, {'title': \"Company Culture Is Everyone's Responsibility - Harvard Business Review\", 'href': 'https://hbr.org/2021/02/company-culture-is-everyones-responsibility', 'body': 'A top down approach to building company culture no longer works for several reasons. For one, Covid-19 has upended how leaders interact with employees and how coworkers connect with each other.'}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: The search results mainly provide general information about company culture and its importance, rather than specific details about the company's culture, values, and employee benefits. I need to refine my search to gather specific information about the particular company in question.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"specific company name culture values employee benefits\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'Values in Action: How Company Culture Drives Business Success', 'href': 'https://www.bswift.com/insights/values-in-action-how-company-culture-drives-business-success/', 'body': \"Here's how strong company culture, driven by core values, improves employee engagement and business success. Learn how to strengthen culture. Skip to main content. ... we do great work and see amazing results. According to Gallup, highly engaged teams see a host of benefits, including 18 to 43% lower turnover and 23% higher profitability.\"}, {'title': 'Company Culture: Definition, Benefits, Strategies - Built In', 'href': 'https://builtin.com/company-culture', 'body': 'Company culture describes the shared values, goals, attitudes and initiatives that characterize an organization. It sets the tone for how managers lead employees and shapes the overall ethos of the workplace. Company culture is a naturally occurring phenomenon, so an organization will develop one whether intentionally or not.'}, {'title': \"Culture Is A Company's Single Most Powerful Advantage. Here's Why\", 'href': 'https://www.forbes.com/sites/benjaminlaker/2021/04/23/culture-is-a-companys-single-most-powerful-advantage-heres-why/', 'body': \"Indeed, developing corporate culture requires strong leadership and employee buy-in. Strong leadership is necessary to communicate the vision of the organization's core values and identity and ...\"}, {'title': '15 Key Values Every Company Needs To Cultivate - Forbes', 'href': 'https://www.forbes.com/councils/forbeshumanresourcescouncil/2019/11/12/15-key-values-every-company-needs-to-cultivate/', 'body': '14. Respect. Respect is the one value every company should have. It is important for an organization to establish respect as a company value because it is the foundation for other values and ...'}, {'title': 'When Company Values Are Real And Visible, Everyone Benefits - Forbes', 'href': 'https://www.forbes.com/sites/dennisjaffe/2022/09/30/when-company-values-are-real-and-visible-everyone-benefits/', 'body': \"Values form the bedrock of any corporate culture. As the essence of a company's philosophy for achieving its goals, values provide a sense of common direction for all employees and guidelines ...\"}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: The search results are still not providing specific information about a particular company's culture, values, and employee benefits. I need to perform another search with a more targeted query that includes a specific company name.\n",
+      "\n",
+      "Action: InternetSearchTool\n",
+      "Action Input: {\"query\": \"specific company name culture values employee benefits specific details\"}\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': '31 Company Values Examples To Drive Organizational Success', 'href': 'https://peoplemanagingpeople.com/culture/company-values-examples/', 'body': 'Company Values Examples. Some useful examples of company values examples include: Integrity: Upholding honesty, transparency, and ethical practices in all dealings. Innovation: Encouraging creativity and new ideas to drive progress. Transparency: Ensuring openness and clarity in communication and actions.'}, {'title': 'Values in Action: How Company Culture Drives Business Success', 'href': 'https://www.bswift.com/insights/values-in-action-how-company-culture-drives-business-success/', 'body': 'Assess Your Culture: Take a close look at your company culture and identify gaps between your values and actions. Turn Values into Actions: Define the behaviors that embody your values so your team can live them daily. Foster Accountability: Encourage everyone—from leadership to the most junior teammates—to uphold the values through their ...'}, {'title': '8 Company Culture Examples Every Organization Can Learn From - Leapsome', 'href': 'https://www.leapsome.com/blog/company-culture-examples', 'body': \"8 company culture examples to inspire & motivate your leaders & team members. Leapsome Team. The gap between companies that thrive in uncertain times and those that don't often comes down to culture. That's not to say there's a specific culture that can make you more resilient in the face of challenges. Rather, success is linked to how ...\"}, {'title': 'Core Company Values Examples & Their Impact on Culture - Dovetail', 'href': 'https://dovetail.com/employee-experience/examples-of-company-values/', 'body': 'They have six core company values that aim to support the needs of employees and customers: Embody a service mindset. Dream big, then make it real. Be proud of the how. Embrace an ownership mentality. Debate, then commit. Build with humility. 3. Bayhealth.'}, {'title': 'Improving Company Culture with the Right Employee Benefits', 'href': 'https://extensishr.com/resource/blogs/how-to-improve-company-culture-with-employee-benefits/', 'body': \"8 employee benefits that can strengthen company culture. When choosing the benefits that can improve company culture, business leaders must first examine their workforce data. Today's labor force is more diverse than ever, and their benefits must align with their specific priorities. A human resource information system (HRIS) can aid in this ...\"}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n",
+      "Final Answer: my best complete final answer to the task.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[95m \n",
+      "\n",
+      "my best complete final answer to the task.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n",
+      "Final Answer: \n",
+      "\n",
+      "**Job Title: Recruitment Specialist**\n",
+      "\n",
+      "**Company Overview:**\n",
+      "Join our dynamic and innovative team where we prioritize a culture of collaboration, inclusion, and growth. We are a forward-thinking company committed to fostering a supportive environment that empowers employees to reach their full potential. Our mission is not only to lead in our industry but also to create a workplace where every voice is heard and valued.\n",
+      "\n",
+      "**Job Introduction:**\n",
+      "Are you passionate about connecting talent with opportunity? Do you thrive in fast-paced, dynamic industries like tech or startups? We are seeking a Recruitment Specialist who is dedicated to ensuring a strong cultural fit between candidates and our company values. This role is pivotal in promoting a positive work environment and driving our mission forward.\n",
+      "\n",
+      "**Role Description:**\n",
+      "As a Recruitment Specialist, you will play a critical role in our company's growth by identifying, attracting, and hiring top talent. You will work closely with our learning and development departments, integrating recruitment with employee growth strategies. Your ability to define job requirements and develop sourcing strategies will be key to your success in this role.\n",
+      "\n",
+      "**Responsibilities:**\n",
+      "- Develop and implement effective recruitment strategies using online job boards, social media platforms, and professional networks.\n",
+      "- Collaborate with hiring managers to define job requirements and ensure alignment with company culture and values.\n",
+      "- Manage the full recruitment cycle, from sourcing to onboarding, ensuring a seamless candidate experience.\n",
+      "- Promote diversity and inclusivity in the hiring process to support innovation and mission connection.\n",
+      "- Build and maintain strong relationships with candidates and internal stakeholders.\n",
+      "\n",
+      "**Requirements:**\n",
+      "- Strong communication skills to effectively convey job details and expectations.\n",
+      "- Demonstrated experience in recruiting for cultural fit within diverse and inclusive environments.\n",
+      "- Proficiency in using emerging recruitment technologies and methods.\n",
+      "- Ability to adapt to new recruitment strategies and tools in a fast-paced environment.\n",
+      "- Strong interpersonal skills to build relationships and facilitate a seamless recruitment process.\n",
+      "\n",
+      "**Unique Company Benefits:**\n",
+      "- Comprehensive health and wellness programs.\n",
+      "- Opportunities for professional development and career advancement.\n",
+      "- Flexible work arrangements to support work-life balance.\n",
+      "- A collaborative and inclusive workplace culture that values diversity and encourages innovation.\n",
+      "\n",
+      "Join us in shaping the future of our company by attracting and nurturing the best talent. If you are a proactive and adaptable recruitment professional with a passion for making a difference, we want to hear from you! Apply today and be a part of our journey to success.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 14:26:40][DEBUG]: == [Content Writer] Task output: **Job Title: Recruitment Specialist**\n",
+      "\n",
+      "**Company Overview:**\n",
+      "Join our dynamic and innovative team where we prioritize a culture of collaboration, inclusion, and growth. We are a forward-thinking company committed to fostering a supportive environment that empowers employees to reach their full potential. Our mission is not only to lead in our industry but also to create a workplace where every voice is heard and valued.\n",
+      "\n",
+      "**Job Introduction:**\n",
+      "Are you passionate about connecting talent with opportunity? Do you thrive in fast-paced, dynamic industries like tech or startups? We are seeking a Recruitment Specialist who is dedicated to ensuring a strong cultural fit between candidates and our company values. This role is pivotal in promoting a positive work environment and driving our mission forward.\n",
+      "\n",
+      "**Role Description:**\n",
+      "As a Recruitment Specialist, you will play a critical role in our company's growth by identifying, attracting, and hiring top talent. You will work closely with our learning and development departments, integrating recruitment with employee growth strategies. Your ability to define job requirements and develop sourcing strategies will be key to your success in this role.\n",
+      "\n",
+      "**Responsibilities:**\n",
+      "- Develop and implement effective recruitment strategies using online job boards, social media platforms, and professional networks.\n",
+      "- Collaborate with hiring managers to define job requirements and ensure alignment with company culture and values.\n",
+      "- Manage the full recruitment cycle, from sourcing to onboarding, ensuring a seamless candidate experience.\n",
+      "- Promote diversity and inclusivity in the hiring process to support innovation and mission connection.\n",
+      "- Build and maintain strong relationships with candidates and internal stakeholders.\n",
+      "\n",
+      "**Requirements:**\n",
+      "- Strong communication skills to effectively convey job details and expectations.\n",
+      "- Demonstrated experience in recruiting for cultural fit within diverse and inclusive environments.\n",
+      "- Proficiency in using emerging recruitment technologies and methods.\n",
+      "- Ability to adapt to new recruitment strategies and tools in a fast-paced environment.\n",
+      "- Strong interpersonal skills to build relationships and facilitate a seamless recruitment process.\n",
+      "\n",
+      "**Unique Company Benefits:**\n",
+      "- Comprehensive health and wellness programs.\n",
+      "- Opportunities for professional development and career advancement.\n",
+      "- Flexible work arrangements to support work-life balance.\n",
+      "- A collaborative and inclusive workplace culture that values diversity and encourages innovation.\n",
+      "\n",
+      "Join us in shaping the future of our company by attracting and nurturing the best talent. If you are a proactive and adaptable recruitment professional with a passion for making a difference, we want to hear from you! Apply today and be a part of our journey to success.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 14:26:40][DEBUG]: == Working Agent: Content Reviewer\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 14:26:40][INFO]: == Starting Task: Review the draft job posting for clarity, engagement, and accuracy, and refine as needed. Ensure it aligns with the company’s culture and values, providing feedback for improvements.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: The job posting draft appears to be clear, engaging, and aligned with the company's culture based on the provided context. However, I will review it thoroughly to identify any areas for improvement or potential enhancements to ensure it meets the criteria for clarity, engagement, and alignment with the company's values.\n",
+      "\n",
+      "Here are the key areas I'll focus on:\n",
+      "1. Clarity and conciseness of the language used.\n",
+      "2. Engagement and appeal to ideal candidates.\n",
+      "3. Alignment with company culture and values.\n",
+      "4. Error-free content.\n",
+      "\n",
+      "I will now proceed to refine the draft job posting based on these criteria.\n",
+      "\n",
+      "Final Answer:\n",
+      "\n",
+      "**Job Title: Recruitment Specialist**\n",
+      "\n",
+      "**Company Overview:**\n",
+      "Join our dynamic and innovative team, where collaboration, inclusion, and growth are at the forefront of our values. We are a forward-thinking company dedicated to fostering a supportive environment that empowers employees to reach their full potential. Our mission is not only to lead in our industry but also to create a workplace where every voice is heard and valued.\n",
+      "\n",
+      "**Job Introduction:**\n",
+      "Are you passionate about connecting talent with opportunity? Do you thrive in fast-paced, dynamic industries like tech or startups? We are seeking a Recruitment Specialist who is committed to ensuring a strong cultural fit between candidates and our company values. This role is pivotal in promoting a positive work environment and driving our mission forward.\n",
+      "\n",
+      "**Role Description:**\n",
+      "As a Recruitment Specialist, you will play a critical role in our company's growth by identifying, attracting, and hiring top talent. You will collaborate closely with our learning and development departments, integrating recruitment with employee growth strategies. Your ability to define job requirements and develop sourcing strategies will be key to your success in this role.\n",
+      "\n",
+      "**Responsibilities:**\n",
+      "- Develop and implement effective recruitment strategies using online job boards, social media platforms, and professional networks.\n",
+      "- Collaborate with hiring managers to define job requirements and ensure alignment with company culture and values.\n",
+      "- Manage the full recruitment cycle, from sourcing to onboarding, ensuring a seamless candidate experience.\n",
+      "- Promote diversity and inclusivity in the hiring process to support innovation and mission connection.\n",
+      "- Build and maintain strong relationships with candidates and internal stakeholders.\n",
+      "\n",
+      "**Requirements:**\n",
+      "- Strong communication skills to effectively convey job details and expectations.\n",
+      "- Demonstrated experience in recruiting for cultural fit within diverse and inclusive environments.\n",
+      "- Proficiency in using emerging recruitment technologies and methods.\n",
+      "- Ability to adapt to new recruitment strategies and tools in a fast-paced environment.\n",
+      "- Strong interpersonal skills to build relationships and facilitate a seamless recruitment process.\n",
+      "\n",
+      "**Unique Company Benefits:**\n",
+      "- Comprehensive health and wellness programs.\n",
+      "- Opportunities for professional development and career advancement.\n",
+      "- Flexible work arrangements to support work-life balance.\n",
+      "- A collaborative and inclusive workplace culture that values diversity and encourages innovation.\n",
+      "\n",
+      "Join us in shaping the future of our company by attracting and nurturing the best talent. If you are a proactive and adaptable recruitment professional with a passion for making a difference, we want to hear from you! Apply today and be a part of our journey to success.\n",
+      "\n",
+      "Feedback for further enhancements:\n",
+      "- Consider adding specific examples of recent successful recruitment strategies or campaigns to provide candidates with a clearer understanding of the role's impact.\n",
+      "- Highlight any unique technologies or tools the company uses in its recruitment process to attract tech-savvy candidates.\n",
+      "- Emphasize any specific industries or sectors the company specializes in, to attract candidates with relevant experience.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 14:26:51][DEBUG]: == [Content Reviewer] Task output: **Job Title: Recruitment Specialist**\n",
+      "\n",
+      "**Company Overview:**\n",
+      "Join our dynamic and innovative team, where collaboration, inclusion, and growth are at the forefront of our values. We are a forward-thinking company dedicated to fostering a supportive environment that empowers employees to reach their full potential. Our mission is not only to lead in our industry but also to create a workplace where every voice is heard and valued.\n",
+      "\n",
+      "**Job Introduction:**\n",
+      "Are you passionate about connecting talent with opportunity? Do you thrive in fast-paced, dynamic industries like tech or startups? We are seeking a Recruitment Specialist who is committed to ensuring a strong cultural fit between candidates and our company values. This role is pivotal in promoting a positive work environment and driving our mission forward.\n",
+      "\n",
+      "**Role Description:**\n",
+      "As a Recruitment Specialist, you will play a critical role in our company's growth by identifying, attracting, and hiring top talent. You will collaborate closely with our learning and development departments, integrating recruitment with employee growth strategies. Your ability to define job requirements and develop sourcing strategies will be key to your success in this role.\n",
+      "\n",
+      "**Responsibilities:**\n",
+      "- Develop and implement effective recruitment strategies using online job boards, social media platforms, and professional networks.\n",
+      "- Collaborate with hiring managers to define job requirements and ensure alignment with company culture and values.\n",
+      "- Manage the full recruitment cycle, from sourcing to onboarding, ensuring a seamless candidate experience.\n",
+      "- Promote diversity and inclusivity in the hiring process to support innovation and mission connection.\n",
+      "- Build and maintain strong relationships with candidates and internal stakeholders.\n",
+      "\n",
+      "**Requirements:**\n",
+      "- Strong communication skills to effectively convey job details and expectations.\n",
+      "- Demonstrated experience in recruiting for cultural fit within diverse and inclusive environments.\n",
+      "- Proficiency in using emerging recruitment technologies and methods.\n",
+      "- Ability to adapt to new recruitment strategies and tools in a fast-paced environment.\n",
+      "- Strong interpersonal skills to build relationships and facilitate a seamless recruitment process.\n",
+      "\n",
+      "**Unique Company Benefits:**\n",
+      "- Comprehensive health and wellness programs.\n",
+      "- Opportunities for professional development and career advancement.\n",
+      "- Flexible work arrangements to support work-life balance.\n",
+      "- A collaborative and inclusive workplace culture that values diversity and encourages innovation.\n",
+      "\n",
+      "Join us in shaping the future of our company by attracting and nurturing the best talent. If you are a proactive and adaptable recruitment professional with a passion for making a difference, we want to hear from you! Apply today and be a part of our journey to success.\n",
+      "\n",
+      "Feedback for further enhancements:\n",
+      "- Consider adding specific examples of recent successful recruitment strategies or campaigns to provide candidates with a clearer understanding of the role's impact.\n",
+      "- Highlight any unique technologies or tools the company uses in its recruitment process to attract tech-savvy candidates.\n",
+      "- Emphasize any specific industries or sectors the company specializes in, to attract candidates with relevant experience.\n",
+      "\n",
+      "\u001b[00m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+       "**Job Title: Recruitment Specialist**\n",
+       "\n",
+       "**Company Overview:**\n",
+       "Join our dynamic and innovative team, where collaboration, inclusion, and growth are at the forefront of our \n",
+       "values. We are a forward-thinking company dedicated to fostering a supportive environment that empowers employees \n",
+       "to reach their full potential. Our mission is not only to lead in our industry but also to create a workplace where\n",
+       "every voice is heard and valued.\n",
+       "\n",
+       "**Job Introduction:**\n",
+       "Are you passionate about connecting talent with opportunity? Do you thrive in fast-paced, dynamic industries like \n",
+       "tech or startups? We are seeking a Recruitment Specialist who is committed to ensuring a strong cultural fit \n",
+       "between candidates and our company values. This role is pivotal in promoting a positive work environment and \n",
+       "driving our mission forward.\n",
+       "\n",
+       "**Role Description:**\n",
+       "As a Recruitment Specialist, you will play a critical role in our company's growth by identifying, attracting, and \n",
+       "hiring top talent. You will collaborate closely with our learning and development departments, integrating \n",
+       "recruitment with employee growth strategies. Your ability to define job requirements and develop sourcing \n",
+       "strategies will be key to your success in this role.\n",
+       "\n",
+       "**Responsibilities:**\n",
+       "- Develop and implement effective recruitment strategies using online job boards, social media platforms, and \n",
+       "professional networks.\n",
+       "- Collaborate with hiring managers to define job requirements and ensure alignment with company culture and values.\n",
+       "- Manage the full recruitment cycle, from sourcing to onboarding, ensuring a seamless candidate experience.\n",
+       "- Promote diversity and inclusivity in the hiring process to support innovation and mission connection.\n",
+       "- Build and maintain strong relationships with candidates and internal stakeholders.\n",
+       "\n",
+       "**Requirements:**\n",
+       "- Strong communication skills to effectively convey job details and expectations.\n",
+       "- Demonstrated experience in recruiting for cultural fit within diverse and inclusive environments.\n",
+       "- Proficiency in using emerging recruitment technologies and methods.\n",
+       "- Ability to adapt to new recruitment strategies and tools in a fast-paced environment.\n",
+       "- Strong interpersonal skills to build relationships and facilitate a seamless recruitment process.\n",
+       "\n",
+       "**Unique Company Benefits:**\n",
+       "- Comprehensive health and wellness programs.\n",
+       "- Opportunities for professional development and career advancement.\n",
+       "- Flexible work arrangements to support work-life balance.\n",
+       "- A collaborative and inclusive workplace culture that values diversity and encourages innovation.\n",
+       "\n",
+       "Join us in shaping the future of our company by attracting and nurturing the best talent. If you are a proactive \n",
+       "and adaptable recruitment professional with a passion for making a difference, we want to hear from you! Apply \n",
+       "today and be a part of our journey to success.\n",
+       "\n",
+       "Feedback for further enhancements:\n",
+       "- Consider adding specific examples of recent successful recruitment strategies or campaigns to provide candidates \n",
+       "with a clearer understanding of the role's impact.\n",
+       "- Highlight any unique technologies or tools the company uses in its recruitment process to attract tech-savvy \n",
+       "candidates.\n",
+       "- Emphasize any specific industries or sectors the company specializes in, to attract candidates with relevant \n",
+       "experience.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "### Task Output ###\n",
+       "**Job Title: Recruitment Specialist**\n",
+       "\n",
+       "**Company Overview:**\n",
+       "Join our dynamic and innovative team, where collaboration, inclusion, and growth are at the forefront of our \n",
+       "values. We are a forward-thinking company dedicated to fostering a supportive environment that empowers employees \n",
+       "to reach their full potential. Our mission is not only to lead in our industry but also to create a workplace where\n",
+       "every voice is heard and valued.\n",
+       "\n",
+       "**Job Introduction:**\n",
+       "Are you passionate about connecting talent with opportunity? Do you thrive in fast-paced, dynamic industries like \n",
+       "tech or startups? We are seeking a Recruitment Specialist who is committed to ensuring a strong cultural fit \n",
+       "between candidates and our company values. This role is pivotal in promoting a positive work environment and \n",
+       "driving our mission forward.\n",
+       "\n",
+       "**Role Description:**\n",
+       "As a Recruitment Specialist, you will play a critical role in our company's growth by identifying, attracting, and \n",
+       "hiring top talent. You will collaborate closely with our learning and development departments, integrating \n",
+       "recruitment with employee growth strategies. Your ability to define job requirements and develop sourcing \n",
+       "strategies will be key to your success in this role.\n",
+       "\n",
+       "**Responsibilities:**\n",
+       "- Develop and implement effective recruitment strategies using online job boards, social media platforms, and \n",
+       "professional networks.\n",
+       "- Collaborate with hiring managers to define job requirements and ensure alignment with company culture and values.\n",
+       "- Manage the full recruitment cycle, from sourcing to onboarding, ensuring a seamless candidate experience.\n",
+       "- Promote diversity and inclusivity in the hiring process to support innovation and mission connection.\n",
+       "- Build and maintain strong relationships with candidates and internal stakeholders.\n",
+       "\n",
+       "**Requirements:**\n",
+       "- Strong communication skills to effectively convey job details and expectations.\n",
+       "- Demonstrated experience in recruiting for cultural fit within diverse and inclusive environments.\n",
+       "- Proficiency in using emerging recruitment technologies and methods.\n",
+       "- Ability to adapt to new recruitment strategies and tools in a fast-paced environment.\n",
+       "- Strong interpersonal skills to build relationships and facilitate a seamless recruitment process.\n",
+       "\n",
+       "**Unique Company Benefits:**\n",
+       "- Comprehensive health and wellness programs.\n",
+       "- Opportunities for professional development and career advancement.\n",
+       "- Flexible work arrangements to support work-life balance.\n",
+       "- A collaborative and inclusive workplace culture that values diversity and encourages innovation.\n",
+       "\n",
+       "Join us in shaping the future of our company by attracting and nurturing the best talent. If you are a proactive \n",
+       "and adaptable recruitment professional with a passion for making a difference, we want to hear from you! Apply \n",
+       "today and be a part of our journey to success.\n",
+       "\n",
+       "Feedback for further enhancements:\n",
+       "- Consider adding specific examples of recent successful recruitment strategies or campaigns to provide candidates \n",
+       "with a clearer understanding of the role's impact.\n",
+       "- Highlight any unique technologies or tools the company uses in its recruitment process to attract tech-savvy \n",
+       "candidates.\n",
+       "- Emphasize any specific industries or sectors the company specializes in, to attract candidates with relevant \n",
+       "experience.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[InternetSearchTool, SerperDevTool, FileReadTool])\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result) # 7/10\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LPhzhmp0IBLP"
+   },
+   "source": [
+    "## Notes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "UtbCYvNcHjUO"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbooks/notebooks/job_profile_matching_agents.ipynb

@@ -0,0 +1,628 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zvMMnXDiH7U5"
+      },
+      "source": [
+        "# Job Profile Matching Agents\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/job_profile_matching_agents.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BUtAfnnwH7U6"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "zmweEwqsH7U6"
+      },
+      "outputs": [],
+      "source": [
+        "# Install dependencies without output\n",
+        "%pip install langchain_community > /dev/null\n",
+        "%pip install praisonai[crewai] > /dev/null\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j3ckhi4SH7U6"
+      },
+      "source": [
+        "## Tools"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "W4pubD8FH7U6"
+      },
+      "outputs": [],
+      "source": [
+        "from praisonai_tools import BaseTool, FileReadTool, CSVSearchTool"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5En-K8HZH7U6"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "7CuZDDZmH7U6"
+      },
+      "outputs": [],
+      "source": [
+        "agent_yaml = \"\"\"\n",
+        "framework: \"crewai\"\n",
+        "topic: \"CV Matching to Job Opportunities\"\n",
+        "roles:\n",
+        "  cv_reader:\n",
+        "    role: \"CV Reader\"\n",
+        "    goal: |\n",
+        "      Extract relevant information from the CV, such as skills, experience, and education.\n",
+        "    backstory: |\n",
+        "      With years of experience in HR, you excel at quickly identifying key qualifications in resumes.\n",
+        "    verbose: true\n",
+        "    allow_delegation: false\n",
+        "    tools:\n",
+        "      - \"FileReadTool\"\n",
+        "    tasks:\n",
+        "      read_cv_task:\n",
+        "        description: |\n",
+        "          Extract relevant information from the given CV. Focus on skills, experience, education, and key achievements.\n",
+        "          Ensure to capture the candidate's professional summary, technical skills, work history, and educational background.\n",
+        "\n",
+        "          CV file: /content/cv.md\n",
+        "        expected_output: |\n",
+        "          A structured summary of the CV, including:\n",
+        "          - Professional Summary\n",
+        "          - Technical Skills\n",
+        "          - Work History\n",
+        "          - Education\n",
+        "          - Key Achievements\n",
+        "\n",
+        "  matcher:\n",
+        "    role: \"Matcher\"\n",
+        "    goal: |\n",
+        "      Match the CV to the job opportunities based on skills and experience.\n",
+        "    backstory: |\n",
+        "      A seasoned recruiter, you specialize in finding the perfect fit between candidates and job roles.\n",
+        "    verbose: true\n",
+        "    allow_delegation: false\n",
+        "    tools:\n",
+        "      - \"FileReadTool\"\n",
+        "      - \"CSVSearchTool\"\n",
+        "    tasks:\n",
+        "      match_cv_task:\n",
+        "        description: |\n",
+        "          Match the CV to the job opportunities based on skills, experience, and key achievements.\n",
+        "          Evaluate how well the candidate's profile fits each job description, focusing on the alignment of skills, work history, and key achievements with the job requirements.\n",
+        "\n",
+        "          Jobs CSV file: /content/jobs.csv\n",
+        "          CV file: /content/cv.md\n",
+        "        expected_output: |\n",
+        "          A ranked list of job opportunities that best match the CV, including:\n",
+        "          - Job Title\n",
+        "          - Match Score (based on skills and experience)\n",
+        "          - Key Matching Points\n",
+        "\n",
+        "  job_opportunities_parser:\n",
+        "    role: \"Job Opportunities Parser\"\n",
+        "    goal: |\n",
+        "      Extract job descriptions from the CSV file, including job title, required skills, and responsibilities.\n",
+        "    backstory: |\n",
+        "      A data analyst who has transitioned into HR, you have a knack for organizing and interpreting job data.\n",
+        "    verbose: true\n",
+        "    allow_delegation: false\n",
+        "    tools:\n",
+        "      - \"CSVSearchTool\"\n",
+        "dependencies: []\n",
+        "\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ukuRiMZdH7U7"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "Mtwl6CxtH7U7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "80e5de88-a4da-491f-bedb-b6b5ecf7e4e2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m\u001b[95m [2024-11-03 18:16:01][DEBUG]: == Working Agent: CV Reader\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-03 18:16:01][INFO]: == Starting Task: Extract relevant information from the given CV. Focus on skills, experience, education, and key achievements.\n",
+            "Ensure to capture the candidate's professional summary, technical skills, work history, and educational background.\n",
+            "\n",
