Merge pull request #534 from MervinPraison/develop

2.2.18

.eslintrc

@@ -1,10 +0,0 @@
-{
-    "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/workflows/claude.yml

@@ -0,0 +1,37 @@
+name: Claude Code
+
+on:
+  issue_comment:
+    types: [created]
+  pull_request_review_comment:
+    types: [created]
+  issues:
+    types: [opened, assigned]
+  pull_request_review:
+    types: [submitted]
+
+jobs:
+  claude:
+    if: |
+      (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
+      (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: read
+      issues: read
+      id-token: write
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+
+      - name: Run Claude Code
+        id: claude
+        uses: anthropics/claude-code-action@beta
+        with:
+          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
+

.github/workflows/python-package.yml

@@ -19,22 +19,48 @@ jobs:
         python-version: ["3.10", "3.11"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
+
     - name: Install dependencies
       run: |
+        cd src/praisonai
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest
+        python -m pip install ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        python -m pip install duckduckgo_search
+        python -m pip install pytest-asyncio pytest-cov
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        
+    - name: Debug API Key Status
+      run: |
+        echo "🔍 Checking API key availability..."
+        if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
+          echo "✅ GitHub secret OPENAI_API_KEY is available"
+          echo "🔑 API key starts with: $(echo "$OPENAI_API_KEY" | cut -c1-7)..."
+        else
+          echo "⚠️ GitHub secret OPENAI_API_KEY is NOT set - using fallback"
+          echo "🔑 Using fallback key: sk-test-key..."
+        fi
+        echo "🌐 API Base: $OPENAI_API_BASE"
+        echo "🤖 Model: $OPENAI_MODEL_NAME"
+        
     # - 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
+    - name: Test with pytest
+      run: |
+        cd src/praisonai && python -m pytest

.github/workflows/python-publish.yml

@@ -21,9 +21,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: '3.x'
     - name: Install dependencies
@@ -31,9 +31,10 @@ jobs:
         python -m pip install --upgrade pip
         pip install build
     - name: Build package
-      run: python -m build
+      run: python -m build src/praisonai
     - name: Publish package
       uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
       with:
         user: __token__
         password: ${{ secrets.PYPI_API_TOKEN }}
+        packages_dir: src/praisonai/dist/

.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 ${{ steps.get_commit.outputs.commit_hash }}
+
+    - name: Setup Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.11'
+        cache: 'pip'
+
+    - name: Install Poetry
+      run: |
+        curl -sSL https://install.python-poetry.org | python3 -
+        echo "$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: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Upload test artifacts
+      if: always()
+      uses: actions/upload-artifact@v4
+      with:
+        name: praisonai-output-${{ 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/test-comprehensive.yml

@@ -0,0 +1,178 @@
+name: Comprehensive Test Suite
+
+on:
+  workflow_dispatch:  # Allow manual triggering
+    inputs:
+      test_type:
+        description: 'Type of tests to run'
+        required: true
+        default: 'all'
+        type: choice
+        options:
+        - all
+        - unit
+        - integration
+        - fast
+        - performance
+        - frameworks
+        - autogen
+        - crewai
+  release:
+    types: [published, prereleased]
+  schedule:
+    # Run comprehensive tests weekly on Sundays at 3 AM UTC
+    - cron: '0 3 * * 0'
+
+jobs:
+  comprehensive-test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.11"]
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+
+    - name: Install dependencies
+      run: |
+        cd src/praisonai
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov pytest-benchmark
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+
+    - name: Run Comprehensive Test Suite
+      run: |
+        # Determine test type from input or default to 'all'
+        TEST_TYPE="${{ github.event.inputs.test_type || 'all' }}"
+        
+        echo "🧪 Running comprehensive test suite (type: $TEST_TYPE)"
+        
+        case $TEST_TYPE in
+          "unit")
+            cd src/praisonai && python tests/test_runner.py --pattern unit
+            ;;
+          "integration")
+            cd src/praisonai && python tests/test_runner.py --pattern integration
+            ;;
+          "fast")
+            cd src/praisonai && python tests/test_runner.py --pattern fast
+            ;;
+          "performance")
+            cd src/praisonai && python tests/test_runner.py --pattern "performance"
+            ;;
+          "frameworks")
+            cd src/praisonai && python tests/test_runner.py --pattern frameworks
+            ;;
+          "autogen")
+            cd src/praisonai && python tests/test_runner.py --pattern autogen
+            ;;
+          "crewai")
+            cd src/praisonai && python tests/test_runner.py --pattern crewai
+            ;;
+          "all"|*)
+            cd src/praisonai && python tests/test_runner.py --pattern all
+            ;;
+        esac
+
+    - name: Generate Comprehensive Test Report
+      if: always()
+      run: |
+        echo "# 📋 Comprehensive Test Report" > comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        echo "**Python Version:** ${{ matrix.python-version }}" >> comprehensive_report.md
+        echo "**Test Type:** ${{ github.event.inputs.test_type || 'all' }}" >> comprehensive_report.md
+        echo "**Trigger:** ${{ github.event_name }}" >> comprehensive_report.md
+        echo "**Date:** $(date -u)" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        
+        echo "## 🧪 Test Categories Covered:" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        echo "### Unit Tests:" >> comprehensive_report.md
+        echo "- ✅ Core agent functionality" >> comprehensive_report.md
+        echo "- ✅ Async operations" >> comprehensive_report.md
+        echo "- ✅ Tool integrations" >> comprehensive_report.md
+        echo "- ✅ UI components" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        
+        echo "### Integration Tests:" >> comprehensive_report.md
+        echo "- ✅ MCP (Model Context Protocol)" >> comprehensive_report.md
+        echo "- ✅ RAG (Retrieval Augmented Generation)" >> comprehensive_report.md
+        echo "- ✅ Base URL API mapping" >> comprehensive_report.md
+        echo "- ✅ Multi-agent workflows" >> comprehensive_report.md
+        echo "- ✅ AutoGen framework integration" >> comprehensive_report.md
+        echo "- ✅ CrewAI framework integration" >> comprehensive_report.md
+        echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
+        echo "- 🖥️ UI frameworks (Gradio, Streamlit)" >> comprehensive_report.md
+        echo "- 📊 Memory and persistence" >> comprehensive_report.md
+        echo "- 🌐 Multi-modal capabilities" >> comprehensive_report.md
+
+        echo "- ✅ AutoGen framework integration" >> comprehensive_report.md
+        echo "- ✅ CrewAI framework integration" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        
+        echo "### Key Features Tested:" >> comprehensive_report.md
+        echo "- 🤖 Agent creation and configuration" >> comprehensive_report.md
+        echo "- 📋 Task management and execution" >> comprehensive_report.md
+        echo "- 🔄 Sync/async workflows" >> comprehensive_report.md
+        echo "- 🛠️ Custom tools and error handling" >> comprehensive_report.md
+        echo "- 🧠 Knowledge bases and RAG" >> comprehensive_report.md
+        echo "- 🔌 MCP server connections" >> comprehensive_report.md
+        echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
+
+    - name: Upload Comprehensive Test Results
+      uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: comprehensive-test-results-python-${{ matrix.python-version }}
+        path: |
+          comprehensive_report.md
+          htmlcov/
+          coverage.xml
+          .coverage
+        retention-days: 30
+
+  test-matrix-summary:
+    runs-on: ubuntu-latest
+    needs: comprehensive-test
+    if: always()
+    
+    steps:
+    - name: Generate Matrix Summary
+      run: |
+        echo "# 🎯 Test Matrix Summary" > matrix_summary.md
+        echo "" >> matrix_summary.md
+        echo "## Python Version Results:" >> matrix_summary.md
+        echo "- Python 3.10: ${{ needs.comprehensive-test.result }}" >> matrix_summary.md  
+        echo "- Python 3.11: ${{ needs.comprehensive-test.result }}" >> matrix_summary.md
+        echo "" >> matrix_summary.md
+        echo "## Overall Status:" >> matrix_summary.md
+        if [ "${{ needs.comprehensive-test.result }}" == "success" ]; then
+          echo "✅ **All tests passed across all Python versions!**" >> matrix_summary.md
+        else
+          echo "❌ **Some tests failed. Check individual job logs for details.**" >> matrix_summary.md
+        fi
+        
+    - name: Upload Matrix Summary
+      uses: actions/upload-artifact@v4
+      with:
+        name: test-matrix-summary
+        path: matrix_summary.md
+        retention-days: 30 

.github/workflows/test-core.yml

@@ -0,0 +1,272 @@
+name: Core Tests
+
+on: 
+  push:
+    branches: [ main, develop ]
+  pull_request:
+    branches: [ main, develop ]
+
+jobs:
+  test-core:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.11]
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+
+    - name: Install dependencies
+      run: |
+        cd src/praisonai
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+        # Also export to current shell session for immediate availability
+        export OPENAI_API_KEY="${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}"
+        export OPENAI_API_BASE="${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}"
+        export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}"
+        export LOGLEVEL=DEBUG
+        # Verify immediate availability
+        echo "🔧 Immediate verification in same step:"
+        echo "  OPENAI_API_KEY length in current session: ${#OPENAI_API_KEY}"
+        echo "  OPENAI_API_KEY starts with sk-: $(echo "$OPENAI_API_KEY" | grep -q '^sk-' && echo 'YES' || echo 'NO')"
+
+    - name: Debug API Key Status
+      run: |
+        echo "🔍 Checking API key availability..."
+        if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
+          echo "✅ GitHub secret OPENAI_API_KEY is available"
+          echo "🔑 API key starts with: $(echo "$OPENAI_API_KEY" | cut -c1-7)..."
+        else
+          echo "⚠️ GitHub secret OPENAI_API_KEY is NOT set - using fallback"
+          echo "🔑 Using fallback key: sk-test-key..."
+        fi
+        echo "🌐 API Base: $OPENAI_API_BASE"
+        echo "🤖 Model: $OPENAI_MODEL_NAME"
+        echo "🐛 Log Level: $LOGLEVEL"
+        echo "📊 Environment Check:"
+        echo "  - OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
+        echo "  - OPENAI_API_BASE: $OPENAI_API_BASE"
+        echo "  - OPENAI_MODEL_NAME: $OPENAI_MODEL_NAME"
+        echo "  - LOGLEVEL: $LOGLEVEL"
+        echo "🔍 Is API key actually set?"
+        echo "  - API key starts with sk-: $(echo "$OPENAI_API_KEY" | grep -q '^sk-' && echo 'YES' || echo 'NO')"
+        echo "  - API key is not test key: $([ "$OPENAI_API_KEY" != 'sk-test-key-for-github-actions-testing-only-not-real' ] && echo 'YES' || echo 'NO')"
+
+    - name: Debug Environment Variables Raw
+      run: |
+        echo "🔧 Raw environment variable check:"
+        echo "OPENAI_API_KEY set: $(if [ -n "$OPENAI_API_KEY" ]; then echo 'YES'; else echo 'NO'; fi)"
+        echo "OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
+        echo "OPENAI_API_KEY first 10 chars: ${OPENAI_API_KEY:0:10}"
+        echo "OPENAI_API_KEY last 5 chars: ${OPENAI_API_KEY: -5}"
+        printenv | grep OPENAI || echo "No OPENAI env vars found"
+
+    - name: Debug Python Environment Variables  
+      run: |
+        python -c "
+        import os
+        print('🐍 Python Environment Variable Check:')
+        api_key = os.environ.get('OPENAI_API_KEY', 'NOT_SET')
+        if api_key != 'NOT_SET':
+            print(f'  ✅ OPENAI_API_KEY: {api_key[:7]}... (length: {len(api_key)})')
+        else:
+            print('  ❌ OPENAI_API_KEY: NOT_SET')
+        print(f'  🌐 OPENAI_API_BASE: {os.environ.get(\"OPENAI_API_BASE\", \"NOT_SET\")}')
+        print(f'  🤖 OPENAI_MODEL_NAME: {os.environ.get(\"OPENAI_MODEL_NAME\", \"NOT_SET\")}')
+        print(f'  📋 All OPENAI env vars:')
+        for key, value in os.environ.items():
+            if key.startswith('OPENAI'):
+                print(f'    {key}: {value[:10] if len(value) > 10 else value}...')
+        "
+
+    - name: Validate API Key
+      run: |
+        echo "🔑 Testing API key validity with minimal OpenAI call..."
+        python -c "
+        import os
+        try:
+            from openai import OpenAI
+            client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
+            response = client.models.list()
+            print('✅ API Key is VALID - OpenAI responded successfully')
+            print(f'📊 Available models: {len(list(response.data))} models found')
+        except Exception as e:
+            print(f'❌ API Key is INVALID - Error: {e}')
+            print('🔍 This explains why all API-dependent tests are failing')
+            print('💡 The GitHub secret OPENAI_API_KEY needs to be updated with a valid key')
+        "
+      continue-on-error: true
+
+    - name: Debug PraisonAI API Key Usage
+      env:
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
+        OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
+        LOGLEVEL: DEBUG
+      run: |
+        echo "🔍 Testing PraisonAI API key usage directly..."
+        cd src/praisonai
+        python -c "
+        import os
+        import sys
+        sys.path.insert(0, '.')
+        
+        # Attempt to import SecretStr, otherwise use a dummy class
+        try:
+            from pydantic.types import SecretStr
+        except ImportError:
+            class SecretStr:  # Dummy class if pydantic is not available in this minimal context
+                def __init__(self, value): self._value = value
+                def get_secret_value(self): return self._value
+
+        def get_key_display_value(key_value):
+            if isinstance(key_value, SecretStr):
+                return key_value.get_secret_value()[:10] if key_value.get_secret_value() else 'EMPTY_SECRET'
+            elif isinstance(key_value, str):
+                return key_value[:10] if key_value != 'nokey' and key_value != 'NOT_SET' else key_value
+            return 'INVALID_TYPE'
+
+        print('🔧 Direct PraisonAI API Key Check:')
+        env_api_key = os.environ.get(\\"OPENAI_API_KEY\\", \\"NOT_SET\\")
+        print(f'Environment OPENAI_API_KEY: {get_key_display_value(env_api_key)}...')
+        
+        from praisonai import PraisonAI
+        praisonai = PraisonAI()
+        
+        print(f'PraisonAI config_list: {praisonai.config_list}')
+        api_key_from_config = praisonai.config_list[0].get('api_key', 'NOT_SET')
+        print(f'API key from PraisonAI config: {get_key_display_value(api_key_from_config)}...')
+        
+        from praisonai.inc.models import PraisonAIModel
+        
+        print('\\\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
+        model_with_explicit_key = PraisonAIModel(
+            model='openai/gpt-4o-mini',
+            base_url=praisonai.config_list[0].get('base_url'),
+            api_key=api_key_from_config # This will be a string from praisonai.config_list
+        )
+        print(f'  Model: {model_with_explicit_key.model}')
+        print(f'  Model name: {model_with_explicit_key.model_name}')
+        print(f'  API key var: {model_with_explicit_key.api_key_var}')
+        # model_with_explicit_key.api_key is now a string, or 'nokey'
+        print(f'  API key (explicitly passed to PraisonAIModel): {get_key_display_value(model_with_explicit_key.api_key)}...')
+        print(f'  Base URL: {model_with_explicit_key.base_url}')
+        
+        try:
+            llm_instance = model_with_explicit_key.get_model()
+            print(f'  ✅ LLM instance created successfully: {type(llm_instance).__name__}')
+            
+            # langchain_openai.ChatOpenAI stores the key in openai_api_key as SecretStr
+            llm_api_key_attr = getattr(llm_instance, 'openai_api_key', 'NOT_FOUND')
+            if llm_api_key_attr != 'NOT_FOUND':
+                 print(f'  LLM instance API key: {get_key_display_value(llm_api_key_attr)}...')
+            else:
+                print(f'  LLM instance API key attribute not found.')
+        except Exception as e:
+            print(f'  ❌ Failed to create LLM instance: {e}')
+            import traceback
+            traceback.print_exc()
+        "
+      continue-on-error: true
+
+    - name: Run Unit Tests
+      run: |
+        cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=term-missing
+
+    - name: Run Integration Tests  
+      run: |
+        cd src/praisonai && python -m pytest tests/integration/ -v --tb=short --disable-warnings
+
+    - name: Debug Directory Structure
+      run: |
+        echo "🔍 Debugging directory structure for CrewAI tests..."
+        cd src/praisonai
+        echo "Current working directory: $(pwd)"
+        echo "📁 Contents of current directory:"
+        ls -la
+        echo ""
+        echo "📁 Contents of tests directory:"
+        ls -la tests/ || echo "❌ tests/ directory not found"
+        echo ""
+        echo "📁 Contents of tests/integration:"
+        ls -la tests/integration/ || echo "❌ tests/integration/ directory not found"
+        echo ""
+        echo "📁 Looking for crewai directory:"
+        find . -name "crewai" -type d 2>/dev/null || echo "❌ No crewai directories found"
+        echo ""
+        echo "📁 Full directory tree of tests:"
+        tree tests/ || find tests/ -type d 2>/dev/null || echo "❌ Cannot explore tests directory"
+
+    - name: Test AutoGen Framework
+      run: |
+        echo "🤖 Testing AutoGen Framework Integration..."
+        cd src/praisonai && python tests/test_runner.py --pattern autogen --verbose
+
+    - name: Test CrewAI Framework
+      run: |
+        echo "⛵ Testing CrewAI Framework Integration..."
+        cd src/praisonai
+        echo "🔍 Trying test runner first..."
+        python tests/test_runner.py --pattern crewai --verbose || {
+          echo "❌ Test runner failed, trying direct pytest..."
+          echo "📁 Current directory: $(pwd)"
+          echo "📁 Looking for CrewAI tests..."
+          find . -name "*crewai*" -type f 2>/dev/null
+          echo "🧪 Trying direct pytest on integration/crewai..."
+          python -m pytest tests/integration/crewai/ -v --tb=short --disable-warnings || {
+            echo "❌ Direct path failed, trying relative path..."
+            python -m pytest integration/crewai/ -v --tb=short --disable-warnings || {
+              echo "❌ All CrewAI test attempts failed"
+              exit 1
+            }
+          }
+        }
+
+    - name: Run Legacy Tests with API Key
+      env:
+        OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
+        OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
+        LOGLEVEL: DEBUG
+      run: |
+        echo "🧪 Running legacy tests with real API key..."
+        echo "🔧 Final environment check before pytest:"
+        echo "  OPENAI_API_KEY set: $([ -n "$OPENAI_API_KEY" ] && echo 'YES' || echo 'NO')"
+        echo "  OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
+        echo "  OPENAI_API_KEY starts with sk-: $(echo "$OPENAI_API_KEY" | grep -q '^sk-' && echo 'YES' || echo 'NO')"
+        export OPENAI_API_KEY="$OPENAI_API_KEY"
+        export OPENAI_API_BASE="$OPENAI_API_BASE"
+        export OPENAI_MODEL_NAME="$OPENAI_MODEL_NAME"
+        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
+
+    - name: Upload Coverage Reports
+      if: matrix.python-version == '3.11'
+      uses: actions/upload-artifact@v4
+      with:
+        name: coverage-reports
+        path: |
+          .coverage
+          htmlcov/
+        retention-days: 7

.github/workflows/test-extended.yml

@@ -0,0 +1,160 @@
+name: Extended Tests
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+  schedule:
+    # Run nightly at 2 AM UTC
+    - cron: '0 2 * * *'
+  workflow_dispatch:  # Allow manual triggering
+
+jobs:
+  test-examples:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.11
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+
+    - name: Install dependencies
+      run: |
+        cd src/praisonai
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+    - name: Test Key Example Scripts
+      run: |
+        echo "🧪 Testing key example scripts from praisonai-agents..."
+        
+        # Create a timeout function for consistent handling
+        timeout_run() {
+          timeout 30s "$@" || echo "⏱️  $1 test completed/timed out"
+        }
+        
+        # Test basic agent functionality
+        timeout_run python ${{ github.workspace }}/src/praisonai-agents/basic-agents.py
+        
+        # Test async functionality  
+        timeout_run python ${{ github.workspace }}/src/praisonai-agents/async_example.py
+        
+        # Test knowledge/RAG functionality
+        timeout_run python ${{ github.workspace }}/src/praisonai-agents/knowledge-agents.py
+        
+        # Test MCP functionality
+        timeout_run python ${{ github.workspace }}/src/praisonai-agents/mcp-basic.py
+        
+        # Test UI functionality
+        timeout_run python ${{ github.workspace }}/src/praisonai-agents/ui.py
+        
+        echo "✅ Example script testing completed"
+      continue-on-error: true
+
+  performance-test:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.11
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+
+    - name: Install dependencies
+      run: |
+        cd src/praisonai
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system pytest pytest-benchmark
+
+    - name: Run Performance Benchmarks
+      run: |
+        echo "🏃 Running performance benchmarks..."
+        python -c "
+        import time
+        import sys
+        import statistics
+        sys.path.insert(0, 'src/praisonai')
+        
+        print('🏃 Testing agent creation performance...')
+        times = []
+        try:
+            from praisonaiagents import Agent
+            for i in range(5):
+                start_time = time.time()
+                agent = Agent(name=f'PerfAgent{i}')
+                times.append(time.time() - start_time)
+            
+            avg_time = statistics.mean(times)
+            print(f'✅ Average agent creation time: {avg_time:.3f}s')
+            print(f'📊 Min: {min(times):.3f}s, Max: {max(times):.3f}s')
+        except Exception as e:
+            print(f'❌ Agent creation benchmark failed: {e}')
+        
+        print('🏃 Testing import performance...')
+        start_time = time.time()
+        try:
+            import praisonaiagents
+            import_time = time.time() - start_time
+            print(f'✅ Import completed in {import_time:.3f}s')
+        except Exception as e:
+            print(f'❌ Import benchmark failed: {e}')
+        
+        print('🏃 Testing memory usage...')
+        try:
+            import psutil
+            import os
+            process = psutil.Process(os.getpid())
+            memory_mb = process.memory_info().rss / 1024 / 1024
+            print(f'📊 Memory usage: {memory_mb:.1f} MB')
+        except ImportError:
+            print('⚠️  psutil not available for memory testing')
+        except Exception as e:
+            print(f'❌ Memory benchmark failed: {e}')
+        "
+      continue-on-error: true
+
+    - name: Generate Performance Report
+      run: |
+        echo "## 📊 Performance Test Results" > performance_report.md
+        echo "" >> performance_report.md
+        echo "### Benchmarks Run:" >> performance_report.md
+        echo "- ⚡ Agent creation speed" >> performance_report.md
+        echo "- 📦 Import performance" >> performance_report.md
+        echo "- 💾 Memory usage" >> performance_report.md
+        echo "- 🧪 Example script execution" >> performance_report.md
+        echo "" >> performance_report.md
+        echo "_Performance results are logged in the CI output above._" >> performance_report.md
+
+    - name: Upload Performance Report
+      uses: actions/upload-artifact@v4
+      with:
+        name: performance-report
+        path: performance_report.md
+        retention-days: 30 

.github/workflows/test-frameworks.yml

@@ -0,0 +1,153 @@
+name: Framework Integration Tests
+
+on:
+  workflow_dispatch:  # Allow manual triggering
+    inputs:
+      framework:
+        description: 'Framework to test'
+        required: true
+        default: 'all'
+        type: choice
+        options:
+        - all
+        - autogen
+        - crewai
+  schedule:
+    # Run framework tests daily at 6 AM UTC
+    - cron: '0 6 * * *'
+  push:
+    paths:
+      - 'tests/integration/autogen/**'
+      - 'tests/integration/crewai/**'
+      - '.github/workflows/test-frameworks.yml'
+
+jobs:
+  framework-tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.11]
+        framework: [autogen, crewai]
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+
+    - name: Install dependencies
+      run: |
+        cd src/praisonai
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+
+    - name: Test ${{ matrix.framework }} Framework
+      run: |
+        echo "🧪 Testing ${{ matrix.framework }} framework integration with Python ${{ matrix.python-version }}"
+        cd src/praisonai && python tests/test_runner.py --pattern ${{ matrix.framework }} --verbose --coverage
+      continue-on-error: false
+
+    - name: Generate Framework Test Report
+      if: always()
+      run: |
+        echo "# 🤖 ${{ matrix.framework }} Framework Test Report" > ${{ matrix.framework }}_report.md
+        echo "" >> ${{ matrix.framework }}_report.md
+        echo "**Framework:** ${{ matrix.framework }}" >> ${{ matrix.framework }}_report.md
+        echo "**Python Version:** ${{ matrix.python-version }}" >> ${{ matrix.framework }}_report.md
+        echo "**Date:** $(date -u)" >> ${{ matrix.framework }}_report.md
+        echo "**Trigger:** ${{ github.event_name }}" >> ${{ matrix.framework }}_report.md
+        echo "" >> ${{ matrix.framework }}_report.md
+        
+        if [ "${{ matrix.framework }}" == "autogen" ]; then
+          echo "## AutoGen Integration Tests" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ AutoGen import verification" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Basic agent creation through PraisonAI" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Conversation flow testing" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Configuration validation" >> ${{ matrix.framework }}_report.md
+        elif [ "${{ matrix.framework }}" == "crewai" ]; then
+          echo "## CrewAI Integration Tests" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ CrewAI import verification" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Basic crew creation through PraisonAI" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Multi-agent workflow testing" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Agent collaboration verification" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Configuration validation" >> ${{ matrix.framework }}_report.md
+        fi
+        
+        echo "" >> ${{ matrix.framework }}_report.md
+        echo "## Test Commands" >> ${{ matrix.framework }}_report.md
+        echo '```bash' >> ${{ matrix.framework }}_report.md
+        echo "# Run ${{ matrix.framework }} tests locally:" >> ${{ matrix.framework }}_report.md
+        echo "python tests/test_runner.py --pattern ${{ matrix.framework }} --verbose" >> ${{ matrix.framework }}_report.md
+        echo '```' >> ${{ matrix.framework }}_report.md
+
+    - name: Upload Framework Test Results
+      uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: ${{ matrix.framework }}-test-results-python-${{ matrix.python-version }}
+        path: |
+          ${{ matrix.framework }}_report.md
+          htmlcov/
+          coverage.xml
+          .coverage
+        retention-days: 14
+
+  framework-summary:
+    runs-on: ubuntu-latest
+    needs: framework-tests
+    if: always()
+    
+    steps:
+    - name: Generate Framework Summary
+      run: |
+        echo "# 🚀 Framework Integration Test Summary" > framework_summary.md
+        echo "" >> framework_summary.md
+        echo "## Test Results by Framework and Python Version:" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "### AutoGen Framework:" >> framework_summary.md
+        echo "- Python 3.11: ${{ needs.framework-tests.result }}" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "### CrewAI Framework:" >> framework_summary.md
+        echo "- Python 3.11: ${{ needs.framework-tests.result }}" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "## Overall Status:" >> framework_summary.md
+        if [ "${{ needs.framework-tests.result }}" == "success" ]; then
+          echo "✅ **All framework integration tests passed!**" >> framework_summary.md
+        else
+          echo "❌ **Some framework tests failed. Check individual job logs for details.**" >> framework_summary.md
+        fi
+        
+        echo "" >> framework_summary.md
+        echo "## Frameworks Tested:" >> framework_summary.md
+        echo "- **AutoGen**: Microsoft's conversational AI framework" >> framework_summary.md
+        echo "- **CrewAI**: Multi-agent collaboration framework" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "## Test Coverage:" >> framework_summary.md
+        echo "- Import verification" >> framework_summary.md
+        echo "- Agent/crew creation" >> framework_summary.md
+        echo "- Workflow execution" >> framework_summary.md
+        echo "- Configuration validation" >> framework_summary.md
+        echo "- Integration with PraisonAI" >> framework_summary.md
+        
+    - name: Upload Framework Summary
+      uses: actions/upload-artifact@v4
+      with:
+        name: framework-test-summary
+        path: framework_summary.md
+        retention-days: 30 

.github/workflows/test-real.yml

@@ -0,0 +1,165 @@
+name: Real End-to-End Tests
+
+# ⚠️ WARNING: This workflow makes real API calls and incurs costs!
+# Only runs when manually triggered to prevent accidental charges
+
+on:
+  workflow_dispatch:  # Manual trigger only
+    inputs:
+      framework:
+        description: 'Framework to test (⚠️ Will incur API costs!)'
+        required: true
+        default: 'none'
+        type: choice
+        options:
+        - none
+        - autogen
+        - crewai
+        - all
+      confirm_costs:
+        description: 'I understand this will make real API calls and may incur costs'
+        required: true
+        type: boolean
+        default: false
+
+jobs:
+  real-tests:
+    runs-on: ubuntu-latest
+    if: ${{ github.event.inputs.confirm_costs == 'true' && github.event.inputs.framework != 'none' }}
+    
+    strategy:
+      matrix:
+        python-version: [3.11]  # Single version to minimize costs
+    
+    steps:
+    - name: 🚨 Cost Warning
+      run: |
+        echo "⚠️  WARNING: This workflow will make real API calls!"
+        echo "💰 This may incur charges on your API accounts"
+        echo "🎯 Framework: ${{ github.event.inputs.framework }}"
+        echo "✅ Cost confirmation: ${{ github.event.inputs.confirm_costs }}"
+        
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+
+    - name: Install dependencies
+      run: |
+        cd src/praisonai
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system pytest pytest-asyncio pytest-cov
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV
+        echo "ANTHROPIC_API_KEY=${{ secrets.ANTHROPIC_API_KEY }}" >> $GITHUB_ENV
+        echo "GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }}" >> $GITHUB_ENV
+
+    - name: Verify API Keys
+      run: |
+        if [ -z "${{ secrets.OPENAI_API_KEY }}" ]; then
+          echo "❌ OPENAI_API_KEY not set in secrets"
+          echo "🔧 Add your API key to repository secrets"
+          exit 1
+        fi
+        echo "✅ API keys configured"
+
+    - name: Run Real AutoGen Tests
+      if: ${{ github.event.inputs.framework == 'autogen' || github.event.inputs.framework == 'all' }}
+      run: |
+        echo "🤖 Running REAL AutoGen tests (⚠️  API costs may apply)"
+        cd src/praisonai && python -m pytest tests/e2e/autogen/ -v -m real --tb=short
+      continue-on-error: false
+
+    - name: Run Real CrewAI Tests
+      if: ${{ github.event.inputs.framework == 'crewai' || github.event.inputs.framework == 'all' }}
+      run: |
+        echo "⛵ Running REAL CrewAI tests (⚠️  API costs may apply)"
+        cd src/praisonai && python -m pytest tests/e2e/crewai/ -v -m real --tb=short
+      continue-on-error: false
+
+    - name: Generate Real Test Report
+      if: always()
+      run: |
+        echo "# 🔥 Real End-to-End Test Report" > real_test_report.md
+        echo "" >> real_test_report.md
+        echo "⚠️  **WARNING: This report represents tests that made real API calls**" >> real_test_report.md
+        echo "" >> real_test_report.md
+        echo "**Framework Tested:** ${{ github.event.inputs.framework }}" >> real_test_report.md
+        echo "**Python Version:** ${{ matrix.python-version }}" >> real_test_report.md
+        echo "**Date:** $(date -u)" >> real_test_report.md
+        echo "**Triggered by:** ${{ github.actor }}" >> real_test_report.md
+        echo "" >> real_test_report.md
+        
+        echo "## 🧪 Test Results" >> real_test_report.md
+        echo "" >> real_test_report.md
+        
+        if [ "${{ github.event.inputs.framework }}" == "autogen" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
+          echo "### AutoGen Real Tests:" >> real_test_report.md
+          echo "- Environment verification" >> real_test_report.md
+          echo "- Agent creation with real API calls" >> real_test_report.md
+          echo "- Configuration validation" >> real_test_report.md
+          echo "" >> real_test_report.md
+        fi
+        
+        if [ "${{ github.event.inputs.framework }}" == "crewai" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
+          echo "### CrewAI Real Tests:" >> real_test_report.md
+          echo "- Environment verification" >> real_test_report.md
+          echo "- Crew creation with real API calls" >> real_test_report.md
+          echo "- Multi-agent setup validation" >> real_test_report.md
+          echo "" >> real_test_report.md
+        fi
+        
+        echo "## 💰 Cost Considerations" >> real_test_report.md
+        echo "- These tests made actual API calls to LLM providers" >> real_test_report.md
+        echo "- Costs depend on your API pricing tier" >> real_test_report.md
+        echo "- Tests are designed to be minimal to reduce costs" >> real_test_report.md
+        echo "- Check your API provider dashboard for actual usage" >> real_test_report.md
+        
+        echo "## 📋 Next Steps" >> real_test_report.md
+        echo "- Review test results for any failures" >> real_test_report.md
+        echo "- Check API usage in your provider dashboard" >> real_test_report.md
+        echo "- Use mock tests (tests/integration/) for routine testing" >> real_test_report.md
+
+    - name: Upload Real Test Results
+      uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: real-test-results-${{ github.event.inputs.framework }}-python-${{ matrix.python-version }}
+        path: |
+          real_test_report.md
+        retention-days: 30
+
+  # Safety job that runs when costs not confirmed
+  safety-check:
+    runs-on: ubuntu-latest
+    if: ${{ github.event.inputs.confirm_costs != 'true' || github.event.inputs.framework == 'none' }}
+    
+    steps:
+    - name: 🛡️ Safety Check Failed
+      run: |
+        echo "🚨 Real tests not executed due to safety checks:"
+        echo ""
+        echo "✅ Costs confirmed: ${{ github.event.inputs.confirm_costs }}"
+        echo "✅ Framework selected: ${{ github.event.inputs.framework }}"
+        echo ""
+        echo "To run real tests:"
+        echo "1. Select a framework (autogen, crewai, or all)"
+        echo "2. Check 'I understand this will make real API calls and may incur costs'"
+        echo "3. Ensure API keys are set in repository secrets"
+        echo ""
+        echo "💡 For cost-free testing, use mock tests instead:"
+        echo "   - Run 'python -m pytest tests/integration/' locally"
+        echo "   - Or trigger other workflows that use mock tests"
+        
+        exit 1 

.github/workflows/unittest.yml

@@ -1,36 +1,503 @@
-name: Run specific unittest
+name: Quick Validation Tests
 
 on: [push, pull_request]
 
 jobs:
-  test:
+  quick-test:
     runs-on: ubuntu-latest
 
     steps:
     - name: Checkout code
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: 3.11
 
-    - name: Install Poetry
+    - name: Install UV
       run: |
-        curl -sSL https://install.python-poetry.org | python3 -
-        export PATH="$HOME/.local/bin:$PATH"
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
 
     - name: Install dependencies
       run: |
-        poetry install
-        poetry run python -m pip install duckduckgo_search
+        cd src/praisonai
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov
 
     - 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 }}" >> $GITHUB_ENV
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
 
