Deployed bf88163 with MkDocs version: 1.6.1

.env.example

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

.eslintrc

@@ -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/FUNDING.yml

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

.github/workflows/docker-publish.yml

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

.github/workflows/python-package.yml

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

.github/workflows/python-publish.yml

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

.github/workflows/release.yml

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

.github/workflows/unittest.yml

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

.gitignore

@@ -1,68 +0,0 @@
-.vscode
-crewai
-.cache
-__pycache__
-chroma.sqlite3
-test
-dist
-.env
-assets
-.idea
-.DS_Store
-.pytest_cache
-praisonAI.egg-info
-flagged
-test.yaml
-db
-praisonai_prompt.txt
-watch.sh
-docs.sh
-other
-output
-
-.files
-threads.db
-threads.db-journal
-
-.chainlit
-!praisonai/ui/config/.chainlit
-
-site
-# Ignore Sphinx build directory
-docs/_build/
-docs/source/
-docs/build/
-
-# Ignore Sphinx cache
-docs/_build/doctrees/
-docs/_build/doctrees/*
-docs/_build/doctrees/.doctrees
-
-# Ignore Sphinx logs and local build files
-docs/_build/.buildinfo
-docs/_build/.buildinfo/*
-docs/_build/.buildinfo/.buildinfo
-
-praisonai/doc
-
-# agentops
-trained_agents_data.pkl
-agentops.log
-
-# crewAI
-crewAI
-
-# virtualenv
-.venv
-
-# Credentials
-credentials.json
-
-# node_modules
-node_modules
-
-outputs
-agents/praisonaiagents/build
-agents/praisonaiagents/dist
-agents/praisonaiagents/praisonaiagents.egg-info
-.codegpt

.praisoncontext

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

.praisonignore

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

.praisoninclude

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

.readthedocs.yml

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

Dockerfile

@@ -1,6 +0,0 @@
-FROM python:3.11-slim
-WORKDIR /app
-COPY . .
-RUN pip install flask praisonai==2.0.32 gunicorn markdown
-EXPOSE 8080
-CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