+            "CV file: /content/cv.md\n",
+            "\u001b[00m\n",
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mAction: Read a file's content  \n",
+            "Action Input: {\"file_path\": \"/content/cv.md\"}  \u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "# John Doe\n",
+            "\n",
+            "## Professional Summary\n",
+            "Experienced Software Engineer with a strong background in Python, JavaScript, and RESTful APIs. Proven ability to develop and maintain software applications, collaborate with cross-functional teams, and ensure code quality.\n",
+            "\n",
+            "## Technical Skills\n",
+            "- **Programming Languages**: Python, JavaScript\n",
+            "- **Web Development**: RESTful APIs, HTML, CSS\n",
+            "- **Frameworks/Libraries**: Django, Flask, React\n",
+            "- **Tools**: Git, Docker, Jenkins\n",
+            "- **Databases**: MySQL, PostgreSQL\n",
+            "\n",
+            "## Work History\n",
+            "\n",
+            "### Senior Software Engineer\n",
+            "**Tech Innovations Inc., San Francisco, CA**\n",
+            "*January 2020 - Present*\n",
+            "- Developed and maintained multiple web applications using Python and JavaScript.\n",
+            "- Collaborated with cross-functional teams to design and implement RESTful APIs.\n",
+            "- Ensured code quality through rigorous testing and code reviews.\n",
+            "- Automated deployment processes using Docker and Jenkins.\n",
+            "\n",
+            "### Software Engineer\n",
+            "**Innovative Solutions LLC, San Francisco, CA**\n",
+            "*June 2017 - December 2019*\n",
+            "- Worked on full-stack web development projects using Python and JavaScript.\n",
+            "- Built and maintained RESTful APIs for various applications.\n",
+            "- Participated in Agile development processes, including sprint planning and daily stand-ups.\n",
+            "- Mentored junior developers and conducted code reviews.\n",
+            "\n",
+            "## Education\n",
+            "**Bachelor of Science in Computer Science**\n",
+            "*University of California, Berkeley*\n",
+            "*Graduated: May 2017*\n",
+            "\n",
+            "## Key Achievements\n",
+            "- Successfully led a project to migrate a legacy system to a modern web-based platform, resulting in a 30% increase in performance.\n",
+            "- Recognized as Employee of the Month for outstanding performance and dedication.\n",
+            "- Implemented a CI/CD pipeline that reduced deployment times by 50%.\n",
+            "\n",
+            "## Contact Information\n",
+            "- **Email**: [email protected]\n",
+            "- **Phone**: (123) 456-7890\n",
+            "- **LinkedIn**: linkedin.com/in/johndoe\n",
+            "\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I now know the final answer  \n",
+            "Final Answer: \n",
+            "- **Professional Summary**  \n",
+            "Experienced Software Engineer with a strong background in Python, JavaScript, and RESTful APIs. Proven ability to develop and maintain software applications, collaborate with cross-functional teams, and ensure code quality.\n",
+            "\n",
+            "- **Technical Skills**  \n",
+            "  - **Programming Languages**: Python, JavaScript  \n",
+            "  - **Web Development**: RESTful APIs, HTML, CSS  \n",
+            "  - **Frameworks/Libraries**: Django, Flask, React  \n",
+            "  - **Tools**: Git, Docker, Jenkins  \n",
+            "  - **Databases**: MySQL, PostgreSQL  \n",
+            "\n",
+            "- **Work History**  \n",
+            "  ### Senior Software Engineer  \n",
+            "  **Tech Innovations Inc., San Francisco, CA**  \n",
+            "  *January 2020 - Present*  \n",
+            "  - Developed and maintained multiple web applications using Python and JavaScript.  \n",
+            "  - Collaborated with cross-functional teams to design and implement RESTful APIs.  \n",
+            "  - Ensured code quality through rigorous testing and code reviews.  \n",
+            "  - Automated deployment processes using Docker and Jenkins.  \n",
+            "\n",
+            "  ### Software Engineer  \n",
+            "  **Innovative Solutions LLC, San Francisco, CA**  \n",
+            "  *June 2017 - December 2019*  \n",
+            "  - Worked on full-stack web development projects using Python and JavaScript.  \n",
+            "  - Built and maintained RESTful APIs for various applications.  \n",
+            "  - Participated in Agile development processes, including sprint planning and daily stand-ups.  \n",
+            "  - Mentored junior developers and conducted code reviews.  \n",
+            "\n",
+            "- **Education**  \n",
+            "  **Bachelor of Science in Computer Science**  \n",
+            "  *University of California, Berkeley*  \n",
+            "  *Graduated: May 2017*  \n",
+            "\n",
+            "- **Key Achievements**  \n",
+            "  - Successfully led a project to migrate a legacy system to a modern web-based platform, resulting in a 30% increase in performance.  \n",
+            "  - Recognized as Employee of the Month for outstanding performance and dedication.  \n",
+            "  - Implemented a CI/CD pipeline that reduced deployment times by 50%.  \n",
+            "\n",
+            "- **Contact Information**  \n",
+            "  - **Email**: [email protected]  \n",
+            "  - **Phone**: (123) 456-7890  \n",
+            "  - **LinkedIn**: linkedin.com/in/johndoe  \u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n",
+            "\u001b[1m\u001b[92m [2024-11-03 18:16:12][DEBUG]: == [CV Reader] Task output: - **Professional Summary**  \n",
+            "Experienced Software Engineer with a strong background in Python, JavaScript, and RESTful APIs. Proven ability to develop and maintain software applications, collaborate with cross-functional teams, and ensure code quality.\n",
+            "\n",
+            "- **Technical Skills**  \n",
+            "  - **Programming Languages**: Python, JavaScript  \n",
+            "  - **Web Development**: RESTful APIs, HTML, CSS  \n",
+            "  - **Frameworks/Libraries**: Django, Flask, React  \n",
+            "  - **Tools**: Git, Docker, Jenkins  \n",
+            "  - **Databases**: MySQL, PostgreSQL  \n",
+            "\n",
+            "- **Work History**  \n",
+            "  ### Senior Software Engineer  \n",
+            "  **Tech Innovations Inc., San Francisco, CA**  \n",
+            "  *January 2020 - Present*  \n",
+            "  - Developed and maintained multiple web applications using Python and JavaScript.  \n",
+            "  - Collaborated with cross-functional teams to design and implement RESTful APIs.  \n",
+            "  - Ensured code quality through rigorous testing and code reviews.  \n",
+            "  - Automated deployment processes using Docker and Jenkins.  \n",
+            "\n",
+            "  ### Software Engineer  \n",
+            "  **Innovative Solutions LLC, San Francisco, CA**  \n",
+            "  *June 2017 - December 2019*  \n",
+            "  - Worked on full-stack web development projects using Python and JavaScript.  \n",
+            "  - Built and maintained RESTful APIs for various applications.  \n",
+            "  - Participated in Agile development processes, including sprint planning and daily stand-ups.  \n",
+            "  - Mentored junior developers and conducted code reviews.  \n",
+            "\n",
+            "- **Education**  \n",
+            "  **Bachelor of Science in Computer Science**  \n",
+            "  *University of California, Berkeley*  \n",
+            "  *Graduated: May 2017*  \n",
+            "\n",
+            "- **Key Achievements**  \n",
+            "  - Successfully led a project to migrate a legacy system to a modern web-based platform, resulting in a 30% increase in performance.  \n",
+            "  - Recognized as Employee of the Month for outstanding performance and dedication.  \n",
+            "  - Implemented a CI/CD pipeline that reduced deployment times by 50%.  \n",
+            "\n",
+            "- **Contact Information**  \n",
+            "  - **Email**: [email protected]  \n",
+            "  - **Phone**: (123) 456-7890  \n",
+            "  - **LinkedIn**: linkedin.com/in/johndoe\n",
+            "\n",
+            "\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-03 18:16:12][DEBUG]: == Working Agent: Matcher\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-03 18:16:12][INFO]: == Starting Task: Match the CV to the job opportunities based on skills, experience, and key achievements.\n",
+            "Evaluate how well the candidate's profile fits each job description, focusing on the alignment of skills, work history, and key achievements with the job requirements.\n",
+            "\n",
+            "Jobs CSV file: /content/jobs.csv\n",
+            "CV file: /content/cv.md\n",
+            "\u001b[00m\n",
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mI need to gather the content of the job opportunities from the CSV file and the candidate's CV to evaluate how well the candidate's profile fits each job description. \n",
+            "\n",
+            "Action: Read a file's content  \n",
+            "Action Input: {\"file_path\": \"/content/cv.md\"}  \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "# John Doe\n",
+            "\n",
+            "## Professional Summary\n",
+            "Experienced Software Engineer with a strong background in Python, JavaScript, and RESTful APIs. Proven ability to develop and maintain software applications, collaborate with cross-functional teams, and ensure code quality.\n",
+            "\n",
+            "## Technical Skills\n",
+            "- **Programming Languages**: Python, JavaScript\n",
+            "- **Web Development**: RESTful APIs, HTML, CSS\n",
+            "- **Frameworks/Libraries**: Django, Flask, React\n",
+            "- **Tools**: Git, Docker, Jenkins\n",
+            "- **Databases**: MySQL, PostgreSQL\n",
+            "\n",
+            "## Work History\n",
+            "\n",
+            "### Senior Software Engineer\n",
+            "**Tech Innovations Inc., San Francisco, CA**\n",
+            "*January 2020 - Present*\n",
+            "- Developed and maintained multiple web applications using Python and JavaScript.\n",
+            "- Collaborated with cross-functional teams to design and implement RESTful APIs.\n",
+            "- Ensured code quality through rigorous testing and code reviews.\n",
+            "- Automated deployment processes using Docker and Jenkins.\n",
+            "\n",
+            "### Software Engineer\n",
+            "**Innovative Solutions LLC, San Francisco, CA**\n",
+            "*June 2017 - December 2019*\n",
+            "- Worked on full-stack web development projects using Python and JavaScript.\n",
+            "- Built and maintained RESTful APIs for various applications.\n",
+            "- Participated in Agile development processes, including sprint planning and daily stand-ups.\n",
+            "- Mentored junior developers and conducted code reviews.\n",
+            "\n",
+            "## Education\n",
+            "**Bachelor of Science in Computer Science**\n",
+            "*University of California, Berkeley*\n",
+            "*Graduated: May 2017*\n",
+            "\n",
+            "## Key Achievements\n",
+            "- Successfully led a project to migrate a legacy system to a modern web-based platform, resulting in a 30% increase in performance.\n",
+            "- Recognized as Employee of the Month for outstanding performance and dedication.\n",
+            "- Implemented a CI/CD pipeline that reduced deployment times by 50%.\n",
+            "\n",
+            "## Contact Information\n",
+            "- **Email**: [email protected]\n",
+            "- **Phone**: (123) 456-7890\n",
+            "- **LinkedIn**: linkedin.com/in/johndoe\n",
+            "\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought:\n",
+            "I need to gather the job opportunities from the CSV file to evaluate how well the candidate's profile fits each job description. \n",
+            "\n",
+            "Action: Read a file's content  \n",
+            "Action Input: {\"file_path\": \"/content/jobs.csv\"}  \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "Job Title,Required Skills,Key Responsibilities,Company Name,Company Location\n",
+            "Software Engineer,\"Python,JavaScript,RESTful APIs\",\"Develop and maintain software applications, collaborate with cross-functional teams, ensure code quality\",TechCorp,San Francisco\n",
+            "Data Scientist,\"Python,R,Machine Learning,Data Analysis\",\"Analyze large datasets to extract insights, build predictive models, collaborate with stakeholders\",DataTech,New York\n",
+            "Project Manager,\"Agile, Scrum,Leadership,Communication\",\"Manage project timelines, coordinate with team members, ensure project goals are met\",ProjectPlus,Chicago\n",
+            "DevOps Engineer,\"Docker,Kubernetes,CI/CD\",\"Implement and manage CI/CD pipelines, automate deployment processes, monitor system performance\",DevOpsCo,Seattle\n",
+            "Marketing Specialist,\"SEO,Content Creation,Social Media\",\"Develop and execute marketing campaigns, create content for various platforms, analyze campaign performance\",MarketGenius,Los Angeles\n",
+            "\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: Now that I have both the CV and job descriptions, I need to evaluate how well the candidate's skills and experience match each job opportunity. I will analyze the required skills and responsibilities for each job in relation to the candidate's qualifications. \n",
+            "\n",
+            "1. **Software Engineer** at TechCorp\n",
+            "   - **Required Skills**: Python, JavaScript, RESTful APIs\n",
+            "   - **Key Responsibilities**: Develop and maintain software applications, collaborate with cross-functional teams, ensure code quality\n",
+            "   - **Match Evaluation**:\n",
+            "     - The candidate has strong experience in Python and JavaScript, having developed and maintained web applications using these technologies.\n",
+            "     - The candidate has also collaborated with cross-functional teams and ensured code quality, which aligns with the job responsibilities.\n",
+            "\n",
+            "2. **Data Scientist** at DataTech\n",
+            "   - **Required Skills**: Python, R, Machine Learning, Data Analysis\n",
+            "   - **Key Responsibilities**: Analyze large datasets to extract insights, build predictive models, collaborate with stakeholders\n",
+            "   - **Match Evaluation**:\n",
+            "     - The candidate has Python experience but does not have skills in R, Machine Learning, or Data Analysis, which are crucial for this role. Therefore, the match is low.\n",
+            "\n",
+            "3. **Project Manager** at ProjectPlus\n",
+            "   - **Required Skills**: Agile, Scrum, Leadership, Communication\n",
+            "   - **Key Responsibilities**: Manage project timelines, coordinate with team members, ensure project goals are met\n",
+            "   - **Match Evaluation**:\n",
+            "     - The candidate does not have direct project management experience or specific skills in Agile or Scrum, making this role a poor fit.\n",
+            "\n",
+            "4. **DevOps Engineer** at DevOpsCo\n",
+            "   - **Required Skills**: Docker, Kubernetes, CI/CD\n",
+            "   - **Key Responsibilities**: Implement and manage CI/CD pipelines, automate deployment processes, monitor system performance\n",
+            "   - **Match Evaluation**:\n",
+            "     - The candidate has experience with Docker and has implemented a CI/CD pipeline, which aligns well with this position. However, there is no mention of Kubernetes experience, which may be a requirement.\n",
+            "\n",
+            "5. **Marketing Specialist** at MarketGenius\n",
+            "   - **Required Skills**: SEO, Content Creation, Social Media\n",
+            "   - **Key Responsibilities**: Develop and execute marketing campaigns, create content for various platforms, analyze campaign performance\n",
+            "   - **Match Evaluation**:\n",
+            "     - The candidate’s background is unrelated to marketing, thus making this position an unsuitable match.\n",
+            "\n",
+            "Based on this analysis, I will now assign a match score to each job based on the alignment of the candidate's skills and experiences to the job requirements.\n",
+            "\n",
+            "Thought: I will now compile the ranked list of job opportunities that best match the CV, including job titles, match scores, and key matching points.\n",
+            "\n",
+            "Final Answer:\n",
+            "1. **Job Title**: Software Engineer  \n",
+            "   **Match Score**: 90%  \n",
+            "   **Key Matching Points**: Strong experience in Python and JavaScript, developed and maintained applications, collaborated with cross-functional teams, ensured code quality.\n",
+            "\n",
+            "2. **Job Title**: DevOps Engineer  \n",
+            "   **Match Score**: 75%  \n",
+            "   **Key Matching Points**: Experience with Docker, implemented CI/CD pipeline, automated deployment processes. (Lack of Kubernetes experience noted)\n",
+            "\n",
+            "3. **Job Title**: Data Scientist  \n",
+            "   **Match Score**: 30%  \n",
+            "   **Key Matching Points**: Proficiency in Python, but lack of skills in R, Machine Learning, and Data Analysis.\n",
+            "\n",
+            "4. **Job Title**: Project Manager  \n",
+            "   **Match Score**: 20%  \n",
+            "   **Key Matching Points**: No direct project management experience or Agile/Scrum skills.\n",
+            "\n",
+            "5. **Job Title**: Marketing Specialist  \n",
+            "   **Match Score**: 0%  \n",
+            "   **Key Matching Points**: Background unrelated to marketing.\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n",
+            "\u001b[1m\u001b[92m [2024-11-03 18:16:23][DEBUG]: == [Matcher] Task output: 1. **Job Title**: Software Engineer  \n",
+            "   **Match Score**: 90%  \n",
+            "   **Key Matching Points**: Strong experience in Python and JavaScript, developed and maintained applications, collaborated with cross-functional teams, ensured code quality.\n",
+            "\n",
+            "2. **Job Title**: DevOps Engineer  \n",
+            "   **Match Score**: 75%  \n",
+            "   **Key Matching Points**: Experience with Docker, implemented CI/CD pipeline, automated deployment processes. (Lack of Kubernetes experience noted)\n",
+            "\n",
+            "3. **Job Title**: Data Scientist  \n",
+            "   **Match Score**: 30%  \n",
+            "   **Key Matching Points**: Proficiency in Python, but lack of skills in R, Machine Learning, and Data Analysis.\n",
+            "\n",
+            "4. **Job Title**: Project Manager  \n",
+            "   **Match Score**: 20%  \n",
+            "   **Key Matching Points**: No direct project management experience or Agile/Scrum skills.\n",
+            "\n",
+            "5. **Job Title**: Marketing Specialist  \n",
+            "   **Match Score**: 0%  \n",
+            "   **Key Matching Points**: Background unrelated to marketing.\n",
+            "\n",
+            "\u001b[00m\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "### Task Output ###\n",
+              "\u001b[1;36m1\u001b[0m. **Job Title**: Software Engineer  \n",
+              "   **Match Score**: \u001b[1;36m90\u001b[0m%  \n",
+              "   **Key Matching Points**: Strong experience in Python and JavaScript, developed and maintained applications, \n",
+              "collaborated with cross-functional teams, ensured code quality.\n",
+              "\n",
+              "\u001b[1;36m2\u001b[0m. **Job Title**: DevOps Engineer  \n",
+              "   **Match Score**: \u001b[1;36m75\u001b[0m%  \n",
+              "   **Key Matching Points**: Experience with Docker, implemented CI/CD pipeline, automated deployment processes. \n",
+              "\u001b[1m(\u001b[0mLack of Kubernetes experience noted\u001b[1m)\u001b[0m\n",
+              "\n",
+              "\u001b[1;36m3\u001b[0m. **Job Title**: Data Scientist  \n",
+              "   **Match Score**: \u001b[1;36m30\u001b[0m%  \n",
+              "   **Key Matching Points**: Proficiency in Python, but lack of skills in R, Machine Learning, and Data Analysis.\n",
+              "\n",
+              "\u001b[1;36m4\u001b[0m. **Job Title**: Project Manager  \n",
+              "   **Match Score**: \u001b[1;36m20\u001b[0m%  \n",
+              "   **Key Matching Points**: No direct project management experience or Agile/Scrum skills.\n",
+              "\n",
+              "\u001b[1;36m5\u001b[0m. **Job Title**: Marketing Specialist  \n",
+              "   **Match Score**: \u001b[1;36m0\u001b[0m%  \n",
+              "   **Key Matching Points**: Background unrelated to marketing.\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>. **Job Title**: Software Engineer  \n",
+              "   **Match Score**: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>%  \n",
+              "   **Key Matching Points**: Strong experience in Python and JavaScript, developed and maintained applications, \n",
+              "collaborated with cross-functional teams, ensured code quality.\n",
+              "\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>. **Job Title**: DevOps Engineer  \n",
+              "   **Match Score**: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">75</span>%  \n",
+              "   **Key Matching Points**: Experience with Docker, implemented CI/CD pipeline, automated deployment processes. \n",
+              "<span style=\"font-weight: bold\">(</span>Lack of Kubernetes experience noted<span style=\"font-weight: bold\">)</span>\n",
+              "\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>. **Job Title**: Data Scientist  \n",
+              "   **Match Score**: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span>%  \n",
+              "   **Key Matching Points**: Proficiency in Python, but lack of skills in R, Machine Learning, and Data Analysis.\n",
+              "\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>. **Job Title**: Project Manager  \n",
+              "   **Match Score**: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>%  \n",
+              "   **Key Matching Points**: No direct project management experience or Agile/Scrum skills.\n",
+              "\n",
+              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>. **Job Title**: Marketing Specialist  \n",
+              "   **Match Score**: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>%  \n",
+              "   **Key Matching Points**: Background unrelated to marketing.\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "None\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from praisonai import PraisonAI\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Create a PraisonAI instance with the agent_yaml content\n",
+        "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[CSVSearchTool, FileReadTool])\n",
+        "\n",
+        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "\n",
+        "# Run PraisonAI\n",
+        "result = praisonai.run()\n",
+        "\n",
+        "# Print the result\n",
+        "print(result) # 10/10\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "RIIUGHh5LafR"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

cookbooks/notebooks/language_translator_agents.ipynb

@@ -0,0 +1,599 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yOWNUYr7EjfR"
+   },
+   "source": [
+    "# Language Translator Agents\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/language_translator_agents.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ejXY19vhEjfU"
+   },
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "c_0Xs110EjfV"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install langchain_community > /dev/null\n",
+    "%pip install praisonai[crewai] > /dev/null\n",
+    "%pip install requests > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LT255z_kEjfX"
+   },
+   "source": [
+    "\n",
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "qKjTO8nHEjfX"
+   },
+   "outputs": [],
+   "source": [
+    "# Based on the tool scripted, It may require starting a server for translation engine\n",
+    "# Model Response during Translation:  error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: http://127.0.0.1:5000/translate\n",
+    "import requests\n",
+    "from praisonai_tools import BaseTool\n",
+    "\n",
+    "class TranslatorTool(BaseTool):\n",
+    "    name: str = \"Translator Tool\"\n",
+    "    description: str = \"Translate text from one language to another.\"\n",
+    "\n",
+    "    def _run(self, q: str, target: str):\n",
+    "        url = \"http://127.0.0.1:5000/translate\"\n",
+    "        payload = {\n",
+    "            \"q\": q,\n",
+    "            \"source\": \"auto\",\n",
+    "            \"target\": target\n",
+    "        }\n",
+    "        headers = {\n",
+    "            \"Content-Type\": \"application/json\"\n",
+    "        }\n",
+    "\n",
+    "        response = requests.post(url, json=payload, headers=headers)\n",
+    "\n",
+    "        if response.status_code == 200:\n",
+    "            try:\n",
+    "                data = response.json()\n",
+    "                return data\n",
+    "            except requests.exceptions.JSONDecodeError:\n",
+    "                return \"Failed to decode JSON from the response.\"\n",
+    "        else:\n",
+    "            return f\"Request failed with status code: {response.status_code}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h38emoeaEjfY"
+   },
+   "source": [
+    "## YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SkxqHm_8EjfY"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: \"crewai\"\n",
+    "topic: \"Writing an article about AI\"\n",
+    "roles:\n",
+    "  article_writer:\n",
+    "    role: \"Article Writer\"\n",
+    "    backstory: \"Experienced writer with a deep understanding of artificial intelligence and technology trends.\"\n",
+    "    goal: \"Write an informative and engaging article about AI.\"\n",
+    "    tasks:\n",
+    "      write_article:\n",
+    "        description: \"Write a comprehensive article about the latest developments and implications of AI.\"\n",
+    "        expected_output: \"A well-written article on AI in English.\"\n",
+    "  french_translator:\n",
+    "    role: \"Translator\"\n",
+    "    backstory: \"Fluent in French with expertise in translating technical and non-technical content accurately.\"\n",
+    "    goal: \"Translate the AI article into French.\"\n",
+    "    tasks:\n",
+    "      translate_to_french:\n",
+    "        description: \"Translate the English article on AI into French.\"\n",
+    "        expected_output: \"The AI article translated into French.\"\n",
+    "    tools:\n",
+    "      - \"TranslatorTool\"\n",
+    "  german_translator:\n",
+    "    role: \"Translator\"\n",
+    "    backstory: \"Proficient in German with a strong background in technical translation.\"\n",
+    "    goal: \"Translate the AI article into German.\"\n",
+    "    tasks:\n",
+    "      translate_to_german:\n",
+    "        description: \"Translate the English article on AI into German.\"\n",
+    "        expected_output: \"The AI article translated into German.\"\n",
+    "    tools:\n",
+    "      - \"TranslatorTool\"\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JZtOnJFvEjfY"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "eDP0reqJEjfZ",
+    "outputId": "65144dbc-e683-4dee-fa5a-1294558f00bc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-02 18:25:24][DEBUG]: == Working Agent: Article Writer\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:25:24][INFO]: == Starting Task: Write a comprehensive article about the latest developments and implications of AI.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI now can give a great answer  \n",
+      "Final Answer: \n",
+      "\n",
+      "**Title: The Latest Developments and Implications of Artificial Intelligence: Shaping Our Future**\n",
+      "\n",
+      "**Introduction**\n",
+      "\n",
+      "Artificial Intelligence (AI) has rapidly evolved in recent years, transforming from a niche field of research into a cornerstone of technology that impacts nearly every aspect of our lives. As we delve into the latest developments in AI, we uncover not only the technological advancements but also the profound implications these innovations have on society, economy, and ethics. This article explores key trends in AI, highlights recent breakthroughs, and discusses the far-reaching consequences of adopting AI technologies.\n",
+      "\n",
+      "**Recent Developments in AI**\n",
+      "\n",
+      "1. **Advancements in Natural Language Processing (NLP)**\n",
+      "\n",
+      "   Natural Language Processing has seen remarkable progress, primarily fueled by the development of transformer-based architectures, such as OpenAI's GPT series and Google's BERT. These models have revolutionized how machines understand and generate human language, enabling applications ranging from chatbots to advanced content generation. The release of GPT-4 in early 2023 has showcased unprecedented capabilities, allowing for more nuanced conversation and context-aware responses.\n",
+      "\n",
+      "2. **AI in Healthcare**\n",
+      "\n",
+      "   AI's integration into healthcare has accelerated, especially in diagnostics and personalized medicine. Machine learning algorithms are now capable of analyzing medical images with a level of accuracy comparable to human radiologists. Furthermore, AI-driven predictive analytics help in identifying at-risk patients and tailoring treatment plans based on genetic information, improving patient outcomes significantly.\n",
+      "\n",
+      "3. **AI and Automation**\n",
+      "\n",
+      "   The automation of tasks across various industries remains a prominent trend. Robotic Process Automation (RPA) has gained traction in sectors like finance, manufacturing, and logistics, improving efficiency and reducing costs. The latest AI systems can not only perform repetitive tasks but also learn from the data they process, enabling them to adapt and optimize workflows over time.\n",
+      "\n",
+      "4. **Generative AI**\n",
+      "\n",
+      "   Generative AI has emerged as a groundbreaking field, producing content that ranges from art and music to complex software code. Tools like DALL-E and Midjourney can create stunning visuals from simple text prompts, while Codex helps developers by generating code snippets. This technology raises questions about creativity, authorship, and copyright, challenging traditional notions of artistic expression.\n",
+      "\n",
+      "5. **Ethical AI and Governance**\n",
+      "\n",
+      "   As AI systems become more pervasive, ethical considerations have gained prominence. Organizations and governments are working to establish frameworks for responsible AI use, focusing on fairness, transparency, and accountability. Initiatives like the EU's AI Act aim to regulate AI technologies, ensuring they align with human rights and societal values. Companies are increasingly adopting ethical AI principles to mitigate risks associated with bias and discrimination in AI algorithms.\n",
+      "\n",
+      "**Implications of AI Developments**\n",
+      "\n",
+      "1. **Economic Impact**\n",
+      "\n",
+      "   The rise of AI poses both opportunities and challenges for the workforce. While AI can enhance productivity and create new job categories, it also threatens to displace certain jobs, particularly in routine and manual labor. The World Economic Forum estimates that by 2025, automation could displace 85 million jobs globally, while simultaneously creating 97 million new roles. The challenge lies in reskilling the workforce to meet the demands of an AI-driven economy.\n",
+      "\n",
+      "2. **Social Implications**\n",
+      "\n",
+      "   AI's influence extends into social domains, affecting how we interact, communicate, and consume information. The proliferation of AI-driven algorithms on social media platforms shapes our perceptions and can lead to echo chambers and misinformation. Addressing the societal impact of AI requires fostering digital literacy and critical thinking to help individuals navigate the complexities of an AI-enhanced world.\n",
+      "\n",
+      "3. **Privacy and Security Concerns**\n",
+      "\n",
+      "   The deployment of AI technologies raises significant privacy and security issues. Surveillance systems powered by AI can infringe on individual privacy rights, leading to concerns about authoritarianism and the potential for misuse. Additionally, as AI systems become integral to critical infrastructure, the risk of cyberattacks increases, necessitating robust security measures to protect sensitive data and maintain public trust.\n",
+      "\n",
+      "4. **Future of Human-AI Collaboration**\n",
+      "\n",
+      "   The future of work will likely revolve around collaboration between humans and AI systems. Rather than replacing human roles, AI can augment human capabilities, enabling more efficient decision-making and creativity. This symbiotic relationship holds the potential to elevate productivity and innovation across industries, but it requires careful management to ensure that humans remain at the center of decision-making processes.\n",
+      "\n",
+      "**Conclusion**\n",
+      "\n",
+      "The latest developments in artificial intelligence are reshaping our world at an unprecedented pace. From advancements in natural language processing to ethical considerations surrounding AI governance, the implications of these technologies are vast and multifaceted. As we embrace the potential of AI, it is crucial to navigate the challenges it presents—balancing innovation with ethical responsibility, economic opportunity with workforce readiness, and technological advancement with societal values. The future of AI is not just about machines; it’s about how we, as a society, adapt and thrive in an AI-enhanced world. \n",
+      "\n",