-    - name: Run specific unittest
+    - name: Backup Root Config Files
       run: |
-        poetry run python -m unittest tests.test.TestExamples
+        echo "🔄 Backing up root configuration files to prevent default file resolution interference..."
+        echo "ℹ️  PraisonAI automatically uses 'agents.yaml' in working directory when no file specified"
+        echo "ℹ️  This can interfere with test files that specify their own YAML files"
+        if [ -f "agents.yaml" ]; then
+          mv agents.yaml agents.yaml.backup
+          echo "✅ Moved root agents.yaml to agents.yaml.backup"
+          echo "   - This prevents default file fallback during tests"
+        fi
+        if [ -f "tools.py" ]; then
+          mv tools.py tools.py.backup  
+          echo "✅ Moved tools.py to tools.py.backup"
+        fi
+
+    - name: Debug API Key Status
+      run: |
+        echo "🔍 Checking API key availability..."
+        if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
+          echo "✅ GitHub secret OPENAI_API_KEY is available"
+          echo "🔑 API key starts with: $(echo "$OPENAI_API_KEY" | cut -c1-7)..."
+        else
+          echo "⚠️ GitHub secret OPENAI_API_KEY is NOT set - using fallback"
+          echo "🔑 Using fallback key: sk-test-key..."
+        fi
+        echo "🌐 API Base: $OPENAI_API_BASE"
+        echo "🤖 Model: $OPENAI_MODEL_NAME"
+        echo "🐛 Log Level: $LOGLEVEL"
+        echo "📊 Environment Check:"
+        echo "  - OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
+        echo "  - OPENAI_API_BASE: $OPENAI_API_BASE"
+        echo "  - OPENAI_MODEL_NAME: $OPENAI_MODEL_NAME"
+        echo "  - LOGLEVEL: $LOGLEVEL"
+
+    - name: Debug Python Environment Variables
+      run: |
+        python -c "
+        import os
+        print('🐍 Python Environment Variable Check:')
+        api_key = os.environ.get('OPENAI_API_KEY', 'NOT_SET')
+        if api_key != 'NOT_SET':
+            print(f'  ✅ OPENAI_API_KEY: {api_key[:7]}... (length: {len(api_key)})')
+        else:
+            print('  ❌ OPENAI_API_KEY: NOT_SET')
+        print(f'  🌐 OPENAI_API_BASE: {os.environ.get(\"OPENAI_API_BASE\", \"NOT_SET\")}')
+        print(f'  🤖 OPENAI_MODEL_NAME: {os.environ.get(\"OPENAI_MODEL_NAME\", \"NOT_SET\")}')
+        print(f'  📋 All OPENAI env vars:')
+        for key, value in os.environ.items():
+            if key.startswith('OPENAI'):
+                print(f'    {key}: {value[:10] if len(value) > 10 else value}...')
+        "
+
+    - name: Find Researcher Role Source
+      run: |
+        echo "🔍 Hunting for the mysterious 'Researcher' role..."
+        cd src/praisonai
+        python -c "
+        import os
+        import yaml
+        import glob
+        
+        print('📋 Searching for Researcher role in all YAML files:')
+        yaml_files = glob.glob('src/praisonai/tests/*.yaml')
+        
+        for yaml_file in yaml_files:
+            try:
+                with open(yaml_file, 'r') as f:
+                    config = yaml.safe_load(f)
+                
+                # Check if any role contains 'researcher'
+                roles = config.get('roles', {})
+                for role_key, role_data in roles.items():
+                    role_name = role_data.get('role', '')
+                    if 'researcher' in role_key.lower() or 'researcher' in role_name.lower():
+                        print(f'  🎯 FOUND in {yaml_file}:')
+                        print(f'    Framework: {config.get(\"framework\", \"NOT_SET\")}')
+                        print(f'    Role key: {role_key}')
+                        print(f'    Role name: {role_name}')
+                        print(f'    All roles: {list(roles.keys())}')
+                        print()
+            except Exception as e:
+                print(f'  ❌ Error reading {yaml_file}: {e}')
+        
+        print('🔍 Checking for default configurations...')
+        # Check if there are any default configs or hardcoded roles
+        try:
+            import praisonai
+            print(f'  PraisonAI package location: {praisonai.__file__}')
+            
+            # Check if there are any example YAML files in the package
+            package_dir = os.path.dirname(praisonai.__file__)
+            for root, dirs, files in os.walk(package_dir):
+                for file in files:
+                    if file.endswith(('.yaml', '.yml')):
+                        file_path = os.path.join(root, file)
+                        print(f'  📁 Found YAML in package: {file_path}')
+        except Exception as e:
+            print(f'  ❌ Error checking package: {e}')
+        "
+      continue-on-error: true
+
+    - name: Trace AutoGen Execution Path
+      run: |
+        echo "🔍 Tracing AutoGen execution to find where it diverges..."
+        cd src/praisonai
+        python -c "
+        import os
+        import sys
+        sys.path.insert(0, '.')
+        
+        try:
+            from praisonai import PraisonAI
+            from praisonai.agents_generator import AgentsGenerator
+            
+            # Test the exact execution path
+            print('🎯 Testing AutoGen execution path:')
+            
+            praisonai = PraisonAI(agent_file='tests/autogen-agents.yaml')
+            print(f'  1. PraisonAI framework: \"{praisonai.framework}\"')
+            
+            agents_gen = AgentsGenerator(
+                agent_file='tests/autogen-agents.yaml',
+                framework=praisonai.framework,
+                config_list=praisonai.config_list
+            )
+            print(f'  2. AgentsGenerator framework: \"{agents_gen.framework}\"')
+            
+            # Load the YAML to check what it contains
+            import yaml
+            with open('src/praisonai/tests/autogen-agents.yaml', 'r') as f:
+                config = yaml.safe_load(f)
+            
+            framework = agents_gen.framework or config.get('framework')
+            print(f'  3. Final framework decision: \"{framework}\"')
+            print(f'  4. Available frameworks:')
+            
+            # Check framework availability
+            try:
+                import autogen
+                print(f'    ✅ AutoGen available')
+            except ImportError:
+                print(f'    ❌ AutoGen NOT available')
+                
+            try:
+                from praisonaiagents import Agent
+                print(f'    ✅ PraisonAI agents available')
+            except ImportError:
+                print(f'    ❌ PraisonAI agents NOT available')
+                
+            try:
+                from crewai import Agent
+                print(f'    ✅ CrewAI available')
+            except ImportError:
+                print(f'    ❌ CrewAI NOT available')
+            
+            print(f'  5. Roles in YAML: {list(config.get(\"roles\", {}).keys())}')
+            
+            # Now test the actual framework execution
+            if framework == 'autogen':
+                print(f'  6. ✅ Should execute _run_autogen')
+            elif framework == 'praisonai':
+                print(f'  6. ❌ Would execute _run_praisonai (WRONG!)')
+            else:
+                print(f'  6. ❌ Would execute _run_crewai (DEFAULT FALLBACK)')
+                
+        except Exception as e:
+            print(f'❌ Error tracing execution: {e}')
+            import traceback
+            traceback.print_exc()
+        "
+      continue-on-error: true
+
+    - name: Debug YAML Loading and Roles
+      run: |
+        echo "🔍 Tracing YAML file loading and role creation..."
+        cd src/praisonai
+        python -c "
+        import os
+        import sys
+        import yaml
+        sys.path.insert(0, '.')
+        
+        print('📁 Available YAML files in src/praisonai/tests/:')
+        import glob
+        yaml_files = glob.glob('tests/*.yaml')
+        for f in yaml_files:
+            print(f'  {f}')
+        
+        print()
+        print('📋 Content of autogen-agents.yaml:')
+        with open('tests/autogen-agents.yaml', 'r') as f:
+            config = yaml.safe_load(f)
+        print(f'  Framework: {config.get(\"framework\")}')
+        print(f'  Topic: {config.get(\"topic\")}')
+        print(f'  Roles: {list(config.get(\"roles\", {}).keys())}')
+        for role_key, role_data in config.get('roles', {}).items():
+            print(f'    {role_key} -> {role_data.get(\"role\", \"NO_ROLE\")}')
+        
+        print()
+        print('🔍 Checking if execution uses a different YAML:')
+        
+        # Check other YAML files for 'Researcher' role
+        for yaml_file in yaml_files:
+            try:
+                with open(yaml_file, 'r') as f:
+                    test_config = yaml.safe_load(f)
+                roles = test_config.get('roles', {})
+                for role_key, role_data in roles.items():
+                    if 'researcher' in role_key.lower() or 'researcher' in role_data.get('role', '').lower():
+                        print(f'  🎯 FOUND Researcher in {yaml_file}!')
+                        print(f'    Framework: {test_config.get(\"framework\")}')
+                        print(f'    Role key: {role_key} -> {role_data.get(\"role\")}')
+            except:
+                pass
+        "
+      continue-on-error: true
+
+    - name: Debug Framework Detection
+      run: |
+        echo "🔍 Testing framework detection and config flow..."
+        python -c "
+        import os
+        import sys
+        import yaml
+        sys.path.insert(0, '.')
+        
+        print('🔧 Testing framework detection:')
+        
+        # Load the YAML file
+        with open('tests/autogen-agents.yaml', 'r') as f:
+            config = yaml.safe_load(f)
+            
+        print(f'  📋 YAML framework: {config.get(\"framework\", \"NOT_SET\")}')
+        print(f'  📋 YAML topic: {config.get(\"topic\", \"NOT_SET\")}')
+        
+        try:
+            from praisonai import PraisonAI
+            from praisonai.agents_generator import AgentsGenerator
+            
+            # Test PraisonAI initialization
+            praisonai = PraisonAI(agent_file='tests/autogen-agents.yaml')
+            print(f'  🎯 PraisonAI framework: {praisonai.framework}')
+            
+            # Test AgentsGenerator initialization
+            agents_gen = AgentsGenerator(
+                agent_file='tests/autogen-agents.yaml',
+                framework=praisonai.framework,
+                config_list=praisonai.config_list
+            )
+            print(f'  ⚙️ AgentsGenerator framework: {agents_gen.framework}')
+            print(f'  ⚙️ Final framework decision: {agents_gen.framework or config.get(\"framework\")}')
+            
+            # Check config_list
+            print(f'  🔑 Config list model: {praisonai.config_list[0].get(\"model\")}')
+            print(f'  🔑 Config list API key: {praisonai.config_list[0].get(\"api_key\", \"NOT_SET\")[:10]}...')
+            
+        except Exception as e:
+            print(f'❌ Error in framework detection: {e}')
+        "
+      continue-on-error: true
+
+    - name: Debug PraisonAIModel API Key Flow
+      run: |
+        echo "🔍 Testing PraisonAIModel API key handling..."
+        cd src/praisonai
+        python -c "
+        import os
+        import sys
+        sys.path.insert(0, '.')
+        
+        print('🔑 Environment API Key Check:')
+        env_key = os.environ.get('OPENAI_API_KEY', 'NOT_FOUND')
+        print(f'  OPENAI_API_KEY: {env_key[:10] if env_key != \"NOT_FOUND\" else \"NOT_FOUND\"}...')
+        
+        try:
+            from praisonai.inc.models import PraisonAIModel
+            
+            # Test PraisonAIModel with openai/gpt-4o-mini (from YAML)
+            model = PraisonAIModel(model='openai/gpt-4o-mini')
+            
+            print('🤖 PraisonAIModel Configuration:')
+            print(f'  model: {model.model}')
+            print(f'  model_name: {model.model_name}')
+            print(f'  api_key_var: {model.api_key_var}')
+            print(f'  api_key: {model.api_key[:10] if model.api_key != \"nokey\" else \"DEFAULT_NOKEY\"}...')
+            print(f'  base_url: {model.base_url}')
+            
+            if model.api_key == 'nokey':
+                print('❌ FOUND THE ISSUE: PraisonAIModel is using default \"nokey\" instead of environment variable!')
+            else:
+                print('✅ PraisonAIModel has valid API key from environment')
+                
+        except Exception as e:
+            print(f'❌ Error testing PraisonAIModel: {e}')
+        "
+      continue-on-error: true
+
+    - name: Validate API Key
+      run: |
+        echo "🔑 Testing API key validity with minimal OpenAI call..."
+        python -c "
+        import os
+        try:
+            from openai import OpenAI
+            client = OpenAI(api_key=os.environ.get('OPENAI_API_KEY'))
+            # Make a minimal API call to test key validity
+            response = client.models.list()
+            print('✅ API Key is VALID - OpenAI responded successfully')
+            print(f'📊 Available models: {len(list(response.data))} models found')
+        except Exception as e:
+            print(f'❌ API Key is INVALID - Error: {e}')
+            print('🔍 This explains why all API-dependent tests are failing')
+            print('💡 The GitHub secret OPENAI_API_KEY needs to be updated with a valid key')
+        "
+      continue-on-error: true
+
+    - name: Test Direct PraisonAI Execution
+      run: |
+        echo "🧪 Testing direct PraisonAI execution (what works locally)..."
+        python -m praisonai src/praisonai/tests/autogen-agents.yaml
+      continue-on-error: true
+
+    - name: Comprehensive Execution Debug
+      run: |
+        echo "🔍 Comprehensive debugging of PraisonAI execution path..."
+        cd src/praisonai
+        python -c "
+        import os
+        import sys
+        import yaml
+        sys.path.insert(0, '.')
+        
+        print('=' * 60)
+        print('🔍 COMPREHENSIVE EXECUTION DEBUG')
+        print('=' * 60)
+        
+        # Check current working directory
+        print(f'📁 Current working directory: {os.getcwd()}')
+        
+        # List files in current directory
+        print('📋 Files in current directory:')
+        for f in os.listdir('.'):
+            print(f'  {f}')
+        
+        print()
+        print('📋 Files in src/praisonai/tests/ directory:')
+        for f in os.listdir('tests'):
+            if f.endswith('.yaml'):
+                print(f'  src/praisonai/tests/{f}')
+        
+        # Check if root agents.yaml exists
+        print()
+        if os.path.exists('agents.yaml'):
+            print('❌ ROOT agents.yaml EXISTS (this is the problem!)')
+            with open('agents.yaml', 'r') as f:
+                root_config = yaml.safe_load(f)
+            print(f'  Root framework: {root_config.get(\"framework\")}')
+            print(f'  Root roles: {list(root_config.get(\"roles\", {}).keys())}')
+        else:
+            print('✅ ROOT agents.yaml does NOT exist (good!)')
+            
+        # Test the actual execution path
+        print()
+        print('🎯 Testing EXACT execution path:')
+        
+        try:
+            from praisonai import PraisonAI
+            from praisonai.agents_generator import AgentsGenerator
+            
+            # Test with full path
+            test_file = 'src/praisonai/tests/autogen-agents.yaml'
+            print(f'  Using test file: {test_file}')
+            print(f'  File exists: {os.path.exists(test_file)}')
+            
+            # Load the test file directly
+            with open(test_file, 'r') as f:
+                test_config = yaml.safe_load(f)
+            print(f'  Test file framework: {test_config.get(\"framework\")}')
+            print(f'  Test file roles: {list(test_config.get(\"roles\", {}).keys())}')
+            
+            # Create PraisonAI instance
+            praisonai = PraisonAI(agent_file=test_file)
+            print(f'  PraisonAI.agent_file: {praisonai.agent_file}')
+            print(f'  PraisonAI.framework: {praisonai.framework}')
+            
+            # Create AgentsGenerator
+            agents_gen = AgentsGenerator(
+                agent_file=test_file,
+                framework=praisonai.framework,
+                config_list=praisonai.config_list
+            )
+            print(f'  AgentsGenerator.agent_file: {agents_gen.agent_file}')
+            print(f'  AgentsGenerator.framework: {agents_gen.framework}')
+            
+            # Test what would happen in generate_crew_and_kickoff
+            print()
+            print('🔧 Testing generate_crew_and_kickoff logic:')
+            
+            # Simulate the loading logic
+            if agents_gen.agent_yaml:
+                loaded_config = yaml.safe_load(agents_gen.agent_yaml)
+                print('  Would load from agent_yaml')
+            else:
+                if agents_gen.agent_file == '/app/api:app' or agents_gen.agent_file == 'api:app':
+                    agents_gen.agent_file = 'agents.yaml'
+                    print(f'  Would change agent_file to: {agents_gen.agent_file}')
+                try:
+                    with open(agents_gen.agent_file, 'r') as f:
+                        loaded_config = yaml.safe_load(f)
+                    print(f'  Successfully loaded: {agents_gen.agent_file}')
+                except FileNotFoundError:
+                    print(f'  FileNotFoundError: {agents_gen.agent_file}')
+                    loaded_config = None
+            
+            if loaded_config:
+                final_framework = agents_gen.framework or loaded_config.get('framework')
+                print(f'  Final framework decision: {final_framework}')
+                print(f'  Loaded roles: {list(loaded_config.get(\"roles\", {}).keys())}')
+                
+                if 'researcher' in loaded_config.get('roles', {}):
+                    print('  ❌ FOUND Researcher role in loaded config!')
+                else:
+                    print('  ✅ No Researcher role in loaded config')
+                    
+        except Exception as e:
+            print(f'❌ Error during execution debug: {e}')
+            import traceback
+            traceback.print_exc()
+        "
+      continue-on-error: true
+
+    - name: Run Fast Tests
+      run: |
+        # Run the fastest, most essential tests
+        cd src/praisonai && python tests/test_runner.py --pattern fast
+
+    - name: Run Real API Tests
+      run: |
+        echo "🔑 Running real API tests with actual OpenAI API key..."
+        cd src/praisonai && python -m pytest tests/test_agents_playbook.py -v --tb=short --disable-warnings -m real
+      continue-on-error: true
+
+    - name: Run E2E Real Tests
+      run: |
+        echo "🧪 Running E2E real tests..."
+        cd src/praisonai && python -m pytest tests/e2e/ -v --tb=short --disable-warnings -m real
+      continue-on-error: true
+
+    - name: Run Legacy Example Tests
+      run: |
+        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
+      continue-on-error: true
+
+    - name: Restore Root Config Files
+      run: |
+        echo "🔄 Restoring root configuration files..."
+        if [ -f "agents.yaml.backup" ]; then
+          mv agents.yaml.backup agents.yaml
+          echo "✅ Restored agents.yaml"
+        fi
+        if [ -f "tools.py.backup" ]; then
+          mv tools.py.backup tools.py
+          echo "✅ Restored tools.py"
+        fi
+      if: always()  # Always run this step, even if previous steps failed

.gitignore

@@ -1,3 +1,4 @@
+workspace.code-workspace
 .vscode
 crewai
 .cache
@@ -51,6 +52,10 @@ agentops.log
 
 # crewAI
 crewAI
+!tests/integration/crewai
+!tests/e2e/crewai
+!src/praisonai/tests/integration/crewai
+!src/praisonai/tests/e2e/crewai
 
 # virtualenv
 .venv
@@ -64,4 +69,9 @@ node_modules
 outputs
 agents/praisonaiagents/build
 agents/praisonaiagents/dist
-agents/praisonaiagents/praisonaiagents.egg-info
+agents/praisonaiagents/praisonaiagents.egg-info
+.codegpt
+.praison
+# Local Netlify folder
+.netlify
+.qodo

README.md

@@ -20,270 +20,477 @@
 
 </div>
 
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
 
 <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>
+  <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>
 
-## Different User Interfaces:
-
-| 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) |
-
-| Other Features | Description | Docs |
-|---|---|---|
-| **Train** | Fine-tune LLMs using your custom data | [https://docs.praison.ai/train](https://docs.praison.ai/train) |
-
-
-## 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
-
 ## Key Features
 
 - 🤖 Automated AI Agents Creation
-- 🔄 Use CrewAI or AutoGen Framework
+- 🔄 Self Reflection AI Agents
+- 🧠 Reasoning AI Agents
+- 👁️ Multi Modal AI Agents
+- 🤝 Multi Agent Collaboration
+- 🎭 AI Agent Workflow
+- 📚 Add Custom Knowledge
+- 🧠 Agents with Short and Long Term Memory
+- 📄 Chat with PDF Agents
+- 💻 Code Interpreter Agents
+- 📚 RAG Agents
+- 🤔 Async & Parallel Processing
+- 🔄 Auto Agents
+- 🔢 Math Agents
+- 🎯 Structured Output Agents
+- 🔗 LangChain Integrated Agents
+- 📞 Callback Agents
+- 🤏 Mini AI Agents
+- 🛠️ 100+ Custom Tools
+- 📄 YAML Configuration
 - 💯 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
 
-## TL;DR Multi Agents
+## Using Python Code
 
+Light weight package dedicated for coding:
+```bash
+pip install praisonaiagents
+```
+
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
+
+### 1. Single Agent
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent
+agent = Agent(instructions="Your are a helpful AI assistant")
+agent.start("Write a movie script about a robot in Mars")
+```
+
+Run:
+```bash
+python app.py
+```
+
+### 2. Multi Agents
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Using No Code
+
+### Auto Mode:
 ```bash
 pip install praisonai
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init create a movie script about dog in moon
-praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
 ```
 
-## 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
+## Using JavaScript Code
 
 ```bash
-pip install praisonai
+npm install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
 ```
 
-## Initialise
+```javascript
+const { Agent } = require('praisonai');
+const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
+agent.start('Write a movie script about a robot in Mars');
+```
 
+![PraisonAI CLI Demo](docs/demo/praisonai-cli-demo.gif)
+
+## AI Agents Flow
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[ ]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+    end
+    
+    subgraph Agent2[ ]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+    end
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
+```
+
+## AI Agents with Tools
+
+Create AI agents that can use tools to interact with external systems and perform actions.
+
+```mermaid
+flowchart TB
+    subgraph Tools
+        direction TB
+        T3[Internet Search]
+        T1[Code Execution]
+        T2[Formatting]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    T3 --> A1
+    T1 --> A2
+    T2 --> A3
+
+    style Tools fill:#189AB4,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Memory
+
+Create AI agents with memory capabilities for maintaining context and information across tasks.
+
+```mermaid
+flowchart TB
+    subgraph Memory
+        direction TB
+        STM[Short Term]
+        LTM[Long Term]
+    end
+
+    subgraph Store
+        direction TB
+        DB[(Vector DB)]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    Memory <--> Store
+    Store <--> A1
+    Store <--> A2
+    Store <--> A3
+
+    style Memory fill:#189AB4,color:#fff
+    style Store fill:#2E8B57,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Different Processes
+
+### Sequential Process
+
+The simplest form of task execution where tasks are performed one after another.
+
+```mermaid
+graph LR
+    Input[Input] --> A1
+    subgraph Agents
+        direction LR
+        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
+    end
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class A1,A2,A3 process
+    class Agents transparent
+```
+
+### Hierarchical Process
+
+Uses a manager agent to coordinate task execution and agent assignments.
+
+```mermaid
+graph TB
+    Input[Input] --> Manager
+    
+    subgraph Agents
+        Manager[Manager Agent]
+        
+        subgraph Workers
+            direction LR
+            W1[Worker 1]
+            W2[Worker 2]
+            W3[Worker 3]
+        end
+        
+        Manager --> W1
+        Manager --> W2
+        Manager --> W3
+    end
+    
+    W1 --> Manager
+    W2 --> Manager
+    W3 --> Manager
+    Manager --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Manager,W1,W2,W3 process
+    class Agents,Workers transparent
+```
+
+### Workflow Process
+
+Advanced process type supporting complex task relationships and conditional execution.
+
+```mermaid
+graph LR
+    Input[Input] --> Start
+    
+    subgraph Workflow
+        direction LR
+        Start[Start] --> C1{Condition}
+        C1 --> |Yes| A1[Agent 1]
+        C1 --> |No| A2[Agent 2]
+        A1 --> Join
+        A2 --> Join
+        Join --> A3[Agent 3]
+    end
+    
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Start,A1,A2,A3,Join process
+    class C1 decision
+    class Workflow transparent
+```
+
+#### Agentic Routing Workflow
+
+Create AI agents that can dynamically route tasks to specialized LLM instances.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Out[Out]
+    LLM2 --> Out
+    LLM3 --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Orchestrator Worker
+
+Create AI agents that orchestrate and distribute tasks among specialized workers.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Synthesizer[Synthesizer]
+    LLM2 --> Synthesizer
+    LLM3 --> Synthesizer
+    Synthesizer --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Autonomous Workflow
+
+Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.
+
+```mermaid
+flowchart LR
+    Human[Human] <--> LLM[LLM Call]
+    LLM -->|ACTION| Environment[Environment]
+    Environment -->|FEEDBACK| LLM
+    LLM --> Stop[Stop]
+    
+    style Human fill:#8B0000,color:#fff
+    style LLM fill:#2E8B57,color:#fff
+    style Environment fill:#8B0000,color:#fff
+    style Stop fill:#333,color:#fff
+```
+
+#### Agentic Parallelization
+
+Create AI agents that can execute tasks in parallel for improved performance.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM2[LLM Call 2]
+    In --> LLM1[LLM Call 1]
+    In --> LLM3[LLM Call 3]
+    LLM1 --> Aggregator[Aggregator]
+    LLM2 --> Aggregator
+    LLM3 --> Aggregator
+    Aggregator --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Aggregator fill:#fff,color:#000
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Prompt Chaining
+
+Create AI agents with sequential prompt chaining for complex workflows.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
+    Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
+    Gate -->|Fail| Exit[Exit]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+    style Exit fill:#8B0000,color:#fff
+```
+
+#### Agentic Evaluator Optimizer
+
+Create AI agents that can generate and optimize solutions through iterative feedback.
+
+```mermaid
+flowchart LR
+    In[In] --> Generator[LLM Call Generator] 
+    Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
+    Evaluator -->|REJECTED + FEEDBACK| Generator
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Evaluator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Repetitive Agents
+
+Create AI agents that can efficiently handle repetitive tasks through automated loops.
+
+```mermaid
+flowchart LR
+    In[Input] --> LoopAgent[("Looping Agent")]
+    LoopAgent --> Task[Task]
+    Task --> |Next iteration| LoopAgent
+    Task --> |Done| Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style LoopAgent fill:#2E8B57,color:#fff,shape:circle
+    style Task fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Adding Models
+
+<div align="center">
+  <a href="https://docs.praison.ai/models">
+    <p align="center">
+      <img src="https://img.shields.io/badge/%F0%9F%93%9A_Models-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Models" />
+    </p>
+  </a>
+</div>
+
+## Ollama Integration
 ```bash
-export OPENAI_API_KEY="Enter your API key"
+export OPENAI_BASE_URL=http://localhost:11434/v1
 ```
 
-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.
-
+## Groq Integration
+Replace xxxx with Groq API KEY:
 ```bash
-praisonai --init create a movie script about dog in moon
+export OPENAI_API_KEY=xxxxxxxxxxx
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
 ```
 
-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/
+## No Code Options
 
 ## Agents Playbook
 
 ### Simple Playbook Example
 
+Create `agents.yaml` file and add the code below:
+
 ```yaml
-framework: crewai
+framework: praisonai
 topic: Artificial Intelligence
 roles:
   screenwriter:
@@ -296,90 +503,27 @@ roles:
         expected_output: "Complete script ready for production."
 ```
 
+*To run the playbook:*
+```bash
+praisonai agents.yaml
+```
+
 ## Use 100+ Models
 
 - https://docs.praison.ai/models/
+<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>
 
-## Include praisonai package in your project
+## Development:
 
-- https://docs.praison.ai/developers/wrapper
-- https://docs.praison.ai/developers/wrapper-tools/
+Below is used for development only.
 