Dockerfile.chat

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

Dockerfile.dev

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

Dockerfile.ui

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

LICENSE

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

README.md

@@ -1,582 +0,0 @@
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="docs/logo/dark.png" />
-    <source media="(prefers-color-scheme: light)" srcset="docs/logo/light.png" />
-    <img alt="PraisonAI Logo" src="docs/logo/light.png" />
-  </picture>
-</p>
-
-<p align="center">
-<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" /></a>
-<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" /></a>
-<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a>
-</p>
-
-<div align="center">
-
-# Praison AI
-
-<a href="https://trendshift.io/repositories/9130" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison%2FPraisonAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-
-</div>
-
-PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration.
-
-<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>
-
-## Key Features
-
-- 🤖 Automated AI Agents Creation
-- 🔄 Self Reflection AI Agents
-- 🧠 Reasoning AI Agents
-- 👁️ Multi Modal AI Agents
-- 🤝 Multi Agent Collaboration
-- ⚡ AI Agent Workflow
-- 🔄 Use CrewAI or AutoGen Framework
-- 💯 100+ LLM Support
-- 💻 Chat with ENTIRE Codebase
-- 🎨 Interactive UIs
-- 📄 YAML-based Configuration
-- 🛠️ Custom Tool Integration
-- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
-- 🖼️ Vision Language Model (VLM) Support
-- 🎙️ Real-time Voice Interaction
-
-## Using No Code
-
-### Auto Mode:
-```bash
-pip install praisonai
-export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-praisonai --auto create a movie script about Robots in Mars
-```
-
-### Initialise Mode:
-```bash
-pip install praisonai
-export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-praisonai --init create a movie script about Robots in Mars
-praisonai
-```
-
-## Using Coding
-
-Light weight package dedicated for coding:
-```bash
-pip install praisonaiagents
-```
-
-```bash
-export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-```
-
-Create app.py file and add the code below:
-```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-# 1. Create agents
-researcher = Agent(
-    name="Researcher",
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI and data science",
-    backstory="""You are an expert at a technology research group, 
-    skilled in identifying trends and analyzing complex data.""",
-    verbose=True,
-    llm="gpt-4o",
-    markdown=True
-)
-writer = Agent(
-    name="Writer",
-    role="Tech Content Strategist",
-    goal="Craft compelling content on tech advancements",
-    backstory="""You are a content strategist known for 
-    making complex tech topics interesting and easy to understand.""",
-    llm="gpt-4o",
-    markdown=True
-)
-
-# 2. Define Tasks
-task1 = Task(
-    name="research_task",
-    description="""Analyze 2024's AI advancements. 
-    Find major trends, new technologies, and their effects.""",
-    expected_output="""A detailed report on 2024 AI advancements""",
-    agent=researcher
-)
-
-task2 = Task(
-    name="writing_task",
-    description="""Create a blog post about major AI advancements using the insights you have.
-    Make it interesting, clear, and suited for tech enthusiasts. 
-    It should be at least 4 paragraphs long.""",
-    expected_output="A blog post of at least 4 paragraphs",
-    agent=writer,
-)
-
-agents = PraisonAIAgents(
-    agents=[researcher, writer],
-    tasks=[task1, task2],
-    verbose=False,
-    process="hierarchical",
-    manager_llm="gpt-4o"
-)
-
-result = agents.start()
-```
-
-Run:
-```bash
-python app.py
-```
-
-## Ollama Integration
-```bash
-export OPENAI_BASE_URL=http://localhost:11434/v1
-```
-
-## Groq Integration
-Replace xxxx with Groq API KEY:
-```bash
-export OPENAI_API_KEY=xxxxxxxxxxx
-export OPENAI_BASE_URL=https://api.groq.com/openai/v1
-```
-
-## Logging
-```bash
-export LOGLEVEL=info
-```
-
-Advanced logging:
-```bash
-export LOGLEVEL=debug
-```
-
-<div align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="docs/images/architecture-dark.png" />
-    <source media="(prefers-color-scheme: light)" srcset="docs/images/architecture-light.png" />
-    <img alt="PraisonAI Architecture" src="docs/images/architecture-light.png" />
-  </picture>
-</div>
-
-## 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
-
-
-
-## TL;DR Multi Agents
-
-```bash
-pip install praisonai
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init create a movie script about dog in moon
-praisonai
-```
-
-## Table of Contents
-
-- [Installation](#installation)
-- [Initialise](#initialise)
-- [Run](#run)
-- [Full Automatic Mode](#full-automatic-mode)
-- [User Interface](#user-interface)
-- [Praison AI Chat](#praison-ai-chat)
-- [Create Custom Tools](#create-custom-tools)
-- [Agents Playbook](#agents-playbook)
-- [Include praisonai package in your project](#include-praisonai-package-in-your-project)
-- [Commands to Install Dev Dependencies](#commands-to-install-dependencies)
-- [Other Models](#other-models)
-- [Contributing](#contributing)
-- [Star History](#star-history)
-
-## Installation Multi Agents
-
-```bash
-pip install praisonai
-```
-
-## Initialise
-
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
-
-Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
-
-Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
-
-```bash
-praisonai --init create a movie script about dog in moon
-```
-
-This will automatically create agents.yaml file in the current directory.
-
-### To initialise with a specific agent framework (Optional):
-
-```bash
-praisonai --framework autogen --init create movie script about cat in mars
-```
-
-## Run
-
-```bash
-praisonai
-```
-
-or
-
-```bash
-python -m praisonai
-```
-
-### Specify the agent framework (Optional):
-
-```bash
-praisonai --framework autogen
-```
-
-### Full Automatic Mode
-
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```
-
-## User Interface
-
-## PraisonAI User Interfaces:
-
-| Interface | Description                                | URL                                                                   |
-| --------- | ------------------------------------------ | --------------------------------------------------------------------- |