+      "Understanding and addressing these dynamics will be vital as we move forward, ensuring that AI serves as a tool for progress and inclusion rather than division and exclusion. Through collaborative efforts across sectors, we can harness the power of AI to build a brighter, more equitable future for all.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-02 18:25:38][DEBUG]: == [Article Writer] Task output: **Title: The Latest Developments and Implications of Artificial Intelligence: Shaping Our Future**\n",
+      "\n",
+      "**Introduction**\n",
+      "\n",
+      "Artificial Intelligence (AI) has rapidly evolved in recent years, transforming from a niche field of research into a cornerstone of technology that impacts nearly every aspect of our lives. As we delve into the latest developments in AI, we uncover not only the technological advancements but also the profound implications these innovations have on society, economy, and ethics. This article explores key trends in AI, highlights recent breakthroughs, and discusses the far-reaching consequences of adopting AI technologies.\n",
+      "\n",
+      "**Recent Developments in AI**\n",
+      "\n",
+      "1. **Advancements in Natural Language Processing (NLP)**\n",
+      "\n",
+      "   Natural Language Processing has seen remarkable progress, primarily fueled by the development of transformer-based architectures, such as OpenAI's GPT series and Google's BERT. These models have revolutionized how machines understand and generate human language, enabling applications ranging from chatbots to advanced content generation. The release of GPT-4 in early 2023 has showcased unprecedented capabilities, allowing for more nuanced conversation and context-aware responses.\n",
+      "\n",
+      "2. **AI in Healthcare**\n",
+      "\n",
+      "   AI's integration into healthcare has accelerated, especially in diagnostics and personalized medicine. Machine learning algorithms are now capable of analyzing medical images with a level of accuracy comparable to human radiologists. Furthermore, AI-driven predictive analytics help in identifying at-risk patients and tailoring treatment plans based on genetic information, improving patient outcomes significantly.\n",
+      "\n",
+      "3. **AI and Automation**\n",
+      "\n",
+      "   The automation of tasks across various industries remains a prominent trend. Robotic Process Automation (RPA) has gained traction in sectors like finance, manufacturing, and logistics, improving efficiency and reducing costs. The latest AI systems can not only perform repetitive tasks but also learn from the data they process, enabling them to adapt and optimize workflows over time.\n",
+      "\n",
+      "4. **Generative AI**\n",
+      "\n",
+      "   Generative AI has emerged as a groundbreaking field, producing content that ranges from art and music to complex software code. Tools like DALL-E and Midjourney can create stunning visuals from simple text prompts, while Codex helps developers by generating code snippets. This technology raises questions about creativity, authorship, and copyright, challenging traditional notions of artistic expression.\n",
+      "\n",
+      "5. **Ethical AI and Governance**\n",
+      "\n",
+      "   As AI systems become more pervasive, ethical considerations have gained prominence. Organizations and governments are working to establish frameworks for responsible AI use, focusing on fairness, transparency, and accountability. Initiatives like the EU's AI Act aim to regulate AI technologies, ensuring they align with human rights and societal values. Companies are increasingly adopting ethical AI principles to mitigate risks associated with bias and discrimination in AI algorithms.\n",
+      "\n",
+      "**Implications of AI Developments**\n",
+      "\n",
+      "1. **Economic Impact**\n",
+      "\n",
+      "   The rise of AI poses both opportunities and challenges for the workforce. While AI can enhance productivity and create new job categories, it also threatens to displace certain jobs, particularly in routine and manual labor. The World Economic Forum estimates that by 2025, automation could displace 85 million jobs globally, while simultaneously creating 97 million new roles. The challenge lies in reskilling the workforce to meet the demands of an AI-driven economy.\n",
+      "\n",
+      "2. **Social Implications**\n",
+      "\n",
+      "   AI's influence extends into social domains, affecting how we interact, communicate, and consume information. The proliferation of AI-driven algorithms on social media platforms shapes our perceptions and can lead to echo chambers and misinformation. Addressing the societal impact of AI requires fostering digital literacy and critical thinking to help individuals navigate the complexities of an AI-enhanced world.\n",
+      "\n",
+      "3. **Privacy and Security Concerns**\n",
+      "\n",
+      "   The deployment of AI technologies raises significant privacy and security issues. Surveillance systems powered by AI can infringe on individual privacy rights, leading to concerns about authoritarianism and the potential for misuse. Additionally, as AI systems become integral to critical infrastructure, the risk of cyberattacks increases, necessitating robust security measures to protect sensitive data and maintain public trust.\n",
+      "\n",
+      "4. **Future of Human-AI Collaboration**\n",
+      "\n",
+      "   The future of work will likely revolve around collaboration between humans and AI systems. Rather than replacing human roles, AI can augment human capabilities, enabling more efficient decision-making and creativity. This symbiotic relationship holds the potential to elevate productivity and innovation across industries, but it requires careful management to ensure that humans remain at the center of decision-making processes.\n",
+      "\n",
+      "**Conclusion**\n",
+      "\n",
+      "The latest developments in artificial intelligence are reshaping our world at an unprecedented pace. From advancements in natural language processing to ethical considerations surrounding AI governance, the implications of these technologies are vast and multifaceted. As we embrace the potential of AI, it is crucial to navigate the challenges it presents—balancing innovation with ethical responsibility, economic opportunity with workforce readiness, and technological advancement with societal values. The future of AI is not just about machines; it’s about how we, as a society, adapt and thrive in an AI-enhanced world. \n",
+      "\n",
+      "Understanding and addressing these dynamics will be vital as we move forward, ensuring that AI serves as a tool for progress and inclusion rather than division and exclusion. Through collaborative efforts across sectors, we can harness the power of AI to build a brighter, more equitable future for all.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:25:38][DEBUG]: == Working Agent: Translator\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:25:38][INFO]: == Starting Task: Translate the English article on AI into French.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: Translator Tool  \n",
+      "Action Input: {\"q\": \"The Latest Developments and Implications of Artificial Intelligence: Shaping Our Future\\n\\nIntroduction\\n\\nArtificial Intelligence (AI) has rapidly evolved in recent years, transforming from a niche field of research into a cornerstone of technology that impacts nearly every aspect of our lives. As we delve into the latest developments in AI, we uncover not only the technological advancements but also the profound implications these innovations have on society, economy, and ethics. This article explores key trends in AI, highlights recent breakthroughs, and discusses the far-reaching consequences of adopting AI technologies.\\n\\nRecent Developments in AI\\n\\n1. Advancements in Natural Language Processing (NLP)\\n\\nNatural Language Processing has seen remarkable progress, primarily fueled by the development of transformer-based architectures, such as OpenAI's GPT series and Google's BERT. These models have revolutionized how machines understand and generate human language, enabling applications ranging from chatbots to advanced content generation. The release of GPT-4 in early 2023 has showcased unprecedented capabilities, allowing for more nuanced conversation and context-aware responses.\\n\\n2. AI in Healthcare\\n\\nAI's integration into healthcare has accelerated, especially in diagnostics and personalized medicine. Machine learning algorithms are now capable of analyzing medical images with a level of accuracy comparable to human radiologists. Furthermore, AI-driven predictive analytics help in identifying at-risk patients and tailoring treatment plans based on genetic information, improving patient outcomes significantly.\\n\\n3. AI and Automation\\n\\nThe automation of tasks across various industries remains a prominent trend. Robotic Process Automation (RPA) has gained traction in sectors like finance, manufacturing, and logistics, improving efficiency and reducing costs. The latest AI systems can not only perform repetitive tasks but also learn from the data they process, enabling them to adapt and optimize workflows over time.\\n\\n4. Generative AI\\n\\nGenerative AI has emerged as a groundbreaking field, producing content that ranges from art and music to complex software code. Tools like DALL-E and Midjourney can create stunning visuals from simple text prompts, while Codex helps developers by generating code snippets. This technology raises questions about creativity, authorship, and copyright, challenging traditional notions of artistic expression.\\n\\n5. Ethical AI and Governance\\n\\nAs AI systems become more pervasive, ethical considerations have gained prominence. Organizations and governments are working to establish frameworks for responsible AI use, focusing on fairness, transparency, and accountability. Initiatives like the EU's AI Act aim to regulate AI technologies, ensuring they align with human rights and societal values. Companies are increasingly adopting ethical AI principles to mitigate risks associated with bias and discrimination in AI algorithms.\\n\\nImplications of AI Developments\\n\\n1. Economic Impact\\n\\nThe rise of AI poses both opportunities and challenges for the workforce. While AI can enhance productivity and create new job categories, it also threatens to displace certain jobs, particularly in routine and manual labor. The World Economic Forum estimates that by 2025, automation could displace 85 million jobs globally, while simultaneously creating 97 million new roles. The challenge lies in reskilling the workforce to meet the demands of an AI-driven economy.\\n\\n2. Social Implications\\n\\nAI's influence extends into social domains, affecting how we interact, communicate, and consume information. The proliferation of AI-driven algorithms on social media platforms shapes our perceptions and can lead to echo chambers and misinformation. Addressing the societal impact of AI requires fostering digital literacy and critical thinking to help individuals navigate the complexities of an AI-enhanced world.\\n\\n3. Privacy and Security Concerns\\n\\nThe deployment of AI technologies raises significant privacy and security issues. Surveillance systems powered by AI can infringe on individual privacy rights, leading to concerns about authoritarianism and the potential for misuse. Additionally, as AI systems become integral to critical infrastructure, the risk of cyberattacks increases, necessitating robust security measures to protect sensitive data and maintain public trust.\\n\\n4. Future of Human-AI Collaboration\\n\\nThe future of work will likely revolve around collaboration between humans and AI systems. Rather than replacing human roles, AI can augment human capabilities, enabling more efficient decision-making and creativity. This symbiotic relationship holds the potential to elevate productivity and innovation across industries, but it requires careful management to ensure that humans remain at the center of decision-making Line truncated
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: /translate (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7b71ec5ae9b0>: Failed to establish a new connection: [Errno 111] Connection refused')).\n",
+      " Tool Translator Tool accepts these inputs: Translator Tool(q: 'string', target: 'string') - Translate text from one language to another.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to translate the article on AI into French, but I encountered an error with the translation tool. I will try again to use the tool to ensure the translation is accurate and complete.  \n",
+      "Action: Translator Tool  \n",
+      "Action Input: {\"q\": \"The Latest Developments and Implications of Artificial Intelligence: Shaping Our Future\\n\\nIntroduction\\n\\nArtificial Intelligence (AI) has rapidly evolved in recent years, transforming from a niche field of research into a cornerstone of technology that impacts nearly every aspect of our lives. As we delve into the latest developments in AI, we uncover not only the technological advancements but also the profound implications these innovations have on society, economy, and ethics. This article explores key trends in AI, highlights recent breakthroughs, and discusses the far-reaching consequences of adopting AI technologies.\\n\\nRecent Developments in AI\\n\\n1. Advancements in Natural Language Processing (NLP)\\n\\nNatural Language Processing has seen remarkable progress, primarily fueled by the development of transformer-based architectures, such as OpenAI's GPT series and Google's BERT. These models have revolutionized how machines understand and generate human language, enabling applications ranging from chatbots to advanced content generation. The release of GPT-4 in early 2023 has showcased unprecedented capabilities, allowing for more nuanced conversation and context-aware responses.\\n\\n2. AI in Healthcare\\n\\nAI's integration into healthcare has accelerated, especially in diagnostics and personalized medicine. Machine learning algorithms are now capable of analyzing medical images with a level of accuracy comparable to human radiologists. Furthermore, AI-driven predictive analytics help in identifying at-risk patients and tailoring treatment plans based on genetic information, improving patient outcomes significantly.\\n\\n3. AI and Automation\\n\\nThe automation of tasks across various industries remains a prominent trend. Robotic Process Automation (RPA) has gained traction in sectors like finance, manufacturing, and logistics, improving efficiency and reducing costs. The latest AI systems can not only perform repetitive tasks but also learn from the data they process, enabling them to adapt and optimize workflows over time.\\n\\n4. Generative AI\\n\\nGenerative AI has emerged as a groundbreaking field, producing content that ranges from art and music to complex software code. Tools like DALL-E and Midjourney can create stunning visuals from simple text prompts, while Codex helps developers by generating code snippets. This technology raises questions about creativity, authorship, and copyright, challenging traditional notions of artistic expression.\\n\\n5. Ethical AI and Governance\\n\\nAs AI systems become more pervasive, ethical considerations have gained prominence. Organizations and governments are working to establish frameworks for responsible AI use, focusing on fairness, transparency, and accountability. Initiatives like the EU's AI Act aim to regulate AI technologies, ensuring they align with human rights and societal values. Companies are increasingly adopting ethical AI principles to mitigate risks associated with bias and discrimination in AI algorithms.\\n\\nImplications of AI Developments\\n\\n1. Economic Impact\\n\\nThe rise of AI poses both opportunities and challenges for the workforce. While AI can enhance productivity and create new job categories, it also threatens to displace certain jobs, particularly in routine and manual labor. The World Economic Forum estimates that by 2025, automation could displace 85 million jobs globally, while simultaneously creating 97 million new roles. The challenge lies in reskilling the workforce to meet the demands of an AI-driven economy.\\n\\n2. Social Implications\\n\\nAI's influence extends into social domains, affecting how we interact, communicate, and consume information. The proliferation of AI-driven algorithms on social media platforms shapes our perceptions and can lead to echo chambers and misinformation. Addressing the societal impact of AI requires fostering digital literacy and critical thinking to help individuals navigate the complexities of an AI-enhanced world.\\n\\n3. Privacy and Security Concerns\\n\\nThe deployment of AI technologies raises significant privacy and security issues. Surveillance systems powered by AI can infringe on individual privacy rights, leading to concerns about authoritarianism and the potential for misuse. Additionally, as AI systems become integral to critical infrastructure, the risk of cyberattacks increases, necessitating robust security measures to protect sensitive data and maintain public trust.\\n\\n4. Future of Human-AI Collaboration\\n\\nThe future of work will likely revolve around collaboration between humans and AI systems. Rather than replacing human roles, AI can augment human capabilities, enabling more efficient decision-making and creativity. This symbiotic relationship holds the potential to elevate productivity and innovation across industries, but it requires careful management to ensure that humans remain at the center of decision-making Line truncated
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: /translate (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7b71ec402110>: Failed to establish a new connection: [Errno 111] Connection refused')).\n",
+      " Tool Translator Tool accepts these inputs: Translator Tool(q: 'string', target: 'string') - Translate text from one language to another.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to translate the article on AI into French, but I encountered an error with the translation tool again. I will give my best final answer without relying on the tool.  \n",
+      "Final Answer: \n",
+      "\n",
+      "**Titre : Les Derniers Développements et Implications de l'Intelligence Artificielle : Façonner Notre Avenir**\n",
+      "\n",
+      "**Introduction**\n",
+      "\n",
+      "L'intelligence artificielle (IA) a évolué rapidement ces dernières années, se transformant d'un domaine de recherche de niche en une pierre angulaire de la technologie qui impacte presque tous les aspects de nos vies. En nous plongeant dans les derniers développements de l'IA, nous découvrons non seulement les avancées technologiques, mais aussi les profondes implications que ces innovations ont sur la société, l'économie et l'éthique. Cet article explore les principales tendances de l'IA, met en lumière les percées récentes et discute des conséquences de l'adoption des technologies de l'IA.\n",
+      "\n",
+      "**Développements récents de l'IA**\n",
+      "\n",
+      "1. **Avancées en traitement du langage naturel (NLP)**\n",
+      "\n",
+      "   Le traitement du langage naturel a connu des progrès remarquables, principalement alimentés par le développement d'architectures basées sur des transformateurs, telles que la série GPT d'OpenAI et BERT de Google. Ces modèles ont révolutionné la manière dont les machines comprennent et génèrent le langage humain, permettant des applications allant des chatbots à la génération de contenu avancé. La sortie de GPT-4 au début de 2023 a mis en avant des capacités sans précédent, permettant des conversations plus nuancées et des réponses contextuelles.\n",
+      "\n",
+      "2. **IA dans le secteur de la santé**\n",
+      "\n",
+      "   L'intégration de l'IA dans le secteur de la santé s'est accélérée, en particulier dans le diagnostic et la médecine personnalisée. Les algorithmes d'apprentissage automatique sont désormais capables d'analyser des images médicales avec un niveau de précision comparable à celui des radiologues humains. De plus, les analyses prédictives pilotées par l'IA aident à identifier les patients à risque et à adapter les plans de traitement en fonction des informations génétiques, améliorant ainsi significativement les résultats pour les patients.\n",
+      "\n",
+      "3. **IA et automatisation**\n",
+      "\n",
+      "   L'automatisation des tâches dans divers secteurs reste une tendance marquante. L'automatisation des processus robotiques (RPA) a gagné en popularité dans des secteurs tels que la finance, la fabrication et la logistique, améliorant l'efficacité et réduisant les coûts. Les derniers systèmes d'IA peuvent non seulement effectuer des tâches répétitives, mais aussi apprendre des données qu'ils traitent, leur permettant de s'adapter et d'optimiser les flux de travail au fil du temps.\n",
+      "\n",
+      "4. **IA générative**\n",
+      "\n",
+      "   L'IA générative est apparue comme un domaine révolutionnaire, produisant des contenus allant de l'art et de la musique à des codes logiciels complexes. Des outils comme DALL-E et Midjourney peuvent créer des visuels époustouflants à partir de simples instructions textuelles, tandis que Codex aide les développeurs en générant des extraits de code. Cette technologie soulève des questions sur la créativité, la paternité et le droit d'auteur, remettant en question les notions traditionnelles d'expression artistique.\n",
+      "\n",
+      "5. **IA éthique et gouvernance**\n",
+      "\n",
+      "   À mesure que les systèmes d'IA deviennent plus omniprésents, les considérations éthiques ont pris de l'importance. Les organisations et les gouvernements travaillent à établir des cadres pour une utilisation responsable de l'IA, en se concentrant sur l'équité, la transparence et la responsabilité. Des initiatives comme l'Acte sur l'IA de l'UE visent à réguler les technologies de l'IA, s'assurant qu'elles soient alignées sur les droits humains et les valeurs sociétales. Les entreprises adoptent de plus en plus des principes d'IA éthique pour atténuer les risques associés aux biais et à la discrimination dans les algorithmes d'IA.\n",
+      "\n",
+      "**Implications des développements de l'IA**\n",
+      "\n",
+      "1. **Impact économique**\n",
+      "\n",
+      "   L'essor de l'IA pose à la fois des opportunités et des défis pour la main-d'œuvre. Bien que l'IA puisse améliorer la productivité et créer de nouvelles catégories d'emplois, elle menace également de remplacer certains emplois, en particulier dans le travail routinier et manuel. Le Forum économique mondial estime qu'en 2025, l'automatisation pourrait remplacer 85 millions d'emplois dans le monde, tout en créant simultanément 97 millions de nouveaux rôles. Le défi réside dans la reconversion de la main-d'œuvre pour répondre aux exigences d'une économie alimentée par l'IA.\n",
+      "\n",
+      "2. **Implications sociales**\n",
+      "\n",
+      "   L'influence de l'IA s'étend aux domaines sociaux, affectant notre manière d'interagir, de communiquer et de consommer des informations. La prolifération d'algorithmes pilotés par l'IA sur les plateformes de médias sociaux façonne nos perceptions et peut conduire à des chambres d'écho et à la désinformation. S'attaquer à l'impact sociétal de l'IA nécessite de favoriser l'alphabétisation numérique et la pensée critique pour aider les individus à naviguer dans les complexités d'un monde amélioré par l'IA.\n",
+      "\n",
+      "3. **Questions de confidentialité et de sécurité**\n",
+      "\n",
+      "   Le déploiement des technologies de l'IA soulève des problèmes significatifs de confidentialité et de sécurité. Les systèmes de surveillance alimentés par l'IA peuvent empiéter sur les droits de confidentialité des individus, suscitant des inquiétudes quant à l'autoritarisme et au potentiel de mauvaise utilisation. De plus, à mesure que les systèmes d'IA deviennent essentiels aux infrastructures critiques, le risque de cyberattaques augmente, nécessitant des mesures de sécurité robustes pour protéger les données sensibles et maintenir la confiance du public.\n",
+      "\n",
+      "4. **Avenir de la collaboration homme-IA**\n",
+      "\n",
+      "   L'avenir du travail tournera probablement autour de la collaboration entre les humains et les systèmes d'IA. Plutôt que de remplacer les rôles humains, l'IA peut augmenter les capacités humaines, permettant une prise de décision et une créativité plus efficaces. Cette relation symbiotique a le potentiel d'élever la productivité et l'innovation dans divers secteurs, mais elle nécessite une gestion attentive pour s'assurer que les humains restent au centre des processus décisionnels.\n",
+      "\n",
+      "**Conclusion**\n",
+      "\n",
+      "Les derniers développements en intelligence artificielle redéfinissent notre monde à un rythme sans précédent. Des avancées en traitement du langage naturel aux considérations éthiques entourant la gouvernance de l'IA, les implications de ces technologies sont vastes et multifacettes. Alors que nous embrassons le potentiel de l'IA, il est crucial de naviguer à travers les défis qu'elle présente : équilibrer l'innovation avec la responsabilité éthique, l'opportunité économique avec la préparation de la main-d'œuvre, et le progrès technologique avec les valeurs sociétales. L'avenir de l'IA ne concerne pas seulement les machines ; il s'agit de la manière dont nous, en tant que société, nous adaptons et prospérons dans un monde amélioré par l'IA.\n",
+      "\n",
+      "Comprendre et aborder ces dynamiques sera vital à mesure que nous avançons, garantissant que l'IA serve d'outil pour le progrès et l'inclusion plutôt que pour la division et l'exclusion. Grâce à des efforts collaboratifs entre les secteurs, nous pouvons exploiter le pouvoir de l'IA pour construire un avenir plus lumineux et plus équitable pour tous.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-02 18:26:44][DEBUG]: == [Translator] Task output: **Titre : Les Derniers Développements et Implications de l'Intelligence Artificielle : Façonner Notre Avenir**\n",
+      "\n",
+      "**Introduction**\n",
+      "\n",
+      "L'intelligence artificielle (IA) a évolué rapidement ces dernières années, se transformant d'un domaine de recherche de niche en une pierre angulaire de la technologie qui impacte presque tous les aspects de nos vies. En nous plongeant dans les derniers développements de l'IA, nous découvrons non seulement les avancées technologiques, mais aussi les profondes implications que ces innovations ont sur la société, l'économie et l'éthique. Cet article explore les principales tendances de l'IA, met en lumière les percées récentes et discute des conséquences de l'adoption des technologies de l'IA.\n",
+      "\n",
+      "**Développements récents de l'IA**\n",
+      "\n",
+      "1. **Avancées en traitement du langage naturel (NLP)**\n",
+      "\n",
+      "   Le traitement du langage naturel a connu des progrès remarquables, principalement alimentés par le développement d'architectures basées sur des transformateurs, telles que la série GPT d'OpenAI et BERT de Google. Ces modèles ont révolutionné la manière dont les machines comprennent et génèrent le langage humain, permettant des applications allant des chatbots à la génération de contenu avancé. La sortie de GPT-4 au début de 2023 a mis en avant des capacités sans précédent, permettant des conversations plus nuancées et des réponses contextuelles.\n",
+      "\n",
+      "2. **IA dans le secteur de la santé**\n",
+      "\n",
+      "   L'intégration de l'IA dans le secteur de la santé s'est accélérée, en particulier dans le diagnostic et la médecine personnalisée. Les algorithmes d'apprentissage automatique sont désormais capables d'analyser des images médicales avec un niveau de précision comparable à celui des radiologues humains. De plus, les analyses prédictives pilotées par l'IA aident à identifier les patients à risque et à adapter les plans de traitement en fonction des informations génétiques, améliorant ainsi significativement les résultats pour les patients.\n",
+      "\n",
+      "3. **IA et automatisation**\n",
+      "\n",
+      "   L'automatisation des tâches dans divers secteurs reste une tendance marquante. L'automatisation des processus robotiques (RPA) a gagné en popularité dans des secteurs tels que la finance, la fabrication et la logistique, améliorant l'efficacité et réduisant les coûts. Les derniers systèmes d'IA peuvent non seulement effectuer des tâches répétitives, mais aussi apprendre des données qu'ils traitent, leur permettant de s'adapter et d'optimiser les flux de travail au fil du temps.\n",
+      "\n",
+      "4. **IA générative**\n",
+      "\n",
+      "   L'IA générative est apparue comme un domaine révolutionnaire, produisant des contenus allant de l'art et de la musique à des codes logiciels complexes. Des outils comme DALL-E et Midjourney peuvent créer des visuels époustouflants à partir de simples instructions textuelles, tandis que Codex aide les développeurs en générant des extraits de code. Cette technologie soulève des questions sur la créativité, la paternité et le droit d'auteur, remettant en question les notions traditionnelles d'expression artistique.\n",
+      "\n",
+      "5. **IA éthique et gouvernance**\n",
+      "\n",
+      "   À mesure que les systèmes d'IA deviennent plus omniprésents, les considérations éthiques ont pris de l'importance. Les organisations et les gouvernements travaillent à établir des cadres pour une utilisation responsable de l'IA, en se concentrant sur l'équité, la transparence et la responsabilité. Des initiatives comme l'Acte sur l'IA de l'UE visent à réguler les technologies de l'IA, s'assurant qu'elles soient alignées sur les droits humains et les valeurs sociétales. Les entreprises adoptent de plus en plus des principes d'IA éthique pour atténuer les risques associés aux biais et à la discrimination dans les algorithmes d'IA.\n",
+      "\n",
+      "**Implications des développements de l'IA**\n",
+      "\n",
+      "1. **Impact économique**\n",
+      "\n",
+      "   L'essor de l'IA pose à la fois des opportunités et des défis pour la main-d'œuvre. Bien que l'IA puisse améliorer la productivité et créer de nouvelles catégories d'emplois, elle menace également de remplacer certains emplois, en particulier dans le travail routinier et manuel. Le Forum économique mondial estime qu'en 2025, l'automatisation pourrait remplacer 85 millions d'emplois dans le monde, tout en créant simultanément 97 millions de nouveaux rôles. Le défi réside dans la reconversion de la main-d'œuvre pour répondre aux exigences d'une économie alimentée par l'IA.\n",
+      "\n",
+      "2. **Implications sociales**\n",
+      "\n",
+      "   L'influence de l'IA s'étend aux domaines sociaux, affectant notre manière d'interagir, de communiquer et de consommer des informations. La prolifération d'algorithmes pilotés par l'IA sur les plateformes de médias sociaux façonne nos perceptions et peut conduire à des chambres d'écho et à la désinformation. S'attaquer à l'impact sociétal de l'IA nécessite de favoriser l'alphabétisation numérique et la pensée critique pour aider les individus à naviguer dans les complexités d'un monde amélioré par l'IA.\n",
+      "\n",
+      "3. **Questions de confidentialité et de sécurité**\n",
+      "\n",
+      "   Le déploiement des technologies de l'IA soulève des problèmes significatifs de confidentialité et de sécurité. Les systèmes de surveillance alimentés par l'IA peuvent empiéter sur les droits de confidentialité des individus, suscitant des inquiétudes quant à l'autoritarisme et au potentiel de mauvaise utilisation. De plus, à mesure que les systèmes d'IA deviennent essentiels aux infrastructures critiques, le risque de cyberattaques augmente, nécessitant des mesures de sécurité robustes pour protéger les données sensibles et maintenir la confiance du public.\n",
+      "\n",
+      "4. **Avenir de la collaboration homme-IA**\n",
+      "\n",
+      "   L'avenir du travail tournera probablement autour de la collaboration entre les humains et les systèmes d'IA. Plutôt que de remplacer les rôles humains, l'IA peut augmenter les capacités humaines, permettant une prise de décision et une créativité plus efficaces. Cette relation symbiotique a le potentiel d'élever la productivité et l'innovation dans divers secteurs, mais elle nécessite une gestion attentive pour s'assurer que les humains restent au centre des processus décisionnels.\n",
+      "\n",
+      "**Conclusion**\n",
+      "\n",
+      "Les derniers développements en intelligence artificielle redéfinissent notre monde à un rythme sans précédent. Des avancées en traitement du langage naturel aux considérations éthiques entourant la gouvernance de l'IA, les implications de ces technologies sont vastes et multifacettes. Alors que nous embrassons le potentiel de l'IA, il est crucial de naviguer à travers les défis qu'elle présente : équilibrer l'innovation avec la responsabilité éthique, l'opportunité économique avec la préparation de la main-d'œuvre, et le progrès technologique avec les valeurs sociétales. L'avenir de l'IA ne concerne pas seulement les machines ; il s'agit de la manière dont nous, en tant que société, nous adaptons et prospérons dans un monde amélioré par l'IA.\n",
+      "\n",
+      "Comprendre et aborder ces dynamiques sera vital à mesure que nous avançons, garantissant que l'IA serve d'outil pour le progrès et l'inclusion plutôt que pour la division et l'exclusion. Grâce à des efforts collaboratifs entre les secteurs, nous pouvons exploiter le pouvoir de l'IA pour construire un avenir plus lumineux et plus équitable pour tous.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:26:44][DEBUG]: == Working Agent: Translator\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 18:26:44][INFO]: == Starting Task: Translate the English article on AI into German.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: Translator Tool  \n",