-## 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:
-
-1. **Install all dependencies, including dev dependencies:**
-
-   ```sh
-   poetry install
-   ```
-
-2. **Install only documentation dependencies:**
-
-   ```sh
-   poetry install --with docs
-   ```
-
-3. **Install only test dependencies:**
-
-   ```sh
-   poetry install --with test
-   ```
-
-4. **Install only dev dependencies:**
-
-   ```sh
-   poetry install --with dev
-   ```
-
-This configuration ensures that your development dependencies are correctly categorized and installed as needed.
-
-### Using uv (Fast Python Package Installer)
+### Using uv
 ```bash
 # Install uv if you haven't already
 pip install uv
@@ -402,18 +546,34 @@ uv pip install -r pyproject.toml --extra "crewai,autogen"
 - Submit a pull request via GitHub's web interface.
 - Await feedback from project maintainers.
 
+## Other Features
+
+- 🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
+- 💻 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
+
 ## 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 |
 |-------|--------|
+| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
+| Reasoning Data Generating Agent | [![Reasoning Data](https://img.youtube.com/vi/fUT332Y2zA8/0.jpg)](https://www.youtube.com/watch?v=fUT332Y2zA8) |
+| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
+| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
+| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
+| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
+| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
+| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
+| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
 | 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) |
@@ -426,7 +586,5 @@ Praison AI is an open-sourced software licensed under the **[MIT license](https:
 | 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) |
+| Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |
 
-## License
-
-Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.

agents/example.py

@@ -1,166 +0,0 @@
-import os
-import logging
-from rich.console import Console
-from rich.panel import Panel
-from rich.text import Text
-from praisonaiagents import Agent, Task, Agents, error_logs
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(filename)s:%(lineno)d %(levelname)s %(message)s",
-    datefmt="[%X]"
-)
-
-def my_callback(output):
-    logging.info(f"Callback function called after task execution: {output.description}")
-    logging.info(f"Task output: {output}")
-
-def main():
-    # Make sure OPENAI_API_KEY is set
-    if not os.environ.get("OPENAI_API_KEY"):
-        raise ValueError("Please set OPENAI_API_KEY environment variable")
-
-    # Define tools
-    search_tool = {
-        "type": "function",
-        "function": {
-            "name": "search_tool",
-            "description": "Use this to perform search queries",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "query": {"type": "string"},
-                },
-                "required": ["query"],
-            },
-        },
-    }
-    get_weather_tool = {
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get the weather for a location",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string"},
-                },
-                "required": ["location"],
-            },
-        },
-    }
-
-    # 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=[search_tool],
-        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.""",
-        verbose=True,
-        allow_delegation=True,
-        llm="gpt-4o",
-        tools=[get_weather_tool],
-        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=[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=[get_weather_tool]
-    )
-
-    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 = Agents(
-        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
-    console = Console()
-    
-    # Print task results
-    console.print("\n=== Task Results ===")
-    for task_id, task_status in result['task_status'].items():
-        console.print(f"Task {task_id}: {task_status}")
-        if task_result := result['task_results'].get(task_id):
-            console.print(f"Output: {task_result.raw[:200]}...")  # Show first 200 chars
-
-    # Print task details
-    console.print("\n=== Task Details ===")
-    for i in range(4):
-        console.print(agents.get_task_details(i))
-
-    # Print agent details
-    console.print("\n=== Agent Details ===")
-    console.print(agents.get_agent_details('Researcher'))
-    console.print(agents.get_agent_details('Writer'))
-
-    # Print any errors
-    if error_logs:
-        console.print(Panel.fit(Text("Errors Encountered:", style="bold red"), title="Error Summary", border_style="red"))
-        for err in error_logs:
-            console.print(f"- {err}")
-            if "parsing self-reflection json" in err:
-                console.print("  Reason: The self-reflection JSON response was not valid JSON.")
-            elif "Error: Task with ID" in err:
-                console.print("  Reason: Task ID referenced does not exist.")
-            elif "saving task output to file" in err:
-                console.print("  Reason: Possible file permissions or invalid path.")
-            else:
-                console.print("  Reason not identified")
-
-if __name__ == "__main__":
-    main() 

agents/praisonaiagents/agent/__init__.py

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

agents/praisonaiagents/agent/agent.py

@@ -1,351 +0,0 @@
-import logging
-import json
-import time
-from typing import List, Optional, Any, Dict, Union, Literal
-from rich.console import Console
-from rich.live import Live
-from ..main import (
-    display_error,
-    display_tool_call,
-    display_instruction,
-    display_interaction,
-    display_generating,
-    display_self_reflection,
-    ReflectionOutput,
-    client,
-    error_logs
-)
-
-class Agent:
-    def __init__(
-        self,
-        name: str,
-        role: str,
-        goal: str,
-        backstory: str,
-        llm: Optional[Union[str, Any]] = "gpt-4o-mini",
-        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 = False,
-        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: bool = True,
-        max_reflection_iter: int = 3
-    ):
-        self.name = name
-        self.role = role
-        self.goal = goal
-        self.backstory = backstory
-        self.llm = llm
-        self.tools = tools if tools else []
-        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.self_reflect = self_reflect
-        self.max_reflection_iter = max_reflection_iter
-
-    def execute_tool(self, function_name, arguments):
-        logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
-        if function_name == "get_weather":
-            location = arguments.get("location", "Unknown Location")
-            return {"temperature": "25C", "condition": "Sunny", "location": location}
-        elif function_name == "search_tool":
-            query = arguments.get("query", "AI trends in 2024")
-            return {"results": [
-                {"title": "AI advancements in 2024", "link": "url1", "summary": "Lots of advancements"},
-                {"title": "New trends in AI", "link": "url2", "summary": "New trends being found"}
-            ]}
-        else:
-            return f"Tool '{function_name}' is not recognized"
-
-    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):
-        console = Console()
-        start_time = time.time()
-        logging.debug(f"{self.name} sending messages to LLM: {messages}")
-
-        formatted_tools = []
-        if tools:
-            for tool in tools:
-                if isinstance(tool, dict):
-                    formatted_tools.append(tool)
-                elif hasattr(tool, "to_openai_tool"):
-                    formatted_tools.append(tool.to_openai_tool())
-                elif isinstance(tool, str):
-                    formatted_tools.append({
-                        "type": "function",
-                        "function": {
-                            "name": tool,
-                            "description": f"This is a tool called {tool}",
-                            "parameters": {
-                                "type": "object",
-                                "properties": {},
-                            },
-                        }
-                    })
-                else:
-                    display_error(f"Warning: 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 = ""
-                with Live(display_generating("", start_time), refresh_per_second=4) 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))
-
-                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):
-        if self.use_system_prompt:
-            system_prompt = f"""{self.backstory}\n
-            Your Role: {self.role}\n
-            Your Goal: {self.goal}
-            """
-        else:
-            system_prompt = None
-        
-        messages = []
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-        messages.extend(self.chat_history)
-        messages.append({"role": "user", "content": prompt})
-
-        final_response_text = None
-        reflection_count = 0
-        start_time = time.time()
-
-        while True:
-            try:
-                if self.verbose:
-                    display_instruction(f"Agent {self.name} is processing prompt: {prompt}")
-
-                formatted_tools = []
-                if tools:
-                    for tool in tools:
-                        if isinstance(tool, dict):
-                            formatted_tools.append(tool)
-                        elif hasattr(tool, "to_openai_tool"):
-                            formatted_tools.append(tool.to_openai_tool())
-                        elif isinstance(tool, str):
-                            formatted_tools.append({
-                                "type": "function",
-                                "function": {
-                                    "name": tool,
-                                    "description": f"This is a tool called {tool}",
-                                    "parameters": {
-                                        "type": "object",
-                                        "properties": {},
-                                    },
-                                }
-                            })
-                        else:
-                            display_error(f"Warning: Tool {tool} not recognized")
-
-                response = self._chat_completion(messages, temperature=temperature, tools=formatted_tools if formatted_tools else None)
-                if not response:
-                    return None
-                    
-                tool_calls = getattr(response.choices[0].message, 'tool_calls', None)
-
-                if tool_calls:
-                    messages.append({
-                        "role": "assistant",
-                        "content": 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)
-
-                        if tool_result:
-                            if self.verbose:
-                                display_tool_call(f"Function '{function_name}' returned: {tool_result}")
-                            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()
-                else:
-                    response_text = response.choices[0].message.content.strip()
-
-                if not self.self_reflect:
-                    self.chat_history.append({"role": "user", "content": 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(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
-                    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.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: reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'")
-
-                    messages.append({"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"})
-
-                    if reflection_output.satisfactory == "yes":
-                        if self.verbose:
-                            display_self_reflection("Agent marked the response as satisfactory")
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
-                        return response_text
-
-                    logging.debug(f"{self.name} reflection not satisfactory, requesting regeneration.")
-                    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()
-                except Exception as e:
-                    display_error(f"Error in parsing self-reflection json {e}. Retrying")
-                    logging.error("Reflection parsing failed.", exc_info=True)
-                    messages.append({"role": "assistant", "content": f"Self Reflection failed."})
-                
-                reflection_count += 1
-
-                self.chat_history.append({"role": "user", "content": 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(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
-                return response_text
-            except Exception as e:
-                display_error(f"Error in chat: {e}")
-                return None 

agents/praisonaiagents/agents/agents.py

@@ -1,318 +0,0 @@
-import os
-import time
-import json
-import logging
-from typing import Any, Dict, Optional
-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
-
-class PraisonAIAgents:
-    def __init__(self, agents, tasks, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
-        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
-        self.manager_llm = manager_llm
-        for task in tasks:
-            self.add_task(task)
-            task.status = "not started"
-
-    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
-
-    def execute_task(self, task_id):
-        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 == "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}")
-        agent_output = executor_agent.chat(task_prompt, tools=task.tools)
-        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 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 run_task(self, task_id):
-        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):
-        if self.process == "sequential":
-            for task_id in self.tasks:
-                if self.tasks[task_id].status != "completed":
-                    self.run_task(task_id)
-        elif self.process == "hierarchical":
-            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 = self.add_task(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}")
-                    self.run_task(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 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}
-        } 

agents/praisonaiagents/main.py

@@ -1,112 +0,0 @@
-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
-
-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 = []
-
-def display_interaction(message: str, response: str, markdown: bool = True, generation_time: Optional[float] = None):
-    console = Console()
-    if generation_time is not None:
-        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
-    else:
-        console.print(Text("Response Generation Complete", 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 white"), title="Response", border_style="cyan"))
-
-def display_self_reflection(message: str):
-    console = Console()
-    console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
-
-def display_instruction(message: str):
-    console = Console()
-    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
-
-def display_tool_call(message: str):
-    console = Console()
-    console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
-
-def display_error(message: str):
-    console = Console()
-    console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
-    # Store errors
-    error_logs.append(message)
-
-def display_generating(content: str = "", start_time: Optional[float] = None):
-    elapsed_str = ""
-    if start_time is not None:
-        elapsed = time.time() - start_time
-        elapsed_str = f" {elapsed:.1f}s"
-    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/task/task.py

@@ -1,48 +0,0 @@
-import logging
-from typing import List, Optional, Dict, Any, Type
-from pydantic import BaseModel
-from ..main import TaskOutput
-from ..agent.agent import Agent
-
-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[Any] = None,
-        status: str = "not started",
-        result: Optional[TaskOutput] = None,
-        create_directory: Optional[bool] = False,
-        id: Optional[int] = None
-    ):
-        self.description = description
-        self.expected_output = expected_output
-        self.name = name
-        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.id = id
-
-        if self.output_json and self.output_pydantic:
-            raise ValueError("Only one output type can be defined")
-
-    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}')" 

agents/pyproject.toml

@@ -1,16 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "praisonaiagents"
-version = "0.0.4"
-description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
-authors = [
-    { name="Mervin Praison" }
-]
-dependencies = [
-    "pydantic",
-    "rich",
-    "openai"
-] 

agents/uv.lock

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chainlit.md

@@ -1 +0,0 @@
-# Welcome to Praison AI! 🚀🤖

cookbooks/agents_playbook/agents-scrapewebitetool.yaml

@@ -1,42 +0,0 @@
-framework: crewai
-topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
-roles:
-  story_developer:
-    backstory: Experienced in crafting compelling narratives for a diverse audience.
-    goal: Develop an engaging storyline for a movie about cats in Mars
-    role: Story Developer
-    tasks:
-      storyline_creation:
-        description: Create a captivating storyline for a movie about cats in Mars
-          inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
-        expected_output: Detailed storyline with key plot points, character motivations,
-          and setting descriptions.
-    tools:
-    - 'ScrapeWebsiteTool'
-  screenwriter:
-    backstory: Proficient in translating complex narratives into engaging screenplay
-      format.
-    goal: Transform the storyline into a script
-    role: Screenwriter
-    tasks:
-      script_creation:
-        description: Transform the developed storyline into a script for the movie
-          about cats in Mars.
-        expected_output: Complete script with dialogues, scenes, and stage directions
-          ready for production.
-    tools:
-    - ''
-  director:
-    backstory: Experienced in visual storytelling and bringing scripts to life on
-      screen.
-    goal: Oversee the production of the movie
-    role: Director
-    tasks:
-      movie_production:
-        description: Take the script and lead the team to produce the movie 'Cats
-          in Mars' based on the provided content and script.
-        expected_output: Completed movie ready for distribution with compelling visuals
-          and engaging storytelling.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/create-movie-agents.yaml

@@ -1,42 +0,0 @@
-framework: crewai
-topic: create a movie about cat in mars based on https://www.grammarly.com/blog/how-to-write-a-story/
-roles:
-  story_developer:
-    backstory: Experienced in crafting compelling narratives for a diverse audience.
-    goal: Develop an engaging storyline for a movie about cats in Mars
-    role: Story Developer
-    tasks:
-      storyline_creation:
-        description: Create a captivating storyline for a movie about cats in Mars
-          inspired by the blog post at https://www.grammarly.com/blog/how-to-write-a-story/
-        expected_output: Detailed storyline with key plot points, character motivations,
-          and setting descriptions.
-    tools:
-    - 'ScrapeWebsiteTool'
-  screenwriter:
-    backstory: Proficient in translating complex narratives into engaging screenplay
-      format.
-    goal: Transform the storyline into a script
-    role: Screenwriter
-    tasks:
-      script_creation:
-        description: Transform the developed storyline into a script for the movie
-          about cats in Mars.
-        expected_output: Complete script with dialogues, scenes, and stage directions
-          ready for production.
-    tools:
-    - ''
-  director:
-    backstory: Experienced in visual storytelling and bringing scripts to life on
-      screen.
-    goal: Oversee the production of the movie
-    role: Director
-    tasks:
-      movie_production:
-        description: Take the script and lead the team to produce the movie 'Cats
-          in Mars' based on the provided content and script.
-        expected_output: Completed movie ready for distribution with compelling visuals
-          and engaging storytelling.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/examples/researchers.yml

@@ -1,41 +0,0 @@
-framework: "crewai"
-topic: "Artificial Intelligence"
-
-roles:
-  researcher:
-    role: "Senior Data Researcher"
-    goal: "Uncover cutting-edge developments in {topic}"
-    backstory: > 
-      You're a seasoned researcher with a knack for uncovering the latest
-      developments in {topic}. Known for your ability to find the most relevant
-      information and present it in a clear and concise manner.
-    tools:
-      - search_tool: 
-          engine: google
-    tasks:
-      research_task:
-        description: >
-          Conduct thorough research about {topic}, focusing on the latest 
-          trends and technologies.
-        expected_output: > 
-          A list of 10 bullet points summarizing key findings with links to 
-          relevant sources. 
-
-  reporting_analyst:
-    role: "Reporting Analyst"
-    goal: "Create detailed reports based on {topic} data analysis and research findings."
-    backstory: >
-      You're a meticulous analyst with a keen eye for detail. You're known for 
-      your ability to turn complex data into clear and concise reports, making 
-      it easy for others to understand and act on the information you provide.
-    tasks:
-      reporting_task:
-        description: >
-          Review the research findings and create a comprehensive report on 
-          {topic}. Include insights, potential opportunities, and recommendations.
-        expected_output: >
-          A well-structured report in Markdown format, ready for distribution.
-
-dependencies:
- - task: reporting_task
-   depends_on: research_task  

cookbooks/agents_playbook/examples/scriptwriters.yml

@@ -1,41 +0,0 @@
-framework: "autogen"
-topic: "Artificial Intelligence"
-
-roles:
-  narrative_designer:
-    role: "Narrative Designer"
-    goal: "Craft engaging storylines and narratives for {topic}"
-    backstory: >
-      You're a creative visionary skilled in weaving complex narratives and 
-      developing engaging story worlds within {topic}. Your expertise lies in 
-      conceptualizing the overarching story and ensuring it resonates with the audience.
-    tasks:
-      story_concept_development:
-        description: >
-          Develop a compelling story concept for {topic}, focusing on originality, 
-          thematic depth, and audience engagement. Outline the main narrative arcs,
-          characters, and settings.
-        expected_output: >
-          A comprehensive document outlining the story concept, including key narrative 
-          arcs, character bios, and settings descriptions.
-
-  scriptwriter:
-    role: "Scriptwriter"
-    goal: "Transform story concepts into detailed scripts for {topic}"
-    backstory: >
-      With a flair for dialogue and a deep understanding of pacing and structure,
-      you excel at bringing narratives to life through scriptwriting. Your work 
-      turns story concepts into blueprints for compelling {topic} experiences.
-    tasks:
-      scriptwriting_task:
-        description: >
-          Based on the narrative concept, write a detailed script for {topic}. 
-          Include dialogue, stage directions, and scene descriptions that bring 
-          the story to life.
-        expected_output: >
-          A detailed script ready for production, including dialogue, stage 
-          directions, and scene descriptions.
-
-dependencies:
- - task: scriptwriting_task
-   depends_on: story_concept_development  

cookbooks/agents_playbook/finance-research.yaml

@@ -1,43 +0,0 @@
-framework: crewai
-topic: research about nvidia growth
-roles:
-  data_collector:
-    backstory: An experienced researcher with the ability to efficiently collect and
-      organize vast amounts of data.
-    goal: Gather information on Nvidia's growth
-    role: Data Collector
-    tasks:
-      data_collection_task:
-        description: Collect data on Nvidia's growth from various sources such as
-          financial reports, news articles, and company announcements.
-        expected_output: A comprehensive document detailing data points on Nvidia's
-          growth over the years.
-    tools:
-    - 'YouSearchTool'
-  data_analyst:
-    backstory: Specializes in extracting insights from large datasets, proficient
-      in quantitative and qualitative analysis.
-    goal: Analyze the collected data to identify trends and patterns
-    role: Data Analyst
-    tasks:
-      data_analysis_task:
-        description: Analyze the collected data to identify key trends and patterns
-          in Nvidia's growth.
-        expected_output: An analytical report summarizing trends, patterns, and key
-          growth metrics of Nvidia.
-    tools:
-    - ''
-  report_preparer:
-    backstory: Experienced in creating detailed reports and presentations, turning
-      analytical data into actionable insights.
-    goal: Generate a final report on Nvidia's growth
-    role: Report Preparer
-    tasks:
-      report_preparation_task:
-        description: Create a detailed report based on the analysis, highlighting
-          Nvidia's growth trajectory.
-        expected_output: A polished, comprehensive report summarizing Nvidia's growth
-          with visual aids and key insights.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/internet-research-agents.yaml

@@ -1,26 +0,0 @@
-framework: crewai
-topic: research about the latest AI News and prepare a detailed report
-roles:
-  research_analyst:
-    backstory: Experienced in gathering and analyzing data related to AI news trends.
-    goal: Analyze AI News trends
-    role: Research Analyst
-    tasks:
-      gather_data:
-        description: Conduct in-depth research on the latest AI News trends from reputable
-          sources.
-        expected_output: Comprehensive report on current AI News trends.
-    tools:
-    - 'InternetSearchTool'
-  content_creator:
-    backstory: Skilled in writing informative and engaging content about AI News.
-    goal: Create detailed reports on AI News
-    role: Content Creator
-    tasks:
-      compile_report:
-        description: Compile all gathered data into a detailed report with analysis
-          and insights.
-        expected_output: Well-structured report on the latest AI News with key insights.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/medical-research-pubmed-agents.yaml

@@ -1,39 +0,0 @@
-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
-    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.
-    tools:
-    - 'PubmedQueryRun'
-  medical_writer:
-    backstory: Skilled in translating complex medical information into accessible
-      content.
-    goal: Compile comprehensive content on lung disease causes
-    role: Medical Writer
-    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.
-    tools:
-    - ''
-  editor:
-    backstory: Proficient in editing medical content for accuracy and clarity.
-    goal: Review and refine content on lung disease causes
-    role: Editor
-    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.
-    tools:
-    - ''
-dependencies: []

cookbooks/praisonai-googlecolab.ipynb

@@ -1,311 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "cW47IVIvdyii",
-        "outputId": "dad73c8c-7506-440b-da37-8d8b21f7813f",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/173.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.7/173.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[33mWARNING: embedchain 0.1.124 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.124 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.123 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.123 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.122 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.122 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.121 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.121 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.120 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.120 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.119 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.119 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.118 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.118 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.117 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.117 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m160.7/160.7 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.2/76.2 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.3/54.3 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m525.5/525.5 kB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m72.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.3/55.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.6/202.6 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m327.6/327.6 kB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.1/115.1 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.6/21.6 MB\u001b[0m \u001b[31m73.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.0/64.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.5/52.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m342.3/342.3 kB\u001b[0m \u001b[31m29.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.6/57.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m774.0/774.0 kB\u001b[0m \u001b[31m42.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m38.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.2/233.2 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m273.8/273.8 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.8/172.8 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.7/67.7 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.7/94.7 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.6/131.6 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m71.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.9/302.9 kB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.2/93.2 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m88.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m85.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m342.2/342.2 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.8/295.8 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m58.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m481.7/481.7 kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.7/63.7 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m81.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.9/254.9 kB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m248.1/248.1 kB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.1/442.1 kB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m856.7/856.7 kB\u001b[0m \u001b[31m48.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.3/73.3 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m75.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m91.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m425.7/425.7 kB\u001b[0m \u001b[31m32.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.1/164.1 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m78.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.4/194.4 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m72.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m80.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Building wheel for praisonai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install -Uq \"praisonai[crewai]\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "myh-_QCuHfOs",
-        "outputId": "5384b8e0-e56c-4833-a829-2593ba6c6393"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1m\u001b[95m [2024-10-31 21:43:52][DEBUG]: == Working Agent: Space Researcher\u001b[00m\n",
-            "\u001b[1m\u001b[95m [2024-10-31 21:43:52][INFO]: == Starting Task: Research and compile information about exoplanets discovered in the last decade.\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: my best complete final answer to the task.  \n",
-            "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-            "\n",
-            "1. **Kepler-452b**: Discovered in 2015 by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \"cousin.\" It is about 1.6 times the size of Earth and orbits within the habitable zone of a G-type star similar to our Sun, located approximately 1,400 light-years away from Earth. Its potential for hosting life lies in its Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of detailed atmospheric data.\n",
-            "\n",
-            "2. **Proxima Centauri b**: Found in 2016, this exoplanet orbits the closest star to our solar system, Proxima Centauri, at a distance of just 4.24 light-years. Proxima Centauri b is roughly 1.27 times the size of Earth and resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a prime candidate for future studies on habitability and atmospheric composition.\n",
-            "\n",
-            "3. **TRAPPIST-1 System**: Discovered in 2017, the TRAPPIST-1 system contains seven Earth-sized exoplanets, three of which lie in the star's habitable zone. Located about 39 light-years away, these planets are of significant interest due to their potential for liquid water. The compact nature of the system and the small, cool star around which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space Telescope.\n",
-            "\n",
-            "4. **LHS 1140 b**: This exoplanet was discovered in 2017 and is located approximately 41 light-years away. It is about 1.4 times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-            "\n",
-            "5. **K2-18b**: Announced in 2019, K2-18b is situated 124 light-years from Earth and is a super-Earth with a mass about 8 times that of our planet. It orbits within the habitable zone of its star, and water vapor has been detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-            "\n",
-            "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the progress made in identifying worlds that could potentially host life. Advances in technology and observational techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the age-old question of whether we are alone in the universe.\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n",
-            "\u001b[1m\u001b[92m [2024-10-31 21:44:05][DEBUG]: == [Space Researcher] Task output: my best complete final answer to the task.  \n",
-            "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-            "\n",
-            "1. **Kepler-452b**: Discovered in 2015 by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \"cousin.\" It is about 1.6 times the size of Earth and orbits within the habitable zone of a G-type star similar to our Sun, located approximately 1,400 light-years away from Earth. Its potential for hosting life lies in its Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of detailed atmospheric data.\n",
-            "\n",
-            "2. **Proxima Centauri b**: Found in 2016, this exoplanet orbits the closest star to our solar system, Proxima Centauri, at a distance of just 4.24 light-years. Proxima Centauri b is roughly 1.27 times the size of Earth and resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a prime candidate for future studies on habitability and atmospheric composition.\n",
-            "\n",
-            "3. **TRAPPIST-1 System**: Discovered in 2017, the TRAPPIST-1 system contains seven Earth-sized exoplanets, three of which lie in the star's habitable zone. Located about 39 light-years away, these planets are of significant interest due to their potential for liquid water. The compact nature of the system and the small, cool star around which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space Telescope.\n",
-            "\n",
-            "4. **LHS 1140 b**: This exoplanet was discovered in 2017 and is located approximately 41 light-years away. It is about 1.4 times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-            "\n",
-            "5. **K2-18b**: Announced in 2019, K2-18b is situated 124 light-years from Earth and is a super-Earth with a mass about 8 times that of our planet. It orbits within the habitable zone of its star, and water vapor has been detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-            "\n",
-            "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the progress made in identifying worlds that could potentially host life. Advances in technology and observational techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the age-old question of whether we are alone in the universe.\n",
-            "\n",
-            "\u001b[00m\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "### Task Output ###\n",
-              "my best complete final answer to the task.  \n",
-              "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the \n",
-              "discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most \n",
-              "significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-              "\n",
-              "\u001b[1;36m1\u001b[0m. **Kepler-452b**: Discovered in \u001b[1;36m2015\u001b[0m by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \n",
-              "\u001b[32m\"cousin.\"\u001b[0m It is about \u001b[1;36m1.6\u001b[0m times the size of Earth and orbits within the habitable zone of a G-type star similar to \n",
-              "our Sun, located approximately \u001b[1;36m1\u001b[0m,\u001b[1;36m400\u001b[0m light-years away from Earth. Its potential for hosting life lies in its \n",
-              "Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of \n",
-              "detailed atmospheric data.\n",
-              "\n",
-              "\u001b[1;36m2\u001b[0m. **Proxima Centauri b**: Found in \u001b[1;36m2016\u001b[0m, this exoplanet orbits the closest star to our solar system, Proxima \n",
-              "Centauri, at a distance of just \u001b[1;36m4.24\u001b[0m light-years. Proxima Centauri b is roughly \u001b[1;36m1.27\u001b[0m times the size of Earth and \n",
-              "resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a \n",
-              "prime candidate for future studies on habitability and atmospheric composition.\n",
-              "\n",
-              "\u001b[1;36m3\u001b[0m. **TRAPPIST-\u001b[1;36m1\u001b[0m System**: Discovered in \u001b[1;36m2017\u001b[0m, the TRAPPIST-\u001b[1;36m1\u001b[0m system contains seven Earth-sized exoplanets, three of\n",
-              "which lie in the star's habitable zone. Located about \u001b[1;36m39\u001b[0m light-years away, these planets are of significant \n",
-              "interest due to their potential for liquid water. The compact nature of the system and the small, cool star around \n",
-              "which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space \n",
-              "Telescope.\n",
-              "\n",
-              "\u001b[1;36m4\u001b[0m. **LHS \u001b[1;36m1140\u001b[0m b**: This exoplanet was discovered in \u001b[1;36m2017\u001b[0m and is located approximately \u001b[1;36m41\u001b[0m light-years away. It is \n",
-              "about \u001b[1;36m1.4\u001b[0m times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky \n",
-              "composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-              "\n",
-              "\u001b[1;36m5\u001b[0m. **K2-18b**: Announced in \u001b[1;36m2019\u001b[0m, K2-18b is situated \u001b[1;36m124\u001b[0m light-years from Earth and is a super-Earth with a mass \n",
-              "about \u001b[1;36m8\u001b[0m times that of our planet. It orbits within the habitable zone of its star, and water vapor has been \n",
-              "detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-              "\n",
-              "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the \n",
-              "progress made in identifying worlds that could potentially host life. Advances in technology and observational \n",
-              "techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the \n",
-              "age-old question of whether we are alone in the universe.\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",
-              "my best complete final answer to the task.  \n",
-              "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the \n",
-              "discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most \n",
-              "significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>. **Kepler-452b**: Discovered in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2015</span> by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \n",
-              "<span style=\"color: #008000; text-decoration-color: #008000\">\"cousin.\"</span> It is about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> times the size of Earth and orbits within the habitable zone of a G-type star similar to \n",
-              "our Sun, located approximately <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span> light-years away from Earth. Its potential for hosting life lies in its \n",
-              "Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of \n",
-              "detailed atmospheric data.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>. **Proxima Centauri b**: Found in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2016</span>, this exoplanet orbits the closest star to our solar system, Proxima \n",
-              "Centauri, at a distance of just <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.24</span> light-years. Proxima Centauri b is roughly <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.27</span> times the size of Earth and \n",
-              "resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a \n",
-              "prime candidate for future studies on habitability and atmospheric composition.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>. **TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> System**: Discovered in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2017</span>, the TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> system contains seven Earth-sized exoplanets, three of\n",
-              "which lie in the star's habitable zone. Located about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">39</span> light-years away, these planets are of significant \n",
-              "interest due to their potential for liquid water. The compact nature of the system and the small, cool star around \n",
-              "which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space \n",
-              "Telescope.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>. **LHS <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1140</span> b**: This exoplanet was discovered in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2017</span> and is located approximately <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41</span> light-years away. It is \n",
-              "about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.4</span> times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky \n",
-              "composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>. **K2-18b**: Announced in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2019</span>, K2-18b is situated <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">124</span> light-years from Earth and is a super-Earth with a mass \n",
-              "about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> times that of our planet. It orbits within the habitable zone of its star, and water vapor has been \n",
-              "detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-              "\n",
-              "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the \n",
-              "progress made in identifying worlds that could potentially host life. Advances in technology and observational \n",
-              "techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the \n",
-              "age-old question of whether we are alone in the universe.\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "None\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "import yaml\n",
-        "from praisonai import PraisonAI\n",
-        "from google.colab import userdata\n",
-        "\n",
-        "# Example agent_yaml content\n",
-        "agent_yaml = \"\"\"\n",
-        "framework: \"crewai\"\n",
-        "topic: \"Space Exploration\"\n",
-        "\n",
-        "roles:\n",
-        "  astronomer:\n",
-        "    role: \"Space Researcher\"\n",
-        "    goal: \"Discover new insights about {topic}\"\n",
-        "    backstory: \"You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos.\"\n",
-        "    tasks:\n",
-        "      investigate_exoplanets:\n",
-        "        description: \"Research and compile information about exoplanets discovered in the last decade.\"\n",
-        "        expected_output: \"A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth.\"\n",
-        "\"\"\"\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') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "\n",
-        "# Run PraisonAI\n",
-        "result = praisonai.run()\n",
-        "\n",
-        "# Print the result\n",
-        "print(result)"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

cookbooks/praisonai-tools-googlecolab.ipynb

@@ -1,358 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-tools-googlecolab.ipynb)"
-      ],
-      "metadata": {
-        "id": "MHwYRTSdlNUS"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "cW47IVIvdyii",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "ad31a979-acb4-4200-9ce8-439ab4193594"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/173.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.7/173.7 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[33mWARNING: embedchain 0.1.124 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m160.7/160.7 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m273.8/273.8 kB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.8/172.8 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.7/67.7 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.2/93.2 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m48.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m342.2/342.2 kB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.8/295.8 kB\u001b[0m \u001b[31m22.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m58.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m481.7/481.7 kB\u001b[0m \u001b[31m38.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.7/63.7 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m93.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.9/254.9 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m248.1/248.1 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.1/442.1 kB\u001b[0m \u001b[31m32.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m856.7/856.7 kB\u001b[0m \u001b[31m53.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.3/73.3 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m37.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m72.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m425.7/425.7 kB\u001b[0m \u001b[31m32.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.1/164.1 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m98.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.4/194.4 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m71.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m94.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Building wheel for praisonai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install -Uq \"praisonai[crewai]\" duckduckgo_search"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "Qwds0ad96Pco"
-      },
-      "outputs": [],
-      "source": [
-        "# tools.py\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"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "myh-_QCuHfOs",
-        "outputId": "8024c5db-cdf3-4099-cf15-86c33c35f795"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1m\u001b[95m [2024-10-31 17:13:31][DEBUG]: == Working Agent: Space Researcher\u001b[00m\n",
-            "\u001b[1m\u001b[95m [2024-10-31 17:13:31][INFO]: == Starting Task: Research and compile information about exoplanets discovered in the last decade.\u001b[00m\n",
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
-            "\u001b[32;1m\u001b[1;3mTo compile a comprehensive report on exoplanets discovered in the last decade, I should start by searching for relevant information on recent exoplanet discoveries, focusing on their size, potential habitability, and distance from Earth. \n",
-            "\n",
-            "Action: InternetSearchTool\n",
-            "Action Input: {\"query\": \"exoplanets discovered in the last decade size habitability distance from Earth\"}\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': 'Earth-Size, Habitable Zone Planet Found Hidden in Early NASA Kepler ...', 'href': 'https://www.nasa.gov/news-release/earth-size-habitable-zone-planet-found-hidden-in-early-nasa-kepler-data/', 'body': \"A team of transatlantic scientists, using reanalyzed data from NASA's Kepler space telescope, has discovered an Earth-size exoplanet orbiting in its star's habitable zone, the area around a star where a rocky planet could support liquid water. Scientists discovered this planet, called Kepler-1649c, when looking through old observations from ...\"}, {'title': 'Largest Batch of Earth-size Habitable Zone Planets Found ...', 'href': 'https://science.nasa.gov/exoplanets/trappist1/', 'body': \"TRAPPIST-1: Largest Batch of Earth-sized Exoplanets The most studied planetary system, aside from our own solar system, lies about 40 light-years away. We've looked at the seven rocky exoplanets orbiting the TRAPPIST-1 star with ground and space telescopes like Spitzer, Kepler, Hubble, and, now, the James Webb Space Telescope. In March 2023, the first science […]\"}, {'title': 'NASA Telescope Reveals Largest Batch of Earth-Size ...', 'href': 'https://www.nasa.gov/news-release/nasa-telescope-reveals-largest-batch-of-earth-size-habitable-zone-planets-around-single-star/', 'body': '\"The seven wonders of TRAPPIST-1 are the first Earth-size planets that have been found orbiting this kind of star,\" said Michael Gillon, lead author of the paper and the principal investigator of the TRAPPIST exoplanet survey at the University of Liege, Belgium. \"It is also the best target yet for studying the atmospheres of potentially ...'}, {'title': 'NASA finds potentially habitable Earth-sized exoplanet ...', 'href': 'https://www.usatoday.com/story/news/nation/2024/05/28/nasa-exoplanet-discovery/73882055007/', 'body': '0:00. 0:59. NASA announced the discovery of a planet 40 light years from Earth that orbits every 12.8 days and is possibly even habitable. Gliese 12 b is a \"super Earth exoplanet\" that is nearly ...'}, {'title': \"NASA space telescope finds Earth-size exoplanet that's\", 'href': 'https://www.space.com/gliese-12-b-tess-exoplanet-hunt-for-life', 'body': \"The exoplanet — which the team found with NASA's Transiting Exoplanet Survey Satellite (TESS) — is estimated to have a width around 1.1 times that of Earth, making it similar to our planet as ...\"}]\n",
-            "\u001b[00m\n",
-            "\u001b[32;1m\u001b[1;3mThought: I have gathered multiple sources discussing exoplanet discoveries, including details about their size, potential habitability, and distance from Earth. I will now compile this information into a comprehensive report.\n",
-            "\n",
-            "Final Answer:\n",
-            "\n",
-            "### Exoplanet Discoveries in the Last Decade\n",
-            "\n",
-            "1. **Kepler-1649c**\n",
-            "   - **Size:** Earth-sized\n",
-            "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support liquid water.\n",
-            "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact distance wasn't specified in the initial search result.\n",
-            "\n",
-            "2. **TRAPPIST-1 System**\n",
-            "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-            "   - **Habitability:** The TRAPPIST-1 system is one of the most studied, and it provides the best targets for studying the atmospheres of potentially habitable planets.\n",
-            "   - **Distance from Earth:** About 40 light-years away.\n",
-            "\n",
-            "3. **Gliese 12 b**\n",
-            "   - **Size:** Classified as a \"super Earth\" exoplanet, with a width approximately 1.1 times that of Earth.\n",
-            "   - **Habitability:** Potentially habitable and orbits every 12.8 days, suggesting it could support liquid water.\n",
-            "   - **Distance from Earth:** 40 light-years away.\n",
-            "\n",
-            "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit within habitable zones. The proximity of these planets, particularly the TRAPPIST-1 system and Gliese 12 b, offers promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n",
-            "\u001b[1m\u001b[92m [2024-10-31 17:13:47][DEBUG]: == [Space Researcher] Task output: ### Exoplanet Discoveries in the Last Decade\n",
-            "\n",
-            "1. **Kepler-1649c**\n",
-            "   - **Size:** Earth-sized\n",
-            "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support liquid water.\n",
-            "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact distance wasn't specified in the initial search result.\n",
-            "\n",
-            "2. **TRAPPIST-1 System**\n",
-            "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-            "   - **Habitability:** The TRAPPIST-1 system is one of the most studied, and it provides the best targets for studying the atmospheres of potentially habitable planets.\n",
-            "   - **Distance from Earth:** About 40 light-years away.\n",
-            "\n",
-            "3. **Gliese 12 b**\n",
-            "   - **Size:** Classified as a \"super Earth\" exoplanet, with a width approximately 1.1 times that of Earth.\n",
-            "   - **Habitability:** Potentially habitable and orbits every 12.8 days, suggesting it could support liquid water.\n",
-            "   - **Distance from Earth:** 40 light-years away.\n",
-            "\n",
-            "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit within habitable zones. The proximity of these planets, particularly the TRAPPIST-1 system and Gliese 12 b, offers promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\n",
-            "\n",
-            "\u001b[00m\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "### Task Output ###\n",
-              "### Exoplanet Discoveries in the Last Decade\n",
-              "\n",
-              "\u001b[1;36m1\u001b[0m. **Kepler-1649c**\n",
-              "   - **Size:** Earth-sized\n",
-              "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support \n",
-              "liquid water.\n",
-              "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact \n",
-              "distance wasn't specified in the initial search result.\n",
-              "\n",
-              "\u001b[1;36m2\u001b[0m. **TRAPPIST-\u001b[1;36m1\u001b[0m System**\n",
-              "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-              "   - **Habitability:** The TRAPPIST-\u001b[1;36m1\u001b[0m system is one of the most studied, and it provides the best targets for \n",
-              "studying the atmospheres of potentially habitable planets.\n",
-              "   - **Distance from Earth:** About \u001b[1;36m40\u001b[0m light-years away.\n",
-              "\n",
-              "\u001b[1;36m3\u001b[0m. **Gliese \u001b[1;36m12\u001b[0m b**\n",
-              "   - **Size:** Classified as a \u001b[32m\"super Earth\"\u001b[0m exoplanet, with a width approximately \u001b[1;36m1.1\u001b[0m times that of Earth.\n",
-              "   - **Habitability:** Potentially habitable and orbits every \u001b[1;36m12.8\u001b[0m days, suggesting it could support liquid water.\n",
-              "   - **Distance from Earth:** \u001b[1;36m40\u001b[0m light-years away.\n",
-              "\n",
-              "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit \n",
-              "within habitable zones. The proximity of these planets, particularly the TRAPPIST-\u001b[1;36m1\u001b[0m system and Gliese \u001b[1;36m12\u001b[0m b, offers \n",
-              "promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\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",
-              "### Exoplanet Discoveries in the Last Decade\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>. **Kepler-1649c**\n",
-              "   - **Size:** Earth-sized\n",
-              "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support \n",
-              "liquid water.\n",
-              "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact \n",
-              "distance wasn't specified in the initial search result.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>. **TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> System**\n",
-              "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-              "   - **Habitability:** The TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> system is one of the most studied, and it provides the best targets for \n",
-              "studying the atmospheres of potentially habitable planets.\n",
-              "   - **Distance from Earth:** About <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span> light-years away.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>. **Gliese <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> b**\n",
-              "   - **Size:** Classified as a <span style=\"color: #008000; text-decoration-color: #008000\">\"super Earth\"</span> exoplanet, with a width approximately <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.1</span> times that of Earth.\n",
-              "   - **Habitability:** Potentially habitable and orbits every <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.8</span> days, suggesting it could support liquid water.\n",
-              "   - **Distance from Earth:** <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span> light-years away.\n",
-              "\n",
-              "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit \n",
-              "within habitable zones. The proximity of these planets, particularly the TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> system and Gliese <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> b, offers \n",
-              "promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "None\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "import yaml\n",
-        "from praisonai import PraisonAI\n",
-        "from google.colab import userdata\n",
-        "\n",
-        "# Example agent_yaml content\n",
-        "agent_yaml = \"\"\"\n",
-        "framework: \"crewai\"\n",
-        "topic: \"Space Exploration\"\n",
-        "\n",
-        "roles:\n",
-        "  astronomer:\n",
-        "    role: \"Space Researcher\"\n",
-        "    goal: \"Discover new insights about {topic}\"\n",
-        "    backstory: \"You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos.\"\n",
-        "    tasks:\n",
-        "      investigate_exoplanets:\n",
-        "        description: \"Research and compile information about exoplanets discovered in the last decade.\"\n",
-        "        expected_output: \"A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth.\"\n",
-        "    tools:\n",
-        "      - \"InternetSearchTool\"\n",
-        "\"\"\"\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",
-        "\n",
-        "# Run PraisonAI\n",
-        "result = praisonai.run()\n",
-        "\n",
-        "# Print the result\n",
-        "print(result)"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

cookbooks/python/10_wikipedia_praison_ai_integration.py

@@ -1,67 +0,0 @@
-# tools.py
-from langchain_community.utilities import WikipediaAPIWrapper
-from praisonai_tools import BaseTool
-
-class WikipediaSearchTool(BaseTool):
-    name: str = "WikipediaSearchTool"
-    description: str = "Search Wikipedia for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
-        results = api_wrapper.load(query=query)
-        return results
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "research about Nvidia growth"
-roles:
-  data_collector:
-    role: "Data Collector"
-    backstory: "An experienced researcher with the ability to efficiently collect and organize vast amounts of data."
-    goal: "Gather information on Nvidia's growth by providing the Ticket Symbol to YahooFinanceNewsTool"
-    tasks:
-      data_collection_task:
-        description: "Collect data on Nvidia's growth from various sources such as financial reports, news articles, and company announcements."
-        expected_output: "A comprehensive document detailing data points on Nvidia's growth over the years."
-    tools:
-      - "WikipediaSearchTool"
-  data_analyst:
-    role: "Data Analyst"
-    backstory: "Specializes in extracting insights from large datasets, proficient in quantitative and qualitative analysis."
-    goal: "Analyze the collected data to identify trends and patterns"
-    tasks:
-      data_analysis_task:
-        description: "Analyze the collected data to identify key trends and patterns in Nvidia's growth."
-        expected_output: "An analytical report summarizing trends, patterns, and key growth metrics of Nvidia."
-    tools: []
-  report_preparer:
-    role: "Report Preparer"
-    backstory: "Experienced in creating detailed reports and presentations, turning analytical data into actionable insights."
-    goal: "Generate a final report on Nvidia's growth"
-    tasks:
-      report_preparation_task:
-        description: "Create a detailed report based on the analysis, highlighting Nvidia's growth trajectory."
-        expected_output: "A polished, comprehensive report summarizing Nvidia's growth with visual aids and key insights."
-    tools: []
-dependencies: []
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/11_crawl4ai_and_praison_ai_integration.py

@@ -1,97 +0,0 @@
-# tools.py
-import os
-from crawl4ai import WebCrawler
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-from pydantic import BaseModel, Field
-from praisonai_tools import BaseTool
-
-class ModelFee(BaseModel):
-    llm_model_name: str = Field(..., description="Name of the model.")
-    input_fee: str = Field(..., description="Fee for input token for the model.")
-    output_fee: str = Field(..., description="Fee for output token for the model.")
-
-class ModelFeeTool(BaseTool):
-    name: str = "ModelFeeTool"
-    description: str = "Extracts model fees for input and output tokens from the given pricing page."
-
-    def _run(self, url: str):
-        crawler = WebCrawler()
-        crawler.warmup()
-
-        result = crawler.run(
-            url=url,
-            word_count_threshold=1,
-            extraction_strategy= LLMExtractionStrategy(
-                provider="openai/gpt-4o",
-                api_token=os.getenv('OPENAI_API_KEY'), 
-                schema=ModelFee.schema(),
-                extraction_type="schema",
-                instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
-                Do not miss any models in the entire content. One extracted model JSON format should look like this: 
-                {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
-            ),            
-            bypass_cache=True,
-        )
-        return result.extracted_content
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "extract model pricing from websites"
-roles:
-  web_scraper:
-    role: "Web Scraper"
-    backstory: |
-      An expert in web scraping with a deep understanding of extracting structured
-      data from online sources.
-
-      https://openai.com/api/pricing/
-      https://www.anthropic.com/pricing
-      https://cohere.com/pricing
-    goal: "Gather model pricing data from various websites"
-    tasks:
-      scrape_model_pricing:
-        description: "Scrape model pricing information from the provided list of websites."
-        expected_output: "Raw HTML or JSON containing model pricing data."
-    tools:
-      - "ModelFeeTool"
-  data_cleaner:
-    role: "Data Cleaner"
-    backstory: "Specialist in data cleaning, ensuring that all collected data is accurate and properly formatted."
-    goal: "Clean and organize the scraped pricing data"
-    tasks:
-      clean_pricing_data:
-        description: "Process the raw scraped data to remove any duplicates and inconsistencies, and convert it into a structured format."
-        expected_output: "Cleaned and organized JSON or CSV file with model pricing data."
-    tools: []
-  data_analyzer:
-    role: "Data Analyzer"
-    backstory: "Data analysis expert focused on deriving actionable insights from structured data."
-    goal: "Analyze the cleaned pricing data to extract insights"
-    tasks:
-      analyze_pricing_data:
-        description: "Analyze the cleaned data to extract trends, patterns, and insights on model pricing."
-        expected_output: "Detailed report summarizing model pricing trends and insights."
-    tools: []
-dependencies: []
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/1_business_product_launch_agents.py

@@ -1,66 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-
-# Example agents_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agents_yaml = """framework: "crewai"
-topic: "Product Development"
-roles:
-  market_research_analyst:
-    role: "Market Research Analyst"
-    backstory: "Expert at understanding market demand, target audience, and competition for products like the product. Skilled in developing marketing strategies to reach a wide audience."
-    goal: "Analyze the market demand for the product and suggest marketing strategies using InternetSearchTool."
-    tasks:
-      market_analysis_task:
-        description: "Analyze the market demand for the product. Current month is Jan 2024. Write a report on the ideal customer profile and marketing strategies to reach the widest possible audience. Include at least 10 bullet points addressing key marketing areas. Search the internet using InternetSearchTool."
-        expected_output: "Report on the ideal customer profile and marketing strategies, with at least 10 bullet points addressing key marketing areas."
-    tools:
-      - InternetSearchTool
-  technology_expert:
-    role: "Technology Expert"
-    backstory: "Visionary in current and emerging technological trends, especially in products like the product. Identifies which technologies are best suited for different business models."
-    goal: "Assess technological feasibilities and requirements for producing high-quality the product"
-    tasks:
-      technology_assessment_task:
-        description: "Assess the technological aspects of manufacturing high-quality the product. Write a report detailing necessary technologies and manufacturing approaches. Include at least 10 bullet points on key technological areas."
-        expected_output: "Report detailing necessary technologies and manufacturing approaches, including at least 10 bullet points on key technological areas."
-  business_consultant:
-    role: "Business Development Consultant"
-    backstory: "Seasoned in shaping business strategies for products like the product. Understands scalability and potential revenue streams to ensure long-term sustainability."
-    goal: "Evaluate the business model for the product, focusing on scalability and revenue streams"
-    tasks:
-      business_model_evaluation_task:
-        description: "Summarize the market and technological reports and evaluate the business model for the product. Write a report on the scalability and revenue streams for the product. Include at least 10 bullet points on key business areas. Give Business Plan, Goals, and Timeline for the product launch. Current month is Jan 2024."
-        expected_output: "Comprehensive business model evaluation report including scalability, revenue streams, at least 10 key business areas, Business Plan, Goals, and Timeline for the product launch."
-      """
-
-
-# Create a PraisonAI instance with the agent_yaml content and tools
-praisonai = PraisonAI(
-    agent_yaml=agents_yaml,
-    tools=[InternetSearchTool]  # Just pass the class directly in a list
-)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/2_create_story_book_agent.py

@@ -1,80 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-# Example agent_yaml content
-
-import os
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Children's Storybook Creation"
-roles:
-  story_outliner:
-    role: "Story Outliner"
-    backstory: "An imaginative creator who lays the foundation of captivating stories for children."
-    goal: "Develop an outline for a children's storybook about Animals, including chapter titles and characters for 5 chapters."
-    tasks:
-      task_outline:
-        description: "Create an outline for the children's storybook about Animals, detailing chapter titles and character descriptions for 5 chapters."
-        expected_output: "A structured outline document containing 5 chapter titles, with detailed character descriptions and the main plot points for each chapter."
-    tools:
-      - InternetSearchTool
-  story_writer:
-    role: "Story Writer"
-    backstory: "A talented storyteller who brings to life the world and characters outlined, crafting engaging and imaginative tales for children."
-    goal: "Write the full content of the story for all 5 chapters, each chapter 100 words, weaving together the narratives and characters outlined."
-    tasks:
-      task_write:
-        description: "Using the outline provided, write the full story content for all chapters, ensuring a cohesive and engaging narrative for children. Each chapter should contain approximately 100 words. Include the title of the story at the top."
-        expected_output: "A complete manuscript of the children's storybook about Animals with 5 chapters, each approximately 100 words, following the provided outline and integrating the characters and plot points into a cohesive narrative."
-  image_generator:
-    role: "Image Generator"
-    backstory: "A creative AI specialized in visual storytelling, bringing each chapter to life through imaginative imagery."
-    goal: "Generate one image per chapter based on the content provided by the story writer. Start with chapter number, chapter content, character details, detailed location information, and detailed items in the location where the activity happens. Generate a total of 5 images one by one. Final output should contain all 5 images in JSON format."
-    tasks:
-      task_image_generate:
-        description: "Generate 5 images that capture the essence of the children's storybook about Animals, aligning with the themes, characters, and narratives outlined in the chapters. Do it one by one."
-        expected_output: "A set of digital images that visually represent each chapter of the children's storybook, incorporating elements from the characters and plot as described in the chapters. The images should be suitable for inclusion in the storybook as illustrations."
-  content_formatter:
-    role: "Content Formatter"
-    backstory: "A meticulous formatter who enhances the readability and presentation of the storybook."
-    goal: "Format the written story content in markdown, including images at the beginning of each chapter."
-    tasks:
-      task_format_content:
-        description: "Format the story content in markdown, including an image at the beginning of each chapter."
-        expected_output: "The entire storybook content formatted in markdown, with each chapter title followed by the corresponding image and the chapter content."
-        output_file: "story.md"
-  markdown_to_pdf_creator:
-    role: "PDF Converter"
-    backstory: "An efficient converter that transforms Markdown files into professionally formatted PDF documents."
-    goal: "Convert the Markdown file to a PDF document. 'story.md' is the markdown file name."
-    tasks:
-      task_markdown_to_pdf:
-        description: "Convert a Markdown file to a PDF document, ensuring the preservation of formatting, structure, and embedded images using the mdpdf library."
-        expected_output: "A PDF file generated from the Markdown input, accurately reflecting the content with proper formatting. The PDF should be ready for sharing or printing."
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/3_crewai_open_interpreter.py

@@ -1,51 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Command Line Operations"
-
-roles:
-  cli_agent:
-    role: "Software Engineer"
-    backstory: "Expert in command line operations, creating and executing code."
-    goal: "Always use Executor Tool. Ability to perform CLI operations, write programs, and execute using Executor Tool."
-    tools:
-      - "CLITool.execute_cli_command"
-    tasks:
-      cli_task:
-        description: "Identify the operating system."
-        expected_output: "Successfully identifies the OS."
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/4_rag_using_tools.py

@@ -1,75 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-              framework: "crewai"
-              topic: "News Analysis"
-
-              roles:
-                news_search_agent:
-                  role: "News Searcher"
-                  backstory: "Expert in analyzing and generating key points from news content for quick updates."
-                  goal: "Generate key points for each news article from the latest news."
-                  tools:
-                    - "SearchNewsDB.news"
-                  allow_delegation: true
-                  tasks:
-                    news_search_task:
-                      description: "Search for 'AI 2024' and create key points for each news."
-                      expected_output: "Key points for each news article related to 'AI 2024'."
-                writer_agent:
-                  role: "Writer"
-                  backstory: "Expert in crafting engaging narratives from complex information."
-                  goal: "Identify all the topics received. Use the Get News Tool to verify each topic to search. Use the Search tool for detailed exploration of each topic. Summarize the retrieved information in depth for every topic."
-                  tools:
-                    - "GetNews.news"
-                    - "DuckDuckGoSearchRun"
-                  allow_delegation: true
-                  tasks:
-                    writer_task:
-                      description: |
-                        Go step by step.
-                        Step 1: Identify all the topics received.
-                        Step 2: Use the Get News Tool to verify each topic by going through one by one.
-                        Step 3: Use the Search tool to search for information on each topic one by one.
-                        Step 4: Go through every topic and write an in-depth summary of the information retrieved.
-                        Don't skip any topic.
-                      expected_output: "An in-depth summary of the information retrieved for each topic."
-
-              dependencies:
-                - task: "writer_task"
-                  depends_on:
-                    - "news_search_task"
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/5_job_search.py

@@ -1,94 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Job Search Assistance"
-
-roles:
-  job_searcher_agent:
-    role: "Job Searcher"
-    backstory: "You are actively searching for job opportunities in your field, ready to utilize and expand your skill set in a new role."
-    goal: "Search for jobs in the field of interest, focusing on enhancing relevant skills."
-    tools:
-      - "JobSearchTools.search_jobs"
-    allow_delegation: true
-    tasks:
-      job_search_task:
-        description: |
-          Search for current job openings for the Senior Data Scientist role in New York using the Job Search tool. Find 5 vacant positions in total. Emphasize the key skills required. The tool accepts input in JSON format with the following schema: 'role': '<role>', 'location': '<location>', 'num_results': <number>. Ensure to format the input accordingly.
-        expected_output: "A list of 5 Senior Data Scientist job openings in New York, including key skills required for each position."
-
-  skills_development_agent:
-    role: "Skills Development Advisor"
-    backstory: "As a skills development advisor, you assist job searchers in identifying crucial skills for their target roles and recommend ways to develop these skills."
-    goal: "Identify key skills required for jobs of interest and advise on improving them."
-    allow_delegation: true
-    tasks:
-      skills_highlighting_task:
-        description: |
-          Based on the identified job openings, list the key skills required for each position separately. Provide recommendations on how candidates can acquire or improve these skills through courses, self-study, or practical experience.
-        expected_output: "A detailed list of key skills for each position with recommendations on how to develop them."
-
-  interview_preparation_coach:
-    role: "Interview Preparation Coach"
-    backstory: "Expert in coaching job searchers on successful interview techniques, including mock interviews and feedback."
-    goal: "Enhance interview skills, focusing on common questions, presentation, and communication."
-    allow_delegation: true
-    tasks:
-      interview_preparation_task:
-        description: |
-          Prepare job searchers for interviews by conducting mock interviews and offering feedback on their responses, presentation, and communication skills, for each role separately.
-        expected_output: "Feedback and guidance on interview skills for each role, including common questions and tips on presentation and communication."
-
-  career_advisor:
-    role: "Career Advisor"
-    backstory: "Experienced in guiding candidates through their job search journey, offering personalized advice on career development and application processes."
-    goal: "Assist in resume building, LinkedIn profile optimization, and networking strategies."
-    allow_delegation: true
-    tasks:
-      career_advisory_task:
-        description: |
-          Offer guidance on resume building, optimizing LinkedIn profiles, and effective networking strategies to enhance job application success, for each role separately.
-        expected_output: "Personalized advice on resume enhancement, LinkedIn optimization, and networking strategies for each position."
-
-dependencies:
-  - task: "skills_highlighting_task"
-    depends_on:
-      - "job_search_task"
-  - task: "interview_preparation_task"
-    depends_on:
-      - "job_search_task"
-  - task: "career_advisory_task"
-    depends_on:
-      - "job_search_task"
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/6_rag_2-0.py

@@ -1,103 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "AI in healthcare"
-
-roles:
-  manager:
-    role: "Project Manager"
-    backstory: |
-      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.
-    goal: "Coordinate the project to ensure a seamless integration of research findings into compelling narratives"
-    verbose: true
-    allow_delegation: true
-    max_iter: 10
-    max_rpm: 20
-    tasks:
-      manager_task:
-        description: |
-          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.
-        expected_output: "A final comprehensive report that combines the research findings and narrative on AI in healthcare."
-
-  researcher:
-    role: "Senior Researcher"
-    backstory: |
-      Driven by curiosity, you're at the forefront of innovation, eager to explore and share
-      knowledge that could change the world.
-    goal: "Uncover groundbreaking technologies around AI in healthcare"
-    verbose: true
-    tools:
-      - "search_tool"
-      - "ContentTools.read_content"
-    tasks:
-      list_ideas:
-        description: "List of 5 interesting ideas to explore for an article about AI in healthcare."
-        expected_output: "Bullet point list of 5 ideas for an article."
-        async_execution: true
-      list_important_history:
-        description: "Research the history of AI in healthcare and identify the 5 most important events."
-        expected_output: "Bullet point list of 5 important events."
-        async_execution: true
-
-  writer:
-    role: "Writer"
-    backstory: |
-      With a flair for simplifying complex topics, you craft engaging narratives that captivate
-      and educate, bringing new discoveries to light in an accessible manner.
-    goal: "Narrate compelling tech stories around AI in healthcare"
-    verbose: true
-    tools:
-      - "search_tool"
-      - "ContentTools.read_content"
-    tasks:
-      write_article:
-        description: |
-          Compose an insightful article on AI in healthcare, including its history and the latest interesting ideas.
-        expected_output: "A 4-paragraph article about AI in healthcare."
-        context:
-          - "list_ideas"
-          - "list_important_history"
-        callback: "callback_function"
-
-dependencies:
-  - task: "write_article"
-    depends_on:
-      - "list_ideas"
-      - "list_important_history"
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/7_crew_ai_example_code.py

@@ -1,69 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """framework: "crewai"
-topic: "AI Advancements in 2024"
-roles:
-  researcher:
-    role: "Senior Research Analyst"
-    backstory: |
-      You are an expert at a technology research group, skilled in identifying trends and analyzing complex data.
-    goal: "Uncover cutting-edge developments in AI and data science"
-    verbose: true
-    allow_delegation: false
-    tools:
-      - "search_tool"
-    tasks:
-      task1:
-        description: |
-          Analyze 2024's AI advancements. Find major trends, new technologies, and their effects. Provide a detailed report.
-        expected_output: "A detailed report on major AI trends, new technologies, and their effects in 2024."
-  writer:
-    role: "Tech Content Strategist"
-    backstory: |
-      You are a content strategist known for making complex tech topics interesting and easy to understand.
-    goal: "Craft compelling content on tech advancements"
-    verbose: true
-    allow_delegation: true
-    tasks:
-      task2:
-        description: |
-          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.
-        expected_output: "An engaging blog post of at least 4 paragraphs about major AI advancements, suitable for tech enthusiasts."
-dependencies:
-  - task: "task2"
-    depends_on:
-      - "task1"
-"""
-
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/8_libre_translate.py

@@ -1,83 +0,0 @@
-# tools.py
-import requests
-from praisonai_tools import BaseTool
-
-class TranslatorTool(BaseTool):
-    name: str = "Translator Tool"
-    description: str = "Translate text from one language to another."
-
-    def _run(self, q: str, target: str):
-        url = "http://127.0.0.1:5000/translate"
-        payload = {
-            "q": q,
-            "source": "auto",  # Automatically detect the source language
-            "target": target
-        }
-        headers = {
-            "Content-Type": "application/json"
-        }
-
-        response = requests.post(url, json=payload, headers=headers)
-
-        if response.status_code == 200:
-            try:
-                data = response.json()
-                return data
-            except requests.exceptions.JSONDecodeError:
-                return "Failed to decode JSON from the response."
-        else:
-            return f"Request failed with status code: {response.status_code}"
-        
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Writing an article about AI"
-roles:
-  article_writer:
-    role: "Article Writer"
-    backstory: "Experienced writer with a deep understanding of artificial intelligence and technology trends."
-    goal: "Write an informative and engaging article about AI."
-    tasks:
-      write_article:
-        description: "Write a comprehensive article about the latest developments and implications of AI."
-        expected_output: "A well-written article on AI in English."
-  french_translator:
-    role: "Translator"
-    backstory: "Fluent in French with expertise in translating technical and non-technical content accurately."
-    goal: "Translate the AI article into French."
-    tasks:
-      translate_to_french:
-        description: "Translate the English article on AI into French."
-        expected_output: "The AI article translated into French."
-    tools:
-      - "TranslatorTool"
-  german_translator:
-    role: "Translator"
-    backstory: "Proficient in German with a strong background in technical translation."
-    goal: "Translate the AI article into German."
-    tasks:
-      translate_to_german:
-        description: "Translate the English article on AI into German."
-        expected_output: "The AI article translated into German."
-    tools:
-      - "TranslatorTool"
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/9_mem0_praison_ai_integration.py

@@ -1,126 +0,0 @@
-# tools.py
-from mem0 import Memory
-from praisonai_tools import BaseTool
-
-class AddMemoryTool(BaseTool):
-    name: str = "Add Memory Tool"
-    description: str = ("This tool allows storing a new memory with user ID and optional metadata.\n"
-                        "Example:\n"
-                        "   - Input: text='I am working on improving my tennis skills. Suggest some online courses.', user_id='alice', metadata={'category': 'hobbies'}\n"
-                        "   - Output: Memory added with summary 'Improving her tennis skills. Looking for online suggestions.'")
-
-    def _run(self, text: str, user_id: str, metadata: dict = None):
-        m = Memory()
-        result = m.add(text, user_id=user_id, metadata=metadata)
-        return result
-
-class GetAllMemoriesTool(BaseTool):
-    name: str = "Get All Memories Tool"
-    description: str = ("This tool retrieves all stored memories.\n"
-                        "Example:\n"
-                        "   - Input: action='get_all'\n"
-                        "   - Output: List of all stored memories.")
-
-    def _run(self):
-        m = Memory()
-        result = m.get_all()
-        return result
-
-class SearchMemoryTool(BaseTool):
-    name: str = "Search Memory Tool"
-    description: str = ("This tool searches for specific memories based on a query and user ID.\n"
-                        "Example:\n"
-                        "   - Input: query='What are Alice's hobbies?', user_id='alice'\n"
-                        "   - Output: Search results related to Alice's hobbies.")
-
-    def _run(self, query: str, user_id: str):
-        m = Memory()
-        result = m.search(query=query, user_id=user_id)
-        return result
-
-class UpdateMemoryTool(BaseTool):
-    name: str = "Update Memory Tool"
-    description: str = ("This tool updates an existing memory by memory ID and new data.\n"
-                        "Example:\n"
-                        "   - Input: memory_id='cb032b42-0703-4b9c-954d-77c36abdd660', data='Likes to play tennis on weekends'\n"
-                        "   - Output: Memory updated to 'Likes to play tennis on weekends.'")
-
-    def _run(self, memory_id: str, data: str):
-        m = Memory()
-        result = m.update(memory_id=memory_id, data=data)
-        return result
-
-class MemoryHistoryTool(BaseTool):
-    name: str = "Memory History Tool"
-    description: str = ("This tool gets the history of changes made to a specific memory by memory ID.\n"
-                        "Example:\n"
-                        "   - Input: memory_id='cb032b42-0703-4b9c-954d-77c36abdd660'\n"
-                        "   - Output: History of the specified memory.")
-
-    def _run(self, memory_id: str):
-        m = Memory()
-        result = m.history(memory_id=memory_id)
-        return result
-
-
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "personal memory management"
-roles:
-  data_analyst:
-    role: "Data Analyst"
-    backstory: "Skilled in analyzing data to find trends and insights."
-    goal: "Analyze the data to provide useful insights and recommendations."
-    tasks:
-      research_ai_news:
-        description: "Analyze the latest AI news from July 2024 and trends to provide insights and recommendations."
-        expected_output: "Report with insights on the latest AI trends and personalized recommendations."
-    tools:
-      - "InternetSearchTool"
-  user_support:
-    role: "User Support"
-    backstory: "Expert in assisting users with queries and issues related to memory management."
-    goal: "Provide support to users for adding information to memories."
-    tasks:
-      support_users:
-        description: "Assist users in adding information to memories."
-        expected_output: "User queries resolved and memories managed effectively."
-    tools:
-      - "AddMemoryTool"
-      - "GetAllMemoriesTool"
-      - "SearchMemoryTool"
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/story.md

@@ -1,49 +0,0 @@
-```markdown
-# The Secret Meeting Spot
-
-![The Secret Meeting Spot](A serene and vibrant illustration of the Enchanted Forest, with Luna the Wise Owl perched on a branch of the ancient oak tree. Max the Brave Squirrel stands excitedly next to the tree, and Ella the Curious Fawn gazes at a mysterious map held by Luna. The forest is lush with magical flora and a soft, mystical glow, capturing the sense of wonder and the beginning of their adventure.)
-
-## Chapter 1: The Secret Meeting Spot
-
-Luna the Wise Owl had called for a secret meeting in the heart of the Enchanted Forest. Perched on the branch of the ancient oak tree, she awaited her friends with a mysterious map clutched in her talons. Max the Brave Squirrel scampered up to the tree, his eyes gleaming with excitement. Ella the Curious Fawn approached, her gaze fixed on the map that promised an adventure like no other. The forest around them was alive with magical flora, casting a soft, mystical glow that hinted at the wonders to come.
-
----
-
-# The Mysterious Riverbank
-
-![The Mysterious Riverbank](A lively scene at the bustling riverbank, featuring Benny the Playful Beaver busy constructing a sturdy wooden bridge. Luna, Max, Ella, and Benny are depicted crossing the bridge, while the river sparkles beneath them. Strange markings on the trees along the riverbank hint at the presence of others, creating an atmosphere of mystery and excitement.)
-
-## Chapter 2: The Mysterious Riverbank
-
-The group made their way to the bustling riverbank, where Benny the Playful Beaver was hard at work constructing a sturdy wooden bridge. Luna, Max, Ella, and Benny crossed the bridge together, the river sparkling beneath them. As they journeyed along the riverbank, they noticed strange markings on the trees, hinting at the presence of others. The air was filled with an atmosphere of mystery and excitement, urging them forward on their quest.
-
----
-
-# The Hidden Glade of Whispers
-
-![The Hidden Glade of Whispers](A magical hidden glade where the wind whispers secrets. Mia the Shy Rabbit stands with Luna, Max, Ella, and Benny, all gathered around as they decipher the whispers. Sunlight filters through the trees, and the glade is filled with ethereal light and soft colors, reflecting the growing bond and shared purpose of the friends as they solve the riddle together.)
-
-## Chapter 3: The Hidden Glade of Whispers
-
-In the heart of the forest lay the Hidden Glade of Whispers, a place where the wind itself shared secrets. Mia the Shy Rabbit joined Luna, Max, Ella, and Benny as they gathered to decipher the whispers. Sunlight filtered through the trees, casting an ethereal light that enveloped the glade in soft colors. It was here that the bond between the friends grew stronger, their shared purpose guiding them as they unraveled the riddle together.
-
----
-
-# The Cavern of Echoes
-
-![The Cavern of Echoes](Inside the dark and mysterious cavern, illuminated by Oliver the Inventive Raccoon's gadgets. The friends, including Luna, Max, Ella, Benny, and Mia, navigate the maze-like tunnels, facing challenges together. The echoes play tricks on their minds, portrayed with swirling shadows and light, highlighting their teamwork and trust as they discover a crucial clue.)
-
-## Chapter 4: The Cavern of Echoes
-
-The journey led them to the Cavern of Echoes, a dark and mysterious place where Oliver the Inventive Raccoon's gadgets illuminated their path. Together, Luna, Max, Ella, Benny, and Mia navigated the maze-like tunnels, each echo playing tricks on their minds. Swirling shadows and light portrayed the challenges they faced, their teamwork and trust shining through as they discovered a crucial clue that would lead them to their goal.
-
----
-
-# The Treasure of Friendship
-
-![The Treasure of Friendship](A serene meadow bathed in the warm glow of the setting sun, where the friends uncover a chest filled with seeds and a heartfelt message. The group, united and joyful, plants the seeds together, symbolizing growth and new adventures. The scene captures the essence of friendship and the realization of their true treasure, with a backdrop of nature's beauty and promise of more to come.)
-
-## Chapter 5: The Treasure of Friendship
-
-Finally, the friends emerged into a serene meadow bathed in the warm glow of the setting sun. There, they uncovered a chest filled with seeds and a heartfelt message. United and joyful, they planted the seeds together, a symbol of growth and new adventures on the horizon. The scene captured the essence of friendship, the realization of their true treasure, and the promise of nature's beauty and more adventures to come.
-```

cookbooks/tools/tools.py

@@ -1,72 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-from langchain_community.tools.pubmed.tool import PubmedQueryRun
-from langchain_community.tools.reddit_search.tool import RedditSearchRun
-# pip install tavily-python
-from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
-from langchain_community.utilities.you import YouSearchAPIWrapper
-# pip install youtube_search
-from langchain_community.tools import YouTubeSearchTool
-# pip install wikipedia
-from langchain_community.utilities import WikipediaAPIWrapper
-
-class TavilyTool(BaseTool):
-    name: str = "TavilyTool"
-    description: str = "Search Tavily for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = TavilySearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-
-class YouSearchTool(BaseTool):
-    name: str = "You Search Tool"
-    description: str = "Search You.com for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = YouSearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-
-class WikipediaSearchTool(BaseTool):
-    name: str = "WikipediaSearchTool"
-    description: str = "Search Wikipedia for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
-        results = api_wrapper.load(query=query)
-        return results
-
-# pip install open-interpreter
-from interpreter import interpreter
-from praisonai_tools import BaseTool
-
-class OpenInterpreterTool(BaseTool):
-    name: str = "Open Interpreter Tool"
-    description: str = "Use Open Interpreter configured to create and execute code based on natural language commands."
-
-    def __init__(self):
-        super().__init__()
-        interpreter.auto_run = True
-
-    def _run(self, code: str):
-        result = interpreter.chat(code)
-        return result
-
-# Example usage
-if __name__ == "__main__":
-    tool = OpenInterpreterTool()
-    code = "Plot AAPL and META's normalized stock prices"
-    output = tool._run(code)
-    print(output)

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.7 gunicorn markdown
+RUN pip install flask praisonai==2.2.18 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docker/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==2.2.18" \
+    "praisonai[chat]" \
+    "embedchain[github,youtube]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "chat"]

docker/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==2.2.18" \
+    "praisonai[ui]" \
+    "praisonai[chat]" \
+    "praisonai[realtime]" \
+    "embedchain[github,youtube]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "ui"]

docker/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==2.2.18" \
+    "praisonai[ui]" \
+    "praisonai[crewai]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "ui"]

docker/docker-compose.yml

@@ -0,0 +1,21 @@
+services:
+  ui:
+    build:
+      context: .
+      dockerfile: Dockerfile.ui
+    volumes:
+      - .:/app
+    ports:
+      - "8082:8082"
+    environment:
+      FLASK_ENV: development
+      CHAINLIT_PORT: "8082"
+      CHAINLIT_HOST: "0.0.0.0"
+      OPENAI_API_KEY: ${OPENAI_API_KEY}
+      CHAINLIT_AUTH_SECRET: ${CHAINLIT_AUTH_SECRET}
+    command: praisonai ui --host 0.0.0.0
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8082"]
+      interval: 10s
+      timeout: 5s
+      retries: 3

docker/services.yaml

@@ -0,0 +1,5 @@
+framework: praisonaiagents
+services:
+  - ui
+  - chat
+  - realtime

docs/agents/agents.mdx

@@ -0,0 +1,124 @@
+---
+title: "AI Agents"
+description: "Overview of all available PraisonAI agents and their capabilities"
+icon: "robot"
+---
+
+PraisonAI provides a diverse set of specialized agents for various tasks. Each agent is designed with specific capabilities and tools to handle different types of tasks effectively.
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[ ]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+    end
+    
+    subgraph Agent2[ ]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+    end
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
+```
+
+## Data & Analysis
+
+<CardGroup cols={2}>
+  <Card title="Data Analyst" icon="chart-line" href="/agents/data-analyst">
+    Analyze data from various sources, create visualizations, and generate insights.
+  </Card>
+  <Card title="Finance" icon="money-bill-trend-up" href="/agents/finance">
+    Track stocks, analyze financial data, and provide investment recommendations.
+  </Card>
+  <Card title="Research" icon="magnifying-glass-chart" href="/agents/research">
+    Conduct comprehensive research and analysis across various topics.
+  </Card>
+  <Card title="Wikipedia" icon="book" href="/agents/wikipedia">
+    Search and extract information from Wikipedia articles.
+  </Card>
+</CardGroup>
+
+## Media & Content
+
+<CardGroup cols={2}>
+  <Card title="Image Analysis" icon="image" href="/agents/image">
+    Analyze and understand visual content from images.
+  </Card>
+  <Card title="Image to Text" icon="text" href="/agents/image-to-text">
+    Convert images to textual descriptions and extract text content.
+  </Card>
+  <Card title="Video" icon="video" href="/agents/video">
+    Analyze video content and extract meaningful information.
+  </Card>
+  <Card title="Markdown" icon="markdown" href="/agents/markdown">
+    Generate and format content in Markdown syntax.
+  </Card>
+</CardGroup>
+
+## Search & Recommendations
+
+<CardGroup cols={2}>
+  <Card title="Web Search" icon="globe" href="/agents/websearch">
+    Perform intelligent web searches and gather information.
+  </Card>
+  <Card title="Recommendation" icon="thumbs-up" href="/agents/recommendation">
+    Generate personalized recommendations based on preferences.
+  </Card>
+  <Card title="Shopping" icon="shop" href="/agents/shopping">
+    Compare prices and find the best deals across stores.
+  </Card>
+  <Card title="Planning" icon="calendar" href="/agents/planning">
+    Create travel plans and detailed itineraries.
+  </Card>
+</CardGroup>
+
+## Development
+
+<CardGroup cols={2}>
+  <Card title="Programming" icon="code" href="/agents/programming">
+    Write, analyze, and debug code across multiple languages.
+  </Card>
+  <Card title="Single Agent" icon="circle-1" href="/agents/single">
+    Simple, focused agent for basic tasks without external tools.
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+Each agent can be easily initialized and customized for your specific needs. Here's a basic example:
+
+```python
+from praisonaiagents import Agent
+
+# Create an agent with specific instructions
+agent = Agent(instructions="Your task-specific instructions")
+
+# Start the agent with a task
+response = agent.start("Your task description")
+```
+
+For more detailed information about each agent, click on the respective cards above.

docs/agents/data-analyst.mdx

@@ -0,0 +1,120 @@
+---
+title: "Data Analyst Agent"
+sidebarTitle: "Data Analyst"
+description: "Learn how to create AI agents for data analysis and insights generation."
+icon: "chart-line"
+---
+
+```mermaid
+flowchart LR
+    In[Data Source] --> Reader[Data Reader]
+    Reader --> Analyzer[Data Analyzer]
+    Analyzer --> Generator[Insights Generator]
+    Generator --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Reader fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Data Analyst Agent can read data from various sources, analyze it, and generate insights.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `data_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import (
+            read_csv, read_excel, write_csv, write_excel,
+            filter_data, get_summary, group_by, pivot_table
+        )
+
+        agent = Agent(
+            instructions="You are a Data Analyst Agent",
+            tools=[
+                read_csv, read_excel, write_csv, write_excel,
+                filter_data, get_summary, group_by, pivot_table
+            ]
+        )
+
+        # Start analysis with a specific task
+        agent.start(f"""
+            Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+            Analyse the data and give me the insights
+            read_csv to read the file
+        """)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Data Analysis Workflow
+
+The Data Analyst Agent is designed to perform comprehensive data analysis tasks using a suite of specialized tools. Here's how it works:
+
+1. **Data Reading**: The agent can read data from various sources:
+   - CSV files using `read_csv`
+   - Excel files using `read_excel`
+
+2. **Data Analysis**: Multiple analysis tools are available:
+   - `filter_data`: Filter datasets based on conditions
+   - `get_summary`: Generate statistical summaries
+   - `group_by`: Group data by specific columns
+   - `pivot_table`: Create pivot tables for analysis
+
+3. **Data Export**: Results can be exported to:
+   - CSV format using `write_csv`
+   - Excel format using `write_excel`
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Multiple Data Sources" icon="table">
+    Support for both CSV and Excel file formats.
+  </Card>
+  <Card title="Analysis Tools" icon="calculator">
+    Comprehensive suite of analysis tools including filtering, summarization, and pivoting.
+  </Card>
+  <Card title="Data Export" icon="file-export">
+    Export capabilities to various formats.
+  </Card>
+  <Card title="Automated Insights" icon="lightbulb">
+    Automatic generation of data insights and patterns.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing stock data
+agent.start("""
+    1. Read 'stock_data.csv'
+    2. Filter data for the last 30 days
+    3. Calculate daily returns
+    4. Generate summary statistics
+    5. Export results to 'analysis_results.xlsx'
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out other specialized agents like the [Finance Agent](/agents/finance) for specific use cases

docs/agents/finance.mdx

@@ -0,0 +1,97 @@
+---
+title: "Finance Agent"
+sidebarTitle: "Finance"
+description: "Learn how to create AI agents for financial analysis and investment recommendations."
+icon: "money-bill-trend-up"
+---
+
+```mermaid
+flowchart LR
+    In[Request] --> Price[Stock Price]
+    Price --> Info[Stock Info]
+    Info --> History[Historical Data]
+    History --> Out[Investment Advice]
+    
+    style In fill:#8B0000,color:#fff
+    style Price fill:#2E8B57,color:#fff
+    style Info fill:#2E8B57,color:#fff
+    style History fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Finance Agent can analyze stock market data and provide investment recommendations.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `finance_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+        agent = Agent(
+            instructions="You are a Research Agent",
+            tools=[get_stock_price, get_stock_info, get_historical_data]
+        )
+        agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")
+        ```
+    </Step>
+</Steps>
+
+## Understanding Financial Analysis
+
+The Finance Agent specializes in stock market analysis and investment recommendations using three key components:
+
+1. **Real-time Price Data**: Uses `get_stock_price` to fetch current market prices
+2. **Company Information**: Retrieves company details using `get_stock_info`
+3. **Historical Analysis**: Analyzes trends with `get_historical_data`
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Real-time Data" icon="clock">
+    Access to current stock market prices and information.
+  </Card>
+  <Card title="Company Analysis" icon="building">
+    Detailed company information and fundamentals.
+  </Card>
+  <Card title="Historical Trends" icon="chart-line">
+    Analysis of historical price movements and patterns.
+  </Card>
+  <Card title="Investment Advice" icon="money-bill">
+    AI-powered investment recommendations.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing specific stocks
+agent.start("""
+    1. Get current prices for AAPL and GOOGL
+    2. Retrieve company information
+    3. Analyze 6-month historical data
+    4. Provide investment recommendations
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex financial analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Data Analyst Agent](/agents/data-analyst) for detailed data analysis capabilities