-| **UI**    | Multi Agents such as CrewAI or AutoGen     | [https://docs.praisonai.com/ui/ui](https://docs.praison.ai/ui/ui)     |
-| **Chat**  | Chat with 100+ LLMs, single AI Agent       | [https://docs.praisonai.com/ui/chat](https://docs.praison.ai/ui/chat) |
-| **Code**  | Chat with entire Codebase, single AI Agent | [https://docs.praisonai.com/ui/code](https://docs.praison.ai/ui/code) |
-
-```bash
-pip install -U "praisonai[ui]"
-export OPENAI_API_KEY="Enter your API key"
-chainlit create-secret
-export CHAINLIT_AUTH_SECRET=xxxxxxxx
-praisonai ui
-```
-
-or
-
-```
-python -m praisonai ui
-```
-
-## Praison AI Chat
-
-- https://docs.praison.ai/chat/
-
-```bash
-pip install "praisonai[chat]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai chat
-```
-
-### Internet Search
-
-Praison AI Chat and Praison AI Code now includes internet search capabilities using Crawl4AI and Tavily, allowing you to retrieve up-to-date information during your conversations.
-
-### Vision Language Model Support
-
-You can now upload images and ask questions based on them using Vision Language Models. This feature enables visual understanding and analysis within your chat sessions.
-
-## Praison AI Code
-
-```bash
-pip install "praisonai[code]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai code
-```
-
-### Internet Search
-
-Praison AI Code also includes internet search functionality, enabling you to find relevant code snippets and programming information online.
-
-## Create Custom Tools
-
-- https://docs.praison.ai/tools/custom/
-
-## Agents Playbook
-
-### Simple Playbook Example
-
-```yaml
-framework: crewai
-topic: Artificial Intelligence
-roles:
-  screenwriter:
-    backstory: "Skilled in crafting scripts with engaging dialogue about {topic}."
-    goal: Create scripts from concepts.
-    role: Screenwriter
-    tasks:
-      scriptwriting_task:
-        description: "Develop scripts with compelling characters and dialogue about {topic}."
-        expected_output: "Complete script ready for production."
-```
-
-## Use 100+ Models
-
-- https://docs.praison.ai/models/
-
-## Include praisonai package in your project
-
-- https://docs.praison.ai/developers/wrapper
-- https://docs.praison.ai/developers/wrapper-tools/
-
-## Option 1: Using RAW YAML
-
-```python
-from praisonai import PraisonAI
-
-# Example agent_yaml content
-agent_yaml = """
-framework: "crewai"
-topic: "Space Exploration"
-
-roles:
-  astronomer:
-    role: "Space Researcher"
-    goal: "Discover new insights about {topic}"
-    backstory: "You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos."
-    tasks:
-      investigate_exoplanets:
-        description: "Research and compile information about exoplanets discovered in the last decade."
-        expected_output: "A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth."
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)
-```
-
-## Option 2: Using separate agents.yaml file
-
-Note: Please create agents.yaml file before hand.
-
-```python
-from praisonai import PraisonAI
-
-def basic(): # Basic Mode
-    praisonai = PraisonAI(agent_file="agents.yaml")
-    praisonai.run()
-
-if __name__ == "__main__":
-    basic()
-```
-
-## Commands to Install Dependencies:
-
-### Using uv
-```bash
-# Install uv if you haven't already
-pip install uv
-
-# Install from requirements
-uv pip install -r pyproject.toml
-
-# Install with extras
-uv pip install -r pyproject.toml --extra code
-uv pip install -r pyproject.toml --extra "crewai,autogen"
-```
-
-## Contributing
-
-- Fork on GitHub: Use the "Fork" button on the repository page.
-- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
-- Create a branch: `git checkout -b new-feature`
-- Make changes and commit: `git commit -am "Add some feature"`
-- Push to your fork: `git push origin new-feature`
-- Submit a pull request via GitHub's web interface.
-- Await feedback from project maintainers.
-
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
-
-## License
-
-Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
-
-## Video Tutorials
-
-| Topic | Video |
-|-------|--------|
-| Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
-| Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
-| Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
-| Firecrawl Integration | [![Firecrawl](https://img.youtube.com/vi/UoqUDcLcOYo/0.jpg)](https://www.youtube.com/watch?v=UoqUDcLcOYo) |
-| User Interface | [![UI](https://img.youtube.com/vi/tg-ZjNl3OCg/0.jpg)](https://www.youtube.com/watch?v=tg-ZjNl3OCg) |
-| Crawl4AI Integration | [![Crawl4AI](https://img.youtube.com/vi/KAvuVUh0XU8/0.jpg)](https://www.youtube.com/watch?v=KAvuVUh0XU8) |
-| Chat Interface | [![Chat](https://img.youtube.com/vi/sw3uDqn2h1Y/0.jpg)](https://www.youtube.com/watch?v=sw3uDqn2h1Y) |
-| Code Interface | [![Code](https://img.youtube.com/vi/_5jQayO-MQY/0.jpg)](https://www.youtube.com/watch?v=_5jQayO-MQY) |
-| Mem0 Integration | [![Mem0](https://img.youtube.com/vi/KIGSgRxf1cY/0.jpg)](https://www.youtube.com/watch?v=KIGSgRxf1cY) |
-| Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
-| Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
-| Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
-
-## License
-
-Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
-
-## Local Docker Development with Live Reload
-
-To facilitate local development with live reload, you can use Docker. Follow the steps below:
-
-1. **Create a `Dockerfile.dev`**:
-    ```dockerfile
-    FROM python:3.11-slim
-
-    WORKDIR /app
-
-    COPY . .
-
-    RUN pip install flask praisonai==2.0.18 watchdog
-
-    EXPOSE 5555
-
-    ENV FLASK_ENV=development
-
-    CMD ["flask", "run", "--host=0.0.0.0"]
-    ```
-
-2. **Create a `docker-compose.yml`**:
-    ```yaml
-    version: '3.8'
-
-    services:
-      app:
-        build:
-          context: .
-          dockerfile: Dockerfile.dev
-        volumes:
-          - .:/app
-        ports:
-          - "5555:5555"
-        environment:
-          FLASK_ENV: development
-        command: flask run --host=0.0.0.0
-
-      watch:
-        image: alpine:latest
-        volumes:
-          - .:/app
-        command: sh -c "apk add --no-cache inotify-tools && while inotifywait -r -e modify,create,delete /app; do kill -HUP 1; done"
-    ```
-
-3. **Run Docker Compose**:
-    ```bash
-    docker-compose up
-    ```
-
-This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.