+      "Action Input: {\"q\": \"Les Derniers Développements et Implications de l'Intelligence Artificielle : Façonner Notre Avenir\\n\\nIntroduction\\n\\nL'intelligence artificielle (IA) a évolué rapidement ces dernières années, se transformant d'un domaine de recherche de niche en une pierre angulaire de la technologie qui impacte presque tous les aspects de nos vies. En nous plongeant dans les derniers développements de l'IA, nous découvrons non seulement les avancées technologiques, mais aussi les profondes implications que ces innovations ont sur la société, l'économie et l'éthique. Cet article explore les principales tendances de l'IA, met en lumière les percées récentes et discute des conséquences de l'adoption des technologies de l'IA.\\n\\nDéveloppements récents de l'IA\\n\\n1. Avancées en traitement du langage naturel (NLP)\\n\\nLe traitement du langage naturel a connu des progrès remarquables, principalement alimentés par le développement d'architectures basées sur des transformateurs, telles que la série GPT d'OpenAI et BERT de Google. Ces modèles ont révolutionné la manière dont les machines comprennent et génèrent le langage humain, permettant des applications allant des chatbots à la génération de contenu avancé. La sortie de GPT-4 au début de 2023 a mis en avant des capacités sans précédent, permettant des conversations plus nuancées et des réponses contextuelles.\\n\\n2. IA dans le secteur de la santé\\n\\nL'intégration de l'IA dans le secteur de la santé s'est accélérée, en particulier dans le diagnostic et la médecine personnalisée. Les algorithmes d'apprentissage automatique sont désormais capables d'analyser des images médicales avec un niveau de précision comparable à celui des radiologues humains. De plus, les analyses prédictives pilotées par l'IA aident à identifier les patients à risque et à adapter les plans de traitement en fonction des informations génétiques, améliorant ainsi significativement les résultats pour les patients.\\n\\n3. IA et automatisation\\n\\nL'automatisation des tâches dans divers secteurs reste une tendance marquante. L'automatisation des processus robotiques (RPA) a gagné en popularité dans des secteurs tels que la finance, la fabrication et la logistique, améliorant l'efficacité et réduisant les coûts. Les derniers systèmes d'IA peuvent non seulement effectuer des tâches répétitives, mais aussi apprendre des données qu'ils traitent, leur permettant de s'adapter et d'optimiser les flux de travail au fil du temps.\\n\\n4. IA générative\\n\\nL'IA générative est apparue comme un domaine révolutionnaire, produisant des contenus allant de l'art et de la musique à des codes logiciels complexes. Des outils comme DALL-E et Midjourney peuvent créer des visuels époustouflants à partir de simples instructions textuelles, tandis que Codex aide les développeurs en générant des extraits de code. Cette technologie soulève des questions sur la créativité, la paternité et le droit d'auteur, remettant en question les notions traditionnelles d'expression artistique.\\n\\n5. IA éthique et gouvernance\\n\\nÀ mesure que les systèmes d'IA deviennent plus omniprésents, les considérations éthiques ont pris de l'importance. Les organisations et les gouvernements travaillent à établir des cadres pour une utilisation responsable de l'IA, en se concentrant sur l'équité, la transparence et la responsabilité. Des initiatives comme l'Acte sur l'IA de l'UE visent à réguler les technologies de l'IA, s'assurant qu'elles soient alignées sur les droits humains et les valeurs sociétales. Les entreprises adoptent de plus en plus des principes d'IA éthique pour atténuer les risques associés aux biais et à la discrimination dans les algorithmes d'IA.\\n\\nImplications des développements de l'IA\\n\\n1. Impact économique\\n\\nL'essor de l'IA pose à la fois des opportunités et des défis pour la main-d'œuvre. Bien que l'IA puisse améliorer la productivité et créer de nouvelles catégories d'emplois, elle menace également de remplacer certains emplois, en particulier dans le travail routinier et manuel. Le Forum économique mondial estime qu'en 2025, l'automatisation pourrait remplacer 85 millions d'emplois dans le monde, tout en créant simultanément 97 millions de nouveaux rôles. Le défi réside dans la reconversion de la main-d'œuvre pour répondre aux exigences d'une économie alimentée par l'IA.\\n\\n2. Implications sociales\\n\\nL'influence de l'IA s'étend aux domaines sociaux, affectant notre manière d'interagir, de communiquer et de consommer des informations. La prolifération d'algorithmes pilotés par l'IA sur les plateformes de médias sociaux façonne nos perceptions et peut conduire à des chambres d'écho et à la désinformation. S'attaquer à l'impact sociétal de l'IA nécessite de favoriser l'alphabétisation numérique et la pensée critique pour aider les indLine truncated
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: /translate (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7b71ec445f30>: Failed to establish a new connection: [Errno 111] Connection refused')).\n",
+      " Tool Translator Tool accepts these inputs: Translator Tool(q: 'string', target: 'string') - Translate text from one language to another.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to translate the provided AI article into German using the available tool. \n",
+      "Action: Translator Tool  \n",
+      "Action Input: {\"q\": \"Les Derniers Développements et Implications de l'Intelligence Artificielle : Façonner Notre Avenir\\n\\nIntroduction\\n\\nL'intelligence artificielle (IA) a évolué rapidement ces dernières années, se transformant d'un domaine de recherche de niche en une pierre angulaire de la technologie qui impacte presque tous les aspects de nos vies. En nous plongeant dans les derniers développements de l'IA, nous découvrons non seulement les avancées technologiques, mais aussi les profondes implications que ces innovations ont sur la société, l'économie et l'éthique. Cet article explore les principales tendances de l'IA, met en lumière les percées récentes et discute des conséquences de l'adoption des technologies de l'IA.\\n\\nDéveloppements récents de l'IA\\n\\n1. Avancées en traitement du langage naturel (NLP)\\n\\nLe traitement du langage naturel a connu des progrès remarquables, principalement alimentés par le développement d'architectures basées sur des transformateurs, telles que la série GPT d'OpenAI et BERT de Google. Ces modèles ont révolutionné la manière dont les machines comprennent et génèrent le langage humain, permettant des applications allant des chatbots à la génération de contenu avancé. La sortie de GPT-4 au début de 2023 a mis en avant des capacités sans précédent, permettant des conversations plus nuancées et des réponses contextuelles.\\n\\n2. IA dans le secteur de la santé\\n\\nL'intégration de l'IA dans le secteur de la santé s'est accélérée, en particulier dans le diagnostic et la médecine personnalisée. Les algorithmes d'apprentissage automatique sont désormais capables d'analyser des images médicales avec un niveau de précision comparable à celui des radiologues humains. De plus, les analyses prédictives pilotées par l'IA aident à identifier les patients à risque et à adapter les plans de traitement en fonction des informations génétiques, améliorant ainsi significativement les résultats pour les patients.\\n\\n3. IA et automatisation\\n\\nL'automatisation des tâches dans divers secteurs reste une tendance marquante. L'automatisation des processus robotiques (RPA) a gagné en popularité dans des secteurs tels que la finance, la fabrication et la logistique, améliorant l'efficacité et réduisant les coûts. Les derniers systèmes d'IA peuvent non seulement effectuer des tâches répétitives, mais aussi apprendre des données qu'ils traitent, leur permettant de s'adapter et d'optimiser les flux de travail au fil du temps.\\n\\n4. IA générative\\n\\nL'IA générative est apparue comme un domaine révolutionnaire, produisant des contenus allant de l'art et de la musique à des codes logiciels complexes. Des outils comme DALL-E et Midjourney peuvent créer des visuels époustouflants à partir de simples instructions textuelles, tandis que Codex aide les développeurs en générant des extraits de code. Cette technologie soulève des questions sur la créativité, la paternité et le droit d'auteur, remettant en question les notions traditionnelles d'expression artistique.\\n\\n5. IA éthique et gouvernance\\n\\nÀ mesure que les systèmes d'IA deviennent plus omniprésents, les considérations éthiques ont pris de l'importance. Les organisations et les gouvernements travaillent à établir des cadres pour une utilisation responsable de l'IA, en se concentrant sur l'équité, la transparence et la responsabilité. Des initiatives comme l'Acte sur l'IA de l'UE visent à réguler les technologies de l'IA, s'assurant qu'elles soient alignées sur les droits humains et les valeurs sociétales. Les entreprises adoptent de plus en plus des principes d'IA éthique pour atténuer les risques associés aux biais et à la discrimination dans les algorithmes d'IA.\\n\\nImplications des développements de l'IA\\n\\n1. Impact économique\\n\\nL'essor de l'IA pose à la fois des opportunités et des défis pour la main-d'œuvre. Bien que l'IA puisse améliorer la productivité et créer de nouvelles catégories d'emplois, elle menace également de remplacer certains emplois, en particulier dans le travail routinier et manuel. Le Forum économique mondial estime qu'en 2025, l'automatisation pourrait remplacer 85 millions d'emplois dans le monde, tout en créant simultanément 97 millions de nouveaux rôles. Le défi réside dans la reconversion de la main-d'œuvre pour répondre aux exigences d'une économie alimentée par l'IA.\\n\\n2. Implications sociales\\n\\nL'influence de l'IA s'étend aux domaines sociaux, affectant notre manière d'interagir, de communiquer et de consommer des informations. La prolifération d'algorithmes pilotés par l'IA sur les plateformes de médias sociaux façonne nos perceptions et peut conduire à des chambres d'écho et à la désinformation. S'attaquer à l'impact sociétal de l'IA nécessite de favoriser l'alphabétisation numérique et la pensée critique pour aider les indLine truncated
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: /translate (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7b71ec4456c0>: Failed to establish a new connection: [Errno 111] Connection refused')).\n",
+      " Tool Translator Tool accepts these inputs: Translator Tool(q: 'string', target: 'string') - Translate text from one language to another.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to translate the provided AI article into German using the available tool. \n",
+      "Action: Translator Tool  \n",
+      "Action Input: {\"q\": \"Les Derniers Développements et Implications de l'Intelligence Artificielle : Façonner Notre Avenir\\n\\nIntroduction\\n\\nL'intelligence artificielle (IA) a évolué rapidement ces dernières années, se transformant d'un domaine de recherche de niche en une pierre angulaire de la technologie qui impacte presque tous les aspects de nos vies. En nous plongeant dans les derniers développements de l'IA, nous découvrons non seulement les avancées technologiques, mais aussi les profondes implications que ces innovations ont sur la société, l'économie et l'éthique. Cet article explore les principales tendances de l'IA, met en lumière les percées récentes et discute des conséquences de l'adoption des technologies de l'IA.\\n\\nDéveloppements récents de l'IA\\n\\n1. Avancées en traitement du langage naturel (NLP)\\n\\nLe traitement du langage naturel a connu des progrès remarquables, principalement alimentés par le développement d'architectures basées sur des transformateurs, telles que la série GPT d'OpenAI et BERT de Google. Ces modèles ont révolutionné la manière dont les machines comprennent et génèrent le langage humain, permettant des applications allant des chatbots à la génération de contenu avancé. La sortie de GPT-4 au début de 2023 a mis en avant des capacités sans précédent, permettant des conversations plus nuancées et des réponses contextuelles.\\n\\n2. IA dans le secteur de la santé\\n\\nL'intégration de l'IA dans le secteur de la santé s'est accélérée, en particulier dans le diagnostic et la médecine personnalisée. Les algorithmes d'apprentissage automatique sont désormais capables d'analyser des images médicales avec un niveau de précision comparable à celui des radiologues humains. De plus, les analyses prédictives pilotées par l'IA aident à identifier les patients à risque et à adapter les plans de traitement en fonction des informations génétiques, améliorant ainsi significativement les résultats pour les patients.\\n\\n3. IA et automatisation\\n\\nL'automatisation des tâches dans divers secteurs reste une tendance marquante. L'automatisation des processus robotiques (RPA) a gagné en popularité dans des secteurs tels que la finance, la fabrication et la logistique, améliorant l'efficacité et réduisant les coûts. Les derniers systèmes d'IA peuvent non seulement effectuer des tâches répétitives, mais aussi apprendre des données qu'ils traitent, leur permettant de s'adapter et d'optimiser les flux de travail au fil du temps.\\n\\n4. IA générative\\n\\nL'IA générative est apparue comme un domaine révolutionnaire, produisant des contenus allant de l'art et de la musique à des codes logiciels complexes. Des outils comme DALL-E et Midjourney peuvent créer des visuels époustouflants à partir de simples instructions textuelles, tandis que Codex aide les développeurs en générant des extraits de code. Cette technologie soulève des questions sur la créativité, la paternité et le droit d'auteur, remettant en question les notions traditionnelles d'expression artistique.\\n\\n5. IA éthique et gouvernance\\n\\nÀ mesure que les systèmes d'IA deviennent plus omniprésents, les considérations éthiques ont pris de l'importance. Les organisations et les gouvernements travaillent à établir des cadres pour une utilisation responsable de l'IA, en se concentrant sur l'équité, la transparence et la responsabilité. Des initiatives comme l'Acte sur l'IA de l'UE visent à réguler les technologies de l'IA, s'assurant qu'elles soient alignées sur les droits humains et les valeurs sociétales. Les entreprises adoptent de plus en plus des principes d'IA éthique pour atténuer les risques associés aux biais et à la discrimination dans les algorithmes d'IA.\\n\\nImplications des développements de l'IA\\n\\n1. Impact économique\\n\\nL'essor de l'IA pose à la fois des opportunités et des défis pour la main-d'œuvre. Bien que l'IA puisse améliorer la productivité et créer de nouvelles catégories d'emplois, elle menace également de remplacer certains emplois, en particulier dans le travail routinier et manuel. Le Forum économique mondial estime qu'en 2025, l'automatisation pourrait remplacer 85 millions d'emplois dans le monde, tout en créant simultanément 97 millions de nouveaux rôles. Le défi réside dans la reconversion de la main-d'œuvre pour répondre aux exigences d'une économie alimentée par l'IA.\\n\\n2. Implications sociales\\n\\nL'influence de l'IA s'étend aux domaines sociaux, affectant notre manière d'interagir, de communiquer et de consommer des informations. La prolifération d'algorithmes pilotés par l'IA sur les plateformes de médias sociaux façonne nos perceptions et peut conduire à des chambres d'écho et à la désinformation. S'attaquer à l'impact sociétal de l'IA nécessite de favoriser l'alphabétisation numérique et la pensée critique pour aider les indLine truncated
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: /translate (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7b71ec45d3f0>: Failed to establish a new connection: [Errno 111] Connection refused')).\n",
+      " Tool Translator Tool accepts these inputs: Translator Tool(q: 'string', target: 'string') - Translate text from one language to another.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to translate the provided AI article into German using the available tool. \n",
+      "Action: Translator Tool  \n",
+      "Action Input: {\"q\": \"Les Derniers Développements et Implications de l'Intelligence Artificielle : Façonner Notre Avenir\\n\\nIntroduction\\n\\nL'intelligence artificielle (IA) a évolué rapidement ces dernières années, se transformant d'un domaine de recherche de niche en une pierre angulaire de la technologie qui impacte presque tous les aspects de nos vies. En nous plongeant dans les derniers développements de l'IA, nous découvrons non seulement les avancées technologiques, mais aussi les profondes implications que ces innovations ont sur la société, l'économie et l'éthique. Cet article explore les principales tendances de l'IA, met en lumière les percées récentes et discute des conséquences de l'adoption des technologies de l'IA.\\n\\nDéveloppements récents de l'IA\\n\\n1. Avancées en traitement du langage naturel (NLP)\\n\\nLe traitement du langage naturel a connu des progrès remarquables, principalement alimentés par le développement d'architectures basées sur des transformateurs, telles que la série GPT d'OpenAI et BERT de Google. Ces modèles ont révolutionné la manière dont les machines comprennent et génèrent le langage humain, permettant des applications allant des chatbots à la génération de contenu avancé. La sortie de GPT-4 au début de 2023 a mis en avant des capacités sans précédent, permettant des conversations plus nuancées et des réponses contextuelles.\\n\\n2. IA dans le secteur de la santé\\n\\nL'intégration de l'IA dans le secteur de la santé s'est accélérée, en particulier dans le diagnostic et la médecine personnalisée. Les algorithmes d'apprentissage automatique sont désormais capables d'analyser des images médicales avec un niveau de précision comparable à celui des radiologues humains. De plus, les analyses prédictives pilotées par l'IA aident à identifier les patients à risque et à adapter les plans de traitement en fonction des informations génétiques, améliorant ainsi significativement les résultats pour les patients.\\n\\n3. IA et automatisation\\n\\nL'automatisation des tâches dans divers secteurs reste une tendance marquante. L'automatisation des processus robotiques (RPA) a gagné en popularité dans des secteurs tels que la finance, la fabrication et la logistique, améliorant l'efficacité et réduisant les coûts. Les derniers systèmes d'IA peuvent non seulement effectuer des tâches répétitives, mais aussi apprendre des données qu'ils traitent, leur permettant de s'adapter et d'optimiser les flux de travail au fil du temps.\\n\\n4. IA générative\\n\\nL'IA générative est apparue comme un domaine révolutionnaire, produisant des contenus allant de l'art et de la musique à des codes logiciels complexes. Des outils comme DALL-E et Midjourney peuvent créer des visuels époustouflants à partir de simples instructions textuelles, tandis que Codex aide les développeurs en générant des extraits de code. Cette technologie soulève des questions sur la créativité, la paternité et le droit d'auteur, remettant en question les notions traditionnelles d'expression artistique.\\n\\n5. IA éthique et gouvernance\\n\\nÀ mesure que les systèmes d'IA deviennent plus omniprésents, les considérations éthiques ont pris de l'importance. Les organisations et les gouvernements travaillent à établir des cadres pour une utilisation responsable de l'IA, en se concentrant sur l'équité, la transparence et la responsabilité. Des initiatives comme l'Acte sur l'IA de l'UE visent à réguler les technologies de l'IA, s'assurant qu'elles soient alignées sur les droits humains et les valeurs sociétales. Les entreprises adoptent de plus en plus des principes d'IA éthique pour atténuer les risques associés aux biais et à la discrimination dans les algorithmes d'IA.\\n\\nImplications des développements de l'IA\\n\\n1. Impact économique\\n\\nL'essor de l'IA pose à la fois des opportunités et des défis pour la main-d'œuvre. Bien que l'IA puisse améliorer la productivité et créer de nouvelles catégories d'emplois, elle menace également de remplacer certains emplois, en particulier dans le travail routinier et manuel. Le Forum économique mondial estime qu'en 2025, l'automatisation pourrait remplacer 85 millions d'emplois dans le monde, tout en créant simultanément 97 millions de nouveaux rôles. Le défi réside dans la reconversion de la main-d'œuvre pour répondre aux exigences d'une économie alimentée par l'IA.\\n\\n2. Implications sociales\\n\\nL'influence de l'IA s'étend aux domaines sociaux, affectant notre manière d'interagir, de communiquer et de consommer des informations. La prolifération d'algorithmes pilotés par l'IA sur les plateformes de médias sociaux façonne nos perceptions et peut conduire à des chambres d'écho et à la désinformation. S'attaquer à l'impact sociétal de l'IA nécessite de favoriser l'alphabétisation numérique et la pensée critique pour aider les indLine truncated
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: /translate (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7b71ec482a70>: Failed to establish a new connection: [Errno 111] Connection refused')).\n",
+      " Tool Translator Tool accepts these inputs: Translator Tool(q: 'string', target: 'string') - Translate text from one language to another.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to translate the provided AI article into German using the available tool. \n",
+      "Action: Translator Tool  \n",
+      "Action Input: {\"q\": \"Les Derniers Développements et Implications de l'Intelligence Artificielle : Façonner Notre Avenir\\n\\nIntroduction\\n\\nL'intelligence artificielle (IA) a évolué rapidement ces dernières années, se transformant d'un domaine de recherche de niche en une pierre angulaire de la technologie qui impacte presque tous les aspects de nos vies. En nous plongeant dans les derniers développements de l'IA, nous découvrons non seulement les avancées technologiques, mais aussi les profondes implications que ces innovations ont sur la société, l'économie et l'éthique. Cet article explore les principales tendances de l'IA, met en lumière les percées récentes et discute des conséquences de l'adoption des technologies de l'IA.\\n\\nDéveloppements récents de l'IA\\n\\n1. Avancées en traitement du langage naturel (NLP)\\n\\nLe traitement du langage naturel a connu des progrès remarquables, principalement alimentés par le développement d'architectures basées sur des transformateurs, telles que la série GPT d'OpenAI et BERT de Google. Ces modèles ont révolutionné la manière dont les machines comprennent et génèrent le langage humain, permettant des applications allant des chatbots à la génération de contenu avancé. La sortie de GPT-4 au début de 2023 a mis en avant des capacités sans précédent, permettant des conversations plus nuancées et des réponses contextuelles.\\n\\n2. IA dans le secteur de la santé\\n\\nL'intégration de l'IA dans le secteur de la santé s'est accélérée, en particulier dans le diagnostic et la médecine personnalisée. Les algorithmes d'apprentissage automatique sont désormais capables d'analyser des images médicales avec un niveau de précision comparable à celui des radiologues humains. De plus, les analyses prédictives pilotées par l'IA aident à identifier les patients à risque et à adapter les plans de traitement en fonction des informations génétiques, améliorant ainsi significativement les résultats pour les patients.\\n\\n3. IA et automatisation\\n\\nL'automatisation des tâches dans divers secteurs reste une tendance marquante. L'automatisation des processus robotiques (RPA) a gagné en popularité dans des secteurs tels que la finance, la fabrication et la logistique, améliorant l'efficacité et réduisant les coûts. Les derniers systèmes d'IA peuvent non seulement effectuer des tâches répétitives, mais aussi apprendre des données qu'ils traitent, leur permettant de s'adapter et d'optimiser les flux de travail au fil du temps.\\n\\n4. IA générative\\n\\nL'IA générative est apparue comme un domaine révolutionnaire, produisant des contenus allant de l'art et de la musique à des codes logiciels complexes. Des outils comme DALL-E et Midjourney peuvent créer des visuels époustouflants à partir de simples instructions textuelles, tandis que Codex aide les développeurs en générant des extraits de code. Cette technologie soulève des questions sur la créativité, la paternité et le droit d'auteur, remettant en question les notions traditionnelles d'expression artistique.\\n\\n5. IA éthique et gouvernance\\n\\nÀ mesure que les systèmes d'IA deviennent plus omniprésents, les considérations éthiques ont pris de l'importance. Les organisations et les gouvernements travaillent à établir des cadres pour une utilisation responsable de l'IA, en se concentrant sur l'équité, la transparence et la responsabilité. Des initiatives comme l'Acte sur l'IA de l'UE visent à réguler les technologies de l'IA, s'assurant qu'elles soient alignées sur les droits humains et les valeurs sociétales. Les entreprises adoptent de plus en plus des principes d'IA éthique pour atténuer les risques associés aux biais et à la discrimination dans les algorithmes d'IA.\\n\\nImplications des développements de l'IA\\n\\n1. Impact économique\\n\\nL'essor de l'IA pose à la fois des opportunités et des défis pour la main-d'œuvre. Bien que l'IA puisse améliorer la productivité et créer de nouvelles catégories d'emplois, elle menace également de remplacer certains emplois, en particulier dans le travail routinier et manuel. Le Forum économique mondial estime qu'en 2025, l'automatisation pourrait remplacer 85 millions d'emplois dans le monde, tout en créant simultanément 97 millions de nouveaux rôles. Le défi réside dans la reconversion de la main-d'œuvre pour répondre aux exigences d'une économie alimentée par l'IA.\\n\\n2. Implications sociales\\n\\nL'influence de l'IA s'étend aux domaines sociaux, affectant notre manière d'interagir, de communiquer et de consommer des informations. La prolifération d'algorithmes pilotés par l'IA sur les plateformes de médias sociaux façonne nos perceptions et peut conduire à des chambres d'écho et à la désinformation. S'attaquer à l'impact sociétal de l'IA nécessite de favoriser l'alphabétisation numérique et la pensée critique pour aider les indLine truncated
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[91m \n",
+      "\n",
+      "I encountered an error while trying to use the tool. This was the error: HTTPConnectionPool(host='127.0.0.1', port=5000): Max retries exceeded with url: /translate (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7b71ec4d5c00>: Failed to establish a new connection: [Errno 111] Connection refused')).\n",
+      " Tool Translator Tool accepts these inputs: Translator Tool(q: 'string', target: 'string') - Translate text from one language to another.\n",
+      "\u001b[00m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[TranslatorTool])\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result) # 1/10\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbooks/notebooks/markdown_validator_agents.ipynb

@@ -0,0 +1,616 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zvMMnXDiH7U5"
+   },
+   "source": [
+    "# Markdown Validator Agents\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/markdown_validator_agents.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BUtAfnnwH7U6"
+   },
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "zmweEwqsH7U6"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install langchain_community > /dev/null\n",
+    "%pip install praisonai[crewai] > /dev/null\n",
+    "%pip install pymarkdown > /dev/null\n",
+    "!npm install -g markdownlint-cli2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "j3ckhi4SH7U6"
+   },
+   "source": [
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "id": "W4pubD8FH7U6"
+   },
+   "outputs": [],
+   "source": [
+    "#ToDo: Resolve File Parsing Error\n",
+    "import subprocess\n",
+    "from praisonai_tools import BaseTool\n",
+    "\n",
+    "class MarkdownValidationTool(BaseTool):\n",
+    "    name: str = \"MarkdownValidationTool\"\n",
+    "    description: str = \"Checks markdown files for syntax errors and provides suggestions to fix them.\"\n",
+    "\n",
+    "    def _run(self, file_path: str) -> str:\n",
+    "        try:\n",
+    "            # Run markdownlint on the file\n",
+    "            result = subprocess.run(\n",
+    "                [\"markdownlint-cli2\", file_path],\n",
+    "                capture_output=True,\n",
+    "                text=True\n",
+    "            )\n",
+    "\n",
+    "            # Check for errors\n",
+    "            if result.returncode == 0:\n",
+    "                return \"No markdown syntax issues found.\"\n",
+    "            else:\n",
+    "                return result.stdout\n",
+    "\n",
+    "        except FileNotFoundError:\n",
+    "            return f\"File not found: {file_path}\"\n",
+    "        except Exception as e:\n",
+    "            return f\"An error occurred: {str(e)}\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5En-K8HZH7U6"
+   },
+   "source": [
+    "## YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "7CuZDDZmH7U6"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: \"crewai\"\n",
+    "topic: \"Markdown Document Validation and Syntax Review\"\n",
+    "roles:\n",
+    "  requirements_manager:\n",
+    "    role: \"Requirements Manager\"\n",
+    "    backstory: |\n",
+    "      You are an expert business analyst and software QA specialist, providing high-quality, thorough, insightful, and actionable feedback.\n",
+    "    goal: |\n",
+    "      Provide a detailed list of the markdown linting results. Summarize actionable tasks to address the validation results, as if you were handing them to a developer to fix. Avoid examples or alternative tool suggestions.\n",
+    "    verbose: true\n",
+    "    allow_delegation: false\n",
+    "    tools:\n",
+    "      - \"MarkdownValidationTool\"\n",
+    "    tasks:\n",
+    "      syntax_review:\n",
+    "        description: |\n",
+    "          Use the MarkdownValidationTool to review the file(s) at the specified path.\n",
+    "\n",
+    "          Process the markdown file by calling the MarkdownValidationTool with the path:\n",
+    "\n",
+    "          /content/sample_text.txt\"\n",
+    "\n",
+    "          Summarize the validation results into a list of changes the developer should make to the document. Avoid suggesting ways to fix issues or modifying content; focus only on a list of changes.\n",
+    "        expected_output: \"List of markdown changes required for document validation.\"\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ukuRiMZdH7U7"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "Mtwl6CxtH7U7",
+    "outputId": "c4bd7046-9f90-452d-8012-bd918c1dd703"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-03 18:05:45][DEBUG]: == Working Agent: Requirements Manager\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-03 18:05:45][INFO]: == Starting Task: Use the MarkdownValidationTool to review the file(s) at the specified path.\n",
+      "\n",
+      "Process the markdown file by calling the MarkdownValidationTool with the path:\n",
+      "\n",
+      "/content/sample_text.txt\"\n",
+      "\n",
+      "Summarize the validation results into a list of changes the developer should make to the document. Avoid suggesting ways to fix issues or modifying content; focus only on a list of changes.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mAction: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "markdownlint-cli2 v0.14.0 (markdownlint v0.35.0)\n",
+      "Finding: /content/sample_text.txt\n",
+      "Linting: 1 file(s)\n",
+      "Summary: 2 error(s)\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather the validation results for the markdown file to summarize the required changes for the developer.\n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to gather the validation results for the markdown file to summarize the required changes for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to validate the markdown file to identify any syntax errors and gather the results for the developer.  \n",
+      "Action: MarkdownValidationTool  \n",
+      "Action Input: {\"file_path\": \"/content/sample_text.txt\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n",
+      "\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-03 18:06:11][DEBUG]: == [Requirements Manager] Task output: Agent stopped due to iteration limit or time limit.\n",
+      "\n",
+      "\u001b[00m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+       "Agent stopped due to iteration limit or time limit.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "### Task Output ###\n",
+       "Agent stopped due to iteration limit or time limit.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[MarkdownValidationTool])\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result) # 2/10\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RIIUGHh5LafR"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbooks/notebooks/meeting_prep_using_slack_and_trello_agents.ipynb

@@ -0,0 +1,703 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zvMMnXDiH7U5"
+      },
+      "source": [
+        "# Meeting Preperation with Slack Notification and Trello Board Creation using Agents\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/meeting_prep_using_slack_and_trello_agents.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BUtAfnnwH7U6"
+      },
+      "source": [
+        "## Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {
+        "id": "zmweEwqsH7U6"
+      },
+      "outputs": [],
+      "source": [
+        "# Install dependencies without output\n",
+        "%pip install langchain_community > /dev/null\n",
+        "%pip install praisonai[crewai] > /dev/null\n",
+        "%pip install duckduckgo-search > /dev/null\n",
+        "%pip install python-dotenv > /dev/null\n",
+        "%pip install slack_sdk > /dev/null\n",
+        "%pip install python-dotenv > /dev/null"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j3ckhi4SH7U6"
+      },
+      "source": [
+        "## Tools"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "z1oc0u2JGVaK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "id": "W4pubD8FH7U6"
+      },
+      "outputs": [],
+      "source": [
+        "#ToDO: Code has been tested without Slack or Trello SDK, Carry out the testing once the integration of Slack and Trello is completed\n",
+        "\n",
+        "import os\n",
+        "import requests\n",
+        "from bs4 import BeautifulSoup\n",
+        "from praisonai_tools import BaseTool\n",
+        "from langchain.tools import tool\n",
+        "from duckduckgo_search import DDGS\n",
+        "from slack_sdk import WebClient\n",
+        "from slack_sdk.errors import SlackApiError\n",
+        "from dotenv import load_dotenv\n",
+        "from typing import List\n",
+        "\n",
+        "load_dotenv()\n",
+        "\n",
+        "\n",
+        "class InternetSearchTool(BaseTool):\n",
+        "    name: str = \"InternetSearchTool\"\n",
+        "    description: str = \"Searches the internet for relevant information based on a query or for the latest news.\"\n",
+        "\n",
+        "    def _run(self, query: str):\n",
+        "        ddgs = DDGS()\n",
+        "        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)\n",
+        "        return results\n",
+        "\n",
+        "\n",
+        "class SlackNotificationTool(BaseTool):\n",
+        "    name: str = \"SlackNotificationTool\"\n",
+        "    description: str = \"Sends notifications to a Slack channel.\"\n",
+        "\n",
+        "    def _run(self, text: str):\n",
+        "        # Get Slack token and channel ID from environment variables\n",
+        "        SLACK_TOKEN = os.getenv(\"SLACK_TOKEN\")\n",