docs/agents/image-to-text.mdx

@@ -0,0 +1,140 @@
+---
+title: "Image to Text Agent"
+sidebarTitle: "Image to Text"
+description: "Learn how to create AI agents for converting images to textual descriptions and extracting text from images."
+icon: "text"
+---
+
+```mermaid
+flowchart LR
+    In[Image Input] --> OCR[Text Extractor]
+    OCR --> Analyzer[Content Analyzer]
+    Analyzer --> Generator[Description Generator]
+    Generator --> Out[Text Output]
+    
+    style In fill:#8B0000,color:#fff
+    style OCR fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image-to-Text Agent can extract text from images and generate comprehensive descriptions.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `image_to_text.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Image-to-Text Agent
+        image_text_agent = Agent(
+            name="ImageTextConverter",
+            role="Image Text Extraction Specialist",
+            goal="Convert image content to textual descriptions and extract text",
+            backstory="""You are an expert in OCR and image understanding.
+            You excel at extracting text from images and generating detailed descriptions.""",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Create text extraction task
+        extraction_task = Task(
+            name="extract_text",
+            description="Extract all text from this image and describe its layout.",
+            expected_output="Extracted text and layout description",
+            agent=image_text_agent,
+            images=["document.jpg"]
+        )
+
+        # Create description task
+        description_task = Task(
+            name="generate_description",
+            description="Generate a detailed description of the image content.",
+            expected_output="Comprehensive description of visual elements",
+            agent=image_text_agent,
+            images=["scene.jpg"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[image_text_agent],
+            tasks=[extraction_task, description_task],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Image-to-Text Conversion
+
+The Image-to-Text Agent combines multiple capabilities to convert visual content into textual form:
+
+1. **OCR Processing**: Extracts text from images using optical character recognition
+2. **Layout Analysis**: Understands the spatial arrangement of text and visual elements
+3. **Content Description**: Generates natural language descriptions of image content
+4. **Text Formatting**: Preserves text formatting and structure where possible
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Text Extraction" icon="text">
+    Advanced OCR capabilities for text extraction.
+  </Card>
+  <Card title="Layout Understanding" icon="table-layout">
+    Analysis of text and content layout.
+  </Card>
+  <Card title="Content Description" icon="pen-to-square">
+    Detailed descriptions of visual content.
+  </Card>
+  <Card title="Format Preservation" icon="paragraph">
+    Maintains text formatting and structure.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Processing a document image
+document_task = Task(
+    name="process_document",
+    description="Extract text and analyze document layout",
+    expected_output="Extracted text with layout information",
+    agent=image_text_agent,
+    images=["business_document.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+    agents=[image_text_agent],
+    tasks=[document_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document processing
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving text extraction accuracy
+- Check out the [Image Agent](/agents/image) for pure image analysis capabilities

docs/agents/image.mdx

@@ -0,0 +1,139 @@
+---
+title: "Image Analysis Agent"
+sidebarTitle: "Image Analysis"
+description: "Learn how to create AI agents for image analysis and visual content understanding."
+icon: "image"
+---
+
+```mermaid
+flowchart LR
+    In[Image Input] --> Analyzer[Image Analyzer]
+    Analyzer --> Detector[Object Detector]
+    Detector --> Generator[Description Generator]
+    Generator --> Out[Analysis Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Detector fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Image Analysis Agent can process and analyze visual content, detect objects, and generate detailed descriptions.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `image_analysis.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Image Analysis Agent
+        image_agent = Agent(
+            name="ImageAnalyst",
+            role="Image Analysis 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
+        )
+
+        # Create tasks for different types of analysis
+        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=image_agent,
+            images=["https://example.com/landmark.jpg"]
+        )
+
+        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=image_agent,
+            images=["local_image.jpg"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[image_agent],
+            tasks=[task1, task2],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Image Analysis
+
+The Image Analysis Agent is designed to process and understand visual content through several key capabilities:
+
+1. **Image Input**: Supports both URL and local file inputs
+2. **Object Detection**: Identifies and locates objects within images
+3. **Architectural Analysis**: Specialized in analyzing architectural features
+4. **Spatial Understanding**: Describes spatial relationships between objects
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Multiple Input Sources" icon="images">
+    Support for both URL and local image files.
+  </Card>
+  <Card title="Object Detection" icon="magnifying-glass">
+    Advanced object detection and recognition.
+  </Card>
+  <Card title="Architectural Analysis" icon="building">
+    Specialized analysis of architectural features.
+  </Card>
+  <Card title="Detailed Descriptions" icon="pen">
+    Generation of comprehensive visual descriptions.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyzing a landmark image
+landmark_task = Task(
+    name="analyze_famous_landmark",
+    description="Analyze this historical landmark",
+    expected_output="Architectural details and historical significance",
+    agent=image_agent,
+    images=["https://example.com/landmark.jpg"]
+)
+
+# Run single task
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[landmark_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex image analysis workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Video Agent](/agents/video) for video content analysis

docs/agents/markdown.mdx

@@ -0,0 +1,120 @@
+---
+title: "Markdown Agent"
+sidebarTitle: "Markdown"
+description: "Learn how to create AI agents for generating and formatting content in Markdown."
+icon: "markdown"
+---
+
+```mermaid
+flowchart LR
+    In[Content Request] --> Generator[Content Generator]
+    Generator --> Formatter[Markdown Formatter]
+    Formatter --> Validator[Format Validator]
+    Validator --> Out[Markdown Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Formatter fill:#2E8B57,color:#fff
+    style Validator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Markdown Agent can generate and format content in Markdown syntax.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `markdown_generator.py`:
+        ```python
+        from praisonaiagents import Agent
+
+        # Create Markdown Agent
+        markdown_agent = Agent(
+            name="MarkdownWriter",
+            role="Markdown Content Specialist",
+            goal="Generate well-formatted content in Markdown syntax",
+            instructions="You are a Markdown Agent, output in markdown format",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Generate content
+        response = markdown_agent.start(
+            "Write a blog post about artificial intelligence"
+        )
+        
+        # Save to file
+        with open('blog_post.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Markdown Generation
+
+The Markdown Agent specializes in creating properly formatted Markdown content:
+
+1. **Content Generation**: Creates original content based on prompts
+2. **Markdown Formatting**: Applies proper Markdown syntax
+3. **Structure Validation**: Ensures correct formatting
+4. **Document Organization**: Creates well-structured documents
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Content Creation" icon="pen-to-square">
+    Generates original, well-structured content.
+  </Card>
+  <Card title="Markdown Syntax" icon="code">
+    Proper implementation of Markdown formatting.
+  </Card>
+  <Card title="Document Structure" icon="sitemap">
+    Organized document hierarchy and sections.
+  </Card>
+  <Card title="Format Validation" icon="check">
+    Ensures correct Markdown syntax usage.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Generate a technical documentation
+agent = Agent(
+    instructions="You are a Markdown Agent, output in markdown format"
+)
+
+# Generate API documentation
+response = agent.start("""
+    Create technical documentation for a REST API with:
+    - Introduction
+    - Authentication
+    - Endpoints
+    - Examples
+""")
+
+# Save documentation
+with open('api_docs.md', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex document generation
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving content quality
+- Check out the [Documentation Agent](/agents/documentation) for specialized documentation generation

docs/agents/planning.mdx

@@ -0,0 +1,109 @@
+---
+title: "Planning Agent"
+sidebarTitle: "Planning"
+description: "Learn how to create AI agents for trip planning and itinerary generation."
+icon: "calendar"
+---
+
+```mermaid
+flowchart LR
+    In[Request] --> Search[Web Search]
+    Search --> Analyzer[Options Analyzer]
+    Analyzer --> Generator[Plan Generator]
+    Generator --> Out[Itinerary]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Planning Agent can search for travel options and create detailed itineraries.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `travel_planner.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Planning Agent
+        planning_agent = Agent(
+            name="TravelPlanner",
+            role="Travel Planning Specialist",
+            goal="Create comprehensive travel plans and itineraries",
+            instructions="You are a Planning Agent",
+            tools=[duckduckgo]
+        )
+
+        # Generate travel plan
+        response = planning_agent.start(
+            "I want to go to London next week, find me a good hotel and flight"
+        )
+        
+        # Save plan
+        with open('travel_plan.txt', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Travel Planning
+
+The Planning Agent combines multiple capabilities to create comprehensive travel plans:
+
+1. **Web Search**: Uses DuckDuckGo to find current travel options
+2. **Options Analysis**: Evaluates and compares different choices
+3. **Plan Generation**: Creates detailed itineraries
+4. **Recommendations**: Provides personalized suggestions
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Flight Search" icon="plane">
+    Finds and compares flight options.
+  </Card>
+  <Card title="Hotel Booking" icon="hotel">
+    Recommends suitable accommodations.
+  </Card>
+  <Card title="Itinerary Creation" icon="calendar-days">
+    Generates detailed travel schedules.
+  </Card>
+  <Card title="Real-time Data" icon="clock">
+    Uses current pricing and availability.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create a weekend trip plan
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex travel planning
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendations
+- Check out the [Research Agent](/agents/research) for detailed destination research

docs/agents/programming.mdx

@@ -0,0 +1,125 @@
+---
+title: "Programming Agent"
+sidebarTitle: "Programming"
+description: "Learn how to create AI agents for code development, analysis, and execution."
+icon: "code"
+---
+
+```mermaid
+flowchart LR
+    In[Code Request] --> Analyzer[Code Analyzer]
+    Analyzer --> Generator[Code Generator]
+    Generator --> Executor[Code Executor]
+    Executor --> Debugger[Code Debugger]
+    Debugger --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Executor fill:#2E8B57,color:#fff
+    style Debugger fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Programming Agent can analyze, generate, execute, and debug code.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `code_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+        from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+        from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+        agent = Agent(
+            instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
+            tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+        )
+        agent.start(
+            "Write a python script using yfinance to find the stock price of Tesla"
+            "First check if required packages are installed"
+            "Run it using execute_code"
+            "execute_command if you want to run any terminal command"
+            "search internet using duckduckgo if you want to know update python package information"
+            "Analyse the output using analyze_code and fix error if required"
+            "if no package is installed, install it"
+            "then run the code"
+        )
+        ```
+    </Step>
+</Steps>
+
+## Understanding Code Development
+
+The Programming Agent combines multiple tools for comprehensive code development:
+
+1. **Code Tools**:
+   - `execute_code`: Run Python code
+   - `analyze_code`: Analyze code structure
+   - `format_code`: Format code to standards
+   - `lint_code`: Check code quality
+   - `disassemble_code`: View bytecode
+
+2. **Shell Tools**:
+   - `execute_command`: Run terminal commands
+   - `list_processes`: View running processes
+   - `kill_process`: Terminate processes
+   - `get_system_info`: System information
+
+3. **Web Tools**:
+   - `duckduckgo`: Search for programming resources
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Code Execution" icon="play">
+    Run and test code directly.
+  </Card>
+  <Card title="Code Analysis" icon="magnifying-glass-code">
+    Analyze and improve code quality.
+  </Card>
+  <Card title="System Integration" icon="terminal">
+    Execute system commands and manage processes.
+  </Card>
+  <Card title="Package Management" icon="box">
+    Handle dependencies and installations.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create and run a data analysis script
+response = agent.start("""
+    1. Create a script that:
+       - Uses pandas for data analysis
+       - Reads a CSV file
+       - Performs basic statistics
+       - Creates a visualization
+    2. Check and install required packages
+    3. Execute and analyze the results
+""")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex development workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving code quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for specialized data analysis

docs/agents/recommendation.mdx

@@ -0,0 +1,109 @@
+---
+title: "Recommendation Agent"
+sidebarTitle: "Recommendation"
+description: "Learn how to create AI agents for personalized recommendations across various domains."
+icon: "thumbs-up"
+---
+
+```mermaid
+flowchart LR
+    In[User Preferences] --> Search[Content Search]
+    Search --> Analyzer[Preference Analyzer]
+    Analyzer --> Generator[Recommendation Generator]
+    Generator --> Out[Recommendations]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Recommendation Agent can analyze preferences and generate personalized recommendations.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `recommendation_system.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Recommendation Agent
+        recommendation_agent = Agent(
+            name="RecommendationEngine",
+            role="Recommendation Specialist",
+            goal="Generate personalized recommendations based on preferences",
+            instructions="You are a Recommendation Agent",
+            tools=[duckduckgo]
+        )
+
+        # Get recommendations
+        response = recommendation_agent.start(
+            "Recommend me a good movie to watch in 2025"
+        )
+        
+        # Save recommendations
+        with open('recommendations.txt', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Recommendation System
+
+The Recommendation Agent uses multiple approaches to generate personalized suggestions:
+
+1. **Content Search**: Uses DuckDuckGo to find current options
+2. **Preference Analysis**: Understands user preferences
+3. **Recommendation Generation**: Creates personalized suggestions
+4. **Content Filtering**: Filters based on relevance and quality
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Personalization" icon="user">
+    Tailored recommendations based on preferences.
+  </Card>
+  <Card title="Real-time Data" icon="clock">
+    Up-to-date content and information.
+  </Card>
+  <Card title="Multi-domain" icon="layer-group">
+    Recommendations across various categories.
+  </Card>
+  <Card title="Content Filtering" icon="filter">
+    Quality-based content selection.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Get personalized recommendations
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex recommendation systems
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving recommendation accuracy
+- Check out the [Research Agent](/agents/research) for detailed content research

docs/agents/research.mdx

@@ -0,0 +1,108 @@
+---
+title: "Research Agent"
+sidebarTitle: "Research"
+description: "Learn how to create AI agents for conducting comprehensive research and analysis."
+icon: "magnifying-glass-chart"
+---
+
+```mermaid
+flowchart LR
+    In[Research Query] --> Search[Web Search]
+    Search --> Analyzer[Content Analyzer]
+    Analyzer --> Synthesizer[Information Synthesizer]
+    Synthesizer --> Out[Research Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Research Agent can gather, analyze, and synthesize information from various sources.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `research_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Research Agent
+        research_agent = Agent(
+            name="ResearchAssistant",
+            role="Research Specialist",
+            goal="Conduct comprehensive research and analysis",
+            instructions="You are a Research Agent",
+            tools=[duckduckgo]
+        )
+
+        # Conduct research
+        response = research_agent.start(
+            "Research about AI developments in 2024"
+        )
+        
+        # Save research findings
+        with open('research_report.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Research Process
+
+The Research Agent employs a systematic approach to information gathering and analysis:
+
+1. **Web Search**: Uses DuckDuckGo to find relevant sources
+2. **Content Analysis**: Evaluates source credibility and relevance
+3. **Information Synthesis**: Combines findings into coherent insights
+4. **Report Generation**: Creates structured research reports
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Web Research" icon="globe">
+    Comprehensive online information gathering.
+  </Card>
+  <Card title="Content Analysis" icon="microscope">
+    In-depth evaluation of sources.
+  </Card>
+  <Card title="Information Synthesis" icon="layer-group">
+    Integration of multiple sources.
+  </Card>
+  <Card title="Report Generation" icon="file-lines">
+    Structured research documentation.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex research projects
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Data Analyst Agent](/agents/data-analyst) for data-driven research

docs/agents/shopping.mdx

@@ -0,0 +1,109 @@
+---
+title: "Shopping Agent"
+sidebarTitle: "Shopping"
+description: "Learn how to create AI agents for price comparison and shopping assistance."
+icon: "shop"
+---
+
+```mermaid
+flowchart LR
+    In[Product Query] --> Search[Store Search]
+    Search --> Analyzer[Price Analyzer]
+    Analyzer --> Comparator[Price Comparator]
+    Comparator --> Out[Price Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Comparator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Shopping Agent can search for products, compare prices, and provide shopping recommendations.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `shopping_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Shopping Agent
+        shopping_agent = Agent(
+            name="ShoppingAssistant",
+            role="Shopping Specialist",
+            goal="Find the best prices and deals across stores",
+            instructions="You are a Shopping Agent",
+            tools=[duckduckgo]
+        )
+
+        # Search for product prices
+        response = shopping_agent.start(
+            "I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table"
+        )
+        
+        # Save price comparison
+        with open('price_comparison.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Shopping Assistant
+
+The Shopping Agent combines multiple capabilities for effective price comparison:
+
+1. **Store Search**: Uses DuckDuckGo to find retailers
+2. **Price Analysis**: Gathers pricing information
+3. **Price Comparison**: Compares prices across stores
+4. **Deal Finding**: Identifies special offers and discounts
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Price Comparison" icon="scale-balanced">
+    Compare prices across multiple stores.
+  </Card>
+  <Card title="Deal Detection" icon="tag">
+    Find special offers and discounts.
+  </Card>
+  <Card title="Store Analysis" icon="store">
+    Evaluate retailer reliability.
+  </Card>
+  <Card title="Price Tracking" icon="chart-line">
+    Monitor price changes over time.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Compare product prices
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex shopping research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving price accuracy
+- Check out the [Research Agent](/agents/research) for detailed product research