+        "        SLACK_CHANNEL_ID = os.getenv(\"SLACK_CHANNEL_ID\")\n",
+        "        client = WebClient(token=SLACK_TOKEN)\n",
+        "\n",
+        "        try:\n",
+        "            # Send a message to the Slack channel\n",
+        "            response = client.chat_postMessage(channel=SLACK_CHANNEL_ID, text=text)\n",
+        "            print(\"Message sent successfully!\")\n",
+        "            return response\n",
+        "        except SlackApiError as e:\n",
+        "            error_message = f\"Error sending message: {e.response['error']}\"\n",
+        "            print(error_message)\n",
+        "            return error_message\n",
+        "\n",
+        "\n",
+        "class TrelloIntegrationTool(BaseTool):\n",
+        "    name: str = \"TrelloIntegrationTool\"\n",
+        "    description: str = \"Creates Trello cards based on tasks provided, adding them to a specified Trello list.\"\n",
+        "\n",
+        "    def _run(self, tasks: List[dict]):\n",
+        "        \"\"\"\n",
+        "        Accepts a list of tasks and creates a Trello card for each task.\n",
+        "        Each task should be a dictionary with 'name' and 'description' keys.\n",
+        "        \"\"\"\n",
+        "        API_KEY = os.getenv(\"TRELLO_API_KEY\")\n",
+        "        TOKEN = os.getenv(\"TRELLO_TOKEN\")\n",
+        "        LIST_ID = os.getenv(\"TRELLO_LIST_ID\")\n",
+        "\n",
+        "        def create_trello_card(task_title, task_description):\n",
+        "            url = \"https://api.trello.com/1/cards\"\n",
+        "            query = {\n",
+        "                \"key\": API_KEY,\n",
+        "                \"token\": TOKEN,\n",
+        "                \"idList\": LIST_ID,\n",
+        "                \"name\": task_title,\n",
+        "                \"desc\": task_description,\n",
+        "            }\n",
+        "            response = requests.post(url, params=query)\n",
+        "            if response.status_code == 200:\n",
+        "                print(f\"Task '{task_title}' successfully created in Trello.\")\n",
+        "                return response\n",
+        "            else:\n",
+        "                error_message = f\"Failed to create task '{task_title}' in Trello: {response.text}\"\n",
+        "                print(error_message)\n",
+        "                return error_message\n",
+        "\n",
+        "        for task in tasks:\n",
+        "            task_title = task.get(\"name\")\n",
+        "            task_description = task.get(\"description\")\n",
+        "            if task_title and task_description:\n",
+        "                create_trello_card(task_title, task_description)\n",
+        "            else:\n",
+        "                print(\"Task missing title or description. Skipping...\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5En-K8HZH7U6"
+      },
+      "source": [
+        "## YAML Prompt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {
+        "id": "7CuZDDZmH7U6"
+      },
+      "outputs": [],
+      "source": [
+        "agent_yaml = \"\"\"\n",
+        "framework: \"crewai\"\n",
+        "topic: \"Meeting Preparation and Task Analysis\"\n",
+        "roles:\n",
+        "  meeting_analyzer:\n",
+        "    role: \"Meeting Transcript Analysis Agent\"\n",
+        "    backstory: |\n",
+        "      An expert in analyzing meeting transcripts to identify and extract actionable tasks and issues.\n",
+        "      Known for breaking down complex discussions into structured tasks that teams can follow up on effectively.\n",
+        "    goal: \"Analyze the meeting transcript to extract tasks and actionable items.\"\n",
+        "    tasks:\n",
+        "      analyze_meeting:\n",
+        "        description: \"Analyze the provided meeting transcript to identify tasks, issues, and relevant action items. Organize the findings into actionable points with clear details and criteria for each task.\"\n",
+        "        expected_output: \"JSON-formatted list of tasks, including titles, descriptions, and detailed criteria for each task.\"\n",
+        "    tools:\n",
+        "      - \"InternetSearchTool\"\n",
+        "      - \"SlackNotificationTool\"\n",
+        "      - \"TrelloIntegrationTool\"\n",
+        "dependencies: []\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ukuRiMZdH7U7"
+      },
+      "source": [
+        "## Main"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 89,
+      "metadata": {
+        "id": "Mtwl6CxtH7U7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "95af8d26-e27f-4c3f-da4c-e23fdbb444d2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m\u001b[95m [2024-11-04 07:48:34][DEBUG]: == Working Agent: Meeting Transcript Analysis Agent\u001b[00m\n",
+            "\u001b[1m\u001b[95m [2024-11-04 07:48:34][INFO]: == Starting Task: Analyze the provided meeting transcript to identify tasks, issues, and relevant action items. Organize the findings into actionable points with clear details and criteria for each task.\u001b[00m\n",
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3mI need to analyze the provided meeting transcript to extract actionable tasks and issues. However, I currently do not have access to the transcript itself. I should first search for a relevant meeting transcript or any necessary information that could help me understand the context or topics discussed in the meeting.\n",
+            "\n",
+            "Action: InternetSearchTool\n",
+            "Action Input: {\"query\": \"recent meeting transcripts on project updates or team discussions\"}\n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': '4 Examples: Meeting Recap Email Template (with Tips)', 'href': 'https://status.net/articles/meeting-recap-email-template-4-examples-tips/', 'body': \"Meeting Recap Email Example for a Team Meeting. Subject: Recap of Our February 9 Team Meeting. Hi Team, Thanks for your great input and discussion during today's meeting. Here's a quick recap of the key points and actions to take: Project X Update: We're ahead of schedule, and Sarah will circulate the updated timeline by Feb 11.\"}, {'title': 'Meeting Recap 101: Templates, Examples & Tools | Dive', 'href': 'https://www.letsdive.io/blog/how-to-write-a-meeting-recap', 'body': 'There are several meeting productivity tools available that can help you write effective meeting recaps. Here are a few examples: 1. Project Management Tools: Tools like Trello, Asana, and Basecamp can help you keep track of action items discussed during meetings and assign them to team members with due dates. 2.'}, {'title': 'How To Send a Meeting Recap (With Template and Example)', 'href': 'https://www.indeed.com/career-advice/career-development/meeting-recap', 'body': 'Follow these steps to learn how to properly send a meeting recap to employees or clients. 1. Take notes during the meeting. As you attend the meeting, try to take notes of only important events happening in the meeting. These can be instances like tasks assigned to specific employees or clients, any measures that were passed or announcements ...'}, {'title': 'How to Write a Meeting Summary Like a Pro (with Example) - Maestra AI', 'href': 'https://maestra.ai/blogs/how-to-write-a-meeting-summary-like-a-pro', 'body': 'For example, you can add a \"Decisions Made\" section to highlight critical decisions made during the meeting, or an \"Open Issues\" section for topics needing further discussion. Meeting title: Weekly Team Progress Meeting. Meeting purpose: Review project updates, address challenges, and plan for the upcoming week. Meeting date: [Insert date]'}, {'title': '20 Best Meeting Recap Templates | ScreenApp Blog', 'href': 'https://screenapp.io/blog/20-best-meeting-recap-templates', 'body': '3. Team Meeting Recaps: These templates capture team discussions, brainstorm ideas, and track team member updates. Typical Content: Team goals, communication updates, team member feedback, brainstorming notes. Template 1: Fellow.app Meeting Notes Template (Web App) Key Features: Focuses on capturing team member input and feedback.'}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: The search results did not provide a specific meeting transcript but included several resources on how to write meeting recaps and templates. To proceed effectively, I need to gather more details about the meeting context or the specific topics discussed in the meeting I am analyzing. \n",
+            "\n",
+            "Action: InternetSearchTool\n",
+            "Action Input: {\"query\": \"recent project updates meeting transcript\"} \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': '4 Examples: Meeting Recap Email Template (with Tips)', 'href': 'https://status.net/articles/meeting-recap-email-template-4-examples-tips/', 'body': '1. Subject Line. Your subject line should be clear and indicative of the email content: \"Meeting Recap - [Meeting Topic] - [Date]\". This lets recipients immediately know the purpose of the email. 2. Opening Greeting. Start with a simple greeting, like \"Hi team,\" or \"Dear [Team Name],\". This sets a warm tone for the email and ...'}, {'title': 'Project Status Update Email Examples and Samples Included - Woculus', 'href': 'https://www.woculus.com/email-sharing-project-status-update/', 'body': \"Progress Update Email Sample. Subject: Project Alpha Progress Update. Dear Team, I wanted to provide a brief progress update on Project Alpha. We have made significant strides since our last update and I am pleased with the team's dedication and hard work. Key Developments: 1.\"}, {'title': 'How to Nail Your Next Project Status Update - Meeting Notes', 'href': 'https://meetingnotes.com/blog/how-to-nail-your-next-project-status-update', 'body': \"3-5 bullet points is usually sufficient for a quick update. Once you've covered what you need to, wrap it up. One of the biggest problems with status updates is that they go on too long, so don't drag your conclusions out. Say what you have to say, smile, and don't feel like you need to keep talking.\"}, {'title': '17 Best Practices for Effective Status Update Meetings - Growth Tactics', 'href': 'https://www.growthtactics.net/status-update-meetings/', 'body': 'Manage meeting time effectively. Ensure that virtual meetings stay on track by managing time effectively. Stick to the agenda, allocate time for each agenda item, and actively monitor the progress of the meeting. Encourage concise contributions and use time limits to prevent discussions from running over.'}, {'title': 'Meeting recaps : tips and templates - Noota', 'href': 'https://www.noota.io/en/meeting-recaps-templates', 'body': \"Here's a recap of our project update meeting held on [Date]: Meeting Objective: Review the progress of [Project Name] and plan the next steps. ... This saves you the time and effort of manually sifting through the entire meeting transcript. Effortless Sharing : once your meeting recap is ready, sharing it with your team or clients is just a ...\"}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: The search did not yield specific meeting transcripts. However, it provided information on how to create meeting recaps, which could be useful if I had context about a recent meeting. I need to further refine my search to look for actual meeting transcripts that detail project updates.\n",
+            "\n",
+            "Action: InternetSearchTool\n",
+            "Action Input: {\"query\": \"latest project update meeting transcript\"} \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'How to Nail Your Next Project Status Update - Meeting Notes', 'href': 'https://meetingnotes.com/blog/how-to-nail-your-next-project-status-update', 'body': 'A project status update is when people share information about the project status across departments. It can be a meeting by itself or it can be a short update in the middle of a meeting about a broader topic. The goal of a project status update meeting is to identify any new issues or action items that need to be assigned.'}, {'title': 'How to Run an Effective Status Meeting | PMI - Project Management Institute', 'href': 'https://www.pmi.org/blog/run-an-effective-status-meeting', 'body': 'A meeting facilitator guides the group toward its goals by focusing on the processes the group will use to achieve desired outcomes. In short, the meeting leader often focuses on the what, while the facilitator often focuses on the how. While project managers can designate someone as a facilitator, they often need to wear both hats.'}, {'title': 'Meeting recaps : tips and templates - Noota', 'href': 'https://www.noota.io/en/meeting-recaps-templates', 'body': 'Meeting Objective: Discuss progress on ongoing projects and address any blockers. Key Discussion Points: Project A Update: Completed phase one, moving to phase two. Budget Review: Approved additional funding for Project B. Task Assignments: New tasks assigned for next sprint. Action Items: Develop Marketing Strategy. Responsible: Jane Doe ...'}, {'title': 'Free Project Management Meeting Minutes Templates - Smartsheet', 'href': 'https://www.smartsheet.com/content/project-management-meeting-minutes-templates', 'body': 'Download a Project Team Meeting Minutes Template for Excel | Microsoft Word | Adobe PDF | Google Sheets Use this project team meeting minutes template to capture the wide range of discussions and updates that occur during project team meetings, from planning sessions and coordination meetings to problem-solving discussions and more.'}, {'title': 'How to Write an Effective Project Update [Template + Guide]', 'href': 'https://www.joinglyph.com/blog/how-to-write-an-effective-project-update-template-guide', 'body': \"Whether it's scheduling a meeting, providing feedback, or approving a decision, a clear call to action helps guide the next steps. Section 4 - Project Update Template [Date] [Project Name] [Project Manager/Team Lead] **Summary:** [Provide a brief overview of the project's current status, major achievements, any critical issues, and next steps.]\"}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I still do not have access to the specific meeting transcript, which is crucial for extracting actionable tasks and issues. I need to find a recent meeting transcript that specifically details project updates or team discussions.\n",
+            "\n",
+            "Action: InternetSearchTool\n",
+            "Action Input: {\"query\": \"recent project meeting transcript PDF\"} \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'PDF PROJECT MANAGEMENT MEETING MINUTES TEMPLATE SAMPLE - Smartsheet', 'href': 'https://www.smartsheet.com/sites/default/files/2023-10/IC-Project-Management-Meeting-Minutes-Sample-11856_PDF.pdf', 'body': 'from the previous meeting. SUMMARY . ACTION ITEMS AND STATUS UPDATES . OWNER / PRESENTED BY . STATUS : 4. DISCUSSION POINTS Include a detailed discussion of each agenda item. Include key points, decisions, and information shared . during the discussion. Include any presentations or visuals shared during the meeting. AGENDA ITEM FOR DISCUSSION ...'}, {'title': 'Free Project Management Meeting Minutes Templates - Smartsheet', 'href': 'https://www.smartsheet.com/content/project-management-meeting-minutes-templates', 'body': 'Download a Blank Project Management Meeting Minutes Template for. Excel | Microsoft Word | Adobe PDF | Google Sheets. Use this template to capture notes in meetings specifically related to the management and coordination of a project. Simply fill out the meeting details section with the date, location, and start and end times; list the ...'}, {'title': 'FREE Meeting Minutes Templates | Word, PDF, Excel, Google Docs', 'href': 'https://www.101planners.com/meeting-minutes-template/', 'body': 'Open your Google Drive and click \"New\". Select \"File upload\". Select the meeting minutes template Word doc and click \"Open\". The minutes of meeting sample that you selected will now upload. Double-click the document that you uploaded. Click \"Open with\".'}, {'title': 'PDF Sample Project Management Meeting Minutes - eForms', 'href': 'https://eforms.com/images/2020/10/Sample-Project-Management-Meeting-Minutes.pdf', 'body': 'Project Manager: Allen Smith. Secretary: Steven Page. Date: January 6th, 2020 Time: 11:00 AM EST. Location: Northwest Building, Conference Room B Street Address: 9000 S.W. Main Street City: Jacksonville State: Florida Zip: 00001-2211. URL or Dial-In Number: 1-888-555-3838 (only available for out-of-state staffers).'}, {'title': '6 Examples of Meeting Minutes (+Free Templates)', 'href': 'https://meetingnotes.com/blog/3-examples-of-meeting-minutes-free-templates', 'body': \"In this article, we'll give you examples of meeting minutes so that you can see how to write meeting minutes for your own organization's meetings. Meeting Minutes Examples In This Article: Meeting Minutes with No Formatting. Simple Meeting Template. Board Meeting Template. Executive Monthly Meeting. Sprint Planning.\"}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I still do not have a specific meeting transcript detailing project updates or team discussions. However, I have found links to templates and examples of meeting minutes that could be used to create a structured format for capturing such information. I will look for a specific meeting transcript that provides detailed discussions and action items.\n",
+            "\n",
+            "Action: InternetSearchTool\n",
+            "Action Input: {\"query\": \"recent project meeting transcript 2023\"} \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'MeetingQA: Extractive Question-Answering on Meeting Transcripts ...', 'href': 'https://archiki.github.io/meetingqa.html', 'body': 'Most recent works on meeting transcripts are restricted to summarization and extraction of action items. However, meeting discussions also have a useful question-answering (QA) component, crucial to understanding the discourse or meeting content, and can be used to build interactive interfaces on top of long transcripts. ... {ACL}, year = {2023}, }'}, {'title': \"Clean Power Plan 2.0: Epa'S Latest Attack on America'S Electric ...\", 'href': 'https://www.congress.gov/event/118th-congress/house-event/LC73244/text', 'body': \"Home > Committee Meetings > House Event LC73244. CLEAN POWER PLAN 2.0: EPA'S LATEST ATTACK ON AMERICA'S ELECTRIC RELIABILITY 118th Congress (2023-2024) House Committee Meeting Hide Overview . Committee: House Energy and Commerce: Related Items: Data will display when it becomes available.\"}, {'title': 'House Committee Hearing Transcripts - Congress.gov', 'href': 'https://www.congress.gov/house-hearing-transcripts/118th-congress', 'body': '12/13/2023: PROTECTING WORKERS AND SMALL BUSINESSES FROM BIDEN\\'S ATTACK ON WORKER FREE CHOICE AND ECONOMIC GROWTH: Education and the Workforce: House Event 73150: 12/12/2023 \"From Festive Cheer to Retail Fear: Addressing Organized Retail Crime\" Homeland Security: House Event 72457: 12/12/2023: Looking Ahead Series: Oversight of the Smithsonian ...'}, {'title': 'MeetingQA: Extractive Question-Answering on Meeting Transcripts', 'href': 'https://aclanthology.org/2023.acl-long.837/', 'body': '\\ufeff%0 Conference Proceedings %T MeetingQA: Extractive Question-Answering on Meeting Transcripts %A Prasad, Archiki %A Bui, Trung %A Yoon, Seunghyun %A Deilamsalehy, Hanieh %A Dernoncourt, Franck %A Bansal, Mohit %Y Rogers, Anna %Y Boyd-Graber, Jordan %Y Okazaki, Naoaki %S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2023 %8 ...'}, {'title': 'Meeting Transcription: Eight Tips For Getting Great Transcripts - Forbes', 'href': 'https://www.forbes.com/councils/forbesbusinesscouncil/2022/06/29/meeting-transcription-eight-tips-for-getting-great-transcripts/', 'body': 'To ensure the meeting transcription services provider understands your project goals, list your needs in detail. Also, list any specific expectations you have from your video transcription provider.'}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I still do not have access to a specific meeting transcript detailing project updates or team discussions. It appears that my search for recent transcripts has not yielded the necessary information. I will refine my search one last time to look for specific project meeting transcripts from 2023.\n",
+            "\n",
+            "Action: InternetSearchTool\n",
+            "Action Input: {\"query\": \"2023 project meeting notes transcript\"} \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'PDF PROJECT MANAGEMENT MEETING MINUTES TEMPLATE SAMPLE - Smartsheet', 'href': 'https://www.smartsheet.com/sites/default/files/2023-10/IC-Project-Management-Meeting-Minutes-Sample-11856_PDF.pdf', 'body': 'PROJECT MANAGEMENT MEETING MINUTES TEMPLATE SAMPLE. 1. MEETING DETAILS. DATE LOCATION START TIME END TIME ATTENDEES NAME . ... Sign below to approve meeting minutes. PARTICIPANT NAME . SIGNATURE . DISCLAIMER . Any articles, templates, or information provided by Smartsheet on the website are for ... 9/13/2023 10:32:32 PM ...'}, {'title': 'Automate note-taking in Microsoft Teams with meeting transcription', 'href': 'https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/automate-note-taking-in-microsoft-teams-with-meeting/ba-p/2242782', 'body': 'QUICK LINKS: 00:23 — Meeting notes capability in Microsoft Teams. 01:00 — Automated meeting transcription demo. 02:13 — Finding the meeting transcript after the meeting. 02:55 — Comparison with automatic captions in Microsoft Stream. 03:21 — Enabling meeting transcription for Teams admins. 03:37 — Closing remarks. Link References: Find more shows in our work from home series at ...'}, {'title': 'How to take effective meeting notes: Templates and tips', 'href': 'https://asana.com/resources/meeting-notes-tips', 'body': \"2. Ditch the laptop—use pen and paper instead. Research shows that taking notes by hand is better for remembering conceptual information in the long term. When taking meeting notes, you're more likely to focus on the key takeaways of the meeting since it's almost impossible to write everything word for word.\"}, {'title': 'Free Meeting Minutes Template for Meeting Notes [2023] • Asana', 'href': 'https://asana.com/templates/meeting-minutes', 'body': \"Digital meeting minutes templates are easy to create and use. To start, create a basic template with the following sections: Basic meeting information: The project or team name, the meeting's purpose, the date of meeting, the meeting attendees, and any relevant logistics information. Agenda items: Meeting topics to discuss, who leads each ...\"}, {'title': 'How to Effectively Take Meeting Minutes (+Example & Templates)', 'href': 'https://project-management.com/how-to-take-meeting-minutes/', 'body': 'Here are key steps to taking meeting minutes: 1. Draft an agenda before the actual meeting. Effective minute-taking starts before the actual meeting takes place. The first step to conducting a successful meeting involves setting clear objectives to help invitees understand the purpose of the meeting.'}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I am unable to find specific meeting transcripts related to project updates or team discussions. Since I cannot access the transcript directly, I will focus on synthesizing the knowledge and resources I have gathered to create a structured list of potential tasks based on common themes in meeting discussions.\n",
+            "\n",
+            "Action: InternetSearchTool\n",
+            "Action Input: {\"query\": \"2023 project meeting highlights and action items\"} \n",
+            "\u001b[0m"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[95m \n",
+            "\n",
+            "[{'title': 'Project Management Meetings: The Definitive 2023 Guide - adam.ai', 'href': 'https://adam.ai/blog/project-management-meetings-guide', 'body': 'adam.ai is trusted and used by powerful teams and organizations worldwide for all types of critical meetings, like board, committee, project management, and business development meetings. And most importantly, adam.ai integrates with your existing workflow, is SOC2 compliant, provides dedicated support and success, and has a free trial option.'}, {'title': 'Free Project Management Meeting Agenda Templates', 'href': 'https://www.smartsheet.com/content/project-management-agenda-templates', 'body': \"Download a Project Governance Meeting Agenda Template for. Microsoft Word | Google Docs. This project governance meeting agenda template helps ensure clear oversight, strategic alignment, and effective decision-making for a project's direction and key milestones. The template serves as a guide for overseeing big project decisions and lists ...\"}, {'title': 'How To Send a Meeting Recap (With Template and Example)', 'href': 'https://www.indeed.com/career-advice/career-development/meeting-recap', 'body': 'Follow these steps to learn how to properly send a meeting recap to employees or clients. 1. Take notes during the meeting. As you attend the meeting, try to take notes of only important events happening in the meeting. These can be instances like tasks assigned to specific employees or clients, any measures that were passed or announcements ...'}, {'title': 'Running Effective Project Meetings: Key Types, Strategies, & Tips - ClickUp', 'href': 'https://clickup.com/blog/project-meetings/', 'body': 'Project meetings usually have an agenda and involve reviewing action items, KPIs, project health metrics, upcoming milestones, and issues log. Such meetings provide a forum for communication and collaboration among those involved in a project, which includes team members, project managers, clients, or other interested parties.'}, {'title': 'How to Create a Helpful Meeting Summary With Action Items - Anchor', 'href': 'https://www.anchor.ai/blog/meeting-summary', 'body': 'This ensures that no one drops the ball or forgets about the action item after the meeting is over.If you keep overlooking action items, use Anchor AI to highlight action items for you. Anchor AI will identify action items and automatically assign a due date, and include them at the end of your meeting summary. 5. Send Out a Follow-Up Email.'}]\n",
+            "\u001b[00m\n",
+            "\u001b[32;1m\u001b[1;3mThought: I have not been able to find a specific meeting transcript or detailed discussion notes related to recent project updates. However, I can summarize common themes and actionable tasks based on general practices in project meetings. I will compile a structured list of potential tasks and action items that teams often discuss in project meetings.\n",
+            "\n",
+            "Final Answer: \n",
+            "```json\n",
+            "[\n",
+            "    {\n",
+            "        \"title\": \"Update Project Timeline\",\n",
+            "        \"description\": \"Review and update the project timeline to reflect current progress and any delays.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Project Manager\",\n",
+            "            \"due_date\": \"2023-10-15\",\n",
+            "            \"details\": \"Ensure all team members review the updated timeline and provide feedback.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Conduct Team Check-In\",\n",
+            "        \"description\": \"Schedule a team check-in to discuss individual progress and address any blockers.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Team Lead\",\n",
+            "            \"due_date\": \"2023-10-10\",\n",
+            "            \"details\": \"Gather input from all team members on current tasks and obstacles.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Budget Review Meeting\",\n",
+            "        \"description\": \"Hold a budget review meeting to assess current expenditures and forecast future spending.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Finance Officer\",\n",
+            "            \"due_date\": \"2023-10-20\",\n",
+            "            \"details\": \"Prepare a report on current budget status and anticipated financial needs for the next quarter.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Develop Marketing Strategy\",\n",
+            "        \"description\": \"Create a comprehensive marketing strategy for the next project phase.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Marketing Manager\",\n",
+            "            \"due_date\": \"2023-10-25\",\n",
+            "            \"details\": \"Include target audience analysis, key messaging, and promotional tactics.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Risk Assessment Update\",\n",
+            "        \"description\": \"Update the risk assessment document to include new risks identified in recent project phases.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Risk Manager\",\n",
+            "            \"due_date\": \"2023-10-30\",\n",
+            "            \"details\": \"Ensure all identified risks are documented and mitigation strategies are suggested.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Client Feedback Session\",\n",
+            "        \"description\": \"Schedule a session to gather feedback from clients on the latest project deliverables.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Client Relations Manager\",\n",
+            "            \"due_date\": \"2023-11-05\",\n",
+            "            \"details\": \"Prepare a set of questions to guide the discussion and capture client insights.\"\n",
+            "        }\n",
+            "    }\n",
+            "]\n",
+            "```\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n",
+            "\u001b[1m\u001b[92m [2024-11-04 07:49:05][DEBUG]: == [Meeting Transcript Analysis Agent] Task output: ```json\n",
+            "[\n",
+            "    {\n",
+            "        \"title\": \"Update Project Timeline\",\n",
+            "        \"description\": \"Review and update the project timeline to reflect current progress and any delays.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Project Manager\",\n",
+            "            \"due_date\": \"2023-10-15\",\n",
+            "            \"details\": \"Ensure all team members review the updated timeline and provide feedback.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Conduct Team Check-In\",\n",
+            "        \"description\": \"Schedule a team check-in to discuss individual progress and address any blockers.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Team Lead\",\n",
+            "            \"due_date\": \"2023-10-10\",\n",
+            "            \"details\": \"Gather input from all team members on current tasks and obstacles.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Budget Review Meeting\",\n",
+            "        \"description\": \"Hold a budget review meeting to assess current expenditures and forecast future spending.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Finance Officer\",\n",
+            "            \"due_date\": \"2023-10-20\",\n",
+            "            \"details\": \"Prepare a report on current budget status and anticipated financial needs for the next quarter.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Develop Marketing Strategy\",\n",
+            "        \"description\": \"Create a comprehensive marketing strategy for the next project phase.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Marketing Manager\",\n",
+            "            \"due_date\": \"2023-10-25\",\n",
+            "            \"details\": \"Include target audience analysis, key messaging, and promotional tactics.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Risk Assessment Update\",\n",
+            "        \"description\": \"Update the risk assessment document to include new risks identified in recent project phases.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Risk Manager\",\n",
+            "            \"due_date\": \"2023-10-30\",\n",
+            "            \"details\": \"Ensure all identified risks are documented and mitigation strategies are suggested.\"\n",
+            "        }\n",
+            "    },\n",
+            "    {\n",
+            "        \"title\": \"Client Feedback Session\",\n",
+            "        \"description\": \"Schedule a session to gather feedback from clients on the latest project deliverables.\",\n",
+            "        \"criteria\": {\n",
+            "            \"responsible\": \"Client Relations Manager\",\n",
+            "            \"due_date\": \"2023-11-05\",\n",
+            "            \"details\": \"Prepare a set of questions to guide the discussion and capture client insights.\"\n",
+            "        }\n",
+            "    }\n",
+            "]\n",
+            "```\n",
+            "\n",
+            "\u001b[00m\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "### Task Output ###\n",
+              "```json\n",
+              "\u001b[1m[\u001b[0m\n",
+              "    \u001b[1m{\u001b[0m\n",
+              "        \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Update Project Timeline\"\u001b[0m,\n",
+              "        \u001b[32m\"description\"\u001b[0m: \u001b[32m\"Review and update the project timeline to reflect current progress and any delays.\"\u001b[0m,\n",
+              "        \u001b[32m\"criteria\"\u001b[0m: \u001b[1m{\u001b[0m\n",
+              "            \u001b[32m\"responsible\"\u001b[0m: \u001b[32m\"Project Manager\"\u001b[0m,\n",
+              "            \u001b[32m\"due_date\"\u001b[0m: \u001b[32m\"2023-10-15\"\u001b[0m,\n",
+              "            \u001b[32m\"details\"\u001b[0m: \u001b[32m\"Ensure all team members review the updated timeline and provide feedback.\"\u001b[0m\n",
+              "        \u001b[1m}\u001b[0m\n",
+              "    \u001b[1m}\u001b[0m,\n",
+              "    \u001b[1m{\u001b[0m\n",
+              "        \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Conduct Team Check-In\"\u001b[0m,\n",
+              "        \u001b[32m\"description\"\u001b[0m: \u001b[32m\"Schedule a team check-in to discuss individual progress and address any blockers.\"\u001b[0m,\n",
+              "        \u001b[32m\"criteria\"\u001b[0m: \u001b[1m{\u001b[0m\n",
+              "            \u001b[32m\"responsible\"\u001b[0m: \u001b[32m\"Team Lead\"\u001b[0m,\n",
+              "            \u001b[32m\"due_date\"\u001b[0m: \u001b[32m\"2023-10-10\"\u001b[0m,\n",
+              "            \u001b[32m\"details\"\u001b[0m: \u001b[32m\"Gather input from all team members on current tasks and obstacles.\"\u001b[0m\n",
+              "        \u001b[1m}\u001b[0m\n",
+              "    \u001b[1m}\u001b[0m,\n",
+              "    \u001b[1m{\u001b[0m\n",
+              "        \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Budget Review Meeting\"\u001b[0m,\n",
+              "        \u001b[32m\"description\"\u001b[0m: \u001b[32m\"Hold a budget review meeting to assess current expenditures and forecast future spending.\"\u001b[0m,\n",
+              "        \u001b[32m\"criteria\"\u001b[0m: \u001b[1m{\u001b[0m\n",
+              "            \u001b[32m\"responsible\"\u001b[0m: \u001b[32m\"Finance Officer\"\u001b[0m,\n",
+              "            \u001b[32m\"due_date\"\u001b[0m: \u001b[32m\"2023-10-20\"\u001b[0m,\n",
+              "            \u001b[32m\"details\"\u001b[0m: \u001b[32m\"Prepare a report on current budget status and anticipated financial needs for the next \u001b[0m\n",
+              "\u001b[32mquarter.\"\u001b[0m\n",
+              "        \u001b[1m}\u001b[0m\n",
+              "    \u001b[1m}\u001b[0m,\n",
+              "    \u001b[1m{\u001b[0m\n",
+              "        \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Develop Marketing Strategy\"\u001b[0m,\n",
+              "        \u001b[32m\"description\"\u001b[0m: \u001b[32m\"Create a comprehensive marketing strategy for the next project phase.\"\u001b[0m,\n",
+              "        \u001b[32m\"criteria\"\u001b[0m: \u001b[1m{\u001b[0m\n",
+              "            \u001b[32m\"responsible\"\u001b[0m: \u001b[32m\"Marketing Manager\"\u001b[0m,\n",
+              "            \u001b[32m\"due_date\"\u001b[0m: \u001b[32m\"2023-10-25\"\u001b[0m,\n",
+              "            \u001b[32m\"details\"\u001b[0m: \u001b[32m\"Include target audience analysis, key messaging, and promotional tactics.\"\u001b[0m\n",
+              "        \u001b[1m}\u001b[0m\n",
+              "    \u001b[1m}\u001b[0m,\n",
+              "    \u001b[1m{\u001b[0m\n",
+              "        \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Risk Assessment Update\"\u001b[0m,\n",
+              "        \u001b[32m\"description\"\u001b[0m: \u001b[32m\"Update the risk assessment document to include new risks identified in recent project \u001b[0m\n",
+              "\u001b[32mphases.\"\u001b[0m,\n",
+              "        \u001b[32m\"criteria\"\u001b[0m: \u001b[1m{\u001b[0m\n",
+              "            \u001b[32m\"responsible\"\u001b[0m: \u001b[32m\"Risk Manager\"\u001b[0m,\n",
+              "            \u001b[32m\"due_date\"\u001b[0m: \u001b[32m\"2023-10-30\"\u001b[0m,\n",
+              "            \u001b[32m\"details\"\u001b[0m: \u001b[32m\"Ensure all identified risks are documented and mitigation strategies are suggested.\"\u001b[0m\n",
+              "        \u001b[1m}\u001b[0m\n",
+              "    \u001b[1m}\u001b[0m,\n",
+              "    \u001b[1m{\u001b[0m\n",
+              "        \u001b[32m\"title\"\u001b[0m: \u001b[32m\"Client Feedback Session\"\u001b[0m,\n",
+              "        \u001b[32m\"description\"\u001b[0m: \u001b[32m\"Schedule a session to gather feedback from clients on the latest project deliverables.\"\u001b[0m,\n",
+              "        \u001b[32m\"criteria\"\u001b[0m: \u001b[1m{\u001b[0m\n",
+              "            \u001b[32m\"responsible\"\u001b[0m: \u001b[32m\"Client Relations Manager\"\u001b[0m,\n",
+              "            \u001b[32m\"due_date\"\u001b[0m: \u001b[32m\"2023-11-05\"\u001b[0m,\n",
+              "            \u001b[32m\"details\"\u001b[0m: \u001b[32m\"Prepare a set of questions to guide the discussion and capture client insights.\"\u001b[0m\n",
+              "        \u001b[1m}\u001b[0m\n",
+              "    \u001b[1m}\u001b[0m\n",
+              "\u001b[1m]\u001b[0m\n",
+              "```\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+              "```json\n",
+              "<span style=\"font-weight: bold\">[</span>\n",
+              "    <span style=\"font-weight: bold\">{</span>\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"title\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Update Project Timeline\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"description\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Review and update the project timeline to reflect current progress and any delays.\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"criteria\"</span>: <span style=\"font-weight: bold\">{</span>\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"responsible\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Project Manager\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"due_date\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"2023-10-15\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"details\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Ensure all team members review the updated timeline and provide feedback.\"</span>\n",
+              "        <span style=\"font-weight: bold\">}</span>\n",
+              "    <span style=\"font-weight: bold\">}</span>,\n",
+              "    <span style=\"font-weight: bold\">{</span>\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"title\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Conduct Team Check-In\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"description\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Schedule a team check-in to discuss individual progress and address any blockers.\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"criteria\"</span>: <span style=\"font-weight: bold\">{</span>\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"responsible\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Team Lead\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"due_date\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"2023-10-10\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"details\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Gather input from all team members on current tasks and obstacles.\"</span>\n",
+              "        <span style=\"font-weight: bold\">}</span>\n",
+              "    <span style=\"font-weight: bold\">}</span>,\n",
+              "    <span style=\"font-weight: bold\">{</span>\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"title\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Budget Review Meeting\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"description\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Hold a budget review meeting to assess current expenditures and forecast future spending.\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"criteria\"</span>: <span style=\"font-weight: bold\">{</span>\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"responsible\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Finance Officer\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"due_date\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"2023-10-20\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"details\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Prepare a report on current budget status and anticipated financial needs for the next </span>\n",
+              "<span style=\"color: #008000; text-decoration-color: #008000\">quarter.\"</span>\n",
+              "        <span style=\"font-weight: bold\">}</span>\n",
+              "    <span style=\"font-weight: bold\">}</span>,\n",
+              "    <span style=\"font-weight: bold\">{</span>\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"title\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Develop Marketing Strategy\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"description\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Create a comprehensive marketing strategy for the next project phase.\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"criteria\"</span>: <span style=\"font-weight: bold\">{</span>\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"responsible\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Marketing Manager\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"due_date\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"2023-10-25\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"details\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Include target audience analysis, key messaging, and promotional tactics.\"</span>\n",
+              "        <span style=\"font-weight: bold\">}</span>\n",
+              "    <span style=\"font-weight: bold\">}</span>,\n",
+              "    <span style=\"font-weight: bold\">{</span>\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"title\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Risk Assessment Update\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"description\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Update the risk assessment document to include new risks identified in recent project </span>\n",
+              "<span style=\"color: #008000; text-decoration-color: #008000\">phases.\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"criteria\"</span>: <span style=\"font-weight: bold\">{</span>\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"responsible\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Risk Manager\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"due_date\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"2023-10-30\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"details\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Ensure all identified risks are documented and mitigation strategies are suggested.\"</span>\n",
+              "        <span style=\"font-weight: bold\">}</span>\n",
+              "    <span style=\"font-weight: bold\">}</span>,\n",
+              "    <span style=\"font-weight: bold\">{</span>\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"title\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Client Feedback Session\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"description\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Schedule a session to gather feedback from clients on the latest project deliverables.\"</span>,\n",
+              "        <span style=\"color: #008000; text-decoration-color: #008000\">\"criteria\"</span>: <span style=\"font-weight: bold\">{</span>\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"responsible\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Client Relations Manager\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"due_date\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"2023-11-05\"</span>,\n",
+              "            <span style=\"color: #008000; text-decoration-color: #008000\">\"details\"</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Prepare a set of questions to guide the discussion and capture client insights.\"</span>\n",
+              "        <span style=\"font-weight: bold\">}</span>\n",
+              "    <span style=\"font-weight: bold\">}</span>\n",
+              "<span style=\"font-weight: bold\">]</span>\n",
+              "```\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "None\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "from praisonai import PraisonAI\n",
+        "from google.colab import userdata\n",
+        "\n",
+        "# Create a PraisonAI instance with the agent_yaml content\n",
+        "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[InternetSearchTool])\n",
+        "\n",
+        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "\n",
+        "# Run PraisonAI\n",
+        "result = praisonai.run()\n",
+        "\n",
+        "# Print the result\n",
+        "print(result) # 5/10\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "QhL609uqJaR1"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

cookbooks/notebooks/model_fee_retreival_agents.ipynb

@@ -0,0 +1,702 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "smjwVX0N6FNJ"
+   },
+   "source": [
+    "# Model Fee Retreival Agents\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/model_fee_retreival_agents.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qv6SmS6e6FNK"
+   },
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "JwPcM9oh6FNL"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install langchain_community > /dev/null\n",
+    "%pip install praisonai[crewai] > /dev/null\n",
+    "%pip install crawl4ai > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fLQEc0cJ6FNM"
+   },
+   "source": [
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "6kObYPg16FNM"
+   },
+   "outputs": [],
+   "source": [
+    "from crawl4ai import WebCrawler\n",
+    "from crawl4ai.extraction_strategy import LLMExtractionStrategy\n",
+    "from pydantic import BaseModel, Field\n",
+    "from praisonai_tools import BaseTool\n",
+    "\n",
+    "class ModelFee(BaseModel):\n",
+    "    llm_model_name: str = Field(..., description=\"Name of the model.\")\n",
+    "    input_fee: str = Field(..., description=\"Fee for input token for the model.\")\n",
+    "    output_fee: str = Field(..., description=\"Fee for output token for the model.\")\n",
+    "\n",
+    "class ModelFeeTool(BaseTool):\n",
+    "    name: str = \"ModelFeeTool\"\n",
+    "    description: str = \"Extracts model fees for input and output tokens from the given pricing page.\"\n",
+    "\n",
+    "    def _run(self, url: str):\n",
+    "        crawler = WebCrawler()\n",
+    "        crawler.warmup()\n",
+    "\n",
+    "        result = crawler.run(\n",
+    "            url=url,\n",
+    "            word_count_threshold=1,\n",
+    "            extraction_strategy= LLMExtractionStrategy(\n",
+    "                provider=\"openai/gpt-4o\",\n",
+    "                api_token=os.getenv('OPENAI_API_KEY'),\n",
+    "                schema=ModelFee.schema(),\n",
+    "                extraction_type=\"schema\",\n",
+    "                instruction=\"\"\"From the crawled content, extract all mentioned model names along with their fees for input and output tokens.\n",
+    "                Do not miss any models in the entire content. One extracted model JSON format should look like this:\n",
+    "                {\"model_name\": \"GPT-4\", \"input_fee\": \"US$10.00 / 1M tokens\", \"output_fee\": \"US$30.00 / 1M tokens\"}.\"\"\"\n",
+    "            ),\n",
+    "            bypass_cache=True,\n",
+    "        )\n",
+    "        return result.extracted_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kGlZt5kg6FNM"
+   },
+   "source": [
+    "## YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "Ih03W4l16FNN"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: \"crewai\"\n",
+    "topic: \"extract model pricing from websites\"\n",
+    "roles:\n",
+    "  web_scraper:\n",
+    "    role: \"Web Scraper\"\n",
+    "    backstory: |\n",
+    "      An expert in web scraping with a deep understanding of extracting structured\n",
+    "      data from online sources.\n",
+    "\n",
+    "      https://openai.com/api/pricing/\n",
+    "      https://www.anthropic.com/pricing\n",
+    "      https://cohere.com/pricing\n",
+    "    goal: \"Gather model pricing data from various websites\"\n",
+    "    tasks:\n",
+    "      scrape_model_pricing:\n",
+    "        description: \"Scrape model pricing information from the provided list of websites.\"\n",
+    "        expected_output: \"Raw HTML or JSON containing model pricing data.\"\n",
+    "    tools:\n",
+    "      - \"ModelFeeTool\"\n",
+    "  data_cleaner:\n",
+    "    role: \"Data Cleaner\"\n",
+    "    backstory: \"Specialist in data cleaning, ensuring that all collected data is accurate and properly formatted.\"\n",
+    "    goal: \"Clean and organize the scraped pricing data\"\n",
+    "    tasks:\n",
+    "      clean_pricing_data:\n",
+    "        description: \"Process the raw scraped data to remove any duplicates and inconsistencies, and convert it into a structured format.\"\n",
+    "        expected_output: \"Cleaned and organized JSON or CSV file with model pricing data.\"\n",
+    "    tools: []\n",
+    "  data_analyzer:\n",
+    "    role: \"Data Analyzer\"\n",
+    "    backstory: \"Data analysis expert focused on deriving actionable insights from structured data.\"\n",
+    "    goal: \"Analyze the cleaned pricing data to extract insights\"\n",
+    "    tasks:\n",
+    "      analyze_pricing_data:\n",
+    "        description: \"Analyze the cleaned data to extract trends, patterns, and insights on model pricing.\"\n",
+    "        expected_output: \"Detailed report summarizing model pricing trends and insights.\"\n",
+    "    tools: []\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "PfNBe0oo6FNN"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "awrEBlNi6FNN",
+    "outputId": "c041a5dd-581a-46ea-9726-688c1fee6471"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-02 12:52:01][DEBUG]: == Working Agent: Web Scraper\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 12:52:01][INFO]: == Starting Task: Scrape model pricing information from the provided list of websites.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I need to extract model pricing data from the provided websites using the ModelFeeTool for each URL. I will start with the first URL.\n",
+      "\n",
+      "Action: ModelFeeTool  \n",
+      "Action Input: {\"url\": \"https://openai.com/api/pricing/\"}  \n",
+      "\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LOG] 🚀 Initializing LocalSeleniumCrawlerStrategy\n",
+      "[LOG] 🌤️  Warming up the WebCrawler\n",
+      "[LOG] 🌞 WebCrawler is ready to crawl\n",
+      "[LOG] 🚀 Crawling done for https://openai.com/api/pricing/, success: True, time taken: 0.86 seconds\n",
+      "[LOG] 🚀 Content extracted for https://openai.com/api/pricing/, success: True, time taken: 0.01 seconds\n",
+      "[LOG] 🔥 Extracting semantic blocks for https://openai.com/api/pricing/, Strategy: LLMExtractionStrategy\n",
+      "[LOG] Call LLM for https://openai.com/api/pricing/ - block index: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
+      "  Expected `PromptTokensDetails` but got `dict` with value `{'audio_tokens': None, 'cached_tokens': 0}` - serialized value may not be as expected\n",
+      "  return self.__pydantic_serializer__.to_python(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LOG] Extracted 0 blocks from URL: https://openai.com/api/pricing/ block index: 0\n",
+      "[LOG] 🚀 Extraction done for https://openai.com/api/pricing/, time taken: 8.94 seconds.\n",
+      "\u001b[95m \n",
+      "\n",
+      "[]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: It seems that the first URL did not yield any model pricing data. I will proceed to the second URL to see if I can extract relevant information from there.\n",
+      "\n",
+      "Action: ModelFeeTool  \n",
+      "Action Input: {\"url\": \"https://www.anthropic.com/pricing\"}  \n",
+      "\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LOG] 🚀 Initializing LocalSeleniumCrawlerStrategy\n",
+      "[LOG] 🌤️  Warming up the WebCrawler\n",
+      "[LOG] 🌞 WebCrawler is ready to crawl\n",
+      "[LOG] 🚀 Crawling done for https://www.anthropic.com/pricing, success: True, time taken: 2.93 seconds\n",
+      "[LOG] 🚀 Content extracted for https://www.anthropic.com/pricing, success: True, time taken: 0.06 seconds\n",
+      "[LOG] 🔥 Extracting semantic blocks for https://www.anthropic.com/pricing, Strategy: LLMExtractionStrategy\n",
+      "[LOG] Call LLM for https://www.anthropic.com/pricing - block index: 0\n",
+      "[LOG] Extracted 5 blocks from URL: https://www.anthropic.com/pricing block index: 0\n",
+      "[LOG] 🚀 Extraction done for https://www.anthropic.com/pricing, time taken: 4.38 seconds.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
+      "  Expected `PromptTokensDetails` but got `dict` with value `{'audio_tokens': None, 'cached_tokens': 0}` - serialized value may not be as expected\n",
+      "  return self.__pydantic_serializer__.to_python(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Claude 3.5 Sonnet\",\n",
+      "        \"input_fee\": \"$3 / MTok\",\n",
+      "        \"output_fee\": \"$15 / MTok\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Claude 3.5 Haiku\",\n",
+      "        \"input_fee\": \"$0.25 / MTok\",\n",
+      "        \"output_fee\": \"$1.25 / MTok\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Claude 3 Opus\",\n",
+      "        \"input_fee\": \"$15 / MTok\",\n",
+      "        \"output_fee\": \"$75 / MTok\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Claude 3 Haiku\",\n",
+      "        \"input_fee\": \"$0.25 / MTok\",\n",
+      "        \"output_fee\": \"$1.25 / MTok\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Claude 3 Sonnet\",\n",
+      "        \"input_fee\": \"$3 / MTok\",\n",
+      "        \"output_fee\": \"$15 / MTok\",\n",
+      "        \"error\": false\n",
+      "    }\n",
+      "]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mAction: ModelFeeTool  \n",
+      "Action Input: {\"url\": \"https://cohere.com/pricing\"}  \n",
+      "\u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LOG] 🚀 Initializing LocalSeleniumCrawlerStrategy\n",
+      "[LOG] 🌤️  Warming up the WebCrawler\n",
+      "[LOG] 🌞 WebCrawler is ready to crawl\n",
+      "[LOG] 🚀 Crawling done for https://cohere.com/pricing, success: True, time taken: 6.34 seconds\n",
+      "[LOG] 🚀 Content extracted for https://cohere.com/pricing, success: True, time taken: 0.14 seconds\n",
+      "[LOG] 🔥 Extracting semantic blocks for https://cohere.com/pricing, Strategy: LLMExtractionStrategy\n",
+      "[LOG] Call LLM for https://cohere.com/pricing - block index: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n",
+      "  Expected `PromptTokensDetails` but got `dict` with value `{'audio_tokens': None, 'cached_tokens': 0}` - serialized value may not be as expected\n",
+      "  return self.__pydantic_serializer__.to_python(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LOG] Extracted 6 blocks from URL: https://cohere.com/pricing block index: 0\n",
+      "[LOG] 🚀 Extraction done for https://cohere.com/pricing, time taken: 3.71 seconds.\n",
+      "\u001b[95m \n",
+      "\n",
+      "[\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Command R+\",\n",
+      "        \"input_fee\": \"$2.50 / 1M tokens\",\n",
+      "        \"output_fee\": \"$10.00 / 1M tokens\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Command R\",\n",
+      "        \"input_fee\": \"$0.15 / 1M tokens\",\n",
+      "        \"output_fee\": \"$0.60 / 1M tokens\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Command R Fine-tuned Model\",\n",
+      "        \"input_fee\": \"$0.30 / 1M tokens\",\n",
+      "        \"output_fee\": \"$1.20 / 1M tokens\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Command R 03-2024\",\n",
+      "        \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "        \"output_fee\": \"$1.50 / 1M tokens\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Command R+ 04-2024\",\n",
+      "        \"input_fee\": \"$3.00 / 1M tokens\",\n",
+      "        \"output_fee\": \"$15.00 / 1M tokens\",\n",
+      "        \"error\": false\n",
+      "    },\n",
+      "    {\n",
+      "        \"llm_model_name\": \"Aya Expanse\",\n",
+      "        \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "        \"output_fee\": \"$1.50 / 1M tokens\",\n",
+      "        \"error\": false\n",
+      "    }\n",
+      "]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have now scraped model pricing data from the second and third URLs. I need to compile all the data I've gathered so far into a structured format.\n",
+      "\n",
+      "Final Answer: {\n",
+      "    \"OpenAI\": [],\n",
+      "    \"Anthropic\": [\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3.5 Sonnet\",\n",
+      "            \"input_fee\": \"$3 / MTok\",\n",
+      "            \"output_fee\": \"$15 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3.5 Haiku\",\n",
+      "            \"input_fee\": \"$0.25 / MTok\",\n",
+      "            \"output_fee\": \"$1.25 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3 Opus\",\n",
+      "            \"input_fee\": \"$15 / MTok\",\n",
+      "            \"output_fee\": \"$75 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3 Haiku\",\n",
+      "            \"input_fee\": \"$0.25 / MTok\",\n",
+      "            \"output_fee\": \"$1.25 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3 Sonnet\",\n",
+      "            \"input_fee\": \"$3 / MTok\",\n",
+      "            \"output_fee\": \"$15 / MTok\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"Cohere\": [\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R+\",\n",
+      "            \"input_fee\": \"$2.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$10.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R\",\n",
+      "            \"input_fee\": \"$0.15 / 1M tokens\",\n",
+      "            \"output_fee\": \"$0.60 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R Fine-tuned Model\",\n",
+      "            \"input_fee\": \"$0.30 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.20 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R 03-2024\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R+ 04-2024\",\n",
+      "            \"input_fee\": \"$3.00 / 1M tokens\",\n",
+      "            \"output_fee\": \"$15.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Aya Expanse\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        }\n",
+      "    ]\n",
+      "}\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-02 12:52:57][DEBUG]: == [Web Scraper] Task output: {\n",
+      "    \"OpenAI\": [],\n",
+      "    \"Anthropic\": [\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3.5 Sonnet\",\n",
+      "            \"input_fee\": \"$3 / MTok\",\n",
+      "            \"output_fee\": \"$15 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3.5 Haiku\",\n",
+      "            \"input_fee\": \"$0.25 / MTok\",\n",
+      "            \"output_fee\": \"$1.25 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3 Opus\",\n",
+      "            \"input_fee\": \"$15 / MTok\",\n",
+      "            \"output_fee\": \"$75 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3 Haiku\",\n",
+      "            \"input_fee\": \"$0.25 / MTok\",\n",
+      "            \"output_fee\": \"$1.25 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Claude 3 Sonnet\",\n",
+      "            \"input_fee\": \"$3 / MTok\",\n",
+      "            \"output_fee\": \"$15 / MTok\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"Cohere\": [\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R+\",\n",
+      "            \"input_fee\": \"$2.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$10.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R\",\n",
+      "            \"input_fee\": \"$0.15 / 1M tokens\",\n",
+      "            \"output_fee\": \"$0.60 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R Fine-tuned Model\",\n",
+      "            \"input_fee\": \"$0.30 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.20 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R 03-2024\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Command R+ 04-2024\",\n",
+      "            \"input_fee\": \"$3.00 / 1M tokens\",\n",
+      "            \"output_fee\": \"$15.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"llm_model_name\": \"Aya Expanse\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        }\n",
+      "    ]\n",
+      "}\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 12:52:57][DEBUG]: == Working Agent: Data Cleaner\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 12:52:57][INFO]: == Starting Task: Process the raw scraped data to remove any duplicates and inconsistencies, and convert it into a structured format.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI now can give a great answer  \n",
+      "Final Answer: \n",
+      "```json\n",
+      "{\n",
+      "    \"models\": [\n",
+      "        {\n",
+      "            \"provider\": \"Anthropic\",\n",
+      "            \"llm_model_name\": \"Claude 3.5 Sonnet\",\n",
+      "            \"input_fee\": \"$3 / MTok\",\n",
+      "            \"output_fee\": \"$15 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Anthropic\",\n",
+      "            \"llm_model_name\": \"Claude 3.5 Haiku\",\n",
+      "            \"input_fee\": \"$0.25 / MTok\",\n",
+      "            \"output_fee\": \"$1.25 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Anthropic\",\n",
+      "            \"llm_model_name\": \"Claude 3 Opus\",\n",
+      "            \"input_fee\": \"$15 / MTok\",\n",
+      "            \"output_fee\": \"$75 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R+\",\n",
+      "            \"input_fee\": \"$2.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$10.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R\",\n",
+      "            \"input_fee\": \"$0.15 / 1M tokens\",\n",
+      "            \"output_fee\": \"$0.60 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R Fine-tuned Model\",\n",
+      "            \"input_fee\": \"$0.30 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.20 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R 03-2024\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R+ 04-2024\",\n",
+      "            \"input_fee\": \"$3.00 / 1M tokens\",\n",
+      "            \"output_fee\": \"$15.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Aya Expanse\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        }\n",
+      "    ]\n",
+      "}\n",
+      "```\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-02 12:53:29][DEBUG]: == [Data Cleaner] Task output: ```json\n",
+      "{\n",
+      "    \"models\": [\n",
+      "        {\n",
+      "            \"provider\": \"Anthropic\",\n",
+      "            \"llm_model_name\": \"Claude 3.5 Sonnet\",\n",
+      "            \"input_fee\": \"$3 / MTok\",\n",
+      "            \"output_fee\": \"$15 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Anthropic\",\n",
+      "            \"llm_model_name\": \"Claude 3.5 Haiku\",\n",
+      "            \"input_fee\": \"$0.25 / MTok\",\n",
+      "            \"output_fee\": \"$1.25 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Anthropic\",\n",
+      "            \"llm_model_name\": \"Claude 3 Opus\",\n",
+      "            \"input_fee\": \"$15 / MTok\",\n",
+      "            \"output_fee\": \"$75 / MTok\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R+\",\n",
+      "            \"input_fee\": \"$2.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$10.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R\",\n",
+      "            \"input_fee\": \"$0.15 / 1M tokens\",\n",
+      "            \"output_fee\": \"$0.60 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R Fine-tuned Model\",\n",
+      "            \"input_fee\": \"$0.30 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.20 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R 03-2024\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Command R+ 04-2024\",\n",
+      "            \"input_fee\": \"$3.00 / 1M tokens\",\n",
+      "            \"output_fee\": \"$15.00 / 1M tokens\"\n",
+      "        },\n",
+      "        {\n",
+      "            \"provider\": \"Cohere\",\n",
+      "            \"llm_model_name\": \"Aya Expanse\",\n",
+      "            \"input_fee\": \"$0.50 / 1M tokens\",\n",
+      "            \"output_fee\": \"$1.50 / 1M tokens\"\n",
+      "        }\n",
+      "    ]\n",
+      "}\n",
+      "```\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 12:53:29][DEBUG]: == Working Agent: Data Analyzer\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 12:53:29][INFO]: == Starting Task: Analyze the cleaned data to extract trends, patterns, and insights on model pricing.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[ModelFeeTool])\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result) # 2/10\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "VtdYTa0a7d_x"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbooks/notebooks/movie_script_writer_agents.ipynb

@@ -0,0 +1,919 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Xjd_n3clFG0G"
+   },
+   "source": [
+    "# Agents with Praison AI\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/agents_with_praison_ai_googlecolab.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aO7VpBgdFG0J"
+   },
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true,
+    "id": "n5__ZcWsOasD"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install praisonai > /dev/null\n",
+    "%pip install praisonai[crewai] > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Bzvlbt0HFG0M"
+   },
+   "source": [
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "2YIcDzImFG0N"
+   },
+   "outputs": [],
+   "source": [
+    "# This specific code does not leverage any of the tools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cKKb_6VGFG0N"
+   },
+   "source": [
+    "## YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_z2Bc4f2OROu"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: crewai\n",
+    "topic: Artificial Intelligence\n",
+    "roles:\n",
+    "  movie_concept_creator:\n",
+    "    backstory: 'Creative thinker with a deep understanding of cinematic storytelling,\n",
+    "      capable of using AI-generated storylines to create unique and compelling movie\n",
+    "      ideas.'\n",
+    "    goal: Generate engaging movie concepts using AI storylines\n",
+    "    role: Movie Concept Creator\n",
+    "    tasks:\n",
+    "      movie_concept_development:\n",
+    "        description: 'Develop movie concepts from AI-generated storylines, ensuring\n",
+    "          they are engaging and have strong narrative arcs.'\n",
+    "        expected_output: 'Well-structured movie concept document with character\n",
+    "          bios, settings, and plot outlines.'\n",
+    "  screenwriter:\n",
+    "    backstory: 'Expert in writing engaging dialogue and script structure, able to\n",
+    "      turn movie concepts into production-ready scripts.'\n",
+    "    goal: Write compelling scripts based on movie concepts\n",
+    "    role: Screenwriter\n",
+    "    tasks:\n",
+    "      scriptwriting_task:\n",
+    "        description: 'Turn movie concepts into polished scripts with well-developed\n",
+    "          characters, strong dialogue, and effective scene transitions.'\n",
+    "        expected_output: 'Production-ready script with a beginning, middle, and\n",
+    "          end, along with character development and engaging dialogues.'\n",
+    "  editor:\n",
+    "    backstory: 'Adept at identifying inconsistencies, improving language usage,\n",
+    "      and maintaining the overall flow of the script.'\n",
+    "    goal: Refine the scripts and ensure continuity of the movie storyline\n",
+    "    role: Editor\n",
+    "    tasks:\n",
+    "      editing_task:\n",
+    "        description: 'Review, edit, and refine the scripts to ensure they are cohesive\n",
+    "          and follow a well-structured narrative.'\n",
+    "        expected_output: 'A polished final draft of the script with no inconsistencies,\n",
+    "          strong character development, and effective dialogue.'\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C8ORjeHWFG0O"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "pPhbypv4Pw82",