docs/agents/single.mdx

@@ -0,0 +1,102 @@
+---
+title: "Single Agent"
+sidebarTitle: "Single"
+description: "Learn how to create a basic single-purpose AI agent for simple tasks."
+icon: "circle-1"
+---
+
+```mermaid
+flowchart LR
+    In[Input] --> Agent[Single Agent]
+    Agent --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how to create and use a simple single-purpose agent.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `simple_agent.py`:
+        ```python
+        from praisonaiagents import Agent
+
+        agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+        agent.start("Write a blog post about AI")
+        ```
+    </Step>
+</Steps>
+
+## Understanding Single Agent
+
+The Single Agent is the simplest form of PraisonAI agent:
+
+1. **Simple Setup**: Minimal configuration required
+2. **Single Purpose**: Focused on one specific task
+3. **Direct Execution**: Straightforward input-output flow
+4. **No Dependencies**: No external tools required
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Simple Setup" icon="square-1">
+    Minimal configuration needed.
+  </Card>
+  <Card title="Focused Task" icon="bullseye">
+    Single-purpose execution.
+  </Card>
+  <Card title="Quick Start" icon="bolt">
+    Rapid implementation.
+  </Card>
+  <Card title="No Tools" icon="toolbox">
+    No external dependencies.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Create a content generation agent
+from praisonaiagents import Agent
+
+# Create content agent
+agent = Agent(
+    instructions="You are a Content Generation Agent, create engaging content"
+)
+
+# Generate content
+response = agent.start("""
+    Write a short story about:
+    - A future world
+    - With advanced AI
+    - And human collaboration
+""")
+
+# Save the story
+with open('ai_story.txt', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for more complex workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving output quality
+- Check out other specialized agents like the [Markdown Agent](/agents/markdown) for specific use cases

docs/agents/video.mdx

@@ -0,0 +1,149 @@
+---
+title: "Video Agent"
+sidebarTitle: "Video"
+description: "Learn how to create AI agents for video analysis and content understanding."
+icon: "video"
+---
+
+```mermaid
+flowchart LR
+    In[Video Input] --> Analyzer[Content Analyzer]
+    Analyzer --> Detector[Object Detector]
+    Detector --> Transcriber[Text Transcriber]
+    Transcriber --> Out[Analysis Report]
+    
+    style In fill:#8B0000,color:#fff
+    style Analyzer fill:#2E8B57,color:#fff
+    style Detector fill:#2E8B57,color:#fff
+    style Transcriber fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Video Agent can analyze video content, detect objects, and extract meaningful information.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `video_analyzer.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+
+        # Create Video Analysis Agent
+        video_agent = Agent(
+            name="VideoAnalyst",
+            role="Video Analysis Specialist",
+            goal="Analyze videos to extract meaningful information",
+            backstory="""You are an expert in computer vision and video analysis.
+            You excel at describing content, detecting objects, and understanding context.""",
+            llm="gpt-4o-mini",
+            self_reflect=False
+        )
+
+        # Create video analysis task
+        analysis_task = Task(
+            name="analyze_video",
+            description="""Analyze this video and provide:
+                1. Summary of main events
+                2. Key objects and people
+                3. Text and important information
+                4. Context and setting""",
+            expected_output="Comprehensive video analysis",
+            agent=video_agent,
+            images=["video.mp4"]
+        )
+
+        # Create PraisonAIAgents instance
+        agents = PraisonAIAgents(
+            agents=[video_agent],
+            tasks=[analysis_task],
+            process="sequential",
+            verbose=1
+        )
+
+        # Run analysis
+        agents.start()
+        ```
+    </Step>
+</Steps>
+
+## Understanding Video Analysis
+
+The Video Agent combines multiple capabilities for comprehensive video understanding:
+
+1. **Content Analysis**: Analyzes video scenes and events
+2. **Object Detection**: Identifies objects and people
+3. **Text Extraction**: Captures text shown in videos
+4. **Context Understanding**: Interprets settings and situations
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Scene Analysis" icon="film">
+    Detailed analysis of video scenes.
+  </Card>
+  <Card title="Object Detection" icon="cube">
+    Identification of objects and people.
+  </Card>
+  <Card title="Text Extraction" icon="closed-captioning">
+    Capture of text and captions.
+  </Card>
+  <Card title="Context Analysis" icon="sitemap">
+    Understanding of video context.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Analyze a presentation video
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+video_agent = Agent(
+    name="VideoAnalyst",
+    role="Video Analysis Specialist",
+    goal="Extract information from presentation videos",
+    llm="gpt-4o-mini"
+)
+
+# Create presentation analysis task
+presentation_task = Task(
+    name="analyze_presentation",
+    description="""Analyze this presentation video:
+        1. Extract key points
+        2. Capture slide content
+        3. Note speaker's main arguments
+        4. Summarize Q&A session""",
+    expected_output="Detailed presentation summary",
+    agent=video_agent,
+    images=["presentation.mp4"]
+)
+
+# Run analysis
+agents = PraisonAIAgents(
+    agents=[video_agent],
+    tasks=[presentation_task],
+    process="sequential"
+)
+agents.start()
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex video analysis
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving analysis accuracy
+- Check out the [Image Agent](/agents/image) for still image analysis

docs/agents/websearch.mdx

@@ -0,0 +1,124 @@
+---
+title: "Web Search Agent"
+sidebarTitle: "Web Search"
+description: "Learn how to create AI agents for intelligent web searching and information gathering."
+icon: "globe"
+---
+
+```mermaid
+flowchart LR
+    In[Search Query] --> Search[Web Search]
+    Search --> Filter[Content Filter]
+    Filter --> Summarizer[Content Summarizer]
+    Summarizer --> Out[Search Results]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Filter fill:#2E8B57,color:#fff
+    style Summarizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Web Search Agent can perform intelligent searches and process web content.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `web_search.py`:
+        ```python
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        # Create Web Search Agent
+        search_agent = Agent(
+            name="WebSearcher",
+            role="Web Search Specialist",
+            goal="Perform intelligent web searches and gather information",
+            instructions="You are a Web Search Agent",
+            tools=[duckduckgo]
+        )
+
+        # Perform search
+        response = search_agent.start(
+            "Search about AI developments in 2024"
+        )
+        
+        # Save search results
+        with open('search_results.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Web Search
+
+The Web Search Agent employs sophisticated search strategies:
+
+1. **Query Processing**: Optimizes search queries
+2. **Content Filtering**: Filters relevant results
+3. **Information Extraction**: Extracts key information
+4. **Result Summarization**: Summarizes findings
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Smart Search" icon="magnifying-glass">
+    Intelligent query processing.
+  </Card>
+  <Card title="Content Filtering" icon="filter">
+    Relevance-based filtering.
+  </Card>
+  <Card title="Information Extraction" icon="scissors">
+    Key information extraction.
+  </Card>
+  <Card title="Result Summary" icon="list">
+    Concise result summaries.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Perform topic research
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(
+    instructions="You are a Web Search Agent",
+    tools=[duckduckgo]
+)
+
+# Research specific topic
+response = agent.start("""
+    Search for information about quantum computing:
+    - Recent breakthroughs
+    - Leading companies
+    - Current applications
+    - Future predictions
+""")
+
+# Save research results
+with open('quantum_computing_research.md', 'w') as f:
+    f.write(response)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex search workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving search relevance
+- Check out the [Research Agent](/agents/research) for comprehensive research capabilities

docs/agents/wikipedia.mdx

@@ -0,0 +1,139 @@
+---
+title: "Wikipedia Agent"
+sidebarTitle: "Wikipedia"
+description: "Learn how to create AI agents for searching and extracting information from Wikipedia."
+icon: "book"
+---
+
+```mermaid
+flowchart LR
+    In[Query] --> Search[Wiki Search]
+    Search --> Page[Page Retrieval]
+    Page --> Summary[Content Summary]
+    Summary --> Out[Knowledge Output]
+    
+    style In fill:#8B0000,color:#fff
+    style Search fill:#2E8B57,color:#fff
+    style Page fill:#2E8B57,color:#fff
+    style Summary fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow demonstrating how the Wikipedia Agent can search, retrieve, and summarize Wikipedia content.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `wikipedia_assistant.py`:
+        ```python
+        from praisonaiagents import Agent, Task, PraisonAIAgents
+        from praisonaiagents.tools import (
+            wiki_search,
+            wiki_summary,
+            wiki_page,
+            wiki_random,
+            wiki_language
+        )
+
+        # Create Wikipedia Agent
+        wiki_agent = Agent(
+            name="WikipediaAssistant",
+            role="Wikipedia Research Specialist",
+            goal="Extract and summarize Wikipedia content",
+            instructions="You are a Wikipedia Agent",
+            tools=[
+                wiki_search,
+                wiki_summary,
+                wiki_page,
+                wiki_random,
+                wiki_language
+            ],
+            self_reflect=True,
+            min_reflect=3,
+            max_reflect=5
+        )
+
+        # Research a topic
+        response = wiki_agent.start("""
+            What is the history of AI?
+            First search the history of AI
+            Read the page of the history of AI
+            Get the summary of the page
+        """)
+        
+        # Save research results
+        with open('ai_history.md', 'w') as f:
+            f.write(response)
+        ```
+    </Step>
+</Steps>
+
+## Understanding Wikipedia Research
+
+The Wikipedia Agent provides comprehensive Wikipedia access through multiple tools:
+
+1. **Wiki Search**: `wiki_search` for finding relevant articles
+2. **Page Access**: `wiki_page` for retrieving full articles
+3. **Summarization**: `wiki_summary` for concise overviews
+4. **Random Articles**: `wiki_random` for discovering content
+5. **Language Support**: `wiki_language` for multilingual access
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Article Search" icon="magnifying-glass">
+    Find relevant Wikipedia articles.
+  </Card>
+  <Card title="Content Retrieval" icon="book-open">
+    Access complete article content.
+  </Card>
+  <Card title="Summarization" icon="compress">
+    Generate concise article summaries.
+  </Card>
+  <Card title="Multilingual" icon="language">
+    Access content in multiple languages.
+  </Card>
+</CardGroup>
+
+## Example Usage
+
+```python
+# Example: Research a scientific topic
+from praisonaiagents import Agent
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    self_reflect=True,
+    min_reflect=3,
+    max_reflect=5,
+)
+agent.start(
+    "What is the history of AI?"
+    "First search the history of AI"
+    "Read the page of the history of AI"
+    "Get the summary of the page"
+)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex Wikipedia research
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving research quality
+- Check out the [Research Agent](/agents/research) for broader research capabilities

docs/api-reference/index.mdx

@@ -1,6 +1,7 @@
 ---
 title: 'API Reference'
 description: 'PraisonAI API documentation with framework-specific details'
+icon: "book-open"
 ---
 
 # API Reference
@@ -29,7 +30,7 @@ Features:
 - Built-in tools
 - Structured workflows
 
-### AutoGen Integration
+### AG2 (Formerly AutoGen) Integration
 Requires installation with:
 ```bash
 pip install "praisonai[autogen]"

docs/api.mdx

@@ -1,4 +1,8 @@
-# API Reference
+---
+title: "API Reference"
+description: "Complete API reference for PraisonAI, including core modules, installation options, and framework-specific features"
+icon: "code"
+---
 
 ## Core Modules
 
@@ -19,7 +23,7 @@ from praisonai.auto import AutoGenerator
 ### praisonai.agents_generator
 Framework-specific agent generation and orchestration:
 - CrewAI support (requires `praisonai[crewai]`)
-- AutoGen support (requires `praisonai[autogen]`)
+- AG2 (Formerly AutoGen) support (requires `praisonai[autogen]`)
 
 ```python
 from praisonai.agents_generator import AgentsGenerator
@@ -47,7 +51,7 @@ pip install praisonai
 
 # Framework-specific installations
 pip install "praisonai[crewai]"    # Install with CrewAI support
-pip install "praisonai[autogen]"   # Install with AutoGen support
+pip install "praisonai[autogen]"   # Install with AG2 support
 pip install "praisonai[crewai,autogen]"  # Install both frameworks
 
 # Additional features
@@ -67,9 +71,9 @@ When installing with `pip install "praisonai[crewai]"`, you get:
 - Task delegation capabilities
 - Sequential and parallel task execution
 
-### AutoGen
+### AG2 (Formerly AutoGen)
 When installing with `pip install "praisonai[autogen]"`, you get:
-- AutoGen framework support
+- AG2 framework support
 - PraisonAI tools integration
 - Multi-agent conversation capabilities
 - Code execution environment

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.7 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.2.18 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

docs/api/praisonaiagents/agent/agent.mdx

@@ -0,0 +1,79 @@
+---
+title: "Agent Module"
+description: "Documentation for the praisonaiagents.agent.agent module"
+icon: "user"
+---
+
+# Module praisonaiagents.agent.agent
+
+## Classes
+
+### Agent
+The main class representing an AI agent with specific role, goal, and capabilities.
+
+#### Parameters
+- `name: str` - Name of the agent
+- `role: str` - Role of the agent
+- `goal: str` - Goal the agent aims to achieve
+- `backstory: str` - Background story of the agent
+- `llm: str | Any | None = 'gpt-4o'` - Language model to use
+- `tools: List[Any] | None = None` - List of tools available to the agent
+- `function_calling_llm: Any | None = None` - LLM for function calling
+- `max_iter: int = 20` - Maximum iterations
+- `max_rpm: int | None = None` - Maximum requests per minute
+- `max_execution_time: int | None = None` - Maximum execution time
+- `memory: bool = True` - Enable memory
+- `verbose: bool = True` - Enable verbose output
+- `allow_delegation: bool = False` - Allow task delegation
+- `step_callback: Any | None = None` - Callback for each step
+- `cache: bool = True` - Enable caching
+- `system_template: str | None = None` - System prompt template
+- `prompt_template: str | None = None` - Prompt template
+- `response_template: str | None = None` - Response template
+- `allow_code_execution: bool | None = False` - Allow code execution
+- `max_retry_limit: int = 2` - Maximum retry attempts
+- `respect_context_window: bool = True` - Respect context window size
+- `code_execution_mode: Literal['safe', 'unsafe'] = 'safe'` - Code execution mode
+- `embedder_config: Dict[str, Any] | None = None` - Embedder configuration
+- `knowledge_sources: List[Any] | None = None` - Knowledge sources
+- `use_system_prompt: bool | None = True` - Use system prompt
+- `markdown: bool = True` - Enable markdown
+- `self_reflect: bool = True` - Enable self reflection
+- `max_reflect: int = 3` - Maximum reflections
+- `min_reflect: int = 1` - Minimum reflections
+- `reflect_llm: str | None = None` - LLM for reflection
+
+#### Methods
+- `chat(self, prompt, temperature=0.2, tools=None, output_json=None)` - Chat with the agent
+- `achat(self, prompt, temperature=0.2, tools=None, output_json=None)` - Async version of chat method
+- `clean_json_output(self, output: str) → str` - Clean and extract JSON from response text
+- `clear_history(self)` - Clear chat history
+- `execute_tool(self, function_name, arguments)` - Execute a tool dynamically based on the function name and arguments
+- `_achat_completion(self, response, tools)` - Async version of _chat_completion method
+
+#### Async Support
+The Agent class provides async support through the following methods:
+
+- `achat`: Async version of the chat method for non-blocking communication
+- `_achat_completion`: Internal async method for handling chat completions
+
+Example usage:
+```python
+async def main():
+    agent = Agent(
+        name="AsyncAgent",
+        role="Async Specialist",
+        goal="Handle async operations",
+        backstory="Expert in async processing"
+    )
+    
+    # Use async chat
+    result = await agent.achat(
+        prompt="Your prompt here",
+        tools=your_tools,
+        output_json=your_schema
+    )
+    print(result)
+
+asyncio.run(main())
+```

docs/api/praisonaiagents/agents/agents.mdx

@@ -0,0 +1,113 @@
+---
+title: "Agents Module"
+description: "Documentation for the praisonaiagents.agents.agents module"
+icon: "users"
+---
+
+# Module praisonaiagents.agents.agents
+
+## Functions
+
+### encode_file_to_base64(file_path: str) → str
+Base64-encode a file.
+
+### process_video(video_path: str, seconds_per_frame=2)
+Split video into frames (base64-encoded).
+
+## Classes
+
+### LoopItems
+A Pydantic model for handling loop items.
+
+#### Class Variables
+- `items: List[Any]` - List of items to loop through
+- `model_config` - Model configuration
+
+### PraisonAIAgents
+The main class for managing agents and tasks execution.
+
+#### Parameters
+- `agents: List[Agent]` - List of agents
+- `tasks: List[Task]` - List of tasks
+- `verbose: int = 0` - Verbosity level
+- `completion_checker: Optional[Callable] = None` - Custom completion checker
+- `max_retries: int = 5` - Maximum retry attempts
+- `process: str = "sequential"` - Process type (sequential, workflow, hierarchical)
+- `manager_llm: Optional[str] = None` - Language model for manager agent
+
+#### Methods
+- `add_task(self, task)` - Add a task to the crew
+- `clean_json_output(self, output: str) → str` - Clean JSON output
+- `clear_state(self) → None` - Clear all state values
+- `default_completion_checker(self, task, agent_output)` - Default completion checker
+- `execute_task(self, task_id)` - Execute a specific task
+- `get_agent_details(self, agent_name)` - Get agent details
+- `get_all_tasks_status(self)` - Get status of all tasks
+- `get_state(self, key: str, default: Any = None) → Any` - Get a state value
+- `get_task_details(self, task_id)` - Get task details
+- `get_task_result(self, task_id)` - Get task result
+- `get_task_status(self, task_id)` - Get task status
+- `run_all_tasks(self)` - Execute tasks based on execution mode
+- `run_task(self, task_id)` - Run a specific task
+- `save_output_to_file(self, task, task_output)` - Save task output to file
+- `set_state(self, key: str, value: Any) → None` - Set a state value
+- `start(self)` - Start the crew execution
+- `update_state(self, updates: Dict) → None` - Update multiple state values
+
+#### Async Methods
+- `astart(self)` - Async version of start method
+- `aexecute_task(self, task_id)` - Async version of execute_task
+- `arun_task(self, task_id)` - Async version of run_task
+- `arun_all_tasks(self)` - Async version of run_all_tasks
+
+#### Async Support
+The PraisonAIAgents class provides comprehensive async support through the following methods:
+
+```python
+class PraisonAIAgents:
+    async def astart(self):
+        """Async version of start method"""
+        await self.arun_all_tasks()
+        return {
+            "task_status": self.get_all_tasks_status(),
+            "task_results": self.get_task_results()
+        }
+        
+    async def aexecute_task(self, task_id):
+        """Async version of execute_task method"""
+        # Async task execution implementation
+        
+    async def arun_task(self, task_id):
+        """Async version of run_task method"""
+        # Async task running with retries
+        
+    async def arun_all_tasks(self):
+        """Async version of run_all_tasks method"""
+        # Async execution of all tasks
+```
+
+Example usage:
+```python
+async def main():
+    # Initialize agents and tasks
+    agents = PraisonAIAgents(
+        agents=[async_agent1, async_agent2],
+        tasks=[async_task1, async_task2],
+        process="workflow"
+    )
+    
+    # Start async execution
+    result = await agents.astart()
+    print(result)
+
+# Run the async workflow
+asyncio.run(main())
+```
+
+#### Async Features
+- Full async/await support
+- Parallel task execution
+- Non-blocking operations
+- Efficient resource utilization
+- Mixed sync/async task handling
+- Async process management

docs/api/praisonaiagents/agents/autoagents.mdx

@@ -0,0 +1,187 @@
+---
+title: "AutoAgents Module"
+description: "Documentation for the praisonaiagents.agents.autoagents module"
+icon: "robot"
+---
+
+# Module praisonaiagents.agents.autoagents
+
+The AutoAgents module provides automatic creation and management of AI agents and tasks based on high-level instructions.
+
+## Classes
+
+### AutoAgents
+The main class for automatically creating and managing AI agents and tasks.
+
+#### Parameters
+- `instructions: str` - High-level task description for the agents
+- `tools: Optional[List[Any]] = None` - List of tools available to the agents
+- `verbose: bool = False` - Enable detailed logging
+- `process: str = "sequential"` - Process type (sequential or hierarchical)
+- `manager_llm: Optional[str] = None` - Language model for manager agent
+- `max_retries: int = 5` - Maximum retry attempts
+- `completion_checker: Optional[Any] = None` - Custom completion checker
+- `allow_code_execution: bool = False` - Allow code execution
+- `memory: bool = True` - Enable agent memory
+- `markdown: bool = True` - Enable markdown formatting
+- `self_reflect: bool = False` - Enable agent self-reflection
+- `max_reflect: int = 3` - Maximum reflection iterations
+- `min_reflect: int = 1` - Minimum reflection iterations
+- `llm: Optional[str] = None` - Language model for agents
+- `function_calling_llm: Optional[str] = None` - Language model for tool calling
+- `respect_context_window: bool = True` - Respect model context window
+- `code_execution_mode: str = "safe"` - Code execution mode (safe/unsafe)
+- `embedder_config: Optional[Dict[str, Any]] = None` - Embedder configuration
+- `knowledge_sources: Optional[List[Any]] = None` - Knowledge sources
+- `use_system_prompt: bool = True` - Use system prompts
+- `cache: bool = True` - Enable caching
+- `allow_delegation: bool = False` - Allow task delegation
+- `step_callback: Optional[Any] = None` - Callback for each step
+- `system_template: Optional[str] = None` - Custom system template
+- `prompt_template: Optional[str] = None` - Custom prompt template
+- `response_template: Optional[str] = None` - Custom response template
+- `max_rpm: Optional[int] = None` - Maximum requests per minute
+- `max_execution_time: Optional[int] = None` - Maximum execution time
+- `max_iter: int = 20` - Maximum iterations
+- `reflect_llm: Optional[str] = None` - Language model for reflection
+- `max_agents: int = 3` - Maximum number of agents to create
+
+#### Methods
+
+##### start()
+Start the agents synchronously.
+
+```python
+def start(self):
+    """
+    Creates tasks based on the instructions, then starts execution.
+    Returns the task status and results dictionary.
+    """
+    return super().start()
+```
+
+##### astart()
+Start the agents asynchronously.
+
+```python
+async def astart(self):
+    """
+    Async version of start() method.
+    Creates tasks based on the instructions, then starts execution.
+    Returns the task status and results dictionary.
+    """
+    return await super().astart()
+```
+
+#### Internal Methods
+
+##### _generate_config()
+Generate the configuration for agents and tasks.
+
+```python
+def _generate_config(self) -> AutoAgentsConfig:
+    """
+    Generate the configuration for agents and tasks based on instructions.
+    Returns AutoAgentsConfig object containing agent and task configurations.
+    """
+```
+
+##### _create_agents_and_tasks()
+Create agents and tasks from configuration.
+
+```python
+def _create_agents_and_tasks(self, config: AutoAgentsConfig) -> tuple[List[Agent], List[Task]]:
+    """
+    Create agents and tasks based on the generated configuration.
+    Returns tuple of (agents, tasks).
+    """
+```
+
+##### _assign_tools_to_agent()
+Assign appropriate tools to an agent.
+
+```python
+def _assign_tools_to_agent(self, agent_config: AgentConfig) -> List[Any]:
+    """
+    Assign tools to an agent based on its role and tasks.
+    Returns list of assigned tools.
+    """
+```
+
+## Pydantic Models
+
+### TaskConfig
+Configuration for a task.
+
+#### Attributes
+- `name: str` - Task name
+- `description: str` - Task description
+- `expected_output: str` - Expected output description
+- `tools: List[str]` - Required tools for the task
+
+### AgentConfig
+Configuration for an agent.
+
+#### Attributes
+- `name: str` - Agent name
+- `role: str` - Agent role
+- `goal: str` - Agent goal
+- `backstory: str` - Agent backstory
+- `tools: List[str]` - Required tools
+- `tasks: List[TaskConfig]` - Tasks assigned to the agent
+
+### AutoAgentsConfig
+Overall configuration for AutoAgents.
+
+#### Attributes
+- `main_instruction: str` - Main instruction for the agents
+- `process_type: str` - Process type (sequential/hierarchical)
+- `agents: List[AgentConfig]` - List of agent configurations
+
+## Example Usage
+
+### Basic Usage
+```python
+from praisonaiagents import AutoAgents
+from praisonaiagents.tools import SerperDevTool
+
+agents = AutoAgents(
+    instructions="Research recent AI developments",
+    tools=[SerperDevTool()],
+    verbose=True
+)
+result = agents.start()
+```
+
+### Async Usage
+```python
+async def main():
+    agents = AutoAgents(
+        instructions="Research recent AI developments",
+        tools=[SerperDevTool()],
+        process="hierarchical"
+    )
+    result = await agents.astart()
+
+import asyncio
+asyncio.run(main())
+```
+
+### Advanced Configuration
+```python
+agents = AutoAgents(
+    instructions="Complex research task",
+    tools=[SerperDevTool()],
+    max_agents=5,
+    process="hierarchical",
+    manager_llm="gpt-4o",
+    memory=True,
+    allow_delegation=True,
+    max_execution_time=600,
+    self_reflect=True
+)
+```
+
+<Note>
+  For optimal results, provide clear instructions and appropriate tools for your use case.
+</Note>

docs/api/praisonaiagents/index.mdx

@@ -0,0 +1,52 @@
+---
+title: "API Reference"
+description: "Praison AI Agents - SDK Documentation"
+icon: "code"
+---
+
+<CardGroup cols={2}>
+  <Card title="Agent" icon="user" href="/api/praisonaiagents/agent/agent">
+    Documentation for individual AI agents and their capabilities
+  </Card>
+  <Card title="Agents" icon="users" href="/api/praisonaiagents/agents/agents">
+    Documentation for managing multiple agents and their interactions
+  </Card>
+  <Card title="Task" icon="list-check" href="/api/praisonaiagents/task/task">
+    Documentation for defining and managing tasks
+  </Card>
+  <Card title="Process" icon="diagram-project" href="/api/praisonaiagents/process/process">
+    Documentation for handling task execution flows
+  </Card>
+</CardGroup>
+
+# Module praisonaiagents
+
+## Sub-modules
+* praisonaiagents.agent - Agent module for defining individual AI agents
+* praisonaiagents.agents - Agents module for managing multiple agents
+* praisonaiagents.task - Task module for defining and managing tasks
+* praisonaiagents.process - Process module for handling task execution flows
+* praisonaiagents.main - Main module containing utility functions
+
+## Functions
+
+### clean_triple_backticks(text: str) → str
+Clean triple backticks from text output.
+
+### display_error(message: str, console=None)
+Display error messages in the console.
+
+### display_generating(content: str = '', start_time: float | None = None)
+Display generation status with optional timing information.
+
+### display_instruction(message: str, console=None)
+Display instruction messages in the console.
+
+### display_interaction(message, response, markdown=True, generation_time=None, console=None)
+Display interaction between user and agent.
+
+### display_self_reflection(message: str, console=None)
+Display agent self-reflection messages.
+
+### display_tool_call(message: str, console=None)
+Display tool call messages.

docs/api/praisonaiagents/process/process.mdx

@@ -0,0 +1,98 @@
+---
+title: "Process Module"
+description: "Documentation for the praisonaiagents.process.process module"
+icon: "diagram-project"
+---
+
+# Module praisonaiagents.process.process
+
+## Classes
+
+### Process
+The main class for handling task execution processes in different modes (sequential, hierarchical, workflow).
+
+#### Parameters
+- `tasks: Dict[str, Task]` - Dictionary of tasks to be executed
+- `agents: List[Agent]` - List of available agents
+- `manager_llm: Optional[str] = None` - Language model for manager agent in hierarchical process
+- `verbose: bool = False` - Enable verbose logging
+
+#### Methods
+- `workflow(self)` - Execute tasks in a workflow pattern with support for loops and conditions
+- `sequential(self)` - Execute tasks in sequential order
+- `hierarchical(self)` - Execute tasks in hierarchical order with a manager agent
+
+#### Async Methods
+- `aworkflow(self) -> AsyncGenerator[str, None]` - Async version of workflow process
+- `asequential(self) -> AsyncGenerator[str, None]` - Async version of sequential process
+- `ahierarchical(self) -> AsyncGenerator[str, None]` - Async version of hierarchical process
+
+#### Async Process Support
+Each process type has an async counterpart that supports non-blocking execution:
+
+```python
+class Process:
+    async def aworkflow(self):
+        """Async workflow process execution"""
+        # Async workflow implementation
+        
+    async def asequential(self):
+        """Async sequential process execution"""
+        # Async sequential implementation
+        
+    async def ahierarchical(self):
+        """Async hierarchical process execution"""
+        # Async hierarchical implementation
+```
+
+Example usage:
+```python
+# Create process instance
+process = Process(tasks=tasks, agents=agents)
+
+# Use async workflow
+async for task_id in process.aworkflow():
+    # Handle task execution
+    if tasks[task_id].async_execution:
+        await handle_async_task(task_id)
+    else:
+        handle_sync_task(task_id)
+```
+
+#### Async Process Features
+- Non-blocking task execution
+- Parallel task processing
+- Mixed sync/async task support
+- Efficient resource utilization
+- Async manager coordination (hierarchical)
+- Dynamic task scheduling (workflow)
+
+### LoopItems
+A Pydantic model for handling loop data in workflow process.
+
+#### Parameters
+- `items: List[Any]` - List of items to be processed in a loop
+
+## Process Types
+
+### Sequential Process
+- Tasks are executed one after another in a linear fashion
+- Each task is completed before moving to the next one
+- Simplest form of task execution
+
+### Hierarchical Process
+- Uses a manager agent to coordinate task execution
+- Manager decides task order and agent assignments
+- Provides more complex task orchestration
+- Requires manager_llm parameter
+- Supports dynamic agent assignment
+
+### Workflow Process
+- Supports complex task relationships
+- Features:
+  - Task dependencies
+  - Conditional execution
+  - Loops
+  - Context sharing between tasks
+- Maintains task state and relationships
+- Handles data flow between tasks

docs/api/praisonaiagents/task/task.mdx

@@ -0,0 +1,78 @@
+---
+title: "Task Module"
+description: "Documentation for the praisonaiagents.task.task module"
+icon: "list-check"
+---
+
+# Module praisonaiagents.task.task
+
+## Classes
+
+### Task
+Represents a task to be executed by an agent.
+
+#### Parameters
+- `description: str` - Task description
+- `expected_output: str` - Expected output description
+- `agent: Agent | None = None` - Agent assigned to the task
+- `name: str | None = None` - Task name
+- `tools: List[Any] | None = None` - Tools available for the task
+- `context: List[Task] | None = None` - Context from other tasks
+- `async_execution: bool | None = False` - Enable async execution
+- `config: Dict[str, Any] | None = None` - Task configuration
+- `output_file: str | None = None` - Output file path
+- `output_json: Type[BaseModel] | None = None` - JSON output schema
+- `output_pydantic: Type[BaseModel] | None = None` - Pydantic output schema
+- `callback: Any | None = None` - Task callback
+- `status: str = 'not started'` - Task status
+- `result: TaskOutput | None = None` - Task result
+- `create_directory: bool | None = False` - Create output directory
+- `id: int | None = None` - Task ID
+- `images: List[str] | None = None` - Task images
+- `next_tasks: List[str] | None = None` - Next tasks
+- `task_type: str = 'task'` - Task type
+- `condition: Dict[str, List[str]] | None = None` - Task conditions
+- `is_start: bool = False` - Is start task
+- `loop_state: Dict[str, str | int] | None = None` - Loop state
+
+#### Async Support
+The Task class supports asynchronous execution through the following features:
+
+- `async_execution`: Boolean flag to enable async execution
+- `callback`: Supports both sync and async callback functions
+- `execute_callback`: Internal async method for handling callbacks
+
+Example usage:
+```python
+from typing import Coroutine
+from praisonaiagents import Task, TaskOutput
+
+# Define an async callback
+async def async_callback(output: TaskOutput):
+    await some_async_operation()
+    print(f"Processed: {output.raw}")
+
+# Create task with async execution
+task = Task(
+    description="Async task example",
+    expected_output="Async result",
+    async_execution=True,  # Enable async execution
+    callback=async_callback  # Async callback
+)
+```
+
+#### Callback Types
+The Task class supports both synchronous and asynchronous callbacks:
+```python
+# Type definition
+callback: Optional[Union[
+    Callable[[TaskOutput], Any],  # Sync callback
+    Callable[[TaskOutput], Coroutine[Any, Any, Any]]  # Async callback
+]]
+```
+
+#### Async Task States
+- `not started`: Initial state
+- `in progress`: Task is being executed
+- `completed`: Task finished successfully
+- `failed`: Task execution failed

docs/call.mdx

@@ -1,4 +1,8 @@
-# PraisonAI Call
+---
+title: "PraisonAI Call"
+description: "Guide to PraisonAI's voice-based interaction feature enabling AI customer service through phone calls, including setup and tool integration"
+icon: "phone"
+---
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/m1cwrUG2iAk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 

docs/concepts/agents.mdx

@@ -0,0 +1,368 @@
+---
+title: "Agents"
+description: "Understanding Agents in PraisonAI"
+icon: "user"
+---
+
+# Understanding Agents
+Agents are the core building blocks of PraisonAI. Each agent is an autonomous AI entity with specific roles, goals, and capabilities.
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[ ]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+    end
+    
+    subgraph Agent2[ ]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+    end
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
+```
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Role & Goal" icon="target">
+    Defines the agent's purpose and objectives through role definition and specific goals
+  </Card>
+  <Card title="Capabilities" icon="toolbox">
+    Tools and functions available to the agent for task execution
+  </Card>
+  <Card title="Memory" icon="brain">
+    Context retention and learning capabilities across interactions
+  </Card>
+  <Card title="Language Model" icon="microchip">
+    The underlying AI model powering the agent's intelligence
+  </Card>
+</CardGroup>
+
+## Component Details
+
+### Role and Goal
+<Tip>
+  Clear role and goal definitions are crucial for optimal agent performance.
+</Tip>
+
+| Component | Description | Example |
+|:----------|:------------|:--------|
+| **Role** | Agent's function and expertise | Research Analyst, Code Developer |
+| **Goal** | Specific objectives to achieve | Analyze market trends, Generate reports |
+| **Backstory** | Contextual background | Expert with 10 years of experience |
+
+### Capabilities
+<CodeGroup>
+  ```python Basic
+  agent = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+  ```
+</CodeGroup>
+
+
+<Steps>
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+```python Single Agent
+from praisonaiagents import Agent
+
+agent = Agent(instructions="Your are a helpful AI assistant")
+agent.start("Write a movie script about a robot in Mars")
+```
+
+```python Multi Agents
+from praisonaiagents import Agent, PraisonAIAgents
+
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
+
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```
+</CodeGroup>
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+    ```bash Terminal
+    python app.py
+    ```
+
+    You should see:
+    - Agent initialization
+    - Agents progress
+    - Final results
+    - Generated report
+  </Step>
+</Steps>
+
+## Agent Types
+
+<CardGroup cols={3}>
+  <Card title="Basic Agent" icon="user" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Perfect for straightforward tasks and direct interactions
+      </Accordion>
+      <Accordion title="Features">
+        - Single-purpose focus
+        - Direct user interaction
+        - Limited tool set
+        - Ideal for simple tasks
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Specialized Agent" icon="user-gear" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Experts in specific domains with advanced capabilities
+      </Accordion>
+      <Accordion title="Features">
+        - Domain expertise
+        - Advanced capabilities
+        - Custom tools
+        - Deep knowledge base
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Collaborative Agent" icon="users" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Overview">
+        Designed for team-based operations and complex workflows
+      </Accordion>
+      <Accordion title="Features">
+        - Team interaction
+        - Task delegation
+        - Shared context
+        - Coordinated actions
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+## Best Practices
+
+<Note>
+Always implement proper error handling and resource management in your agent configurations.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Agent Design" icon="compass-drafting" iconType="solid">
+    <Steps>
+      <Step title="Role Definition">
+        Define clear, specific roles for each agent
+      </Step>
+      <Step title="Goal Setting">
+        Set specific, measurable goals
+      </Step>
+      <Step title="Tool Selection">
+        Choose relevant tools for the task
+      </Step>
+      <Step title="Memory Setup">
+        Configure appropriate memory settings
+      </Step>
+    </Steps>
+  </Card>
+
+  <Card title="Agent Interaction" icon="network-wired" iconType="solid">
+    <Steps>
+      <Step title="Communication">
+        Establish clear communication protocols
+      </Step>
+      <Step title="Delegation">
+        Define explicit delegation rules
+      </Step>
+      <Step title="Error Handling">
+        Implement robust error handling
+      </Step>
+      <Step title="Resource Management">
+        Set up efficient resource allocation
+      </Step>
+    </Steps>
+  </Card>
+</CardGroup>
+
+## Async Capabilities
+
+<CardGroup cols={1}>
+  <Card title="Key Features" icon="bolt" iconType="solid">
+    <Tabs>
+      <Tab title="Async Support">
+        ```python
+        async def main():
+            agent = Agent(name="AsyncAgent")
+            result = await agent.aprocess_task()
+        ```
+        - Full async/await support
+        - Non-blocking operations
+        - Enhanced performance
+      </Tab>
+      
+      <Tab title="Parallel Execution">
+        ```python
+        async def parallel_tasks():
+            tasks = [agent1.task(), agent2.task()]
+            results = await asyncio.gather(*tasks)
+        ```
+        - Parallel task execution
+        - Efficient resource usage
+        - Improved throughput
+      </Tab>
+      
+      <Tab title="Integration">
+        ```python
+        @agent.async_tool
+        async def custom_tool():
+            async with aiohttp.ClientSession() as session:
+                # Async operations
+                pass
+        ```
+        - Async tool integration
+        - Callback support
+        - Mixed sync/async operations
+      </Tab>
+    </Tabs>
+  </Card>
+</CardGroup>
+
+## Advanced Features
+
+<CardGroup cols={2}>
+  <Card title="Memory Management" icon="brain">
+    - Short-term conversation memory
+    - Long-term knowledge retention
+    - Context preservation
+  </Card>
+  <Card title="Tool Integration" icon="plug">
+    - Custom tool development
+    - External API integration
+    - Resource access control
+  </Card>
+</CardGroup>
+
+## Async Support
+Agents now support asynchronous operations through the following methods:
+
+- `achat`: Async version of the chat method
+- `astart`: Async version of start method
+- `aexecute_task`: Async version of execute_task method
+- `arun_task`: Async version of run_task method
+- `arun_all_tasks`: Async version of run_all_tasks method
+
+### Example Usage:
+
+```python
+import asyncio
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+async def main():
+    # Create an async agent
+    async_agent = Agent(
+        name="AsyncAgent",
+        role="Async Task Specialist",
+        goal="Perform async operations",
+        backstory="Expert in async operations",
+        tools=[async_search_tool],  # Your async tool
+        verbose=True
+    )
+
+    # Create an async task
+    async_task = Task(
+        description="Perform async operation",
+        expected_output="Async result",
+        agent=async_agent,
+        async_execution=True  # Enable async execution
+    )
+
+    # Create and start agents with async support
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=[async_task],
+        verbose=True
+    )
+    
+    # Start async execution
+    result = await agents.astart()
+    print(result)
+
+# Run the async main function
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Key Features:
+- Full async/await support
+- Parallel task execution
+- Async tool integration
+- Async callback support
+- Mixed sync/async operations
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Create Your First Agent"
+    icon="code"
+    href="/code/quickstart"
+  >
+    Follow our quickstart guide to create your first agent
+  </Card>
+  <Card
+    title="API Reference"
+    icon="book"
+    href="/api/praisonaiagents/agent/agent"
+  >
+    Explore the complete Agent API documentation
+  </Card>
+</CardGroup> 