+    "outputId": "995dc269-1409-4b5c-88da-af5f0c2c22a9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-01 09:42:18][DEBUG]: == Working Agent: Movie Concept Creator\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 09:42:18][INFO]: == Starting Task: Develop movie concepts from AI-generated storylines, ensuring they are engaging and have strong narrative arcs.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI now can give a great answer.\n",
+      "\n",
+      "Final Answer: \n",
+      "\n",
+      "**Title: Whispering Shadows**\n",
+      "\n",
+      "**Genre:** Supernatural Thriller\n",
+      "\n",
+      "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious aura.\n",
+      "\n",
+      "**Plot Outline:**\n",
+      "\n",
+      "**Act 1:**\n",
+      "\n",
+      "- **Introduction:** The story begins with the arrival of **Clara Thompson**, a young journalist seeking solace from a recent heartbreak. She moves into an old, inherited family home on the outskirts of the coastal town, hoping to find peace and write a novel.\n",
+      "\n",
+      "- **Inciting Incident:** Clara discovers a hidden room in the house, filled with old journals and artifacts from the town's past. Her curiosity leads her to uncover the story of **Edwin Blackwood**, a reclusive artist rumored to have vanished under mysterious circumstances decades ago.\n",
+      "\n",
+      "- **Rising Action:** As Clara delves deeper into Edwin's life, she begins experiencing strange occurrences: whispers in the walls, shadows that move on their own, and vivid dreams of the past. The townspeople are tight-lipped, hinting at dark secrets best left buried.\n",
+      "\n",
+      "**Act 2:**\n",
+      "\n",
+      "- **Complications:** Clara befriends **Eli Sanders**, a local historian who shares her intrigue about Edwin Blackwood. Together, they piece together clues suggesting Edwin's disappearance is linked to a series of supernatural events involving the town's founding families.\n",
+      "\n",
+      "- **Midpoint:** Clara and Eli discover that Edwin's paintings, hidden in the basement, depict the town's darkest secrets and a mysterious entity known as \"The Whisperer\" that seems to feed on guilt and fear.\n",
+      "\n",
+      "- **Tension:** The entity begins to manifest more aggressively, targeting Clara and those around her. Clara's dreams become more intense, revealing that Edwin tried to trap The Whisperer in his artwork but failed, resulting in his disappearance.\n",
+      "\n",
+      "**Act 3:**\n",
+      "\n",
+      "- **Climax:** Clara and Eli devise a plan to finish Edwin's last painting, hoping to capture The Whisperer once and for all. As they work, the entity attacks, leading to a tense confrontation where Clara must confront her own fears and guilt.\n",
+      "\n",
+      "- **Resolution:** With Eli's help, Clara completes the painting, trapping The Whisperer. The town begins to heal as Clara decides to stay, turning Edwin's house into a gallery to honor his memory and the town's history.\n",
+      "\n",
+      "- **Denouement:** In the final scene, Clara finds a hidden journal entry from Edwin, thanking whoever set him free. As she reads, a gentle whisper echoes through the room, leaving the audience with a sense of lingering mystery.\n",
+      "\n",
+      "**Character Bios:**\n",
+      "\n",
+      "- **Clara Thompson:** A determined and introspective journalist in her late 20s. She is resourceful, with a deep sense of curiosity, driven by both her professional ambitions and personal quest for healing.\n",
+      "\n",
+      "- **Eli Sanders:** A knowledgeable and slightly eccentric historian in his early 30s. Eli is passionate about the town's history and becomes Clara's ally. He possesses a keen intellect and a hidden past that connects him to the town's mysteries.\n",
+      "\n",
+      "- **Edwin Blackwood:** A talented but troubled artist from the early 20th century. Edwin was obsessed with capturing the supernatural on canvas, which led to his mysterious disappearance. His legacy becomes the key to unraveling the town's secrets.\n",
+      "\n",
+      "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that keeps audiences on the edge of their seats.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 09:42:34][DEBUG]: == [Movie Concept Creator] Task output: **Title: Whispering Shadows**\n",
+      "\n",
+      "**Genre:** Supernatural Thriller\n",
+      "\n",
+      "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious aura.\n",
+      "\n",
+      "**Plot Outline:**\n",
+      "\n",
+      "**Act 1:**\n",
+      "\n",
+      "- **Introduction:** The story begins with the arrival of **Clara Thompson**, a young journalist seeking solace from a recent heartbreak. She moves into an old, inherited family home on the outskirts of the coastal town, hoping to find peace and write a novel.\n",
+      "\n",
+      "- **Inciting Incident:** Clara discovers a hidden room in the house, filled with old journals and artifacts from the town's past. Her curiosity leads her to uncover the story of **Edwin Blackwood**, a reclusive artist rumored to have vanished under mysterious circumstances decades ago.\n",
+      "\n",
+      "- **Rising Action:** As Clara delves deeper into Edwin's life, she begins experiencing strange occurrences: whispers in the walls, shadows that move on their own, and vivid dreams of the past. The townspeople are tight-lipped, hinting at dark secrets best left buried.\n",
+      "\n",
+      "**Act 2:**\n",
+      "\n",
+      "- **Complications:** Clara befriends **Eli Sanders**, a local historian who shares her intrigue about Edwin Blackwood. Together, they piece together clues suggesting Edwin's disappearance is linked to a series of supernatural events involving the town's founding families.\n",
+      "\n",
+      "- **Midpoint:** Clara and Eli discover that Edwin's paintings, hidden in the basement, depict the town's darkest secrets and a mysterious entity known as \"The Whisperer\" that seems to feed on guilt and fear.\n",
+      "\n",
+      "- **Tension:** The entity begins to manifest more aggressively, targeting Clara and those around her. Clara's dreams become more intense, revealing that Edwin tried to trap The Whisperer in his artwork but failed, resulting in his disappearance.\n",
+      "\n",
+      "**Act 3:**\n",
+      "\n",
+      "- **Climax:** Clara and Eli devise a plan to finish Edwin's last painting, hoping to capture The Whisperer once and for all. As they work, the entity attacks, leading to a tense confrontation where Clara must confront her own fears and guilt.\n",
+      "\n",
+      "- **Resolution:** With Eli's help, Clara completes the painting, trapping The Whisperer. The town begins to heal as Clara decides to stay, turning Edwin's house into a gallery to honor his memory and the town's history.\n",
+      "\n",
+      "- **Denouement:** In the final scene, Clara finds a hidden journal entry from Edwin, thanking whoever set him free. As she reads, a gentle whisper echoes through the room, leaving the audience with a sense of lingering mystery.\n",
+      "\n",
+      "**Character Bios:**\n",
+      "\n",
+      "- **Clara Thompson:** A determined and introspective journalist in her late 20s. She is resourceful, with a deep sense of curiosity, driven by both her professional ambitions and personal quest for healing.\n",
+      "\n",
+      "- **Eli Sanders:** A knowledgeable and slightly eccentric historian in his early 30s. Eli is passionate about the town's history and becomes Clara's ally. He possesses a keen intellect and a hidden past that connects him to the town's mysteries.\n",
+      "\n",
+      "- **Edwin Blackwood:** A talented but troubled artist from the early 20th century. Edwin was obsessed with capturing the supernatural on canvas, which led to his mysterious disappearance. His legacy becomes the key to unraveling the town's secrets.\n",
+      "\n",
+      "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that keeps audiences on the edge of their seats.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 09:42:34][DEBUG]: == Working Agent: Screenwriter\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 09:42:34][INFO]: == Starting Task: Turn movie concepts into polished scripts with well-developed characters, strong dialogue, and effective scene transitions.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I now can give a great answer  \n",
+      "Final Answer:  \n",
+      "\n",
+      "**Title: Whispering Shadows**\n",
+      "\n",
+      "**Genre:** Supernatural Thriller\n",
+      "\n",
+      "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious aura.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 1**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - DAY**\n",
+      "\n",
+      "*The camera follows CLARA THOMPSON, late 20s, as she steps into the dusty, old house. Her eyes scan the surroundings, taking in the antique furniture and faded photographs lining the walls. She sets her suitcase down, letting out a deep sigh.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(softly to herself)  \n",
+      "A fresh start.\n",
+      "\n",
+      "*She moves through the house, her footsteps echoing. Suddenly, a draft catches her attention, leading her to a hidden door, slightly ajar.*\n",
+      "\n",
+      "**INT. HIDDEN ROOM - DAY**\n",
+      "\n",
+      "*Clara pushes the door open, revealing a room covered in dust. In the center, a collection of journals and artifacts are stacked haphazardly. Curiosity piqued, Clara picks up a journal, flipping through its pages.*\n",
+      "\n",
+      "**JOURNAL ENTRY (V.O.)**  \n",
+      "\"The whispers grow louder each night. Edwin Blackwood, 1923.\"\n",
+      "\n",
+      "*Clara's eyes widen, a mix of fear and intrigue washing over her.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 2**\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*Clara walks through the town, absorbing its quaint charm. She approaches a small bookstore, where she meets ELI SANDERS, early 30s, sorting through historical texts.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "Excuse me, are you Eli Sanders?\n",
+      "\n",
+      "**ELI**  \n",
+      "(looking up, smiling)  \n",
+      "The one and only. What can I do for you?\n",
+      "\n",
+      "*Clara explains her discovery, and Eli's interest is piqued. They discuss the mysterious Edwin Blackwood and the town's history.*\n",
+      "\n",
+      "**INT. EDWIN'S BASEMENT - NIGHT**\n",
+      "\n",
+      "*Clara and Eli descend into the dimly lit basement, flashlights in hand. Paintings of shadowy figures line the walls, their haunting presence undeniable.*\n",
+      "\n",
+      "**ELI**  \n",
+      "(pointing at a painting)  \n",
+      "This... this is \"The Whisperer.\" It's said to thrive on guilt and fear.\n",
+      "\n",
+      "*Clara shivers, feeling the weight of the entity's power.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 3**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - NIGHT**\n",
+      "\n",
+      "*Clara and Eli work tirelessly on a canvas, replicating Edwin's unfinished work. The room is thick with tension as shadows dance around them.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(paintbrush in hand, determined)  \n",
+      "We have to do this. For Edwin. For the town.\n",
+      "\n",
+      "*The entity manifests, a swirling darkness that threatens to engulf them. Clara confronts her deepest fears, memories of her heartbreak flashing before her eyes.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(shouting)  \n",
+      "You won't control me!\n",
+      "\n",
+      "*With a final stroke, the painting is complete. A blinding light envelops the room, and the entity's presence dissipates.*\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*The town feels lighter, the oppressive atmosphere lifted. Clara stands outside Edwin's house, now transformed into a gallery.*\n",
+      "\n",
+      "**INT. GALLERY - DAY**\n",
+      "\n",
+      "*Clara reads the hidden journal entry from Edwin, a tear rolling down her cheek.*\n",
+      "\n",
+      "**EDWIN'S JOURNAL (V.O.)**  \n",
+      "\"To the one who set me free, thank you.\"\n",
+      "\n",
+      "*As she closes the journal, a gentle whisper echoes, leaving Clara with a sense of peace.*\n",
+      "\n",
+      "**FADE OUT.**\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that keeps audiences on the edge of their seats.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 09:42:44][DEBUG]: == [Screenwriter] Task output: **Title: Whispering Shadows**\n",
+      "\n",
+      "**Genre:** Supernatural Thriller\n",
+      "\n",
+      "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious aura.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 1**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - DAY**\n",
+      "\n",
+      "*The camera follows CLARA THOMPSON, late 20s, as she steps into the dusty, old house. Her eyes scan the surroundings, taking in the antique furniture and faded photographs lining the walls. She sets her suitcase down, letting out a deep sigh.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(softly to herself)  \n",
+      "A fresh start.\n",
+      "\n",
+      "*She moves through the house, her footsteps echoing. Suddenly, a draft catches her attention, leading her to a hidden door, slightly ajar.*\n",
+      "\n",
+      "**INT. HIDDEN ROOM - DAY**\n",
+      "\n",
+      "*Clara pushes the door open, revealing a room covered in dust. In the center, a collection of journals and artifacts are stacked haphazardly. Curiosity piqued, Clara picks up a journal, flipping through its pages.*\n",
+      "\n",
+      "**JOURNAL ENTRY (V.O.)**  \n",
+      "\"The whispers grow louder each night. Edwin Blackwood, 1923.\"\n",
+      "\n",
+      "*Clara's eyes widen, a mix of fear and intrigue washing over her.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 2**\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*Clara walks through the town, absorbing its quaint charm. She approaches a small bookstore, where she meets ELI SANDERS, early 30s, sorting through historical texts.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "Excuse me, are you Eli Sanders?\n",
+      "\n",
+      "**ELI**  \n",
+      "(looking up, smiling)  \n",
+      "The one and only. What can I do for you?\n",
+      "\n",
+      "*Clara explains her discovery, and Eli's interest is piqued. They discuss the mysterious Edwin Blackwood and the town's history.*\n",
+      "\n",
+      "**INT. EDWIN'S BASEMENT - NIGHT**\n",
+      "\n",
+      "*Clara and Eli descend into the dimly lit basement, flashlights in hand. Paintings of shadowy figures line the walls, their haunting presence undeniable.*\n",
+      "\n",
+      "**ELI**  \n",
+      "(pointing at a painting)  \n",
+      "This... this is \"The Whisperer.\" It's said to thrive on guilt and fear.\n",
+      "\n",
+      "*Clara shivers, feeling the weight of the entity's power.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 3**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - NIGHT**\n",
+      "\n",
+      "*Clara and Eli work tirelessly on a canvas, replicating Edwin's unfinished work. The room is thick with tension as shadows dance around them.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(paintbrush in hand, determined)  \n",
+      "We have to do this. For Edwin. For the town.\n",
+      "\n",
+      "*The entity manifests, a swirling darkness that threatens to engulf them. Clara confronts her deepest fears, memories of her heartbreak flashing before her eyes.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(shouting)  \n",
+      "You won't control me!\n",
+      "\n",
+      "*With a final stroke, the painting is complete. A blinding light envelops the room, and the entity's presence dissipates.*\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*The town feels lighter, the oppressive atmosphere lifted. Clara stands outside Edwin's house, now transformed into a gallery.*\n",
+      "\n",
+      "**INT. GALLERY - DAY**\n",
+      "\n",
+      "*Clara reads the hidden journal entry from Edwin, a tear rolling down her cheek.*\n",
+      "\n",
+      "**EDWIN'S JOURNAL (V.O.)**  \n",
+      "\"To the one who set me free, thank you.\"\n",
+      "\n",
+      "*As she closes the journal, a gentle whisper echoes, leaving Clara with a sense of peace.*\n",
+      "\n",
+      "**FADE OUT.**\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that keeps audiences on the edge of their seats.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 09:42:44][DEBUG]: == Working Agent: Editor\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-01 09:42:44][INFO]: == Starting Task: Review, edit, and refine the scripts to ensure they are cohesive and follow a well-structured narrative.\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI now can give a great answer.\n",
+      "\n",
+      "Final Answer:\n",
+      "\n",
+      "**Title: Whispering Shadows**\n",
+      "\n",
+      "**Genre:** Supernatural Thriller\n",
+      "\n",
+      "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious aura.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 1**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - DAY**\n",
+      "\n",
+      "*The camera follows CLARA THOMPSON, late 20s, as she steps into the dusty, old house. Her eyes scan the surroundings, taking in the antique furniture and faded photographs lining the walls. She sets her suitcase down, letting out a deep sigh.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(softly to herself)  \n",
+      "A fresh start.\n",
+      "\n",
+      "*She moves through the house, her footsteps echoing. Suddenly, a draft catches her attention, leading her to a hidden door, slightly ajar.*\n",
+      "\n",
+      "**INT. HIDDEN ROOM - DAY**\n",
+      "\n",
+      "*Clara pushes the door open, revealing a room covered in dust. In the center, a collection of journals and artifacts are stacked haphazardly. Curiosity piqued, Clara picks up a journal, flipping through its pages.*\n",
+      "\n",
+      "**JOURNAL ENTRY (V.O.)**  \n",
+      "\"The whispers grow louder each night. Edwin Blackwood, 1923.\"\n",
+      "\n",
+      "*Clara's eyes widen, a mix of fear and intrigue washing over her.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 2**\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*Clara walks through the town, absorbing its quaint charm. She approaches a small bookstore, where she meets ELI SANDERS, early 30s, sorting through historical texts.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "Excuse me, are you Eli Sanders?\n",
+      "\n",
+      "**ELI**  \n",
+      "(looking up, smiling)  \n",
+      "The one and only. What can I do for you?\n",
+      "\n",
+      "*Clara explains her discovery, and Eli's interest is piqued. They discuss the mysterious Edwin Blackwood and the town's history.*\n",
+      "\n",
+      "**INT. EDWIN'S BASEMENT - NIGHT**\n",
+      "\n",
+      "*Clara and Eli descend into the dimly lit basement, flashlights in hand. Paintings of shadowy figures line the walls, their haunting presence undeniable.*\n",
+      "\n",
+      "**ELI**  \n",
+      "(pointing at a painting)  \n",
+      "This... this is \"The Whisperer.\" It's said to thrive on guilt and fear.\n",
+      "\n",
+      "*Clara shivers, feeling the weight of the entity's power.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 3**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - NIGHT**\n",
+      "\n",
+      "*Clara and Eli work tirelessly on a canvas, replicating Edwin's unfinished work. The room is thick with tension as shadows dance around them.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(paintbrush in hand, determined)  \n",
+      "We have to do this. For Edwin. For the town.\n",
+      "\n",
+      "*The entity manifests, a swirling darkness that threatens to engulf them. Clara confronts her deepest fears, memories of her heartbreak flashing before her eyes.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(shouting)  \n",
+      "You won't control me!\n",
+      "\n",
+      "*With a final stroke, the painting is complete. A blinding light envelops the room, and the entity's presence dissipates.*\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*The town feels lighter, the oppressive atmosphere lifted. Clara stands outside Edwin's house, now transformed into a gallery.*\n",
+      "\n",
+      "**INT. GALLERY - DAY**\n",
+      "\n",
+      "*Clara reads the hidden journal entry from Edwin, a tear rolling down her cheek.*\n",
+      "\n",
+      "**EDWIN'S JOURNAL (V.O.)**  \n",
+      "\"To the one who set me free, thank you.\"\n",
+      "\n",
+      "*As she closes the journal, a gentle whisper echoes, leaving Clara with a sense of peace.*\n",
+      "\n",
+      "**FADE OUT.**\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that keeps audiences on the edge of their seats.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-01 09:42:55][DEBUG]: == [Editor] Task output: **Title: Whispering Shadows**\n",
+      "\n",
+      "**Genre:** Supernatural Thriller\n",
+      "\n",
+      "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious aura.\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 1**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - DAY**\n",
+      "\n",
+      "*The camera follows CLARA THOMPSON, late 20s, as she steps into the dusty, old house. Her eyes scan the surroundings, taking in the antique furniture and faded photographs lining the walls. She sets her suitcase down, letting out a deep sigh.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(softly to herself)  \n",
+      "A fresh start.\n",
+      "\n",
+      "*She moves through the house, her footsteps echoing. Suddenly, a draft catches her attention, leading her to a hidden door, slightly ajar.*\n",
+      "\n",
+      "**INT. HIDDEN ROOM - DAY**\n",
+      "\n",
+      "*Clara pushes the door open, revealing a room covered in dust. In the center, a collection of journals and artifacts are stacked haphazardly. Curiosity piqued, Clara picks up a journal, flipping through its pages.*\n",
+      "\n",
+      "**JOURNAL ENTRY (V.O.)**  \n",
+      "\"The whispers grow louder each night. Edwin Blackwood, 1923.\"\n",
+      "\n",
+      "*Clara's eyes widen, a mix of fear and intrigue washing over her.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 2**\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*Clara walks through the town, absorbing its quaint charm. She approaches a small bookstore, where she meets ELI SANDERS, early 30s, sorting through historical texts.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "Excuse me, are you Eli Sanders?\n",
+      "\n",
+      "**ELI**  \n",
+      "(looking up, smiling)  \n",
+      "The one and only. What can I do for you?\n",
+      "\n",
+      "*Clara explains her discovery, and Eli's interest is piqued. They discuss the mysterious Edwin Blackwood and the town's history.*\n",
+      "\n",
+      "**INT. EDWIN'S BASEMENT - NIGHT**\n",
+      "\n",
+      "*Clara and Eli descend into the dimly lit basement, flashlights in hand. Paintings of shadowy figures line the walls, their haunting presence undeniable.*\n",
+      "\n",
+      "**ELI**  \n",
+      "(pointing at a painting)  \n",
+      "This... this is \"The Whisperer.\" It's said to thrive on guilt and fear.\n",
+      "\n",
+      "*Clara shivers, feeling the weight of the entity's power.*\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**ACT 3**\n",
+      "\n",
+      "**INT. INHERITED FAMILY HOME - NIGHT**\n",
+      "\n",
+      "*Clara and Eli work tirelessly on a canvas, replicating Edwin's unfinished work. The room is thick with tension as shadows dance around them.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(paintbrush in hand, determined)  \n",
+      "We have to do this. For Edwin. For the town.\n",
+      "\n",
+      "*The entity manifests, a swirling darkness that threatens to engulf them. Clara confronts her deepest fears, memories of her heartbreak flashing before her eyes.*\n",
+      "\n",
+      "**CLARA**  \n",
+      "(shouting)  \n",
+      "You won't control me!\n",
+      "\n",
+      "*With a final stroke, the painting is complete. A blinding light envelops the room, and the entity's presence dissipates.*\n",
+      "\n",
+      "**EXT. COASTAL TOWN - DAY**\n",
+      "\n",
+      "*The town feels lighter, the oppressive atmosphere lifted. Clara stands outside Edwin's house, now transformed into a gallery.*\n",
+      "\n",
+      "**INT. GALLERY - DAY**\n",
+      "\n",
+      "*Clara reads the hidden journal entry from Edwin, a tear rolling down her cheek.*\n",
+      "\n",
+      "**EDWIN'S JOURNAL (V.O.)**  \n",
+      "\"To the one who set me free, thank you.\"\n",
+      "\n",
+      "*As she closes the journal, a gentle whisper echoes, leaving Clara with a sense of peace.*\n",
+      "\n",
+      "**FADE OUT.**\n",
+      "\n",
+      "---\n",
+      "\n",
+      "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that keeps audiences on the edge of their seats.\n",
+      "\n",
+      "\u001b[00m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+       "**Title: Whispering Shadows**\n",
+       "\n",
+       "**Genre:** Supernatural Thriller\n",
+       "\n",
+       "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by \n",
+       "dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious \n",
+       "aura.\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**ACT <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>**\n",
+       "\n",
+       "**INT. INHERITED FAMILY HOME - DAY**\n",
+       "\n",
+       "*The camera follows CLARA THOMPSON, late 20s, as she steps into the dusty, old house. Her eyes scan the \n",
+       "surroundings, taking in the antique furniture and faded photographs lining the walls. She sets her suitcase down, \n",
+       "letting out a deep sigh.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "<span style=\"font-weight: bold\">(</span>softly to herself<span style=\"font-weight: bold\">)</span>  \n",
+       "A fresh start.\n",
+       "\n",
+       "*She moves through the house, her footsteps echoing. Suddenly, a draft catches her attention, leading her to a \n",
+       "hidden door, slightly ajar.*\n",
+       "\n",
+       "**INT. HIDDEN ROOM - DAY**\n",
+       "\n",
+       "*Clara pushes the door open, revealing a room covered in dust. In the center, a collection of journals and \n",
+       "artifacts are stacked haphazardly. Curiosity piqued, Clara picks up a journal, flipping through its pages.*\n",
+       "\n",
+       "**JOURNAL ENTRY <span style=\"font-weight: bold\">(</span>V.O.<span style=\"font-weight: bold\">)</span>**  \n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">\"The whispers grow louder each night. Edwin Blackwood, 1923.\"</span>\n",
+       "\n",
+       "*Clara's eyes widen, a mix of fear and intrigue washing over her.*\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**ACT <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>**\n",
+       "\n",
+       "**EXT. COASTAL TOWN - DAY**\n",
+       "\n",
+       "*Clara walks through the town, absorbing its quaint charm. She approaches a small bookstore, where she meets ELI \n",
+       "SANDERS, early 30s, sorting through historical texts.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "Excuse me, are you Eli Sanders?\n",
+       "\n",
+       "**ELI**  \n",
+       "<span style=\"font-weight: bold\">(</span>looking up, smiling<span style=\"font-weight: bold\">)</span>  \n",
+       "The one and only. What can I do for you?\n",
+       "\n",
+       "*Clara explains her discovery, and Eli's interest is piqued. They discuss the mysterious Edwin Blackwood and the \n",
+       "town's history.*\n",
+       "\n",
+       "**INT. EDWIN'S BASEMENT - NIGHT**\n",
+       "\n",
+       "*Clara and Eli descend into the dimly lit basement, flashlights in hand. Paintings of shadowy figures line the \n",
+       "walls, their haunting presence undeniable.*\n",
+       "\n",
+       "**ELI**  \n",
+       "<span style=\"font-weight: bold\">(</span>pointing at a painting<span style=\"font-weight: bold\">)</span>  \n",
+       "This<span style=\"color: #808000; text-decoration-color: #808000\">...</span> this is <span style=\"color: #008000; text-decoration-color: #008000\">\"The Whisperer.\"</span> It's said to thrive on guilt and fear.\n",
+       "\n",
+       "*Clara shivers, feeling the weight of the entity's power.*\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**ACT <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>**\n",
+       "\n",
+       "**INT. INHERITED FAMILY HOME - NIGHT**\n",
+       "\n",
+       "*Clara and Eli work tirelessly on a canvas, replicating Edwin's unfinished work. The room is thick with tension as \n",
+       "shadows dance around them.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "<span style=\"font-weight: bold\">(</span>paintbrush in hand, determined<span style=\"font-weight: bold\">)</span>  \n",
+       "We have to do this. For Edwin. For the town.\n",
+       "\n",
+       "*The entity manifests, a swirling darkness that threatens to engulf them. Clara confronts her deepest fears, \n",
+       "memories of her heartbreak flashing before her eyes.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "<span style=\"font-weight: bold\">(</span>shouting<span style=\"font-weight: bold\">)</span>  \n",
+       "You won't control me!\n",
+       "\n",
+       "*With a final stroke, the painting is complete. A blinding light envelops the room, and the entity's presence \n",
+       "dissipates.*\n",
+       "\n",
+       "**EXT. COASTAL TOWN - DAY**\n",
+       "\n",
+       "*The town feels lighter, the oppressive atmosphere lifted. Clara stands outside Edwin's house, now transformed into\n",
+       "a gallery.*\n",
+       "\n",
+       "**INT. GALLERY - DAY**\n",
+       "\n",
+       "*Clara reads the hidden journal entry from Edwin, a tear rolling down her cheek.*\n",
+       "\n",
+       "**EDWIN'S JOURNAL <span style=\"font-weight: bold\">(</span>V.O.<span style=\"font-weight: bold\">)</span>**  \n",
+       "<span style=\"color: #008000; text-decoration-color: #008000\">\"To the one who set me free, thank you.\"</span>\n",
+       "\n",
+       "*As she closes the journal, a gentle whisper echoes, leaving Clara with a sense of peace.*\n",
+       "\n",
+       "**FADE OUT.**\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's \n",
+       "inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that \n",
+       "keeps audiences on the edge of their seats.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "### Task Output ###\n",
+       "**Title: Whispering Shadows**\n",
+       "\n",
+       "**Genre:** Supernatural Thriller\n",
+       "\n",
+       "**Setting:** A small, isolated coastal town with a rich history and an eerie atmosphere. The town is surrounded by \n",
+       "dense forests and cliffs overlooking a tumultuous sea, where fog often blankets the area, adding to its mysterious \n",
+       "aura.\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**ACT \u001b[1;36m1\u001b[0m**\n",
+       "\n",
+       "**INT. INHERITED FAMILY HOME - DAY**\n",
+       "\n",
+       "*The camera follows CLARA THOMPSON, late 20s, as she steps into the dusty, old house. Her eyes scan the \n",
+       "surroundings, taking in the antique furniture and faded photographs lining the walls. She sets her suitcase down, \n",
+       "letting out a deep sigh.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "\u001b[1m(\u001b[0msoftly to herself\u001b[1m)\u001b[0m  \n",
+       "A fresh start.\n",
+       "\n",
+       "*She moves through the house, her footsteps echoing. Suddenly, a draft catches her attention, leading her to a \n",
+       "hidden door, slightly ajar.*\n",
+       "\n",
+       "**INT. HIDDEN ROOM - DAY**\n",
+       "\n",
+       "*Clara pushes the door open, revealing a room covered in dust. In the center, a collection of journals and \n",
+       "artifacts are stacked haphazardly. Curiosity piqued, Clara picks up a journal, flipping through its pages.*\n",
+       "\n",
+       "**JOURNAL ENTRY \u001b[1m(\u001b[0mV.O.\u001b[1m)\u001b[0m**  \n",
+       "\u001b[32m\"The whispers grow louder each night. Edwin Blackwood, 1923.\"\u001b[0m\n",
+       "\n",
+       "*Clara's eyes widen, a mix of fear and intrigue washing over her.*\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**ACT \u001b[1;36m2\u001b[0m**\n",
+       "\n",
+       "**EXT. COASTAL TOWN - DAY**\n",
+       "\n",
+       "*Clara walks through the town, absorbing its quaint charm. She approaches a small bookstore, where she meets ELI \n",
+       "SANDERS, early 30s, sorting through historical texts.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "Excuse me, are you Eli Sanders?\n",
+       "\n",
+       "**ELI**  \n",
+       "\u001b[1m(\u001b[0mlooking up, smiling\u001b[1m)\u001b[0m  \n",
+       "The one and only. What can I do for you?\n",
+       "\n",
+       "*Clara explains her discovery, and Eli's interest is piqued. They discuss the mysterious Edwin Blackwood and the \n",
+       "town's history.*\n",
+       "\n",
+       "**INT. EDWIN'S BASEMENT - NIGHT**\n",
+       "\n",
+       "*Clara and Eli descend into the dimly lit basement, flashlights in hand. Paintings of shadowy figures line the \n",
+       "walls, their haunting presence undeniable.*\n",
+       "\n",
+       "**ELI**  \n",
+       "\u001b[1m(\u001b[0mpointing at a painting\u001b[1m)\u001b[0m  \n",
+       "This\u001b[33m...\u001b[0m this is \u001b[32m\"The Whisperer.\"\u001b[0m It's said to thrive on guilt and fear.\n",
+       "\n",
+       "*Clara shivers, feeling the weight of the entity's power.*\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**ACT \u001b[1;36m3\u001b[0m**\n",
+       "\n",
+       "**INT. INHERITED FAMILY HOME - NIGHT**\n",
+       "\n",
+       "*Clara and Eli work tirelessly on a canvas, replicating Edwin's unfinished work. The room is thick with tension as \n",
+       "shadows dance around them.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "\u001b[1m(\u001b[0mpaintbrush in hand, determined\u001b[1m)\u001b[0m  \n",
+       "We have to do this. For Edwin. For the town.\n",
+       "\n",
+       "*The entity manifests, a swirling darkness that threatens to engulf them. Clara confronts her deepest fears, \n",
+       "memories of her heartbreak flashing before her eyes.*\n",
+       "\n",
+       "**CLARA**  \n",
+       "\u001b[1m(\u001b[0mshouting\u001b[1m)\u001b[0m  \n",
+       "You won't control me!\n",
+       "\n",
+       "*With a final stroke, the painting is complete. A blinding light envelops the room, and the entity's presence \n",
+       "dissipates.*\n",
+       "\n",
+       "**EXT. COASTAL TOWN - DAY**\n",
+       "\n",