docs/concepts/knowledge.mdx

@@ -0,0 +1,241 @@
+---
+title: "AI Agents with Knowledge"
+sidebarTitle: "Knowledge"
+description: "Learn how to create AI agents with custom knowledge bases"
+icon: "book"
+---
+
+```mermaid
+flowchart LR
+    subgraph In[Input]
+        PDF[PDF]
+        TXT[TXT]
+        MD[MD]
+    end
+
+    subgraph Router[Vector Store]
+        DB[(Vector DB)]
+    end
+    
+    subgraph Out[Agents]
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+
+    In --> Router
+    Router --> A1
+    Router --> A2
+    Router --> A3
+
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+| Feature | [Knowledge](/concepts/knowledge) | [Memory](/concepts/memory) |
+|---------|--------------------------------|---------------------------|
+| When Used | Pre-loaded before agent execution | Created and updated during runtime |
+| Purpose | Provide static reference information | Store dynamic context and interactions |
+| Storage | Read-only knowledge base | Read-write memory store |
+| Persistence | Permanent until explicitly changed | Can be temporary (STM) or persistent (LTM) |
+| Updates | Manual updates through knowledge files | Automatic updates during agent execution |
+
+## Quick Start
+
+<Steps>
+  <Step title="Install Package">
+    Install PraisonAI Agents with knowledge support:
+    ```bash
+    pip install "praisonaiagents[knowledge]"
+    ```
+  </Step>
+
+  <Step title="Set API Key">
+    Set your OpenAI API key:
+    ```bash
+    export OPENAI_API_KEY=xxxxx
+    ```
+  </Step>
+
+  <Step title="Create Script">
+    Create a new file `app.py`:
+    ```python
+    from praisonaiagents import Agent
+
+    agent = Agent(
+        name="Knowledge Agent",
+        instructions="You answer questions based on the provided knowledge.",
+        knowledge=["small.pdf"]
+    )
+
+    agent.start("What is KAG in one line?")
+    ```
+  </Step>
+</Steps>
+
+## Basic Usage
+
+The simplest way to create a knowledge-based agent is without any configuration:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"]
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Advanced Configuration
+
+For more control over the knowledge base, you can specify a configuration:
+
+```python
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Multi-Agent Knowledge System
+
+For more complex scenarios, you can create a knowledge-based system with multiple agents:
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Define the configuration for the Knowledge instance
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "knowledge_test",
+            "path": ".praison",
+        }
+    }
+}
+
+# Create an agent with knowledge capabilities
+knowledge_agent = Agent(
+    name="KnowledgeAgent",
+    role="Information Specialist",
+    goal="Store and retrieve knowledge efficiently",
+    backstory="Expert in managing and utilizing stored knowledge",
+    knowledge=["sample.pdf"],
+    knowledge_config=config,
+    verbose=True
+)
+
+# Define a task for the agent
+knowledge_task = Task(
+    name="knowledge_task",
+    description="Who is Mervin Praison?",
+    expected_output="Answer to the question",
+    agent=knowledge_agent
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1"
+)
+
+# Start execution
+result = agents.start()
+```
+
+## Understanding Knowledge Configuration
+
+<AccordionGroup>
+  <Accordion title="Vector Store Options">
+    - **Provider**: Choose between different vector store backends (e.g., "chroma")
+    - **Collection Name**: Name for your knowledge collection
+    - **Path**: Location to store the vector database
+  </Accordion>
+  
+  <Accordion title="Supported File Types">
+    - PDF documents (*.pdf)
+    - Text files (*.txt)
+    - Markdown files (*.md, *.mdx)
+    - And more...
+  </Accordion>
+
+  <Accordion title="Multi-Agent Setup">
+    - **Role**: Define specialized roles for knowledge agents
+    - **Goal**: Set specific knowledge management objectives
+    - **Process**: Choose between sequential or parallel execution
+  </Accordion>
+</AccordionGroup>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Custom Knowledge" icon="book">
+    Import your own documents and data as knowledge sources
+  </Card>
+  <Card title="Vector Storage" icon="database">
+    Efficient storage and retrieval of knowledge embeddings
+  </Card>
+  <Card title="Multiple Sources" icon="layer-group">
+    Combine multiple documents and file types
+  </Card>
+  <Card title="Persistent Storage" icon="hard-drive">
+    Save and reuse knowledge bases across sessions
+  </Card>
+</CardGroup>
+
+## Best Practices
+
+1. **Document Preparation**
+   - Clean and well-formatted documents work best
+   - Break large documents into smaller chunks
+   - Use consistent formatting
+
+2. **Knowledge Organization**
+   - Group related documents together
+   - Use meaningful file names
+   - Keep knowledge bases focused and relevant
+
+3. **Performance Optimization**
+   - Monitor vector store size
+   - Clean up unused collections
+   - Use appropriate chunk sizes
+
+4. **Multi-Agent Coordination**
+   - Define clear roles and responsibilities
+   - Set appropriate logging levels for debugging
+   - Use unique collection names for different agent groups
+
+## Next Steps
+
+- Learn about [Memory Management](/concepts/memory) for long-term recall
+- Explore [Tool Integration](/concepts/tools) for enhanced capabilities
+- Check out [Examples](/examples) for implementation ideas

docs/concepts/memory.mdx

@@ -0,0 +1,472 @@
+---
+title: "AI Agents with Memory"
+sidebarTitle: "Memory"
+description: "Learn how to create AI agents with memory capabilities for maintaining context and information across tasks."
+icon: "brain"
+---
+
+```mermaid
+flowchart TB
+    subgraph Memory
+        direction TB
+        STM[Short Term]
+        LTM[Long Term]
+    end
+
+    subgraph Store
+        direction TB
+        DB[(Vector DB)]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    Memory <--> Store
+    Store <--> A1
+    Store <--> A2
+    Store <--> A3
+
+    style Memory fill:#189AB4,color:#fff
+    style Store fill:#2E8B57,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+| Feature | [Knowledge](/concepts/knowledge) | [Memory](/concepts/memory) |
+|---------|--------------------------------|---------------------------|
+| When Used | Pre-loaded before agent execution | Created and updated during runtime |
+| Purpose | Provide static reference information | Store dynamic context and interactions |
+| Storage | Read-only knowledge base | Read-write memory store |
+| Persistence | Permanent until explicitly changed | Can be temporary (STM) or persistent (LTM) |
+| Updates | Manual updates through knowledge files | Automatic updates during agent execution |
+
+## Quick Start
+
+<Tabs>
+  <Tab title="Code">
+    <Steps>
+        <Step title="Install Package">
+            First, install the PraisonAI Agents package:
+            ```bash
+            pip install "praisonaiagents[memory]" duckduckgo_search 
+            ```
+            duckduckgo_search is a tool that allows agents to search the web.
+            It is required for the multiple agents example shown below.
+        </Step>
+
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+
+        <Step title="Create a file">
+            Create a new file `app.py` with the basic setup:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+
+  # Create blog writer agent
+  blog_agent = Agent(
+      role="Blog Writer",
+      goal="Write a blog post about AI",
+      backstory="Expert at writing blog posts",
+      llm="gpt-4o-mini"
+  )
+
+  # Create blog writing task
+  blog_task = Task(
+      description="Write a blog post about AI trends",
+      expected_output="Well-written blog post about AI trends",
+      agent=blog_agent
+  )
+
+  # Create and start the agents with memory enabled
+  agents = PraisonAIAgents(
+      agents=[blog_agent],
+      tasks=[blog_task],
+      memory=True
+  )   
+
+  # Start execution
+  result = agents.start()
+  print(result)
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+  from praisonaiagents.tools import duckduckgo
+
+  # Create research agent with memory
+  research_agent = Agent(
+      role="Research Analyst",
+      goal="Research and document key information about topics",
+      backstory="Expert at analyzing and storing information in memory",
+      llm="gpt-4o-mini",
+      tools=[duckduckgo]
+  )
+
+  # Create blog writer agent
+  blog_agent = Agent(
+      role="Blog Writer",
+      goal="Write a blog post about the research",
+      backstory="Expert at writing blog posts",
+      llm="gpt-4o-mini"
+  )
+
+  # Create research task
+  research_task = Task(
+      description="Research and document key information about AI trends",
+      expected_output="Detailed research findings about AI trends",
+      agent=research_agent
+  )
+
+  # Create blog writing task
+  blog_task = Task(
+      description="Write a blog post about the research findings",
+      expected_output="Well-written blog post based on research",
+      agent=blog_agent
+  )
+
+  # Create and start the agents with advanced memory configuration
+  agents = PraisonAIAgents(
+      agents=[research_agent, blog_agent],
+      tasks=[research_task, blog_task],
+      memory=True
+  )   
+
+  # Start execution
+  result = agents.start()
+  print(result)
+  ```
+</CodeGroup>
+        </Step>
+
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+            ```bash
+            python app.py
+            ```
+        </Step>
+    </Steps>
+  </Tab>
+  <Tab title="No Code">
+    <Steps>
+        <Step title="Install Package">
+            Install the PraisonAI package:
+            ```bash
+            pip install praisonai
+            ```
+        </Step>
+        <Step title="Set API Key">
+            Set your OpenAI API key as an environment variable in your terminal:
+            ```bash
+            export OPENAI_API_KEY=your_api_key_here
+            ```
+        </Step>
+        <Step title="Create a file">
+            Create a new file `agents.yaml` with the basic setup:
+```yaml
+framework: praisonai
+process: sequential
+memory: true
+roles:
+  researcher:
+    backstory: Expert at analyzing and storing information in memory.
+    goal: Research and document key information about topics
+    role: Research Analyst
+    llm: gpt-4o-mini
+    tools:
+      - duckduckgo
+    tasks:
+      research_task:
+        description: Research and document key information about topics.
+        expected_output: Detailed research findings.
+
+  writer:
+    backstory: Expert at writing blog posts.
+    goal: Write a blog post about the research
+    role: Blog Writer
+    llm: gpt-4o-mini
+    tasks:
+      blog_task:
+        description: Write a blog post about the research.
+        expected_output: Well-written blog post based on research.
+```
+        </Step>
+        <Step title="Start Agents">
+            Type this in your terminal to run your agents:
+```bash
+praisonai agents.yaml
+```
+        </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+<Note>
+  **Requirements**
+  - Python 3.10 or higher
+  - OpenAI API key. Generate OpenAI API key [here](https://platform.openai.com/api-keys). Use Other models using [this guide](/models).   
+  - Basic understanding of Python
+</Note>
+
+## Understanding Memory
+
+<Card title="What is Agent Memory?" icon="question">
+  Memory in AI agents enables them to:
+  - Maintain context across multiple tasks
+  - Remember previous interactions and findings
+  - Build upon past knowledge
+  - Share information between agents
+  - Create more coherent and contextual responses
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Context Retention" icon="brain">
+    Maintain information across multiple interactions.
+  </Card>
+  <Card title="Information Sharing" icon="share-nodes">
+    Share knowledge between multiple agents.
+  </Card>
+  <Card title="Long-term Storage" icon="database">
+    Store and retrieve information over extended periods.
+  </Card>
+  <Card title="Memory Types" icon="layer-group">
+    Support for different memory types (short-term, long-term).
+  </Card>
+</CardGroup>
+
+## Multi-Agent Memory
+
+<Tabs>
+  <Tab title="Code">
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from praisonaiagents.tools import duckduckgo
+
+    # Create first agent for research
+    researcher = Agent(
+        role="Research Analyst",
+        goal="Research and analyze market trends",
+        backstory="Expert in market research and data analysis",
+        tools=[duckduckgo],
+        verbose=True
+    )
+
+    # Create second agent for report writing
+    writer = Agent(
+        role="Report Writer",
+        goal="Create comprehensive market reports",
+        backstory="Expert in technical writing and report creation",
+        verbose=True
+    )
+
+    # Create research task
+    research_task = Task(
+        description="Research current market trends",
+        expected_output="Detailed market analysis",
+        agent=researcher
+    )
+
+    # Create writing task
+    report_task = Task(
+        description="Create a market report based on research",
+        expected_output="Comprehensive market report",
+        agent=writer
+    )
+
+    # Create and start the agents with memory
+    agents = PraisonAIAgents(
+        agents=[researcher, writer],
+        tasks=[research_task, report_task],
+        memory=True,
+        process="sequential"
+    )
+
+    # Start execution
+    result = agents.start()
+    ```
+  </Tab>
+  <Tab title="No Code">
+```yaml
+framework: praisonai
+process: sequential
+memory: true
+roles:
+  researcher:
+    backstory: Expert in market research and data analysis.
+    goal: Research and analyze market trends
+    role: Research Analyst
+    tools:
+      - duckduckgo
+    tasks:
+      research_task:
+        description: Research current market trends.
+        expected_output: Detailed market analysis.
+
+  writer:
+    backstory: Expert in technical writing and report creation.
+    goal: Create comprehensive market reports
+    role: Report Writer
+    tasks:
+      report_task:
+        description: Create a market report based on research.
+        expected_output: Comprehensive market report.
+```
+  </Tab>
+</Tabs>
+
+### Configuration Options
+
+```python
+# Create an agent with memory configuration
+agent = Agent(
+    role="Research Analyst",
+    goal="Research and retain information",
+    backstory="Expert in research and analysis",
+    tools=[duckduckgo],
+    verbose=True,  # Enable detailed logging
+    llm="gpt-4o"  # Language model to use
+)
+
+# Create agents with memory options
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    memory=True,  # Enable memory
+    memory_config={
+        "provider": "rag",  # Use RAG for semantic search
+        "use_embedding": True,  # Enable embeddings for better search
+        "short_db": ".praison/short_term.db",  # Path for short-term memory
+        "long_db": ".praison/long_term.db",    # Path for long-term memory
+        "rag_db_path": ".praison/chroma_db"    # Path for vector database
+    }
+)
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Memory Issues" icon="triangle-exclamation">
+    If memory isn't working as expected:
+    - Check memory configuration
+    - Enable verbose mode for debugging
+    - Verify memory provider settings
+  </Card>
+
+  <Card title="Context Flow" icon="code">
+    If context isn't being maintained:
+    - Review task dependencies
+    - Check memory configuration
+    - Verify agent communication
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="../features/autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="../features/mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal results, configure memory settings based on your specific use case requirements and expected interaction patterns.
+</Note>
+
+### Memory Configuration Options
+
+The memory system in PraisonAI supports various configuration options to customize how agents store and retrieve information:
+
+```python
+memory_config = {
+    # Memory Provider
+    "provider": "rag",        # Options: "rag", "mem0", "none"
+    "use_embedding": True,    # Enable semantic search with embeddings
+    
+    # Storage Paths
+    "short_db": ".praison/short_term.db",  # Short-term memory SQLite DB
+    "long_db": ".praison/long_term.db",    # Long-term memory SQLite DB
+    "rag_db_path": ".praison/chroma_db",   # Vector database path
+    
+    # Memory Settings
+    "ttl": 3600,             # Time to live for memory items (in seconds)
+    
+    # Optional Mem0 Config (if using mem0 provider)
+    "config": {
+        "api_key": "...",    # Mem0 API key
+        "org_id": "...",     # Organization ID
+        "project_id": "..."  # Project ID
+    }
+}
+
+# Create agents with memory configuration
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    memory=True,
+    memory_config=memory_config
+)
+```
+
+### Memory Types
+
+PraisonAI's memory system includes several types of memory:
+
+<CardGroup cols={2}>
+  <Card title="Short-term Memory" icon="bolt">
+    - Temporary storage for current context
+    - Automatically cleared between sessions
+    - Fast access for immediate task context
+  </Card>
+  <Card title="Long-term Memory" icon="database">
+    - Persistent storage for important information
+    - Semantic search capabilities with RAG
+    - Quality-based storage decisions
+  </Card>
+</CardGroup>
+
+### Memory Quality Control
+
+PraisonAI includes built-in quality control for memory storage:
+
+```python
+# Example of storing with quality metrics
+agents.memory.store_long_term(
+    text="Important information to remember",
+    metadata={
+        "task_id": "task_123",
+        "agent": "research_agent"
+    },
+    completeness=0.9,    # How complete is the information
+    relevance=0.85,      # How relevant to the task
+    clarity=0.95,        # How clear and well-structured
+    accuracy=0.9,        # How accurate is the information
+    weights={            # Custom weights for quality score
+        "completeness": 0.3,
+        "relevance": 0.3,
+        "clarity": 0.2,
+        "accuracy": 0.2
+    }
+)
+
+# Search with quality filter
+results = agents.memory.search_long_term(
+    query="search query",
+    min_quality=0.8,     # Only return high-quality matches
+    limit=5              # Maximum number of results
+)

docs/concepts/process.mdx

@@ -0,0 +1,453 @@
+---
+title: "Process"
+description: "Understanding Process Types in PraisonAI"
+icon: "diagram-project"
+---
+
+# Understanding Process Types
+
+Process types in PraisonAI define how tasks are executed and how agents collaborate. Each process type offers different patterns for task execution and agent coordination.
+
+## Process Types Overview
+
+<CardGroup cols={3}>
+  <Card
+    title="Sequential Process"
+    icon="arrow-right"
+    href="/sequential-process"
+  >
+    Linear task execution in a predefined order
+  </Card>
+  <Card
+    title="Hierarchical Process"
+    icon="sitemap"
+    href="/hierarchical-process"
+  >
+    Manager-coordinated task execution with dynamic assignment
+  </Card>
+  <Card
+    title="Workflow Process"
+    icon="diagram-project"
+    href="/workflow-process"
+  >
+    Complex task flows with conditional execution and state management
+  </Card>
+</CardGroup>
+
+## Sequential Process
+
+```mermaid
+graph LR
+    Input[Input] --> A1
+    subgraph Agents
+        direction LR
+        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
+    end
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class A1,A2,A3 process
+    class Agents transparent
+```
+
+The simplest form of task execution where tasks are performed one after another.
+
+<Card>
+  ### Characteristics
+  - Linear execution flow
+  - Predictable order
+  - Simple dependency management
+  - Direct task progression
+
+  ### Usage
+  ```python
+  agents = PraisonAIAgents(
+      agents=[agent1, agent2],
+      tasks=[task1, task2, task3],
+      process="sequential"
+  )
+  ```
+</Card>
+
+## Hierarchical Process
+
+```mermaid
+graph TB
+    Input[Input] --> Manager
+    
+    subgraph Agents
+        Manager[Manager Agent]
+        
+        subgraph Workers
+            direction LR
+            W1[Worker 1]
+            W2[Worker 2]
+            W3[Worker 3]
+        end
+        
+        Manager --> W1
+        Manager --> W2
+        Manager --> W3
+    end
+    
+    W1 --> Manager
+    W2 --> Manager
+    W3 --> Manager
+    Manager --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Manager,W1,W2,W3 process
+    class Agents,Workers transparent
+```
+
+Uses a manager agent to coordinate task execution and agent assignments.
+
+<Card>
+  ### Characteristics
+  - Manager-driven coordination
+  - Dynamic task assignment
+  - Flexible execution order
+  - Intelligent resource allocation
+
+  ### Configuration
+  ```python
+  agents = PraisonAIAgents(
+      agents=[manager_agent, worker_agent1, worker_agent2],
+      tasks=[task1, task2],
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+  ```
+</Card>
+
+## Workflow Process
+
+```mermaid
+graph LR
+    Input[Input] --> Start
+    
+    subgraph Workflow
+        direction LR
+        Start[Start] --> C1{Condition}
+        C1 --> |Yes| A1[Agent 1]
+        C1 --> |No| A2[Agent 2]
+        A1 --> Join
+        A2 --> Join
+        Join --> A3[Agent 3]
+    end
+    
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Start,A1,A2,A3,Join process
+    class C1 decision
+    class Workflow transparent
+```
+
+Advanced process type supporting complex task relationships and conditional execution.
+
+<Card>
+  ### Features
+  - Task dependencies
+  - Conditional branching
+  - Loop handling
+  - Context sharing
+  - State management
+
+  ### Implementation
+  ```python
+  task_workflow = {
+      "start_task": {
+          "next": ["decision_task"],
+          "type": "task"
+      },
+      "decision_task": {
+          "type": "decision",
+          "conditions": {
+              "success": ["process_task"],
+              "failure": ["error_task"]
+          }
+      }
+  }
+  ```
+</Card>
+
+## Getting Started
+
+<Steps>
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent Sequential Process
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False,
+      process="sequential"
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents Hierarchical Process
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents 
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      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",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+</Steps>
+<br /><br />
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/yWTH44QPl2A"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+<br />
+
+## Advanced Features
+
+<CardGroup cols={2}>
+  <Card title="State Management" icon="database" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Task State Tracking">
+        Monitor and manage the progress of each task in real-time
+      </Accordion>
+      <Accordion title="Context Preservation">
+        Maintain important information across different stages of execution
+      </Accordion>
+      <Accordion title="Data Flow Control">
+        Manage how data moves between tasks and agents efficiently
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Error Handling" icon="shield-check" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Graceful Recovery">
+        Automatically handle failures and continue execution
+      </Accordion>
+      <Accordion title="Alternative Paths">
+        Switch to backup plans when primary execution fails
+      </Accordion>
+      <Accordion title="Error Reporting">
+        Detailed error logs and diagnostic information
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Monitoring" icon="chart-line" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Progress Tracking">
+        Real-time visibility into task completion status
+      </Accordion>
+      <Accordion title="Performance Metrics">
+        Measure execution time and resource efficiency
+      </Accordion>
+      <Accordion title="Resource Usage">
+        Monitor system resource utilization
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Integration" icon="plug" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="External Systems">
+        Connect with other services and platforms
+      </Accordion>
+      <Accordion title="API Sync">
+        Maintain data consistency across systems
+      </Accordion>
+      <Accordion title="Event Handling">
+        React to system and external events
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+## Async Processing
+
+<Note>
+All process types support asynchronous execution through async generators, enabling efficient parallel processing and non-blocking operations.
+</Note>
+
+### Core Async Methods
+
+<CardGroup cols={3}>
+  <Card title="asequential" icon="arrow-right">
+    Async version of sequential process for non-blocking linear execution
+  </Card>
+  <Card title="aworkflow" icon="diagram-project">
+    Async workflow process for complex parallel task execution
+  </Card>
+  <Card title="ahierarchical" icon="sitemap">
+    Async hierarchical process for distributed task management
+  </Card>
+</CardGroup>
+
+### Process-Specific Features
+
+<Tabs>
+  <Tab title="Sequential Process">
+    <Card>
+      - Tasks execute in order but don't block
+      - Maintains sequence while allowing async operations
+      - Perfect for I/O-heavy tasks
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="sequential",
+              async_mode=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+  
+  <Tab title="Workflow Process">
+    <Card>
+      - Parallel execution of independent tasks
+      - Async branching and merging
+      - Dynamic task scheduling based on conditions
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="workflow",
+              async_mode=True,
+              parallel_tasks=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+  
+  <Tab title="Hierarchical Process">
+    <Card>
+      - Async manager delegation
+      - Parallel subtask execution
+      - Dynamic worker assignment
+      ```python
+      async def main():
+          agents = PraisonAIAgents(
+              process="hierarchical",
+              async_mode=True,
+              dynamic_assignment=True
+          )
+          await agents.astart()
+      ```
+    </Card>
+  </Tab>
+</Tabs>
+
+### Key Benefits
+
+<CardGroup cols={2}>
+  <Card title="Performance" icon="bolt">
+    - Efficient resource utilization
+    - Reduced waiting time
+    - Better throughput
+  </Card>
+  
+  <Card title="Flexibility" icon="puzzle-piece">
+    - Mix sync and async tasks
+    - Adaptable execution patterns
+    - Easy scaling
+  </Card>
+</CardGroup>

docs/concepts/tasks.mdx

@@ -0,0 +1,530 @@
+---
+title: "Tasks"
+description: "Understanding Tasks in PraisonAI"
+icon: "list-check"
+---
+
+# Understanding Tasks
+Tasks are units of work that agents execute. Each task has a clear goal, input requirements, and expected outputs.
+
+```mermaid
+graph LR
+    Input[Input] ---> Task
+    
+    subgraph Task
+        direction LR
+        Role[Role]
+        Goal[Goal]
+        Story[Backstory]
+        Expected[Expected Output]
+        
+        Role --> Goal
+        Goal --> Story
+        Story --> Expected
+    end
+    
+    Task ---> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Role,Goal,Story,Expected process
+    class Task transparent
+```
+
+## Core Components
+
+<CardGroup cols={2}>
+  <Card title="Task Definition" icon="file-lines">
+    Clear description of work to be done and expected outputs
+  </Card>
+  <Card title="Agent Assignment" icon="user-check">
+    Matching tasks with capable agents
+  </Card>
+  <Card title="Tool Access" icon="toolbox">
+    Resources available for task completion
+  </Card>
+  <Card title="Output Handling" icon="file-export">
+    Managing and formatting task results
+  </Card>
+</CardGroup>
+
+## Task Configuration
+
+<CodeGroup>
+  ```python Basic
+  task = Task(
+      description="Research AI trends",
+      expected_output="Summary report",
+      agent=research_agent
+  )
+  ```
+  ```python Advanced
+  task = Task(
+      description="Analyze market data",
+      expected_output="Detailed analysis",
+      agent=analyst_agent,
+      tools=[AnalysisTool()],
+      output_file="analysis.md",
+      async_execution=True
+  )
+  ```
+</CodeGroup>
+
+# Understanding Tasks
+<br />
+```mermaid
+graph LR
+    %% Task Flow Steps
+    Start([▶ Start]) --> TaskDef
+    TaskDef --> AgentAssign
+    AgentAssign --> Exec
+    Exec --> Output([✓ Output])
+    
+    %% Define subgraphs for each step
+    subgraph TaskDef[" "]
+        TD_Icon[📋 Task Definition]
+        TD_Desc[Description & Requirements]
+        
+        TD_Icon --- TD_Desc
+        
+        style TD_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style TD_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph AgentAssign[" "]
+        AA_Icon[🤖 Agent Assignment]
+        AA_Desc[Matching & Delegation]
+        
+        AA_Icon --- AA_Desc
+        
+        style AA_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AA_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Exec[" "]
+        E_Icon[⚙️ Execution]
+        E_Desc[Processing & Tools]
+        
+        E_Icon --- E_Desc
+        
+        style E_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style E_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Style for containers
+    style TaskDef fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style AgentAssign fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Exec fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+<br /><br />
+```mermaid
+graph LR
+    %% Task States
+    Pending([📥 Pending]) --> InProgress([⚡ In Progress])
+    InProgress --> Completed([✅ Completed])
+    InProgress --> Failed([❌ Failed])
+    
+    %% Styling
+    style Pending fill:#2D3047,stroke:#7C90A0,color:#fff
+    style InProgress fill:#5C80BC,stroke:#7C90A0,color:#fff
+    style Completed fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Failed fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+
+## Task Types
+
+<Steps>
+  <Step title="Basic Task">
+    Simple, single-operation tasks with clear inputs and outputs
+  </Step>
+  <Step title="Decision Task">
+    Tasks involving choices and conditional paths
+    ```python
+    decision_task = Task(
+        type="decision",
+        conditions={
+            "success": ["next_task"],
+            "failure": ["error_task"]
+        }
+    )
+    ```
+  </Step>
+  <Step title="Loop Task">
+    Repetitive operations over collections
+    ```python
+    loop_task = Task(
+        type="loop",
+        items=data_list,
+        operation="process_item"
+    )
+    ```
+  </Step>
+</Steps>
+
+## Task Relationships
+
+<Warning>
+  Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.
+</Warning>
+
+### Context Sharing
+```python
+task_a = Task(name="research")
+task_b = Task(
+    name="analyze",
+    context=[task_a]  # Uses task_a's output
+)
+```
+
+### Task Dependencies
+
+| Relationship | Description | Example |
+|:-------------|:------------|:--------|
+| **Sequential** | Tasks run one after another | Research → Analysis → Report |
+| **Parallel** | Independent tasks run simultaneously | Data Collection + Processing |
+| **Conditional** | Tasks depend on previous results | Success → Next Task, Failure → Retry |
+
+## Advanced Features
+
+<CardGroup cols={2}>
+  <Card title="Output Handling" icon="file-code">
+    - Multiple output formats
+    - Structured data validation
+    - File system integration
+  </Card>
+  <Card title="Task Control" icon="sliders">
+    - Execution flow control
+    - Error recovery
+    - Progress tracking
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+<Steps>
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+<CodeGroup>
+  ```python Single Agent
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Multiple Agents
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      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",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+</Steps>
+
+## Best Practices
+
+<Note>
+Design tasks to be atomic and focused. Each task should have a single, clear responsibility.
+</Note>
+
+<CardGroup cols={2}>
+  <Card title="Task Design Principles" icon="compass-drafting" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Clear Task Descriptions">
+        Write precise, unambiguous task descriptions that clearly state the task's purpose and requirements.
+        ```python
+        task = Task(
+            description=(
+                "Analyze Q4 2024 sales data "
+                "and generate growth metrics"
+            ),
+            expected_output="Sales growth report"
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Success Criteria">
+        Define measurable success criteria to ensure task completion can be properly evaluated.
+        ```python
+        task = Task(
+            success_criteria={
+                "format": "markdown",
+                "metrics": [
+                    "growth_rate",
+                    "revenue_change"
+                ],
+                "min_confidence": 0.95
+            }
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Task Dependencies">
+        Specify any dependencies or prerequisites for the task.
+        ```python
+        task = Task(
+            description="Generate final report",
+            dependencies=[
+                data_collection_task,
+                analysis_task
+            ],
+            wait_for_dependencies=True
+        )
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+
+  <Card title="Resource Management" icon="server" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Tool Selection">
+        Choose appropriate tools that match your task's requirements.
+        ```python
+        task = Task(
+            tools=[
+                DataAnalyzer(),
+                ReportGenerator(),
+                MetricsCalculator()
+            ],
+            tool_config={
+                "timeout": 30,
+                "retry": True
+            }
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Error Handling">
+        Implement comprehensive error management strategies.
+        ```python
+        task = Task(
+            error_strategy="retry",
+            max_retries=3,
+            fallback_handler=handler,
+            error_callbacks=[
+                log_error,
+                notify_admin
+            ]
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Resource Limits">
+        Set appropriate resource constraints for optimal performance.
+        ```python
+        task = Task(
+            memory_limit="2GB",
+            timeout=300,
+            max_concurrent=5,
+            cpu_priority="high",
+            auto_scale=True
+        )
+        ```
+      </Accordion>
+
+      <Accordion title="Performance Monitoring">
+        Configure monitoring and logging for task execution.
+        ```python
+        task = Task(
+            monitoring={
+                "metrics": ["duration", "memory", "cpu"],
+                "log_level": "INFO",
+                "trace_enabled": True,
+                "alert_threshold": 0.85
+            }
+        )
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+## Async Task Execution
+
+<CardGroup cols={1}>
+  <Card title="Async Task Features" icon="bolt" iconType="solid">
+    <Tabs>
+      <Tab title="Basic Configuration">
+        ```python
+        async_task = Task(
+            description="Perform async operation",
+            expected_output="Async result",
+            agent=async_agent,
+            async_execution=True,  # Enable async execution
+            callback=async_callback  # Optional async callback
+        )
+        ```
+        - Enable async execution with `async_execution=True`
+        - Perfect for I/O-heavy operations
+        - Supports parallel task execution
+      </Tab>
+      
+      <Tab title="Async Callbacks">
+        ```python
+        async def async_callback(output: TaskOutput):
+            await asyncio.sleep(1)  # Simulate async operation
+            print(f"Processed output: {output.raw}")
+
+        task = Task(
+            description="Task with async callback",
+            expected_output="Result",
+            agent=agent,
+            async_execution=True,
+            callback=async_callback
+        )
+        ```
+        - Support for async callbacks
+        - Process results asynchronously
+        - Handle task completion events
+      </Tab>
+      
+      <Tab title="Mixed Execution">
+        ```python
+        # Create mixed task types
+        sync_task = Task(
+            description="Synchronous operation",
+            async_execution=False
+        )
+
+        async_task = Task(
+            description="Asynchronous operation",
+            async_execution=True
+        )
+
+        # Run with mixed execution
+        agents = PraisonAIAgents(
+            agents=[sync_agent, async_agent],
+            tasks=[sync_task, async_task]
+        )
+        result = await agents.astart()
+        ```
+        - Mix sync and async tasks
+        - Flexible execution patterns
+        - Optimal resource utilization
+      </Tab>
+    </Tabs>
+  </Card>
+</CardGroup>
+
+<Tip>
+**Pro Tip**: Use async execution for tasks that involve I/O operations (like API calls or file operations) to improve overall performance. Keep CPU-intensive tasks synchronous.
+</Tip>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Create Tasks"
+    icon="plus"
+    href="/code/quickstart"
+  >
+    Learn how to create and configure tasks
+  </Card>
+  <Card
+    title="Task API"
+    icon="code"
+    href="/api/praisonaiagents/task/task"
+  >
+    Explore the complete Task API documentation
+  </Card>
+</CardGroup>

docs/concepts/tools.mdx

@@ -0,0 +1,647 @@
+---
+title: "AI Agents with Tools"
+sidebarTitle: "Tools"
+description: "Learn how to create AI agents that can use tools to interact with external systems and perform actions."
+icon: "screwdriver-wrench"
+---
+
+```mermaid
+flowchart TB
+    subgraph Tools
+        direction TB
+        T3[Internet Search]
+        T1[Code Execution]
+        T2[Formatting]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    T3 --> A1
+    T1 --> A2
+    T2 --> A3
+
+    style Tools fill:#189AB4,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+| Feature | [Knowledge](/concepts/knowledge) | [Tools](/concepts/tools) |
+|---------|--------------------------------|---------------------------|
+| Purpose | Static reference information | Dynamic interaction capabilities |
+| Access | Read-only reference | Execute actions and commands |
+| Updates | Manual through files | Real-time through tool calls |
+| Storage | Knowledge base | Assigned to specific agents |
+| Persistence | Permanent until changed | Available during agent execution |
+
+## Quick Start
+
+Tools are functions that agents can use to interact with external systems and perform actions. They are essential for creating agents that can do more than just process text.
+
+## Creating Custom Tool
+<Steps>
+<Step>
+Create any function that you want to use as a tool, that performs a specific task.
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# Tool Implementation
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search using DuckDuckGo
+    
+    Args:
+        query (str): The search query string
+        
+    Returns:
+        List[Dict]: List of search results containing title, URL, and snippet
+    """
+    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
+```
+</Step>
+<Step>
+Assign the tool to an agent
+```python
+    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], ## Add the tool to the agent i.e the function name
+    )
+```
+</Step>
+</Steps>
+
+<Card title="That's it!">
+<Check>You have created a custom tool and assigned it to an agent.</Check>
+</Card>
+
+## Implementing Tools Full Code Example
+<Tabs>
+
+<Tab title="Code">
+<Steps>
+
+  <Step title="Install PraisonAI">
+Install the core package:
+```bash Terminal
+pip install praisonaiagents duckduckgo-search
+```
+  </Step>
+
+  <Step title="Configure Environment">
+  ```bash Terminal
+  export OPENAI_API_KEY=your_openai_key
+  ```
+  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent with Tool">
+    Create `app.py`
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool Implementation
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search using DuckDuckGo
+    
+    Args:
+        query (str): The search query string
+        
+    Returns:
+        List[Dict]: List of search results containing title, URL, and snippet
+    """
+    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
+
+# 2. Assign the tool to an 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. Task Definition
+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",
+)
+
+# 4. Start Agents
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task],
+    process="sequential"
+)
+
+agents.start()
+```
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+```bash Terminal
+python app.py
+```
+  </Step>
+</Steps>
+
+</Tab>
+<Tab title="No Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Custom Tool">
+<Info>
+To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
+</Info>
+Create a new file `tools.py` with the following content:
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    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  
+```
+</Step>
+<Step title="Create Agent">
+
+Create a new file `agents.yaml` with the following content:
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tools:
+      - internet_search_tool # <-- Tool assigned to Agent here
+    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.
+```
+</Step>
+
+<Step title="Start Agents">
+
+Execute your script:
+```bash Terminal
+praisonai agents.yaml
+```
+</Step>
+</Steps>
+</Tab>
+</Tabs>
+
+## In-build Tools in PraisonAI
+
+<CardGroup cols={2}>
+  <Card title="Search Tools" icon="magnifying-glass" href="/tools/search">
+    Tools for searching and retrieving information from various sources
+  </Card>
+  <Card title="Python Tools" icon="python" href="/tools/python_tools">
+    Essential Python utilities for data manipulation and scripting
+  </Card>
+  <Card title="Spider Tools" icon="spider" href="/tools/spider_tools">
+    Web crawling and scraping capabilities for data extraction
+  </Card>
+  <Card title="Arxiv Tools" icon="book" href="/tools/arxiv_tools">
+    Access and search academic papers from arXiv repository
+  </Card>
+  <Card title="Newspaper Tools" icon="newspaper" href="/tools/newspaper_tools">
+    Extract and parse content from news articles and websites
+  </Card>
+  <Card title="DuckDB Tools" icon="database" href="/tools/duckdb_tools">
+    Fast analytical SQL database operations and queries
+  </Card>
+  <Card title="DuckDuckGo Tools" icon="duck" href="/tools/duckduckgo_tools">
+    Web search functionality using DuckDuckGo's API
+  </Card>
+  <Card title="Calculator Tools" icon="calculator" href="/tools/calculator_tools">
+    Perform mathematical calculations and conversions
+  </Card>
+  <Card title="YAML Tools" icon="file-code" href="/tools/yaml_tools">
+    Parse and manipulate YAML format data
+  </Card>
+  <Card title="JSON Tools" icon="brackets-curly" href="/tools/json_tools">
+    Handle JSON data structures and operations
+  </Card>
+  <Card title="Pandas Tools" icon="table" href="/tools/pandas_tools">
+    Data analysis and manipulation using Pandas
+  </Card>
+  <Card title="YFinance Tools" icon="chart-line" href="/tools/yfinance_tools">
+    Fetch financial market data from Yahoo Finance
+  </Card>
+  <Card title="Shell Tools" icon="terminal" href="/tools/shell_tools">
+    Execute shell commands and system operations
+  </Card>
+  <Card title="Wikipedia Tools" icon="book-open" href="/tools/wikipedia_tools">
+    Access and search Wikipedia articles and data
+  </Card>
+  <Card title="XML Tools" icon="code" href="/tools/xml_tools">
+    Process and manipulate XML format data
+  </Card>
+  <Card title="File Tools" icon="file" href="/tools/file_tools">
+    File system operations and management utilities
+  </Card>
+  <Card title="Excel Tools" icon="file-excel" href="/tools/excel_tools">
+    Work with Excel spreadsheets and workbooks
+  </Card>
+  <Card title="CSV Tools" icon="file-csv" href="/tools/csv_tools">
+    Handle CSV file operations and transformations
+  </Card>
+</CardGroup>
+
+## Tools Overview
+
+<CardGroup cols={3}>
+  <Card
+    title="Search Tools"
+    icon="magnifying-glass"
+    iconType="solid"
+  >
+    Tools for searching and retrieving information from various sources
+  </Card>
+
+  <Card
+    title="File Tools"
+    icon="file"
+    iconType="solid"
+  >
+    Tools for reading, writing, and manipulating files
+  </Card>
+
+  <Card
+    title="API Tools"
+    icon="code"
+    iconType="solid"
+  >
+    Tools for interacting with external APIs and services
+  </Card>
+</CardGroup>
+
+## Advanced Tool Features
+
+### Tool Configuration
+<Frame>
+```python
+def configured_tool(
+    query: str,
+    max_results: int = 5,
+    timeout: int = 10
+) -> List[Dict]:
+    """
+    Example of a configurable tool
+    
+    Args:
+        query (str): Search query
+        max_results (int): Maximum number of results
+        timeout (int): Request timeout in seconds
+        
+    Returns:
+        List[Dict]: Search results
+    """
+    # Tool implementation
+    pass
+```
+</Frame>
+
+### Tool Chaining
+<Frame>
+```python
+def chain_tools(input_data: str) -> Dict:
+    """
+    Example of chaining multiple tools
+    
+    Args:
+        input_data (str): Input data
+        
+    Returns:
+        Dict: Processed results
+    """
+    # 1. Search for data
+    search_results = internet_search_tool(input_data)
+    
+    # 2. Process results
+    processed_data = process_tool(search_results)
+    
+    # 3. Format output
+    return format_tool(processed_data)
+```
+</Frame>
+
+### Tool Categories
+
+<CardGroup cols={3}>
+  <Card title="Data Collection Tools">
+    - Web scraping
+    - API integration
+    - Database queries
+  </Card>
+  
+  <Card title="Processing Tools">
+    - Data transformation
+    - Text analysis
+    - Image processing
+  </Card>
+  
+  <Card title="Output Tools">
+    - File generation
+    - Report creation
+    - Data visualization
+  </Card>
+</CardGroup>
+
+## Tool Integration
+
+### Adding Tools to Agents
+<Frame>
+```python
+# Multiple tools
+agent = Agent(
+    name="MultiTool Agent",
+    tools=[
+        internet_search_tool,
+        file_processing_tool,
+        api_integration_tool
+    ]
+)
+```
+</Frame>
+
+### Tool Dependencies
+<Frame>
+```python
+# Tool with dependencies
+def advanced_tool(data: Dict) -> Dict:
+    """
+    Tool that depends on external libraries
+    
+    Args:
+        data (Dict): Input data
+        
+    Returns:
+        Dict: Processed data
+    """
+    try:
+        import required_library
+        # Tool implementation
+        return processed_result
+    except ImportError:
+        raise Exception("Required library not installed")
+```
+</Frame>
+
+## Tool Guidelines
+
+<CardGroup cols={2}>
+  <Card title="Best Practices">
+    1. **Type Hints**
+       - Use Python type hints
+       - Define clear input/output types
+       - Document complex types
+
+    2. **Documentation**
+       - Write clear docstrings
+       - Explain parameters
+       - Provide usage examples
+
+    3. **Error Handling**
+       - Handle exceptions gracefully
+       - Return meaningful errors
+       - Validate inputs
+  </Card>
+
+  <Card title="Tool Types">
+    1. **Search Tools**
+       - Web search
+       - Database queries
+       - Document search
+
+    2. **File Tools**
+       - Read/write operations
+       - File conversion
+       - Data extraction
+
+    3. **API Tools**
+       - REST API calls
+       - GraphQL queries
+       - Service integration
+  </Card>
+</CardGroup>
+
+## Best Practices Summary
+
+<Note>
+Following these best practices will help you create robust, efficient, and secure tools in PraisonAI.
+</Note>
+
+<CardGroup cols={1}>
+  <Card title="Design Principles" icon="compass-drafting" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Single Responsibility">
+        Each tool should have one clear purpose and do it well. Avoid creating tools that try to do too many things.
+        ```python
+        # Good Example
+        def process_image(image: np.array) -> np.array:
+            return processed_image
+
+        # Avoid
+        def process_and_save_and_upload(image):
+            # Too many responsibilities
+            pass
+        ```
+      </Accordion>
+      
+      <Accordion title="Clear Interfaces">
+        Define explicit input/output types and maintain consistent parameter naming.
+        ```python
+        def search_tool(
+            query: str,
+            max_results: int = 10
+        ) -> List[Dict[str, Any]]:
+            """
+            Search for information with clear parameters
+            """
+            pass
+        ```
+      </Accordion>
+      
+      <Accordion title="Documentation">
+        Always include detailed docstrings and type hints.
+        ```python
+        def analyze_text(
+            text: str,
+            language: str = "en"
+        ) -> Dict[str, float]:
+            """
+            Analyze text sentiment and emotions.
+            
+            Args:
+                text: Input text to analyze
+                language: ISO language code
+                
+            Returns:
+                Dict with sentiment scores
+            """
+            pass
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<CardGroup cols={1}>
+  <Card title="Performance Optimization" icon="bolt" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Efficient Processing">
+        Optimize resource usage and processing time.
+        ```python
+        # Use generators for large datasets
+        def process_large_data():
+            for chunk in data_generator():
+                yield process_chunk(chunk)
+        ```
+      </Accordion>
+      
+      <Accordion title="Resource Management">
+        Properly handle resource allocation and cleanup.
+        ```python
+        async with aiohttp.ClientSession() as session:
+            # Resource automatically managed
+            await process_data(session)
+        ```
+      </Accordion>
+      
+      <Accordion title="Caching">
+        Implement caching for frequently accessed data.
+        ```python
+        @cache.memoize(timeout=300)
+        def expensive_operation(data: str) -> Dict:
+            return process_expensive(data)
+        ```
+      </Accordion>
+      
+      <Accordion title="Async Operations">
+        Use async/await for I/O-bound operations.
+        ```python
+        async def fetch_data(urls: List[str]):
+            async with aiohttp.ClientSession() as session:
+                tasks = [fetch_url(session, url) for url in urls]
+                return await asyncio.gather(*tasks)
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<CardGroup cols={1}>
+  <Card title="Security Best Practices" icon="shield-check" iconType="solid">
+    <AccordionGroup>
+      <Accordion title="Input Validation">
+        Always validate and sanitize inputs to prevent security vulnerabilities.
+        ```python
+        def process_user_input(data: str) -> str:
+            if not isinstance(data, str):
+                raise ValueError("Input must be string")
+            return sanitize_input(data.strip())
+        ```
+      </Accordion>
+      
+      <Accordion title="Rate Limiting">
+        Implement rate limiting for API calls to prevent abuse.
+        ```python
+        @rate_limit(calls=100, period=60)
+        async def api_call():
+            return await make_request()
+        ```
+      </Accordion>
+      
+      <Accordion title="API Key Management">
+        Securely handle API keys and credentials using environment variables.
+        ```python
+        # Use environment variables
+        api_key = os.getenv('API_KEY')
+        if not api_key:
+            raise ConfigError("API key not found")
+        ```
+      </Accordion>
+      
+      <Accordion title="Error Masking">
+        Hide sensitive information in error messages to prevent information leakage.
+        ```python
+        try:
+            result = process_sensitive_data()
+        except Exception as e:
+            # Log detailed error internally
+            logger.error(f"Detailed error: {str(e)}")
+            # Return sanitized error to user
+            raise PublicError("Processing failed")
+        ```
+      </Accordion>
+    </AccordionGroup>
+  </Card>
+</CardGroup>
+
+<br />
+
+<Tip>
+**Pro Tip**: Start with these practices from the beginning of your project. It's easier to maintain good practices than to retrofit them later.
+</Tip>

docs/contributing.mdx

@@ -1,4 +1,8 @@
-# Contributing
+---
+title: "Contributing"
+description: "Guide for contributing to PraisonAI through GitHub, including forking, cloning, and submitting pull requests"
+icon: "code-branch"
+---
 
 - Fork on GitHub: Use the "Fork" button on the repository page.
 - Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`

docs/course/agents/01-introduction.mdx

@@ -0,0 +1,71 @@
+---
+title: "Introduction to AI Agents"
+description: "Welcome to the beginner's course on AI Agents"
+icon: "book-open"
+---
+
+# Introduction to AI Agents
+
+Welcome to the beginners' course on AI Agents! This course is designed to help you understand and build your own AI agents, even if you have little to no programming experience.
+
+## What Are AI Agents?
+
+An AI agent is a software program that can:
+- Understand information from its environment
+- Make decisions based on that information
+- Take actions to achieve specific goals
+
+Think of an AI agent like a helpful assistant that can perform tasks for you automatically.
+
+```mermaid
+graph TD
+    A[Perceive Information] --> B[Make Decisions]
+    B --> C[Take Actions]
+    C --> A
+```
+
+## Examples in Daily Life
+
+You might already be using AI agents without realizing it:
+
+- **Virtual Assistants**: Siri, Alexa, and Google Assistant are AI agents that respond to your voice commands
+- **Recommendation Systems**: Netflix, YouTube, and Amazon use AI agents to suggest content you might like
+- **Smart Home Devices**: Devices that adjust temperature or lighting based on your preferences
+
+## What You'll Learn in This Course
+
+Throughout this course, you will:
+
+1. Understand the basic concepts of AI agents
+2. Learn about different types of AI agents
+3. Explore how AI agents make decisions
+4. Discover how to build simple AI agents without complex programming
+5. Create your own functional AI agents
+
+## Who This Course Is For
+
+This course is perfect for:
+- Complete beginners with no prior knowledge of AI
+- Non-technical professionals interested in how AI can solve problems
+- Students curious about artificial intelligence
+- Anyone who wants to understand and use AI agents in practical ways
+
+Let's begin our journey into the fascinating world of AI agents!
+<iframe 
+  id="codeExecutionFrame"
+  src="https://code-execution-server-praisonai.replit.app/?code=import%20openai%0A%0Aclient%20%3D%20openai.OpenAI()%0Aresult%20%3D%20client.chat.completions.create(%0A%20%20%20%20model%3D%22gpt-3.5-turbo%22%2C%0A%20%20%20%20messages%3D%5B%0A%20%20%20%20%20%20%20%20%7B%22role%22%3A%20%22user%22%2C%20%22content%22%3A%20%22Hello%20World%22%7D%0A%20%20%20%20%5D%0A)%0A%0Aprint(result.choices%5B0%5D.message.content)" 
+  width="100%" 
+  height="600px" 
+  frameborder="0"
+  allow="clipboard-read; clipboard-write"
+  scrolling="yes"
+  onload="resizeIframe(this)"
+></iframe>
+
+<IframeWithAutoHeight />
+
+<CardGroup cols={1}>
+  <Card title="Ready to Start?" icon="rocket">
+    Continue to the next lesson to learn about the different types of AI agents.
+  </Card>
+</CardGroup>

docs/course/agents/02-types-of-agents.mdx

@@ -0,0 +1,113 @@
+---
+title: "Types of AI Agents"
+description: "Understanding the different categories of AI agents"
+icon: "sitemap"
+---
+
+# Types of AI Agents
+
+AI agents come in different forms, each designed for specific purposes and with varying levels of complexity. Understanding these types will help you choose the right approach for your needs.
+
+## Simple Reflex Agents
+
+These are the most basic type of AI agents that operate using simple if-then rules.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="lightbulb">
+    - React directly to current input only
+    - No memory of past events
+    - No prediction of future states
+    - Use predefined rules to determine actions
+  </Card>
+</CardGroup>
+
+**Real-world example**: A basic thermostat that turns heating on when temperature drops below a set point.
+
+```mermaid
+graph LR
+    A[Sensor Input] --> B{If-Then Rules}
+    B --> C[Action]
+```
+
+## Model-Based Agents
+
+These agents maintain an internal model of their environment to make better decisions.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="brain">
+    - Build and maintain an internal representation of the world
+    - Can work with partial information
+    - Can consider "what if" scenarios
+    - Make decisions based on their understanding of how the world works
+  </Card>
+</CardGroup>
+
+**Real-world example**: A GPS navigation system that models roads and traffic conditions.
+
+## Goal-Based Agents
+
+These agents make decisions based on how their actions will help achieve specific goals.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="bullseye">
+    - Consider future consequences of actions
+    - Evaluate which actions will lead toward goals
+    - Can plan sequences of actions
+    - Choose actions that maximize goal attainment
+  </Card>
+</CardGroup>
+
+**Real-world example**: A chess-playing AI that plans moves to achieve checkmate.
+
+## Utility-Based Agents
+
+These agents choose actions that maximize a specific utility (or happiness) function.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="chart-line">
+    - Assign a utility value to different possible outcomes
+    - Consider probability and risk
+    - Choose actions that maximize expected utility
+    - Can balance competing objectives
+  </Card>
+</CardGroup>
+
+**Real-world example**: An investment robo-advisor that balances risk and return.
+
+## Learning Agents
+
+These agents improve their performance over time through experience.
+
+<CardGroup cols={1}>
+  <Card title="Key Characteristics" icon="graduation-cap">
+    - Learn from past experiences
+    - Adapt to changing environments
+    - Improve decision-making over time
+    - Can discover new strategies
+  </Card>
+</CardGroup>
+
+**Real-world example**: Recommendation systems that learn your preferences over time.
+
+## Understanding Agent Complexity
+
+<Tip>
+As you move from Simple Reflex Agents toward Learning Agents, both capability and complexity increase. For beginners, starting with simpler agent types is often best.
+</Tip>
+
+## Which Agent Type Should You Build?
+
+The best type of agent for your project depends on:
+
+1. **The problem complexity**: Simple problems may only need reflex agents
+2. **Available data**: Learning agents need training data
+3. **Required adaptability**: Will your agent need to adapt to new situations?
+4. **Available resources**: More complex agents require more computational power
+
+In this course, we'll focus primarily on goal-based and simple learning agents as they provide a good balance of capability and complexity for beginners.
+
+<CardGroup cols={1}>
+  <Card title="Next Steps" icon="arrow-right">
+    In the next lesson, we'll explore how agents process information and make decisions.
+  </Card>
+</CardGroup>

docs/course/agents/03-agent-architecture.mdx

@@ -0,0 +1,157 @@
+---
+title: "Agent Architecture"
+description: "Understanding how AI agents are structured"
+icon: "layer-group"
+---
+
+# Agent Architecture
+
+Understanding how AI agents are structured will help you build more effective agents. This lesson covers the fundamental components of an agent's architecture.
+
+## Basic Components of an AI Agent
+
+Every AI agent, regardless of complexity, has these basic components:
+
+```mermaid
+graph TD
+    A[Sensors/Input] --> B[Processing Unit]
+    B --> C[Decision-Making]
+    C --> D[Actions/Output]
+```
+
+### 1. Input (Sensors)
+
+This is how agents receive information from their environment.
+
+<CardGroup cols={1}>
+  <Card title="Examples of Input" icon="eye">
+    - Text input from users
+    - Data from databases
+    - Image or audio input
+    - API responses
+    - Sensor readings (in physical agents)
+  </Card>
+</CardGroup>
+
+### 2. Processing Unit
+
+This component processes information and converts it into a format the agent can understand.
+
+<CardGroup cols={1}>
+  <Card title="Processing Functions" icon="gears">
+    - Data cleaning and transformation
+    - Feature extraction
+    - Context building
+    - Information retrieval
+    - Pattern recognition
+  </Card>
+</CardGroup>
+
+### 3. Decision-Making Core
+
+The "brain" of the agent that determines what actions to take.
+
+<CardGroup cols={1}>
+  <Card title="Decision Components" icon="brain">
+    - Language models (like GPT-4)
+    - Rule systems
+    - Planning algorithms
+    - Knowledge base
+    - Memory systems
+  </Card>
+</CardGroup>
+
+### 4. Output (Actions)
+
+The actions the agent can perform to achieve its goals.
+
+<CardGroup cols={1}>
+  <Card title="Action Examples" icon="hand">
+    - Generating text responses
+    - Creating visual content
+    - Making API calls
+    - Controlling other systems
+    - Updating databases
+  </Card>
+</CardGroup>
+
+## The Agent Loop
+
+Agents operate in a continuous loop:
+
+```mermaid
+graph LR
+    A[Perceive] --> B[Process]
+    B --> C[Decide]
+    C --> D[Act]
+    D --> A
+```
+
+This cycle allows agents to continuously:
+1. Gather information
+2. Update their understanding
+3. Make new decisions
+4. Take appropriate actions
+
+## PraisonAI Agent Architecture
+
+In the PraisonAI framework, agents follow a specific architecture:
+
+<CardGroup cols={1}>
+  <Card title="PraisonAI Agent Components" icon="puzzle-piece">
+    - **Instructions**: Defines the agent's purpose and behavior
+    - **Language Model**: Powers the agent's intelligence (e.g., GPT-4)
+    - **Memory**: Stores context and previous interactions
+    - **Tools**: Specialized capabilities an agent can use
+  </Card>
+</CardGroup>
+
+### Simple Agent Structure
+
+```python
+from praisonaiagents import Agent
+
+# Create a simple agent
+research_agent = Agent(
+    instructions="Research the latest developments in renewable energy",
+    name="ResearchAgent"
+)
+
+# Start the agent
+research_agent.start()
+```
+
+## Understanding Agent Communication
+
+Multi-agent systems allow agents to communicate with each other:
+
+```mermaid
+graph LR
+    A[Agent 1] --> B[Agent 2]
+    B --> C[Agent 3]
+    C --> A
+```
+
+Each agent can:
+- Pass information to other agents
+- Request assistance from specialized agents
+- Collaborate on complex tasks
+
+## Key Takeaways
+
+<CardGroup cols={2}>
+  <Card title="Component Importance" icon="check">
+    Each component plays a vital role in the agent's functionality
+  </Card>
+  <Card title="Agent Customization" icon="gear">
+    You can customize each component based on your specific needs
+  </Card>
+  <Card title="Component Balance" icon="scale-balanced">
+    A well-designed agent balances all components effectively
+  </Card>
+  <Card title="Continuous Improvement" icon="arrow-trend-up">
+    Agents can be improved by enhancing individual components
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to define effective instructions for your AI agents.

docs/course/agents/04-agent-instructions.mdx

@@ -0,0 +1,187 @@
+---
+title: "Creating Effective Agent Instructions"
+description: "How to write clear instructions for your AI agents"
+icon: "file-lines"
+---
+
+# Creating Effective Agent Instructions
+
+The instructions you give to your AI agent are crucial to its performance. Well-crafted instructions lead to agents that understand their purpose and execute tasks effectively.
+
+## Why Instructions Matter
+
+Instructions serve as the agent's "job description" and guide its behavior:
+
+<CardGroup cols={2}>
+  <Card title="Clarity" icon="glasses">
+    Clear instructions reduce confusion and misinterpretation
+  </Card>
+  <Card title="Focus" icon="bullseye">
+    Specific instructions keep the agent on task
+  </Card>
+  <Card title="Boundaries" icon="border-all">
+    Good instructions define what an agent should and shouldn't do
+  </Card>
+  <Card title="Personality" icon="face-smile">
+    Instructions can shape how an agent communicates
+  </Card>
+</CardGroup>
+
+## Elements of Effective Instructions
+
+### 1. Role Definition
+
+Tell the agent who it is and what its expertise should be.
+
+<CodeGroup>
+```text Good Example
+You are a financial analyst with expertise in market trends and investment strategies.
+```
+
+```text Weak Example
+You are a helper.
+```
+</CodeGroup>
+
+### 2. Purpose and Goals
+
+Clearly state what the agent should accomplish.
+
+<CodeGroup>
+```text Good Example
+Your goal is to analyze company quarterly reports and provide actionable investment insights.
+```
+
+```text Weak Example
+Help with finance stuff.
+```
+</CodeGroup>
+
+### 3. Process Guidelines
+
+Explain how the agent should approach tasks.
+
+<CodeGroup>
+```text Good Example
+First analyze the data trends, then identify key risk factors, and finally make recommendations based on the client's risk tolerance.
+```
+
+```text Weak Example
+Just do a good analysis.
+```
+</CodeGroup>
+
+### 4. Response Format
+
+Specify how you want information presented.
+
+<CodeGroup>
+```text Good Example
+Present your analysis in a structured format with: Summary, Key Findings, Risk Assessment, and Recommendations.
+```
+
+```text Weak Example
+Tell me what you find.
+```
+</CodeGroup>
+
+## Instructions Template
+
+Here's a template you can use to create effective agent instructions:
+
+```
+You are a [ROLE] specializing in [EXPERTISE AREAS].
+
+Your goal is to [MAIN OBJECTIVE].
+
+When approaching tasks:
+1. [FIRST STEP/APPROACH]
+2. [SECOND STEP/APPROACH]
+3. [THIRD STEP/APPROACH]
+
+Present your results in [FORMAT PREFERENCES].
+
+Additional guidelines:
+- [SPECIFIC BEHAVIOR 1]
+- [SPECIFIC BEHAVIOR 2]
+- [SPECIFIC BEHAVIOR 3]
+```
+
+## Examples in PraisonAI
+
+Let's look at how instructions are implemented in PraisonAI:
+
+<CodeGroup>
+```python Simple Example
+from praisonaiagents import Agent
+
+research_agent = Agent(
+    instructions="You are a research assistant specializing in climate science. Your goal is to find the latest research on rising sea levels and summarize key findings in bullet points."
+)
+
+research_agent.start()
+```
+
+```python Detailed Example
+from praisonaiagents import Agent
+
+content_agent = Agent(
+    name="ContentCreator",
+    instructions="""
+    You are a content creation specialist with expertise in digital marketing.
+    
+    Your goal is to create engaging blog content that drives traffic and conversions.
+    
+    When creating content:
+    1. Research the target keyword thoroughly
+    2. Create an attention-grabbing headline
+    3. Develop well-structured content with clear sections
+    4. Include actionable takeaways for readers
+    
+    Present content in markdown format with proper headings, bullet points, and emphasis where appropriate.
+    
+    Additional guidelines:
+    - Maintain a conversational but professional tone
+    - Include 2-3 relevant statistics to support key points
+    - Add a call-to-action at the end of each piece
+    """
+)
+
+content_agent.start("Create a blog post about sustainable living tips")
+```
+</CodeGroup>
+
+## Common Instruction Mistakes
+
+<CardGroup cols={2}>
+  <Card title="Being Too Vague" icon="cloud">
+    Vague instructions lead to unpredictable outputs
+  </Card>
+  <Card title="Information Overload" icon="bomb">
+    Too many details can confuse the agent's priorities
+  </Card>
+  <Card title="Contradictory Guidelines" icon="arrows-split-up-and-left">
+    Conflicting instructions create confusion
+  </Card>
+  <Card title="Missing Context" icon="puzzle-piece-missing">
+    Insufficient background information leads to generic outputs
+  </Card>
+</CardGroup>
+
+## Refining Your Instructions
+
+<Tip>
+Good instructions often require iteration. Start with basic instructions, review the agent's performance, and refine as needed.
+</Tip>
+
+Effective instruction improvement follows this cycle:
+
+```mermaid
+graph TD
+    A[Write Initial Instructions] --> B[Test Agent Performance]
+    B --> C[Identify Issues/Gaps]
+    C --> D[Refine Instructions]
+    D --> B
+```
+
+In the next lesson, we'll explore how agents use tools to extend their capabilities.

docs/course/agents/05-agent-tools.mdx

@@ -0,0 +1,183 @@
+---
+title: "Agent Tools"
+description: "Understanding how tools extend agent capabilities"
+icon: "screwdriver-wrench"
+---
+
+# Agent Tools
+
+Tools allow AI agents to interact with the world and perform specific actions. Think of tools as the "hands" of your agent that let it accomplish tasks beyond just generating text.
+
+## What Are Agent Tools?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="toolbox">
+    Tools are functions or capabilities that agents can access to perform specific actions, retrieve information, or interact with external systems.
+  </Card>
+</CardGroup>
+
+Just as humans use tools to extend their physical abilities, AI agents use digital tools to extend their capabilities.
+
+## Why Tools Matter
+
+Tools transform agents from simple chat interfaces into powerful assistants that can:
+
+<CardGroup cols={2}>
+  <Card title="Access Information" icon="database">
+    Retrieve data from various sources
+  </Card>
+  <Card title="Perform Calculations" icon="calculator">
+    Analyze numbers and data accurately
+  </Card>
+  <Card title="Create Content" icon="pen-to-square">
+    Generate images, code, or other media
+  </Card>
+  <Card title="Control Systems" icon="gears">
+    Interact with other software or hardware
+  </Card>
+</CardGroup>
+
+## Common Types of Tools
+
+### 1. Information Retrieval Tools
+
+These tools help agents access information beyond their training data.
+
+**Examples:**
+- Web search tools
+- Database query tools
+- Document retrieval tools
+
+```python
+# Example of a simple web search tool
+from praisonaiagents import Agent
+
+def web_search(query):
+    # Simplified example
+    return f"Results for: {query}"
+
+agent = Agent(
+    instructions="Research assistant",
+    tools=[web_search]
+)
+```
+
+### 2. Computation Tools
+
+These tools perform calculations or data processing.
+
+**Examples:**
+- Calculator tools
+- Data analysis tools
+- Conversion tools
+
+### 3. Content Creation Tools
+
+These tools help generate or modify various types of content.
+
+**Examples:**
+- Image generation tools
+- Code writing tools
+- Document formatting tools
+
+### 4. External API Tools
+
+These tools connect to third-party services.
+
+**Examples:**
+- Weather API tools
+- Translation API tools
+- E-commerce API tools
+
+## How Tools Work in PraisonAI
+
+In the PraisonAI framework, tools follow a simple pattern:
+
+1. Tools are defined as functions
+2. Tools are registered with an agent
+3. The agent decides when to use appropriate tools
+4. Tool results are incorporated into the agent's reasoning
+
+```mermaid
+graph TD
+    A[Agent Receives Task] --> B{Needs a Tool?}
+    B -->|Yes| C[Select Appropriate Tool]
+    C --> D[Execute Tool]
+    D --> E[Process Tool Results]
+    E --> F[Continue Task]
+    B -->|No| F
+```
+
+## Creating Basic Tools
+
+Here's a simple example of creating and using tools in PraisonAI:
+
+```python
+from praisonaiagents import Agent
+
+# Define a simple calculator tool
+def calculator(expression):
+    try:
+        return str(eval(expression))
+    except:
+        return "Error: Could not calculate"
+
+# Create an agent with the tool
+math_agent = Agent(
+    instructions="You are a math tutor helping students solve problems",
+    tools=[calculator]
+)
+
+# Use the agent
+math_agent.start("Calculate 24 * 17 and explain the steps")
+```
+
+## Tool Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Clear Descriptions" icon="comment">
+    Provide detailed descriptions so the agent knows when to use each tool
+  </Card>
+  <Card title="Error Handling" icon="shield">
+    Make tools robust with proper error handling
+  </Card>
+  <Card title="Focused Purpose" icon="bullseye">
+    Each tool should do one thing well
+  </Card>
+  <Card title="Input Validation" icon="check-double">
+    Validate inputs to prevent misuse
+  </Card>
+</CardGroup>
+
+## Building a Toolbox
+
+As you advance, you'll want to create a collection of tools your agent can use:
+
+```python
+from praisonaiagents import Agent, Tool
+
+# Define multiple tools
+def weather(location):
+    return f"Weather in {location}: Sunny, 75°F"
+
+def translator(text, target_language):
+    return f"Translated to {target_language}: {text} (placeholder)"
+
+def reminder(message, time):
+    return f"Reminder set for {time}: {message}"
+
+# Create tool collection
+tools = [
+    Tool(name="weather", function=weather, description="Get weather for a location"),
+    Tool(name="translator", function=translator, description="Translate text"),
+    Tool(name="reminder", function=reminder, description="Set a reminder")
+]
+
+# Create agent with multiple tools
+assistant = Agent(
+    instructions="You are a helpful personal assistant",
+    tools=tools
+)
+```
+
+In the next lesson, we'll explore how agents use memory to maintain context across interactions.

docs/course/agents/06-agent-memory.mdx

@@ -0,0 +1,167 @@
+---
+title: "Agent Memory"
+description: "Understanding how agents maintain context and learn"
+icon: "brain"
+---
+
+# Agent Memory
+
+Memory enables AI agents to remember past interactions, maintain context, and provide more coherent responses over time. Without memory, agents would treat each interaction as if it was their first.
+
+## Why Memory Matters
+
+<CardGroup cols={2}>
+  <Card title="Context Awareness" icon="comments">
+    Remembers previous parts of a conversation
+  </Card>
+  <Card title="Personalization" icon="user-gear">
+    Recalls user preferences and history
+  </Card>
+  <Card title="Continuous Learning" icon="graduation-cap">
+    Builds on past experiences
+  </Card>
+  <Card title="Task Persistence" icon="list-check">
+    Maintains progress on long-running tasks
+  </Card>
+</CardGroup>
+
+## Types of Agent Memory
+
+### 1. Short-Term Memory (Conversation Memory)
+
+This type of memory stores recent interactions within a session.
+
+```mermaid
+graph LR
+    A[User Input 1] --> B[Agent Response 1]
+    B --> C[User Input 2]
+    C --> D[Agent Response 2]
+    D --> E[User Input 3]
+```
+
+The agent can refer back to earlier messages in the same conversation.
+
+### 2. Long-Term Memory
+
+This allows agents to remember information across different sessions.
+
+<CardGroup cols={1}>
+  <Card title="Use Cases" icon="clock">
+    - Remembering user preferences
+    - Recalling previous tasks completed
+    - Storing learned information
+    - Maintaining relationship history
+  </Card>
+</CardGroup>
+
+### 3. Working Memory
+
+This is where agents process current information and combine it with retrieved memories.
+
+```mermaid
+graph TD
+    A[Current Input] --> D[Working Memory]
+    B[Short-Term Memory] --> D
+    C[Long-Term Memory] --> D
+    D --> E[Agent Response]
+```
+
+## How Memory Works in PraisonAI
+
+In the PraisonAI framework, memory is handled automatically for basic cases and can be customized for more advanced needs:
+
+```python
+from praisonaiagents import Agent
+
+# Basic agent with default memory handling
+agent = Agent(
+    instructions="You are a helpful assistant that remembers our conversations"
+)
+
+# The conversation history is maintained automatically
+agent.start("My name is Alex")
+agent.continue("What's my name?")# Note: TODO: This Feature yet to be developed  # Agent will remember "Alex"
+```
+
+## Memory Limitations
+
+<Tip>
+All AI memories have limitations. They aren't perfect and may occasionally forget or misremember information.
+</Tip>
+
+Common limitations include:
+
+<CardGroup cols={2}>
+  <Card title="Memory Capacity" icon="database">
+    Limit to how much information can be stored
+  </Card>
+  <Card title="Context Window" icon="window">
+    Only a portion of memory fits in the active context
+  </Card>
+  <Card title="Memory Decay" icon="hourglass-half">
+    Older memories may become less accessible
+  </Card>
+  <Card title="Memory Retrieval" icon="magnifying-glass">
+    Finding the right memory at the right time
+  </Card>
+</CardGroup>
+
+## Implementing Persistent Memory
+
+For more advanced applications, you can implement custom memory systems:
+
+```python
+# Note: TODO: This Feature yet to be tested
+from praisonaiagents import Agent, Memory
+
+# Create a simple memory store
+memory = Memory()
+
+# Add information to memory
+memory.add("user_name", "Alex")
+memory.add("favorite_color", "blue")
+
+# Create agent with this memory
+agent = Agent(
+    instructions="You are a personal assistant",
+    memory=memory
+)
+
+# The agent can now access these memories
+agent.start("What's my favorite color?")  # Agent should respond "blue"
+```
+
+## Memory Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Prioritize Important Info" icon="star">
+    Not everything needs to be remembered
+  </Card>
+  <Card title="Verify When Uncertain" icon="question">
+    Have agents confirm uncertain memories
+  </Card>