+       "*The town feels lighter, the oppressive atmosphere lifted. Clara stands outside Edwin's house, now transformed into\n",
+       "a gallery.*\n",
+       "\n",
+       "**INT. GALLERY - DAY**\n",
+       "\n",
+       "*Clara reads the hidden journal entry from Edwin, a tear rolling down her cheek.*\n",
+       "\n",
+       "**EDWIN'S JOURNAL \u001b[1m(\u001b[0mV.O.\u001b[1m)\u001b[0m**  \n",
+       "\u001b[32m\"To the one who set me free, thank you.\"\u001b[0m\n",
+       "\n",
+       "*As she closes the journal, a gentle whisper echoes, leaving Clara with a sense of peace.*\n",
+       "\n",
+       "**FADE OUT.**\n",
+       "\n",
+       "---\n",
+       "\n",
+       "**Themes:** The film explores themes of redemption, the power of art, the weight of history, and confronting one's \n",
+       "inner demons. It combines elements of suspense, mystery, and the supernatural to create an engaging narrative that \n",
+       "keeps audiences on the edge of their seats.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml)\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbooks/notebooks/news_retreival_rag_agents.ipynb

@@ -0,0 +1,511 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Av-vOZZ5v5E8"
+   },
+   "source": [
+    "# News Retreival RAG Agents\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/notebooks/news_retreival_rag_agents.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kbxBz656v5E-"
+   },
+   "source": [
+    "## Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "qq6F4sypv5E_"
+   },
+   "outputs": [],
+   "source": [
+    "# Install dependencies without output\n",
+    "%pip install langchain_community > /dev/null\n",
+    "%pip install praisonai[crewai] > /dev/null\n",
+    "%pip install duckduckgo_search > /dev/null\n",
+    "%pip install requests > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VTrlvE5qv5E_"
+   },
+   "source": [
+    "## Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "yd2zIiJ-v5FA"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import userdata\n",
+    "\n",
+    "import requests\n",
+    "import os\n",
+    "from langchain_community.document_loaders import WebBaseLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from praisonai_tools import BaseTool\n",
+    "from duckduckgo_search import DDGS\n",
+    "\n",
+    "\n",
+    "class InternetSearchTool(BaseTool):\n",
+    "    name: str = \"InternetSearchTool\"\n",
+    "    description: str = \"Search Internet for relevant information based on a query or latest news\"\n",
+    "\n",
+    "    def _run(self, query: str):\n",
+    "        ddgs = DDGS()\n",
+    "        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)\n",
+    "        return results\n",
+    "\n",
+    "\n",
+    "class NewsDBTool(BaseTool):\n",
+    "    name: str = \"NewsDBTool\"\n",
+    "    description: str = \"Fetches and stores news articles based on a specified query in a local database\"\n",
+    "\n",
+    "    def _run(self, query: str):\n",
+    "        NEWSAPI_KEY = userdata.get('NEWSAPI_KEY') or \"ENTER NEWSAPI_KEY HERE\"\n",
+    "        base_url = \"https://newsapi.org/v2/everything\"\n",
+    "\n",
+    "        params = {\n",
+    "            'q': query,\n",
+    "            'sortBy': 'publishedAt',\n",
+    "            'apiKey': NEWSAPI_KEY,\n",
+    "            'language': 'en',\n",
+    "            'pageSize': 5,\n",
+    "        }\n",
+    "\n",
+    "        response = requests.get(base_url, params=params)\n",
+    "        if response.status_code != 200:\n",
+    "            return \"Failed to retrieve news.\"\n",
+    "\n",
+    "        articles = response.json().get('articles', [])\n",
+    "        if not articles:\n",
+    "            return \"No articles found.\"\n",
+    "\n",
+    "        all_splits = []\n",
+    "\n",
+    "        for article in articles:\n",
+    "            loader = WebBaseLoader(article['url'])\n",
+    "            docs = loader.load()\n",
+    "\n",
+    "            text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
+    "            splits = text_splitter.split_documents(docs)\n",
+    "            all_splits.extend(splits)\n",
+    "\n",
+    "        if all_splits:\n",
+    "            embedding_function = OpenAIEmbeddings()\n",
+    "            vectorstore = Chroma.from_documents(all_splits, embedding=embedding_function, persist_directory=\"./chroma_db\")\n",
+    "            vectorstore.persist()\n",
+    "            return \"News articles processed and stored in the database.\"\n",
+    "        else:\n",
+    "            return \"No content available for processing.\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "class RetrieveNewsTool(BaseTool):\n",
+    "    name: str = \"RetrieveNewsTool\"\n",
+    "    description: str = \"Retrieves news articles from the local database using a search query\"\n",
+    "\n",
+    "    def _run(self, query: str):\n",
+    "        embedding_function = OpenAIEmbeddings()\n",
+    "        vectorstore = Chroma(persist_directory=\"./chroma_db\", embedding_function=embedding_function)\n",
+    "\n",
+    "        retriever = vectorstore.similarity_search(query)\n",
+    "\n",
+    "        if retriever:\n",
+    "            return [{\"content\": result.page_content, \"metadata\": result.metadata} for result in retriever]\n",
+    "        else:\n",
+    "            return \"No relevant articles found in the database.\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QLL72soBv5FA"
+   },
+   "source": [
+    "## YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "5olaIncMv5FA"
+   },
+   "outputs": [],
+   "source": [
+    "agent_yaml = \"\"\"\n",
+    "framework: \"crewai\"\n",
+    "topic: \"News Analysis and Reporting\"\n",
+    "roles:\n",
+    "  news_search_agent:\n",
+    "    role: \"News Researcher\"\n",
+    "    backstory: |\n",
+    "      You are an experienced News Researcher, skilled in finding and summarizing key points from the latest news articles.\n",
+    "    goal: |\n",
+    "      Conduct in-depth searches on the latest news, summarize key points for each article, and make the information available for further review.\n",
+    "    verbose: true\n",
+    "    allow_delegation: true\n",
+    "    tools:\n",
+    "      - \"NewsDBTool\"\n",
+    "      - \"RetrieveNewsTool\"\n",
+    "    tasks:\n",
+    "      search_and_summarize_news_task:\n",
+    "        description: |\n",
+    "          Search for the latest news articles based on a specified topic, process them, and generate key points for each article.\n",
+    "        expected_output: |\n",
+    "          A list of key points from recent articles related to the specified topic, ready for further summarization.\n",
+    "  writer_agent:\n",
+    "    role: \"News Content Writer\"\n",
+    "    backstory: |\n",
+    "      As a News Content Writer, you specialize in transforming raw news data into detailed summaries, adding depth and clarity.\n",
+    "    goal: |\n",
+    "      Review the collected news data, verify its relevance, search for additional context, and create comprehensive summaries for each topic.\n",
+    "    verbose: true\n",
+    "    allow_delegation: true\n",
+    "    tools:\n",
+    "      - \"RetrieveNewsTool\"\n",
+    "      - \"InternetSearchTool\"\n",
+    "    tasks:\n",
+    "      analyze_and_write_news_task:\n",
+    "        description: |\n",
+    "          Identify each topic, verify relevance using the retrieved data, then conduct additional web searches to add depth. Summarize each topic in detail.\n",
+    "        expected_output: |\n",
+    "          An in-depth summary of each identified news topic, incorporating all relevant information retrieved from the database and online searches.\n",
+    "dependencies: []\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dKtPRYO1v5FB"
+   },
+   "source": [
+    "## Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "eXK7Tw72v5FB",
+    "outputId": "c944365a-c445-47b6-f7aa-fc87ae3e20d6"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m\u001b[95m [2024-11-02 17:51:51][DEBUG]: == Working Agent: News Researcher\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 17:51:51][INFO]: == Starting Task: Search for the latest news articles based on a specified topic, process them, and generate key points for each article.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI need to conduct a search for the latest news articles on a specified topic to gather key points for further summarization. Since the topic is not specified yet, I will first define a general topic to search for. \n",
+      "\n",
+      "For this exercise, I'll choose the topic \"climate change\" as it is currently a relevant and pressing issue. \n",
+      "\n",
+      "Action: NewsDBTool  \n",
+      "Action Input: {\"query\": \"climate change\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n",
+      "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: Since Chroma 0.4.x the manual persistence method is no longer supported as docs are automatically persisted.\n",
+      "  warn_deprecated(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "News articles processed and stored in the database.\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have successfully stored news articles related to \"climate change\" in the database. Now, I will retrieve these articles to summarize the key points from each one. \n",
+      "\n",
+      "Action: RetrieveNewsTool  \n",
+      "Action Input: {\"query\": \"climate change\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'content': 'I’m hearing so many stories like that from the survivors of the climate-intensified hurricanes that trashed western North Carolina, coastal Florida, and other parts of the Southeastern USA. From the victims of a climate-intensified catastrophe that has wrecked whole towns and torn out roads, flattened forests, washed away homes and put parts of Asheville underwater. I don’t want any more disasters like that, and I’m a climate activist to try to keep nature from getting more violent and destructive, which it will if we keep being violent and destructive toward the climate. But I do want us to know who we are, and how hungry we are for meaning, purpose, and connection, and sometimes disaster lets us see that.', 'metadata': {'language': 'en-US', 'source': 'https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/', 'title': 'Rebecca Solnit: We, the People, Can Make a Better Future ‹ Literary  Hub'}}, {'content': 'When it comes to the climate we want faith in the future, we want the symphony of life to continue with the harmonies, the beauties, the integration of the parts into one harmonious whole to continue. Part of what gives our lives meaning is the confidence or at least hope that these good things will persevere beyond us, that there will be bison grazing the prairies in the year 2124, that there will be whales migrating in the oceans, that wildflowers will bloom in spring and pollinators will come for the nectar and leave with the pollen, that the people we love who are one or six or seventeen or their grandchildren will have a chance to enjoy some of the things we have, that there will be joy and beauty and possibility in the year 2074 and after.', 'metadata': {'language': 'en-US', 'source': 'https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/', 'title': 'Rebecca Solnit: We, the People, Can Make a Better Future ‹ Literary  Hub'}}, {'content': 'Barriers to Environmental Sustainability', 'metadata': {'description': 'Investigate the role of AI in enhancing logistics efficiency and promoting environmental sustainability through advanced route optimization techniques.', 'language': 'en', 'source': 'https://dzone.com/articles/smart-routing-using-ai-for-efficient-logistics', 'title': 'Smart Routing Using AI for Efficient Logistics'}}, {'content': 'Polls offer the false promise of knowing what is going to happen, but what is going to happen in this election is what campaigners, activists, and the electorate make happen. It is not yet decided. We are deciding it with what we do, as voters, as organizers, as voices for truth, justice, inclusion, the reality of the climate crisis and the importance of acting on it. In June, I got to meet one of my heroes, Congressman Jamie Raskin when he gave a keynote for the Third Act chapters in DC, Virginia and Maryland. (Third Act is a climate group founded by Bill McKibben for US people over 60; I’m on its board.) He gave me his memoir of prosecuting the impeachment of Trump after January 6, right after his beloved son Tommy had died by suicide, and there’s a dazzling passage in it that reminds us of the power of participation.\\nHe writes that, during his first campaign, there was', 'metadata': {'language': 'en-US', 'source': 'https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/', 'title': 'Rebecca Solnit: We, the People, Can Make a Better Future ‹ Literary  Hub'}}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have retrieved several articles related to \"climate change.\" Now, I will summarize the key points from each article to make the information available for further review.\n",
+      "\n",
+      "Final Answer:\n",
+      "1. **Title: Rebecca Solnit: We, the People, Can Make a Better Future**  \n",
+      "   - **Source:** [Literary Hub](https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/)  \n",
+      "   - **Key Points:**\n",
+      "     - Survivors of climate-intensified hurricanes have shared their stories, revealing the devastation in places like western North Carolina and coastal Florida.\n",
+      "     - The author emphasizes the need to address climate violence to prevent future disasters and highlights the human desire for meaning and connection, especially in the face of catastrophe.\n",
+      "     - A hopeful vision for the future includes the persistence of life and beauty, such as bison on prairies and whales in oceans, which gives life meaning.\n",
+      "\n",
+      "2. **Title: Smart Routing Using AI for Efficient Logistics**  \n",
+      "   - **Source:** [DZone](https://dzone.com/articles/smart-routing-using-ai-for-efficient-logistics)  \n",
+      "   - **Key Points:**\n",
+      "     - The article discusses the role of AI in improving logistics efficiency.\n",
+      "     - It highlights advanced route optimization techniques that can promote environmental sustainability.\n",
+      "     - The integration of AI is presented as a crucial factor in overcoming barriers to environmental sustainability.\n",
+      "\n",
+      "3. **Title: Rebecca Solnit: We, the People, Can Make a Better Future** (continued)  \n",
+      "   - **Source:** [Literary Hub](https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/)  \n",
+      "   - **Key Points:**\n",
+      "     - The article stresses the importance of participation in elections and activism concerning climate change.\n",
+      "     - It references a keynote from Congressman Jamie Raskin, who inspires action and highlights the importance of organizing and participating in the democratic process.\n",
+      "     - The narrative suggests that the future of climate action is not predetermined; it is shaped by the actions of voters and activists.\n",
+      "\n",
+      "These key points provide an overview of the current discourse around climate change, touching on personal stories, technological advancements, and the importance of civic engagement.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-02 17:52:11][DEBUG]: == [News Researcher] Task output: 1. **Title: Rebecca Solnit: We, the People, Can Make a Better Future**  \n",
+      "   - **Source:** [Literary Hub](https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/)  \n",
+      "   - **Key Points:**\n",
+      "     - Survivors of climate-intensified hurricanes have shared their stories, revealing the devastation in places like western North Carolina and coastal Florida.\n",
+      "     - The author emphasizes the need to address climate violence to prevent future disasters and highlights the human desire for meaning and connection, especially in the face of catastrophe.\n",
+      "     - A hopeful vision for the future includes the persistence of life and beauty, such as bison on prairies and whales in oceans, which gives life meaning.\n",
+      "\n",
+      "2. **Title: Smart Routing Using AI for Efficient Logistics**  \n",
+      "   - **Source:** [DZone](https://dzone.com/articles/smart-routing-using-ai-for-efficient-logistics)  \n",
+      "   - **Key Points:**\n",
+      "     - The article discusses the role of AI in improving logistics efficiency.\n",
+      "     - It highlights advanced route optimization techniques that can promote environmental sustainability.\n",
+      "     - The integration of AI is presented as a crucial factor in overcoming barriers to environmental sustainability.\n",
+      "\n",
+      "3. **Title: Rebecca Solnit: We, the People, Can Make a Better Future** (continued)  \n",
+      "   - **Source:** [Literary Hub](https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/)  \n",
+      "   - **Key Points:**\n",
+      "     - The article stresses the importance of participation in elections and activism concerning climate change.\n",
+      "     - It references a keynote from Congressman Jamie Raskin, who inspires action and highlights the importance of organizing and participating in the democratic process.\n",
+      "     - The narrative suggests that the future of climate action is not predetermined; it is shaped by the actions of voters and activists.\n",
+      "\n",
+      "These key points provide an overview of the current discourse around climate change, touching on personal stories, technological advancements, and the importance of civic engagement.\n",
+      "\n",
+      "\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 17:52:11][DEBUG]: == Working Agent: News Content Writer\u001b[00m\n",
+      "\u001b[1m\u001b[95m [2024-11-02 17:52:11][INFO]: == Starting Task: Identify each topic, verify relevance using the retrieved data, then conduct additional web searches to add depth. Summarize each topic in detail.\n",
+      "\u001b[00m\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3mI will start by reviewing the collected news data to identify the main topics, verify their relevance, and then conduct additional web searches to gather more context. The topics identified are climate change activism and technology's role in logistics and environmental sustainability.\n",
+      "\n",
+      "Action: RetrieveNewsTool  \n",
+      "Action Input: {\"query\": \"climate change activism\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'content': 'I’m hearing so many stories like that from the survivors of the climate-intensified hurricanes that trashed western North Carolina, coastal Florida, and other parts of the Southeastern USA. From the victims of a climate-intensified catastrophe that has wrecked whole towns and torn out roads, flattened forests, washed away homes and put parts of Asheville underwater. I don’t want any more disasters like that, and I’m a climate activist to try to keep nature from getting more violent and destructive, which it will if we keep being violent and destructive toward the climate. But I do want us to know who we are, and how hungry we are for meaning, purpose, and connection, and sometimes disaster lets us see that.', 'metadata': {'language': 'en-US', 'source': 'https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/', 'title': 'Rebecca Solnit: We, the People, Can Make a Better Future ‹ Literary  Hub'}}, {'content': 'Polls offer the false promise of knowing what is going to happen, but what is going to happen in this election is what campaigners, activists, and the electorate make happen. It is not yet decided. We are deciding it with what we do, as voters, as organizers, as voices for truth, justice, inclusion, the reality of the climate crisis and the importance of acting on it. In June, I got to meet one of my heroes, Congressman Jamie Raskin when he gave a keynote for the Third Act chapters in DC, Virginia and Maryland. (Third Act is a climate group founded by Bill McKibben for US people over 60; I’m on its board.) He gave me his memoir of prosecuting the impeachment of Trump after January 6, right after his beloved son Tommy had died by suicide, and there’s a dazzling passage in it that reminds us of the power of participation.\\nHe writes that, during his first campaign, there was', 'metadata': {'language': 'en-US', 'source': 'https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/', 'title': 'Rebecca Solnit: We, the People, Can Make a Better Future ‹ Literary  Hub'}}, {'content': 'Barriers to Environmental Sustainability', 'metadata': {'description': 'Investigate the role of AI in enhancing logistics efficiency and promoting environmental sustainability through advanced route optimization techniques.', 'language': 'en', 'source': 'https://dzone.com/articles/smart-routing-using-ai-for-efficient-logistics', 'title': 'Smart Routing Using AI for Efficient Logistics'}}, {'content': 'Rebecca Solnit\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tWriter, historian, and activist Rebecca Solnit is the author of twenty-five books on feminism, environmental and urban history, popular power, social change and insurrection, wandering and walking, hope and catastrophe. She co-edited the 2023 anthology Not Too Late: Changing the Climate Story from Despair to Possibility. Her other books include Orwell’s Roses; Recollections of My Nonexistence; Hope in the Dark; Men Explain Things to Me; A Paradise Built in Hell: The Extraordinary Communities that Arise in Disaster; and A Field Guide to Getting Lost. A product of the California public education system from kindergarten to graduate school, she writes regularly for the Guardian, serves on the board of the climate group Oil Change International, and in 2022 launched the climate project Not Too Late (nottoolateclimate.com).\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\nhttp://rebeccasolnit.net\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\nPrevious Article\\nSuzanne Nossel is stepping down as PEN America CEO.', 'metadata': {'language': 'en-US', 'source': 'https://lithub.com/rebecca-solnit-we-the-people-can-make-a-better-future-for-all/', 'title': 'Rebecca Solnit: We, the People, Can Make a Better Future ‹ Literary  Hub'}}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have retrieved relevant content related to climate change activism and the role of AI in logistics. The next step is to conduct additional web searches to gather more context and depth on these topics.\n",
+      "\n",
+      "Action: InternetSearchTool  \n",
+      "Action Input: {\"query\": \"climate change activism news 2023\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': \"Thousands march in New York to demand that Biden 'end fossil fuels' - NPR\", 'href': 'https://www.npr.org/2023/09/17/1200027693/thousands-expected-to-march-in-new-york-to-demand-that-biden-end-fossil-fuels', 'body': 'Thousands of activists, indigenous groups, students and others take to the streets of New York for the March to End Fossil Fuels protest on Sunday, Sept. 17, 2023 in New York. Spencer Platt/Getty ...'}, {'title': 'Judge sides with young activists in first-of-its-kind climate change ...', 'href': 'https://www.npr.org/2023/08/14/1193780700/montana-climate-change-trial-ruling', 'body': 'In a landmark climate change trial, a Montana judge sides with youth activists The ruling follows a first-of-its-kind trial in the U.S., adding to a small number of legal decisions around the ...'}, {'title': \"The Most Important Climate Stories of 2023 Aren't All Bad News\", 'href': 'https://www.scientificamerican.com/article/the-most-important-climate-stories-of-2023-arent-all-bad-news/', 'body': 'In 2023 climate news was a mixed bag: we saw unrelenting heat but also glimmers of hope of progress toward reducing greenhouse gas emissions. By Andrea Thompson. A person rides a bicycle as heat ...'}, {'title': 'Meet the Young Activists behind the New Youth Climate Lawsuit', 'href': 'https://www.scientificamerican.com/article/meet-the-young-activists-behind-the-new-youth-climate-lawsuit/', 'body': 'Like the other young activists who agreed to join in the lawsuit, Williams, a Los Angeles resident, said climate change is increasingly disrupting her life. She loves soccer, but smoke from ...'}, {'title': 'Climate Action 30 - Business Insider', 'href': 'https://www.businessinsider.com/climate-action-30-global-leaders-climate-solutions-2023-11?op=1', 'body': 'Climate Action 30 spotlights global activists, academics, founders, executives, and nonprofit and public sector leaders tackling the climate crisis. ... the Harvard Chan C-Change Youth Summit as ...'}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have gathered relevant information regarding climate change activism from both the retrieved articles and the recent web search results. Now, I will also gather more context on the role of AI in logistics to comprehensively summarize both topics.\n",
+      "\n",
+      "Action: InternetSearchTool  \n",
+      "Action Input: {\"query\": \"AI in logistics 2023 news\"}  \u001b[0m"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
+      "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
+      "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[95m \n",
+      "\n",
+      "[{'title': 'How Global Companies Use AI to Prevent Supply Chain Disruptions', 'href': 'https://hbr.org/2023/11/how-global-companies-use-ai-to-prevent-supply-chain-disruptions', 'body': 'Our recent research at Walmart, Tyson Foods, Koch Industries, Maersk, Siemens, and Unilever revealed how these global companies are using advanced AI technologies to plan for and adapt to supply ...'}, {'title': 'AI in Logistics: A Sea of Potential Amidst Waves of Change', 'href': 'https://www.freightos.com/ai-in-logistics-a-sea-of-potential-amidst-waves-of-change/', 'body': 'A significant share (84%) believe AI might trim the workforce. Specifically, 53% anticipate a limited reduction, while 31% foresee substantial cuts. Enterprises, more than SMBs, sense this looming change, with 38% vs. 26% bracing for significant reductions. Notably, a handful of SMBs (7%) harbor hopes of AI birthing new roles, a sentiment ...'}, {'title': 'Logistics Trends 2023/2024: Which Direction for AI?', 'href': 'https://dhl-freight-connections.com/en/trends/logistics-trends-2023-2024/', 'body': 'Review 2023, Outlook 2024 - the Trends Carry On. The keyword for our 2023 trends was \"resilience\". After the supply chain problems caused by the coronavirus and the war in Ukraine, we felt that innovative technologies for the stabilization and robustness of supply chains should be a priority. And as it turned out in 2023, it was not just ...'}, {'title': 'The True Role Of AI In Logistics - Forbes', 'href': 'https://www.forbes.com/councils/forbestechcouncil/2023/08/17/the-true-role-of-ai-in-logistics/', 'body': 'True Artificial Intelligence: Implement Warehouse Automation And Robotics. The integration of AI and robotics has led to significant advancements in warehouse automation.'}, {'title': 'AI in Logistics: Top 23 Use Cases and Applications in 2023 - Markovate', 'href': 'https://markovate.com/blog/ai-applications-in-logistics/', 'body': \"10. AI in Logistics for Supply Chain Transparency. In any supply chain, stuff happens. With AI, you can keep tabs on that stuff every step of the way, from the factory floor to the customer's door, making it easier to fix issues quickly. 11. AI in Logistics for Customer Service.\"}]\n",
+      "\u001b[00m\n",
+      "\u001b[32;1m\u001b[1;3mThought: I have successfully gathered extensive information on climate change activism and the role of AI in logistics. Now, I will synthesize this information into comprehensive summaries for each topic, ensuring that I cover both the retrieved data and the additional context from online searches.\n",
+      "\n",
+      "### Climate Change Activism\n",
+      "\n",
+      "In recent discussions about climate change activism, notable voices, including Rebecca Solnit, emphasize the critical importance of civic participation and collective action in combating climate crises. Survivors of climate-intensified disasters, such as hurricanes affecting western North Carolina and coastal Florida, have shared harrowing accounts of the devastation they faced. These narratives highlight not only the physical destruction of homes and communities but also the emotional and psychological toll of such disasters. Solnit articulates a yearning for meaning and connection that often surfaces during catastrophic events, suggesting that these experiences can galvanize a deeper commitment to environmental activism.\n",
+      "\n",
+      "A pivotal aspect of contemporary climate activism is the role of organized movements and legal actions. For instance, a recent landmark ruling in Montana demonstrated the impact of youth-led initiatives, where a judge sided with young activists in a significant climate change trial. This case is part of a growing trend where the voices of younger generations are increasingly recognized in discussions surrounding climate policy and action.\n",
+      "\n",
+      "Moreover, the March to End Fossil Fuels, held in September 2023, saw thousands of activists, including indigenous groups and students, taking to the streets of New York to demand decisive action from political leaders, particularly President Biden. This demonstration underscores the urgency felt by many regarding the need for a swift transition away from fossil fuel dependency.\n",
+      "\n",
+      "In summary, the current climate activism landscape is marked by a blend of personal testimonies, collective demonstrations, and legal battles, all pushing for a transformative approach to environmental policy and action. The future of climate action is not predetermined; rather, it is shaped by the involvement of voters and activists who are committed to advocating for change.\n",
+      "\n",
+      "### The Role of AI in Logistics and Environmental Sustainability\n",
+      "\n",
+      "As industries continue to evolve, the integration of Artificial Intelligence (AI) in logistics is emerging as a crucial factor for enhancing efficiency and promoting environmental sustainability. Recent advancements in AI technologies are being leveraged by global companies like Walmart, Tyson Foods, and Unilever to navigate and adapt to supply chain disruptions, particularly in light of challenges posed by the COVID-19 pandemic and geopolitical tensions.\n",
+      "\n",
+      "AI's application in logistics includes advanced route optimization techniques that can significantly reduce carbon footprints associated with transportation. By analyzing vast amounts of data, AI systems can identify the most efficient routes for delivery, minimize fuel consumption, and promote sustainable practices within supply chains. This not only benefits the environment but also enhances operational efficiency, allowing companies to respond more agilely to changing market demands.\n",
+      "\n",
+      "Furthermore, the discourse surrounding AI in logistics highlights concerns about workforce displacement, with many anticipating that automation may lead to job reductions. However, there is also optimism that AI could create new roles and opportunities, particularly in areas focused on sustainability and innovation.\n",
+      "\n",
+      "In light of these developments, the ongoing integration of AI in logistics represents a dual opportunity: to streamline operations while also addressing urgent environmental challenges. As businesses increasingly adopt AI-driven solutions, the potential for significant improvements in logistics efficiency and sustainability becomes more pronounced, paving the way for a greener future.\n",
+      "\n",
+      "Final Answer: The detailed summaries above encapsulate the current discourse surrounding climate change activism, highlighting personal stories, collective actions, and the pivotal role of civic engagement. Additionally, the summary on AI in logistics illustrates how technological advancements are being harnessed to enhance operational efficiency and promote environmental sustainability, reflecting a growing recognition of the interconnectedness of technology and ecological responsibility.\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[1m\u001b[92m [2024-11-02 17:52:30][DEBUG]: == [News Content Writer] Task output: The detailed summaries above encapsulate the current discourse surrounding climate change activism, highlighting personal stories, collective actions, and the pivotal role of civic engagement. Additionally, the summary on AI in logistics illustrates how technological advancements are being harnessed to enhance operational efficiency and promote environmental sustainability, reflecting a growing recognition of the interconnectedness of technology and ecological responsibility.\n",
+      "\n",
+      "\u001b[00m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
+       "The detailed summaries above encapsulate the current discourse surrounding climate change activism, highlighting \n",
+       "personal stories, collective actions, and the pivotal role of civic engagement. Additionally, the summary on AI in \n",
+       "logistics illustrates how technological advancements are being harnessed to enhance operational efficiency and \n",
+       "promote environmental sustainability, reflecting a growing recognition of the interconnectedness of technology and \n",
+       "ecological responsibility.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "### Task Output ###\n",
+       "The detailed summaries above encapsulate the current discourse surrounding climate change activism, highlighting \n",
+       "personal stories, collective actions, and the pivotal role of civic engagement. Additionally, the summary on AI in \n",
+       "logistics illustrates how technological advancements are being harnessed to enhance operational efficiency and \n",
+       "promote environmental sustainability, reflecting a growing recognition of the interconnectedness of technology and \n",
+       "ecological responsibility.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from praisonai import PraisonAI\n",
+    "from google.colab import userdata\n",
+    "\n",
+    "# Create a PraisonAI instance with the agent_yaml content\n",
+    "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[NewsDBTool, RetrieveNewsTool, InternetSearchTool])\n",
+    "\n",
+    "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
+    "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "\n",
+    "# Run PraisonAI\n",
+    "result = praisonai.run()\n",
+    "\n",
+    "# Print the result\n",
+    "print(result) # 10/10\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "gmYOTfioyTNj"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}