+  <Card title="Structured Storage" icon="folder-tree">
+    Organize memories by categories
+  </Card>
+  <Card title="Regular Updates" icon="arrows-rotate">
+    Update memories when information changes
+  </Card>
+</CardGroup>
+
+## Memory in Multi-Agent Systems
+
+In systems with multiple agents, memory can be:
+
+1. **Private**: Each agent has its own memories
+2. **Shared**: Agents have access to a common memory store
+3. **Hybrid**: Some memories are private, others are shared
+
+```mermaid
+graph TD
+    A[Agent 1] --> B[Private Memory 1]
+    C[Agent 2] --> D[Private Memory 2]
+    A -.-> E[Shared Memory]
+    C -.-> E
+```
+
+In the next lesson, we'll learn about how to set up multiple agents to work together effectively.

docs/course/agents/07-multi-agent-systems.mdx

@@ -0,0 +1,171 @@
+---
+title: "Multi-Agent Systems"
+description: "How multiple agents can work together"
+icon: "users"
+---
+
+# Multi-Agent Systems
+
+Multi-agent systems allow multiple AI agents to work together, each handling different parts of a complex task. This approach mirrors how human teams collaborate, with each member contributing their specialized skills.
+
+## Why Use Multiple Agents?
+
+<CardGroup cols={2}>
+  <Card title="Specialization" icon="star">
+    Each agent can focus on its area of expertise
+  </Card>
+  <Card title="Complex Tasks" icon="puzzle-piece">
+    Break down difficult problems into manageable pieces
+  </Card>
+  <Card title="Scalability" icon="arrows-up-down">
+    Add more agents as tasks become more complex
+  </Card>
+  <Card title="Diverse Perspectives" icon="lightbulb">
+    Different agents can approach problems differently
+  </Card>
+</CardGroup>
+
+## Basic Multi-Agent Architecture
+
+```mermaid
+graph LR
+    A[Research Agent] --> B[Analysis Agent]
+    B --> C[Writing Agent]
+    C --> D[Final Output]
+```
+
+In this example:
+1. The Research Agent gathers information
+2. The Analysis Agent processes and interprets the data
+3. The Writing Agent creates the final content
+
+## Multi-Agent Patterns
+
+### 1. Pipeline Pattern
+
+Agents work in sequence, with each agent handling a specific stage of the process.
+
+```mermaid
+graph LR
+    A[Agent 1] --> B[Agent 2]
+    B --> C[Agent 3]
+```
+
+**Example Use Case**: Content creation where one agent researches, another drafts, and a third edits.
+
+### 2. Expert Panel Pattern
+
+Multiple specialist agents work in parallel on the same problem, then their outputs are combined.
+
+```mermaid
+graph TD
+    Task --> A[Expert Agent 1]
+    Task --> B[Expert Agent 2]
+    Task --> C[Expert Agent 3]
+    A --> D[Coordination Agent]
+    B --> D
+    C --> D
+    D --> Result
+```
+
+**Example Use Case**: Financial analysis where different experts analyze market trends, economic indicators, and company performance.
+
+### 3. Hierarchical Pattern
+
+A manager agent delegates tasks to worker agents and coordinates their efforts.
+
+```mermaid
+graph TD
+    A[Manager Agent] --> B[Worker Agent 1]
+    A --> C[Worker Agent 2]
+    A --> D[Worker Agent 3]
+    B --> A
+    C --> A
+    D --> A
+```
+
+**Example Use Case**: Project management where a coordinator assigns tasks and integrates results.
+
+## Implementing Multi-Agent Systems in PraisonAI
+
+Here's a simple example of creating a multi-agent system:
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+# Create individual agents
+research_agent = Agent(
+    name="Researcher",
+    instructions="Research the latest trends in renewable energy"
+)
+
+analysis_agent = Agent(
+    name="Analyst",
+    instructions="Analyze the research findings and identify key insights"
+)
+
+writing_agent = Agent(
+    name="Writer", 
+    instructions="Create a clear, engaging report based on the analysis"
+)
+
+# Create a multi-agent system
+agents = PraisonAIAgents(
+    agents=[research_agent, analysis_agent, writing_agent]
+)
+
+# Start the agents
+agents.start()
+```
+
+## Agent Communication
+
+For agents to work together effectively, they need to communicate. This happens through:
+
+<CardGroup cols={2}>
+  <Card title="Output Passing" icon="arrow-right">
+    One agent's output becomes another's input
+  </Card>
+  <Card title="Shared Memory" icon="database">
+    Agents can access common information
+  </Card>
+  <Card title="Message Exchange" icon="comments">
+    Agents can send specific messages to each other
+  </Card>
+  <Card title="Coordination Protocols" icon="traffic-light">
+    Rules that determine how agents interact
+  </Card>
+</CardGroup>
+
+## Challenges in Multi-Agent Systems
+
+<CardGroup cols={2}>
+  <Card title="Coordination" icon="shuffle">
+    Ensuring agents work together smoothly
+  </Card>
+  <Card title="Conflicting Goals" icon="arrows-split-up-and-left">
+    Managing agents with different objectives
+  </Card>
+  <Card title="Information Transfer" icon="exchange">
+    Passing information effectively between agents
+  </Card>
+  <Card title="Resource Management" icon="microchip">
+    Allocating computational resources efficiently
+  </Card>
+</CardGroup>
+
+## Designing Effective Multi-Agent Systems
+
+<Tip>
+Start simple with just 2-3 agents and a clear workflow before scaling to more complex systems.
+</Tip>
+
+Key principles for effective design:
+
+1. **Clear Role Definition**: Each agent should have a specific and well-defined role
+2. **Minimized Dependencies**: Reduce complex interdependencies between agents
+3. **Standardized Communication**: Use consistent formats for information exchange
+4. **Failure Handling**: Plan for cases where an agent fails to complete its task
+5. **Performance Monitoring**: Track how well each agent and the overall system performs
+
+In the next lesson, we'll explore how to create effective agent workflows using the Process component.

docs/course/agents/08-agent-process.mdx

@@ -0,0 +1,152 @@
+---
+title: "Agent Process"
+description: "Understanding agent workflows and processes"
+icon: "diagram-project"
+---
+
+# Agent Process
+
+The Process component in AI agent systems defines how agents work together and the flow of information between them. Think of it as the "playbook" that coordinates how agents complete tasks.
+
+## What is an Agent Process?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="route">
+    A process is a structured workflow that defines the sequence of steps, decision points, and information flow between agents to complete a task.
+  </Card>
+</CardGroup>
+
+## Process Flow Basics
+
+A simple process flow might look like this:
+
+```mermaid
+graph LR
+    A[Start] --> B[Gather Information]
+    B --> C[Process Information]
+    C --> D[Make Decision]
+    D --> E[Take Action]
+    E --> F[Report Results]
+```
+
+Each step could be handled by a different agent or by the same agent in different stages.
+
+## Types of Process Flows
+
+### 1. Sequential Process
+
+Agents work in a straight line, one after another.
+
+```mermaid
+graph LR
+    A[Agent 1] --> B[Agent 2]
+    B --> C[Agent 3]
+    C --> D[Complete]
+```
+
+**Best for**: Tasks with clear, linear stages
+
+### 2. Parallel Process
+
+Multiple agents work simultaneously on different parts of a task.
+
+```mermaid
+graph TD
+    A[Start] --> B[Agent 1]
+    A --> C[Agent 2]
+    A --> D[Agent 3]
+    B --> E[Combine Results]
+    C --> E
+    D --> E
+    E --> F[Complete]
+```
+
+**Best for**: Tasks that can be broken into independent pieces
+
+### 3. Conditional Process
+
+The workflow changes based on decisions or conditions.
+
+```mermaid
+graph TD
+    A[Start] --> B{Decision Point}
+    B -->|Condition A| C[Agent 1]
+    B -->|Condition B| D[Agent 2]
+    C --> E[Complete]
+    D --> E
+```
+
+**Best for**: Tasks requiring different approaches based on circumstances
+
+## Implementing Process in PraisonAI
+
+Here's a simple example of creating a process in PraisonAI:
+
+```python
+from praisonaiagents import Agent, Process, PraisonAIAgents
+
+# Create agents
+research_agent = Agent(
+    name="Researcher",
+    instructions="Research the topic and gather key information about AI"
+)
+
+writing_agent = Agent(
+    name="Writer",
+    instructions="Create content based on the research"
+)
+
+editing_agent = Agent(
+    name="Editor",
+    instructions="Review and improve the content"
+)
+
+# Create the multi-agent system with the process
+agents = PraisonAIAgents(
+    agents=[research_agent, writing_agent, editing_agent],
+    process="hierarchical"
+)
+
+# Start the process
+agents.start()
+```
+
+## Process Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Clear Step Definitions" icon="list-check">
+    Define exactly what each step should accomplish
+  </Card>
+  <Card title="Error Handling" icon="bug">
+    Include steps for handling unexpected situations
+  </Card>
+  <Card title="Feedback Loops" icon="repeat">
+    Allow agents to request clarification when needed
+  </Card>
+  <Card title="Modular Design" icon="cubes">
+    Create reusable process components
+  </Card>
+</CardGroup>
+
+<Tip>
+Start with simple processes and gradually add complexity as you become more comfortable with the system.
+</Tip>
+
+## Monitoring Process Execution
+
+It's important to track how your processes are running:
+
+```python
+agents = PraisonAIAgents(
+    agents=[research_agent, writing_agent, editing_agent],
+    process=content_process,
+    verbose=True  # Enables detailed logging
+)
+```
+
+This will provide information about:
+- Which step is currently running
+- The inputs and outputs of each step
+- Any errors or issues encountered
+
+In the next lesson, we'll explore how to use knowledge bases to enhance agent capabilities.

docs/course/agents/09-knowledge-bases.mdx

@@ -0,0 +1,190 @@
+---
+title: "Knowledge Bases"
+description: "Enhancing agents with specialized knowledge"
+icon: "book"
+---
+
+# Knowledge Bases
+
+Knowledge bases provide agents with specialized information beyond their training data. They help agents answer specific questions, follow guidelines, or work within defined domains.
+
+## What is a Knowledge Base?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="database">
+    A knowledge base is a structured collection of information that an agent can access to answer questions or make decisions.
+  </Card>
+</CardGroup>
+
+Think of a knowledge base as a specialized reference library for your agent.
+
+## Why Use Knowledge Bases?
+
+<CardGroup cols={2}>
+  <Card title="Domain Expertise" icon="user-graduate">
+    Provide specialized information for specific fields
+  </Card>
+  <Card title="Current Information" icon="clock">
+    Include data beyond the agent's training cutoff
+  </Card>
+  <Card title="Custom Guidelines" icon="list">
+    Define rules and policies for the agent to follow
+  </Card>
+  <Card title="Consistent Responses" icon="equals">
+    Ensure agents provide standardized answers
+  </Card>
+</CardGroup>
+
+## Types of Knowledge Bases
+
+### 1. Document Collections
+
+Text documents, articles, guides, or manuals that agents can reference.
+
+```mermaid
+graph TD
+    A[Query] --> B[Search Documents]
+    B --> C[Find Relevant Content]
+    C --> D[Generate Response]
+```
+
+### 2. Structured Data
+
+Databases, tables, or other structured formats that organize information systematically.
+
+<CardGroup cols={1}>
+  <Card title="Examples" icon="table">
+    - Product catalogs with specifications
+    - Customer information databases
+    - Statistical data collections
+    - Reference tables for compliance information
+  </Card>
+</CardGroup>
+
+### 3. Vector Databases
+
+Special databases that store information as numerical representations (vectors) for semantic search.
+
+```mermaid
+graph TD
+    A[User Question] --> B[Convert to Vector]
+    B --> C[Find Similar Vectors]
+    C --> D[Retrieve Related Information]
+    D --> E[Generate Answer]
+```
+
+## Implementing Knowledge Bases in PraisonAI
+
+Here's an example of how to use a knowledge base with an agent:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"]
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Advanced Configuration
+
+For more control over the knowledge base, you can specify a configuration:
+
+```python
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Knowledge Retrieval Process
+
+When an agent uses a knowledge base, this typical process occurs:
+
+```mermaid
+graph LR
+    A[User Question] --> B[Query Processing]
+    B --> C[Search Knowledge Base]
+    C --> D[Rank Results]
+    D --> E[Synthesize Answer]
+```
+
+1. **Query Processing**: The user's question is analyzed
+2. **Search**: The system searches the knowledge base for relevant information
+3. **Ranking**: Results are ranked by relevance
+4. **Synthesis**: The agent creates an answer using the retrieved information
+
+## Best Practices for Knowledge Bases
+
+<CardGroup cols={2}>
+  <Card title="Keep Information Current" icon="arrows-rotate">
+    Regularly update your knowledge base
+  </Card>
+  <Card title="Organize Logically" icon="folder-tree">
+    Structure information in intuitive categories
+  </Card>
+  <Card title="Prioritize Quality" icon="star">
+    Focus on accurate, high-quality information
+  </Card>
+  <Card title="Include Examples" icon="list">
+    Add examples to illustrate complex concepts
+  </Card>
+</CardGroup>
+
+## When to Use Knowledge Bases
+
+Knowledge bases are particularly valuable when:
+
+1. Your agent needs to reference specific information that may change over time
+2. You need to ensure consistent answers to common questions
+3. Your agent needs to follow specific guidelines or protocols
+4. You want to provide expertise in specialized domains
+
+<Tip>
+Start with a small knowledge base focusing on the most important information, then expand as needed.
+</Tip>
+
+## Creating a Simple Knowledge Base
+
+For beginners, you can start with a simple text-based knowledge base:
+
+```
+# Company FAQ Knowledge Base
+
+## Return Policy
+Our return policy allows customers to return products within 30 days of purchase for a full refund.
+
+## Shipping Information
+Standard shipping takes 3-5 business days. Express shipping takes 1-2 business days.
+
+## Product Warranty
+All products come with a 1-year limited warranty covering manufacturing defects.
+```
+
+Save this as a text file and add it to your knowledge base:
+
+```python
+kb = KnowledgeBase()
+kb.add_document("company_faq.txt")
+```
+
+In the next lesson, we'll explore how agents handle tasks and the task management process.

docs/course/agents/10-agent-tasks.mdx

@@ -0,0 +1,274 @@
+---
+title: "Agent Tasks"
+description: "Understanding how agents handle and manage tasks"
+icon: "clipboard-list"
+---
+
+# Agent Tasks
+
+Tasks are the specific units of work that AI agents perform. Understanding how to define and manage tasks is essential for creating effective agent systems.
+
+## What is an Agent Task?
+
+<CardGroup cols={1}>
+  <Card title="Definition" icon="list-check">
+    A task is a specific, well-defined piece of work assigned to an agent with clear inputs, expected outputs, and success criteria.
+  </Card>
+</CardGroup>
+
+Just as human jobs are broken into specific responsibilities, agent systems work best when complex goals are divided into manageable tasks.
+
+## Anatomy of a Task
+
+A well-defined task includes:
+
+<CardGroup cols={2}>
+  <Card title="Objective" icon="bullseye">
+    What the task should accomplish
+  </Card>
+  <Card title="Inputs" icon="arrow-right-to-bracket">
+    Information provided to complete the task
+  </Card>
+  <Card title="Process" icon="gears">
+    Steps to follow or approach to take
+  </Card>
+  <Card title="Outputs" icon="arrow-right-from-bracket">
+    Expected results or deliverables
+  </Card>
+</CardGroup>
+
+## Task Types
+
+### 1. Information Tasks
+
+These tasks involve finding, analyzing, or summarizing information.
+
+**Examples:**
+- Research a topic
+- Summarize a document
+- Extract data from text
+- Answer questions
+
+### 2. Creation Tasks
+
+These tasks involve generating new content.
+
+**Examples:**
+- Write an article
+- Create a marketing plan
+- Generate images (with appropriate tools)
+- Design a workflow
+
+### 3. Analysis Tasks
+
+These tasks involve evaluating information and drawing conclusions.
+
+**Examples:**
+- Analyze data trends
+- Review content for quality
+- Evaluate options
+- Identify patterns
+
+### 4. Interaction Tasks
+
+These tasks involve communicating or working with users or other systems.
+
+**Examples:**
+- Answer customer questions
+- Guide users through processes
+- Collect information from users
+- Notify about events
+
+## Creating Tasks in PraisonAI
+
+### Task Flow
+
+The typical lifecycle of a task follows this pattern:
+
+```mermaid
+graph LR
+    A[Define Task] --> B[Assign to Agent]
+    B --> C[Execute Task]
+    C --> D[Return Results]
+    D --> E[Evaluate Success]
+```
+
+### Task Configuration
+
+<CodeGroup>
+  ```python Basic
+  task = Task(
+      description="Research AI trends",
+      expected_output="Summary report",
+      agent=research_agent
+  )
+  ```
+  ```python Advanced
+  task = Task(
+      description="Analyze market data",
+      expected_output="Detailed analysis",
+      agent=analyst_agent,
+      tools=[AnalysisTool()],
+      output_file="analysis.md",
+      async_execution=True
+  )
+  ```
+</CodeGroup>
+
+# Understanding Tasks
+<br />
+```mermaid
+graph LR
+    %% Task Flow Steps
+    Start([▶ Start]) --> TaskDef
+    TaskDef --> AgentAssign
+    AgentAssign --> Exec
+    Exec --> Output([✓ Output])
+    
+    %% Define subgraphs for each step
+    subgraph TaskDef[" "]
+        TD_Icon[📋 Task Definition]
+        TD_Desc[Description & Requirements]
+        
+        TD_Icon --- TD_Desc
+        
+        style TD_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style TD_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph AgentAssign[" "]
+        AA_Icon[🤖 Agent Assignment]
+        AA_Desc[Matching & Delegation]
+        
+        AA_Icon --- AA_Desc
+        
+        style AA_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AA_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Exec[" "]
+        E_Icon[⚙️ Execution]
+        E_Desc[Processing & Tools]
+        
+        E_Icon --- E_Desc
+        
+        style E_Icon fill:#2D3047,stroke:#7C90A0,color:#fff
+        style E_Desc fill:#5C80BC,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Style for containers
+    style TaskDef fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style AgentAssign fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Exec fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+<br /><br />
+```mermaid
+graph LR
+    %% Task States
+    Pending([📥 Pending]) --> InProgress([⚡ In Progress])
+    InProgress --> Completed([✅ Completed])
+    InProgress --> Failed([❌ Failed])
+    
+    %% Styling
+    style Pending fill:#2D3047,stroke:#7C90A0,color:#fff
+    style InProgress fill:#5C80BC,stroke:#7C90A0,color:#fff
+    style Completed fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Failed fill:#2D3047,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+
+## Task Types
+
+<Steps>
+  <Step title="Basic Task">
+    Simple, single-operation tasks with clear inputs and outputs
+  </Step>
+  <Step title="Decision Task">
+    Tasks involving choices and conditional paths
+    ```python
+    decision_task = Task(
+        type="decision",
+        conditions={
+            "success": ["next_task"],
+            "failure": ["error_task"]
+        }
+    )
+    ```
+  </Step>
+  <Step title="Loop Task">
+    Repetitive operations over collections
+    ```python
+    loop_task = Task(
+        type="loop",
+        items=data_list,
+        operation="process_item"
+    )
+    ```
+  </Step>
+</Steps>
+
+## Task Relationships
+
+<Warning>
+  Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.
+</Warning>
+
+### Context Sharing
+```python
+task_a = Task(name="research")
+task_b = Task(
+    name="analyze",
+    context=[task_a]  # Uses task_a's output
+)
+```
+
+## Task Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Be Specific" icon="magnifying-glass">
+    Clearly define what the task should accomplish
+  </Card>
+  <Card title="One Goal Per Task" icon="check-double">
+    Each task should focus on a single objective
+  </Card>
+  <Card title="Provide Context" icon="circle-info">
+    Include necessary background information
+  </Card>
+  <Card title="Define Success" icon="trophy">
+    Specify what a successful outcome looks like
+  </Card>
+</CardGroup>
+
+<Tip>
+Break complex goals into multiple smaller tasks rather than creating one large, complex task.
+</Tip>
+
+## Common Task Mistakes
+
+<CardGroup cols={2}>
+  <Card title="Vague Instructions" icon="question">
+    Unclear about what the agent should do
+  </Card>
+  <Card title="Too Many Objectives" icon="list-ol">
+    Asking for too many things in one task
+  </Card>
+  <Card title="Missing Context" icon="puzzle-piece-missing">
+    Not providing necessary background information
+  </Card>
+  <Card title="Unrealistic Expectations" icon="mountain-sun">
+    Asking for tasks beyond agent capabilities
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll learn about creating your first complete agent application.

docs/course/agents/11-creating-your-first-agent.mdx

@@ -0,0 +1,172 @@
+---
+title: "Creating Your First Agent"
+description: "A step-by-step guide to building your first AI agent"
+icon: "robot"
+---
+
+# Creating Your First Agent
+
+In this lesson, we'll walk through the process of building a simple AI agent. We'll focus on practical implementation without complex programming.
+
+## What We'll Build
+
+We'll create a basic research assistant agent that can:
+- Understand simple instructions
+- Respond to questions
+- Provide helpful information
+
+## Prerequisites
+
+Before you begin, make sure you have:
+
+1. A basic understanding of the concepts from previous lessons
+2. Python installed on your computer (if following the code examples)
+3. An OpenAI API key (or access to another supported language model)
+
+## Step 1: Installation
+
+First, install the PraisonAI package:
+
+```bash
+pip install praisonaiagents
+```
+
+## Step 2: Setup Your Environment
+
+Create a file called `first_agent.py` and set up your API key:
+
+```python
+import os
+from praisonaiagents import Agent
+
+# Set your API key
+# Option 1: Set it in your environment (recommended)
+# export OPENAI_API_KEY=your_key_here
+
+# Option 2: Set it in your code (not recommended for production)
+# os.environ["OPENAI_API_KEY"] = "your_key_here"
+```
+
+## Step 3: Define Your Agent
+
+Now, let's create a simple agent:
+
+```python
+# Create a research assistant agent
+research_assistant = Agent(
+    name="ResearchAssistant",
+    instructions="""
+    You are a helpful research assistant that provides clear, 
+    accurate information on various topics. When answering:
+    
+    1. Be concise and to the point
+    2. Explain complex concepts in simple terms
+    3. When appropriate, organize information with bullet points
+    4. If you don't know something, admit it instead of guessing
+    """
+)
+```
+
+## Step 4: Start Your Agent
+
+Now let's use our agent to answer a question:
+
+```python
+# Start the agent with a specific query
+response = research_assistant.start("Explain how solar panels work in simple terms")
+
+# Print the response
+print(response)
+```
+
+## Step 5: Run Your Agent
+
+Save the file and run it from your terminal:
+
+```bash
+python first_agent.py
+```
+
+You should see the agent's response explaining how solar panels work in simple terms.
+
+## Experimenting with Your Agent
+
+Try changing the question to see how your agent responds to different topics:
+
+```python
+# Try different questions
+response = research_assistant.start("What are the benefits of exercise?")
+# or
+response = research_assistant.start("How does artificial intelligence work?")
+```
+
+## Modifying Agent Behavior
+
+You can change how your agent behaves by adjusting its instructions:
+
+```python
+# Create an agent with different instructions
+creative_writer = Agent(
+    name="CreativeWriter",
+    instructions="""
+    You are a creative writer that specializes in short, engaging stories.
+    Your writing should:
+    
+    1. Include vivid descriptions
+    2. Have interesting characters
+    3. Include some dialogue
+    4. Have a clear beginning, middle, and end
+    """
+)
+
+# Ask for a story
+story = creative_writer.start("Write a short story about a lost dog finding its way home")
+print(story)
+```
+
+## Examining How It Works
+
+What's happening behind the scenes:
+
+1. The agent receives your instructions and query
+2. The language model (like GPT-4) processes the information
+3. The model generates a response based on the instructions and query
+4. The response is returned to you
+
+```mermaid
+graph TD
+    A[Your Instructions] --> B[Agent]
+    C[Your Query] --> B
+    B --> D[Language Model]
+    D --> E[Generated Response]
+    E --> F[Output to You]
+```
+
+## Common Challenges
+
+If you encounter issues, check these common problems:
+
+<CardGroup cols={2}>
+  <Card title="API Key Issues" icon="key">
+    Make sure your API key is set correctly
+  </Card>
+  <Card title="Connection Problems" icon="wifi">
+    Check your internet connection
+  </Card>
+  <Card title="Unclear Instructions" icon="circle-question">
+    Try making your instructions more specific
+  </Card>
+  <Card title="Rate Limits" icon="hand">
+    You might be making too many API calls
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+Now that you've created your first agent, you can:
+- Experiment with different instructions
+- Try more complex queries
+- Add specialized capabilities (we'll cover this in upcoming lessons)
+- Create agents for specific use cases
+
+Congratulations on creating your first AI agent! In the next lesson, we'll explore how to add tools to enhance your agent's capabilities.

docs/course/agents/12-adding-tools-to-agents.mdx

@@ -0,0 +1,275 @@
+---
+title: "Adding Tools to Agents"
+description: "Enhancing your agents with specialized capabilities"
+icon: "screwdriver-wrench"
+---
+
+# Adding Tools to Agents
+
+In this lesson, we'll learn how to enhance your agents by adding tools. Tools allow agents to perform specific actions beyond just generating text, making them much more useful for real-world tasks.
+
+## What You'll Learn
+
+By the end of this lesson, you'll understand:
+- What agent tools are and why they're useful
+- How to create simple tools for your agents
+- How to integrate tools with your agents
+- Best practices for tool design
+
+## Understanding Agent Tools
+
+Tools give your agents the ability to:
+- Perform calculations
+- Access specific information
+- Interact with external systems
+- Generate different types of content
+
+<Tabs>
+
+<Tab title="Code">
+<Steps>
+
+  <Step title="Install PraisonAI">
+Install the core package:
+```bash Terminal
+pip install praisonaiagents duckduckgo-search
+```
+  </Step>
+
+  <Step title="Configure Environment">
+  ```bash Terminal
+  export OPENAI_API_KEY=your_openai_key
+  ```
+  Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+  Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent with Tool">
+    Create `app.py`
+<CodeGroup>
+```python Single Agent
+from praisonaiagents import Agent
+from duckduckgo_search import DDGS
+
+# 1. Define the tool
+def internet_search_tool(query: str):
+    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
+
+# 2. Assign the tool to an agent
+search_agent = Agent(
+    instructions="Perform internet searches to collect relevant information.",
+    tools=[internet_search_tool] # <--- Tool Assignment
+)
+
+# 3. Start Agent
+search_agent.start("Search about AI job trends in 2025")
+```
+
+```python Multiple Agents
+from praisonaiagents import Agent, PraisonAIAgents
+from duckduckgo_search import DDGS
+
+# 1. Define the tool
+def internet_search_tool(query: str):
+    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
+
+# 2. Assign the tool to an agent
+search_agent = Agent(
+    instructions="Search about AI job trends in 2025",
+    tools=[internet_search_tool] # <--- Tool Assignment
+)
+
+blog_agent = Agent(
+    instructions="Write a blog article based on the previous agent's search results."
+)
+
+# 3. Start Agents
+agents = PraisonAIAgents(agents=[search_agent, blog_agent])
+agents.start()
+```
+</CodeGroup>
+  </Step>
+
+  <Step title="Start Agents">
+    Execute your script:
+```bash Terminal
+python app.py
+```
+  </Step>
+</Steps>
+
+</Tab>
+<Tab title="No Code">
+<Steps>
+<Step title="Install PraisonAI">
+Install the core package and duckduckgo_search package:
+```bash Terminal
+pip install praisonai duckduckgo_search
+```
+</Step>
+<Step title="Create Custom Tool">
+<Info>
+To add additional tools/features you need some coding which can be generated using ChatGPT or any LLM
+</Info>
+Create a new file `tools.py` with the following content:
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    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  
+```
+</Step>
+<Step title="Create Agent">
+
+Create a new file `agents.yaml` with the following content:
+```yaml
+framework: praisonai
+topic: create movie script about cat in mars
+roles:
+  scriptwriter:
+    backstory: Expert in dialogue and script structure, translating concepts into
+      scripts.
+    goal: Write a movie script about a cat in Mars
+    role: Scriptwriter
+    tools:
+      - internet_search_tool # <-- Tool assigned to Agent here
+    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.
+```
+</Step>
+
+<Step title="Start Agents">
+
+Execute your script:
+```bash Terminal
+praisonai agents.yaml
+```
+</Step>
+</Steps>
+</Tab>
+</Tabs>
+
+
+## Creating Custom Tool
+<Steps>
+<Step>
+Create any function that you want to use as a tool, that performs a specific task.
+```python
+from duckduckgo_search import DDGS
+
+def internet_search_tool(query: str):
+    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
+```
+</Step>
+<Step>
+Assign the tool to an agent
+```python
+    data_agent = Agent(
+        instructions="Search about AI job trends in 2025",
+        tools=[internet_search_tool], # <-- Tool Assignment
+    )
+```
+</Step>
+</Steps>
+
+<Card title="That's it!">
+<Check>You have created a custom tool and assigned it to an agent.</Check>
+</Card>
+
+## When Does the Agent Use Tools?
+
+The agent decides when to use tools based on:
+1. The nature of the user's request
+2. The tools available to it
+3. The instructions you've provided
+
+If your agent isn't using tools when expected, you might need to:
+- Make your instructions more explicit about when to use tools
+- Provide more descriptive tool definitions
+- Ensure the tools are appropriate for the task
+
+## Tool Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Specific Purpose" icon="bullseye">
+    Each tool should have a clear, specific purpose
+  </Card>
+  <Card title="Error Handling" icon="shield-check">
+    Tools should handle errors gracefully
+  </Card>
+  <Card title="Descriptive Names" icon="tag">
+    Use clear names that indicate functionality
+  </Card>
+  <Card title="Clear Documentation" icon="file-lines">
+    Include detailed descriptions and examples
+  </Card>
+</CardGroup>
+
+## Real-World Tool Examples
+
+In production systems, you might create tools for:
+
+<CardGroup cols={2}>
+  <Card title="Database Access" icon="database">
+    Retrieve or store information in databases
+  </Card>
+  <Card title="API Integration" icon="link">
+    Connect to external services
+  </Card>
+  <Card title="File Operations" icon="folder">
+    Read from or write to files
+  </Card>
+  <Card title="Image Generation" icon="image">
+    Create images based on descriptions
+  </Card>
+</CardGroup>
+
+## Advanced Tool Concepts
+
+As you become more comfortable with tools, you can explore:
+- Tools that call other tools
+- Tools with complex parameters
+- Tools that maintain state
+- Dynamically generated tools
+
+In the next lesson, we'll explore how to create a multi-agent system for more complex tasks.

docs/course/agents/13-building-multi-agent-system.mdx

@@ -0,0 +1,218 @@
+---
+title: "Building a Multi-Agent System"
+description: "Creating a system with multiple cooperative agents"
+icon: "users"
+---
+
+# Building a Multi-Agent System
+
+In this lesson, we'll create a simple multi-agent system where several agents work together to accomplish a task. This approach allows you to divide complex problems into smaller, more manageable pieces.
+
+## What We'll Build
+
+We'll create a content creation system with three specialized agents:
+1. A research agent to gather information
+2. A writing agent to create content
+3. An editing agent to improve and refine the content
+
+## Step 1: Setting Up
+
+First, let's import the necessary modules and set up our environment:
+
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+# Make sure your API key is set in your environment:
+# export OPENAI_API_KEY=your_openai_key
+```
+
+## Step 2: Creating Specialized Agents
+
+Now, let's create our three specialized agents:
+
+```python
+# Research Agent
+research_agent = Agent(
+    name="Researcher",
+    instructions="""
+    You are a research specialist who excels at finding relevant information.
+    Your job is to:
+    1. Thoroughly research the given topic
+    2. Identify key facts, statistics, and insights
+    3. Organize the information in a clear, structured format
+    4. Provide accurate information with no fabrications
+    5. Focus on the most relevant and current information
+    """
+)
+
+# Writing Agent
+writing_agent = Agent(
+    name="Writer",
+    instructions="""
+    You are a content writer who creates engaging, reader-friendly content.
+    Your job is to:
+    1. Use the research provided to create well-structured content
+    2. Write in a clear, engaging style
+    3. Include an attention-grabbing introduction
+    4. Organize the content with appropriate headings
+    5. Conclude with a summary and call-to-action when appropriate
+    """
+)
+
+# Editing Agent
+editing_agent = Agent(
+    name="Editor",
+    instructions="""
+    You are an editor who refines and improves content.
+    Your job is to:
+    1. Correct any grammatical or spelling errors
+    2. Improve clarity and flow
+    3. Ensure consistency in tone and style
+    4. Check for logical structure and organization
+    5. Enhance readability for the target audience
+    """
+)
+```
+
+## Step 3: Creating the Multi-Agent System
+
+Now, let's combine these agents into a multi-agent system:
+
+```python
+# Create a multi-agent system
+content_team = PraisonAIAgents(
+    agents=[research_agent, writing_agent, editing_agent]
+)
+```
+
+## Step 4: Setting Up the Workflow
+
+Next, we need to define how our agents will work together:
+
+```python
+# Define a topic
+topic = "The Benefits of Regular Exercise"
+
+# Start the multi-agent process
+results = content_team.start(
+    f"""
+    Task: Create a comprehensive blog post about {topic}
+    
+    Process:
+    1. Researcher: Research {topic} and provide key information including:
+       - Main benefits of exercise (physical and mental)
+       - Recommended exercise guidelines
+       - Scientific studies supporting the benefits
+       - Common misconceptions
+    
+    2. Writer: Use the research to create a 500-word blog post about {topic} with:
+       - An engaging introduction
+       - Clear sections with headings
+       - Practical advice for readers
+       - A compelling conclusion
+    
+    3. Editor: Review and improve the blog post by:
+       - Correcting any errors
+       - Enhancing clarity and flow
+       - Ensuring a consistent tone
+       - Making the content more engaging
+    """
+)
+
+# Print the final result
+print(results)
+```
+
+## Understanding the Workflow
+
+Let's examine how the multi-agent system works:
+
+```mermaid
+graph TD
+    A[Start] --> B[Research Agent]
+    B --> C[Writing Agent]
+    C --> D[Editing Agent]
+    D --> E[Final Output]
+```
+
+1. The research agent gathers and organizes relevant information
+2. The writing agent uses that research to create the initial content
+3. The editing agent refines and improves the content
+4. The final polished content is returned
+
+## Customizing Agent Interactions
+
+You can customize how agents interact by modifying the process instructions. For example, you could add feedback loops:
+
+```python
+process_with_feedback = """
+Task: Create a comprehensive blog post about {topic}
+
+Process:
+1. Researcher: Research {topic} and provide key information
+
+2. Writer: Use the research to create a blog post
+
+3. Editor: Review the blog post and provide feedback
+
+4. Writer: Revise the blog post based on the editor's feedback
+
+5. Editor: Make final improvements to the revised blog post
+"""
+```
+
+## Alternative Agent Configurations
+
+You can organize multi-agent systems in different ways:
+
+### Parallel Processing
+
+```python
+parallel_process = """
+Task: Create a marketing campaign for a new product
+
+Process:
+1. Market Researcher: Research the target market and competitors
+   Content Researcher: Research the product features and benefits
+   [These tasks happen in parallel]
+
+2. Strategy Agent: Use both research results to create a marketing strategy
+
+3. Content Creator: Create the marketing materials based on the strategy
+"""
+```
+
+### Expert Panel
+
+```python
+expert_panel = """
+Task: Evaluate a business idea
+
+Process:
+1. Financial Expert: Analyze financial viability
+   Market Expert: Analyze market potential
+   Technical Expert: Analyze technical feasibility
+   [These assessments happen in parallel]
+
+2. Coordinator: Compile all expert opinions into a final recommendation
+"""
+```
+
+## Best Practices for Multi-Agent Systems
+
+<CardGroup cols={2}>
+  <Card title="Clear Role Definition" icon="user-tag">
+    Define exactly what each agent is responsible for
+  </Card>
+  <Card title="Explicit Handoffs" icon="arrow-right-arrow-left">
+    Specify how information passes between agents
+  </Card>
+  <Card title="Avoid Redundancy" icon="copy">
+    Don't have multiple agents do the same work
+  </Card>
+  <Card title="Include Error Handling" icon="triangle-exclamation">
+    Plan for cases where an agent might fail
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll learn how to create a conversational agent that can maintain context across multiple interactions.

docs/course/agents/14-conversational-agents.mdx

@@ -0,0 +1,193 @@
+---
+title: "Building Conversational Agents"
+description: "Creating agents that maintain context in conversations"
+icon: "comments"
+---
+
+# Building Conversational Agents
+
+In this lesson, we'll learn how to create conversational agents that can maintain context across multiple interactions. This is essential for creating assistants that feel natural to interact with.
+
+## What Makes a Good Conversational Agent?
+
+A good conversational agent should:
+- Remember previous parts of the conversation
+- Understand context and references
+- Provide coherent, relevant responses
+- Maintain a consistent personality
+
+## Creating a Simple Conversational Agent
+
+Let's build a basic conversational agent:
+
+```python
+# Note: TODO: This Feature yet to be developed
+from praisonaiagents import Agent
+
+# Create a conversational agent
+chat_agent = Agent(
+    name="Conversational Assistant",
+    instructions="""
+    You are a friendly, helpful conversational assistant.
+    
+    When chatting with users:
+    1. Maintain a warm, friendly tone
+    2. Remember information shared earlier in the conversation
+    3. Ask clarifying questions when needed
+    4. Keep responses concise but informative
+    5. Be helpful while respecting boundaries
+    """
+)
+
+# Start a conversation
+response = chat_agent.start("Hi there! My name is Jamie.")
+print(response)
+
+# Continue the conversation
+response = chat_agent.continue("What can you help me with today?")
+print(response)
+
+# The agent should remember the user's name
+response = chat_agent.continue("Can you remember my name?")
+print(response)
+```
+
+## How Context Works
+
+PraisonAI automatically manages conversation context for you:
+
+```mermaid
+graph TD
+    A[User Message 1] --> B[Agent]
+    B --> C[Response 1]
+    D[User Message 2] --> E[Agent + Context]
+    E --> F[Response 2]
+    G[User Message 3] --> H[Agent + More Context]
+    H --> I[Response 3]
+```
+
+The agent maintains a memory of the conversation, allowing it to refer back to previous messages.
+
+## Conversation Settings
+
+You can customize how your conversational agent behaves:
+
+```python
+# Create an agent with specific conversation settings
+custom_chat_agent = Agent(
+    name="Custom Chat Assistant",
+    instructions="You are a helpful assistant",
+    memory_size=10,  # Remember the last 10 messages
+    temperature=0.7  # Slightly more creative in responses
+)
+```
+
+## Handling Different Types of Conversations
+
+Different use cases require different conversation styles:
+
+<CardGroup cols={2}>
+  <Card title="Customer Support" icon="headset">
+    - Professional and helpful tone
+    - Focus on problem resolution
+    - Remember customer details
+  </Card>
+  <Card title="Educational Tutor" icon="graduation-cap">
+    - Patient and explanatory
+    - Builds on previous lessons
+    - Adjusts to learning pace
+  </Card>
+  <Card title="Personal Assistant" icon="calendar">
+    - Remembers preferences
+    - Provides reminders
+    - Handles scheduling
+  </Card>
+  <Card title="Companion Chat" icon="user-group">
+    - More casual tone
+    - Focuses on engagement
+    - Shows personality
+  </Card>
+</CardGroup>
+
+## Example: Customer Support Agent
+
+Let's create a specialized customer support agent:
+
+```python
+support_agent = Agent(
+    name="Support Agent",
+    instructions="""
+    You are a customer support specialist for a tech company.
+    
+    When helping customers:
+    1. Greet them professionally
+    2. Show empathy for their issues
+    3. Ask for necessary information to troubleshoot
+    4. Provide clear step-by-step solutions
+    5. Confirm if the issue is resolved
+    6. End with an offer for additional help
+    
+    Remember product details and customer information throughout the conversation.
+    """
+)
+
+# Example conversation
+print(support_agent.start("Hi, I'm having trouble logging into my account."))
+print(support_agent.continue("My username is user123."))
+print(support_agent.continue("I've tried resetting my password but I'm not receiving the email."))
+```
+
+## Building Multi-Turn Conversations
+
+To create engaging multi-turn conversations, consider:
+
+1. **Appropriate follow-up questions**: Keep the conversation flowing
+2. **Acknowledgment**: Reference what the user has said
+3. **Progressive disclosure**: Don't overwhelm with information at once
+4. **Conversation repair**: Gracefully handle misunderstandings
+
+## Example: Multi-Turn Educational Conversation
+
+```python
+tutor_agent = Agent(
+    name="Math Tutor",
+    instructions="""
+    You are a patient, encouraging math tutor.
+    
+    When teaching:
+    1. Gauge the student's current understanding
+    2. Explain concepts using clear, simple language
+    3. Provide examples to illustrate points
+    4. Ask questions to check understanding
+    5. Give positive reinforcement for progress
+    
+    Adapt your explanations based on the student's responses.
+    """
+)
+
+# Example tutoring session
+print(tutor_agent.start("Can you help me understand algebra?"))
+print(tutor_agent.continue("I'm struggling with equations like 2x + 5 = 13"))
+print(tutor_agent.continue("So I subtract 5 from both sides first?"))
+print(tutor_agent.continue("Then I divide by 2 to get x = 4?"))
+print(tutor_agent.continue("Could you give me another example to practice?"))
+```
+
+## Conversation Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Clear Transitions" icon="arrow-right">
+    Signal when changing topics
+  </Card>
+  <Card title="Manageable Chunks" icon="layer-group">
+    Break complex information into smaller pieces
+  </Card>
+  <Card title="Engagement Checks" icon="hand">
+    Periodically confirm understanding
+  </Card>
+  <Card title="Personality Consistency" icon="masks-theater">
+    Maintain a consistent tone and style
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to build agents that can search and retrieve information from external sources.

docs/course/agents/15-research-agents.mdx

@@ -0,0 +1,216 @@
+---
+title: "Building Research Agents"
+description: "Creating agents that can gather and analyze information"
+icon: "magnifying-glass"
+---
+
+# Building Research Agents
+
+In this lesson, we'll build agents specialized in gathering, analyzing, and summarizing information. Research agents are valuable for quickly obtaining insights on various topics.
+
+## What is a Research Agent?
+
+A research agent is designed to:
+- Find relevant information on specific topics
+- Analyze and synthesize data
+- Present findings in a clear, structured format
+- Answer questions based on gathered information
+
+## Creating a Basic Research Agent
+
+Let's start by building a simple research agent:
+
+```python
+from praisonaiagents import Agent
+
+# Create a basic research agent
+research_agent = Agent(
+    name="ResearchAgent",
+    instructions="""
+    You are a research specialist who finds and summarizes information.
+    
+    When researching topics:
+    1. Focus on finding accurate, current information
+    2. Organize findings in a logical structure
+    3. Identify key points and insights
+    4. Cite sources when available
+    5. Present information in a clear, concise format
+    """
+)
+
+# Use the research agent
+research_results = research_agent.start("Research the impact of renewable energy on reducing carbon emissions")
+print(research_results)
+```
+
+## Adding Web Search Capability
+
+To make our research agent more powerful, let's add web search capability:
+
+```python
+from praisonaiagents import Agent, Tool
+
+# Define a web search function (simulated for this example)
+def web_search(query):
+    """
+    Simulated web search function.
+    In a real application, this would connect to a search API.
+    
+    Args:
+        query (str): The search query
+        
+    Returns:
+        str: Search results
+    """
+    # This is just a simulation - in a real application, you'd call a search API
+    if "renewable energy" in query.lower():
+        return """
+        Search results for 'renewable energy carbon emissions':
+        
+        1. According to the IEA, renewable energy prevented 2.1 billion tonnes of CO2 emissions in 2022.
+        
+        2. Solar and wind power generate electricity with 95-98% lower carbon emissions than coal-based electricity.
+        
+        3. The IPCC reports that renewable energy could deliver more than half of the emission reductions needed by 2030.
+        """
+    else:
+        return f"Search results for '{query}' (simulated data)"
+
+# Create the web search tool
+search_tool = Tool(
+    name="web_search",
+    function=web_search,
+    description="Search the web for current information on a topic"
+)
+
+# Create a research agent with web search capability
+enhanced_research_agent = Agent(
+    name="EnhancedResearchAgent",
+    instructions="""
+    You are a research specialist who finds and summarizes information.
+    
+    When researching topics:
+    1. Use the web_search tool to find current information
+    2. Analyze and synthesize the search results
+    3. Organize findings in a logical structure
+    4. Identify key points and insights
+    5. Present information in a clear, concise format
+    """,
+    tools=[search_tool]
+)
+
+# Use the enhanced research agent
+enhanced_results = enhanced_research_agent.start("What is the impact of renewable energy on carbon emissions?")
+print(enhanced_results)
+```
+
+## Research Agent with Structured Output
+
+For more organized research results, we can instruct the agent to provide structured output:
+
+```python
+structured_research_agent = Agent(
+    name="StructuredResearchAgent",
+    instructions="""
+    You are a research specialist who finds and organizes information.
+    
+    When researching topics:
+    1. Use available tools to find current information
+    2. Analyze and synthesize the findings
+    
+    Always structure your response in this format:
+    
+    ## Summary
+    [Brief 2-3 sentence overview of the topic]
+    
+    ## Key Facts
+    - [Important fact 1]
+    - [Important fact 2]
+    - [Important fact 3]
+    
+    ## Analysis
+    [Deeper analysis of the information, including implications]
+    
+    ## Sources
+    [List of sources if available]
+    """,
+    tools=[search_tool]
+)
+
+# Use the structured research agent
+structured_results = structured_research_agent.start("Research the latest advancements in quantum computing")
+print(structured_results)
+```
+
+## Specialized Research Agents
+
+Different research tasks require different approaches. Here are some specialized research agents:
+
+### Comparison Research Agent
+
+```python
+comparison_agent = Agent(
+    name="ComparisonAgent",
+    instructions="""
+    You are a research specialist who compares different options or topics.
+    
+    When comparing items:
+    1. Identify the key criteria for comparison
+    2. Research each option thoroughly
+    3. Create a clear side-by-side comparison
+    4. Highlight advantages and disadvantages of each option
+    5. Provide a balanced conclusion
+    
+    Present your findings in a structured table format when appropriate.
+    """,
+    tools=[search_tool]
+)
+
+# Use the comparison agent
+comparison_results = comparison_agent.start("Compare solar and wind energy for residential use")
+print(comparison_results)
+```
+
+### Literature Review Agent
+
+```python
+literature_agent = Agent(
+    name="LiteratureAgent",
+    instructions="""
+    You are a research specialist who reviews academic and professional literature.
+    
+    When reviewing literature:
+    1. Identify key papers, articles, and studies on the topic
+    2. Summarize the main findings and methodologies
+    3. Identify trends, agreements, and contradictions
+    4. Evaluate the strength of evidence
+    5. Highlight gaps in current research
+    
+    Structure your review in a clear, academic format.
+    """,
+    tools=[search_tool]
+)
+
+# Use the literature review agent
+literature_results = literature_agent.start("Review recent research on artificial intelligence in healthcare")
+print(literature_results)
+```
+
+## Research Process Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Define Scope" icon="bullseye">
+    Set clear boundaries for the research
+  </Card>
+  <Card title="Verify Information" icon="check-double">
+    Cross-check facts from multiple sources
+  </Card>
+  <Card title="Consider Bias" icon="scale-balanced">
+    Be aware of potential biases in sources
+  </Card>
+  <Card title="Prioritize Relevance" icon="filter">
+    Focus on information most relevant to the query
+  </Card>
+</CardGroup>
+
+In the next lesson, we'll explore how to build agents that can assist with creative tasks like content creation.