_build/html/_sources/source/modules.rst.txt

@@ -0,0 +1,7 @@
+praisonai
+=========
+
+.. toctree::
+   :maxdepth: 4
+
+   praisonai

_build/html/_sources/source/praisonai.inbuilt_tools.rst.txt

@@ -0,0 +1,21 @@
+praisonai.inbuilt\_tools package
+================================
+
+Submodules
+----------
+
+praisonai.inbuilt\_tools.autogen\_tools module
+----------------------------------------------
+
+.. automodule:: praisonai.inbuilt_tools.autogen_tools
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: praisonai.inbuilt_tools
+   :members:
+   :undoc-members:
+   :show-inheritance:

_build/html/_sources/source/praisonai.rst.txt

@@ -0,0 +1,69 @@
+praisonai package
+=================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   praisonai.inbuilt_tools
+
+Submodules
+----------
+
+praisonai.agents\_generator module
+----------------------------------
+
+.. automodule:: praisonai.agents_generator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+praisonai.auto module
+---------------------
+
+.. automodule:: praisonai.auto
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+praisonai.cli module
+--------------------
+
+.. automodule:: praisonai.cli
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+praisonai.deploy module
+-----------------------
+
+.. automodule:: praisonai.deploy
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+praisonai.test module
+---------------------
+
+.. automodule:: praisonai.test
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+praisonai.version module
+------------------------
+
+.. automodule:: praisonai.version
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: praisonai
+   :members:
+   :undoc-members:
+   :show-inheritance:

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+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
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+ *
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+ */
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+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
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+        };
+    };
+    jQuery.browser = {};
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+}

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+    	margin: 0;
+    }
+
+    .footer {
+    	width: auto;
+    }
+
+    .github {
+        display: none;
+    }
+
+
+
+}
+
+
+
+@media screen and (max-width: 875px) {
+
+    body {
+        margin: 0;
+        padding: 20px 30px;
+    }
+
+    div.documentwrapper {
+        float: none;
+        background: #fff;
+    }
+
+    div.sphinxsidebar {
+        display: block;
+        float: none;
+        width: 102.5%;
+        margin: 50px -30px -20px -30px;
+        padding: 10px 20px;
+        background: #333;
+        color: #FFF;
+    }
+
+    div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p,
+    div.sphinxsidebar h3 a {
+        color: #fff;
+    }
+
+    div.sphinxsidebar a {
+        color: #AAA;
+    }
+
+    div.sphinxsidebar p.logo {
+        display: none;
+    }
+
+    div.document {
+        width: 100%;
+        margin: 0;
+    }
+
+    div.footer {
+        display: none;
+    }
+
+    div.bodywrapper {
+        margin: 0;
+    }
+
+    div.body {
+        min-height: 0;
+        padding: 0;
+    }
+
+    .rtd_doc_footer {
+        display: none;
+    }
+
+    .document {
+        width: auto;
+    }
+
+    .footer {
+        width: auto;
+    }
+
+    .footer {
+        width: auto;
+    }
+
+    .github {
+        display: none;
+    }
+}
+
+
+/* misc. */
+
+.revsys-inline {
+    display: none!important;
+}
+
+/* Hide ugly table cell borders in ..bibliography:: directive output */
+table.docutils.citation, table.docutils.citation td, table.docutils.citation th {
+  border: none;
+  /* Below needed in some edge cases; if not applied, bottom shadows appear */
+  -moz-box-shadow: none;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+
+
+/* relbar */
+
+.related {
+    line-height: 30px;
+    width: 100%;
+    font-size: 0.9rem;
+}
+
+.related.top {
+    border-bottom: 1px solid #EEE;
+    margin-bottom: 20px;
+}
+
+.related.bottom {
+    border-top: 1px solid #EEE;
+}
+
+.related ul {
+    padding: 0;
+    margin: 0;
+    list-style: none;
+}
+
+.related li {
+    display: inline;
+}
+
+nav#rellinks {
+    float: right;
+}
+
+nav#rellinks li+li:before {
+    content: "|";
+}
+
+nav#breadcrumbs li+li:before {
+    content: "\00BB";
+}
+
+/* Hide certain items when printing */
+@media print {
+    div.related {
+        display: none;
+    }
+}

_build/html/_static/basic.css

@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: inherit;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}

_build/html/_static/custom.css

@@ -0,0 +1 @@
+/* This file intentionally left blank. */

_build/html/_static/doctools.js

@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);

_build/html/_static/documentation_options.js

@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '0.0.21',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};

_build/html/_static/jquery.js

@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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_build/html/_static/language_data.js

@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+

_build/html/_static/pygments.css

@@ -0,0 +1,84 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #8f5902; font-style: italic } /* Comment */
+.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
+.highlight .g { color: #000000 } /* Generic */
+.highlight .k { color: #004461; font-weight: bold } /* Keyword */
+.highlight .l { color: #000000 } /* Literal */
+.highlight .n { color: #000000 } /* Name */
+.highlight .o { color: #582800 } /* Operator */
+.highlight .x { color: #000000 } /* Other */
+.highlight .p { color: #000000; font-weight: bold } /* Punctuation */
+.highlight .ch { color: #8f5902; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #8f5902; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #8f5902 } /* Comment.Preproc */
+.highlight .cpf { color: #8f5902; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #8f5902; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #8f5902; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #a40000 } /* Generic.Deleted */
+.highlight .ge { color: #000000; font-style: italic } /* Generic.Emph */
+.highlight .ges { color: #000000 } /* Generic.EmphStrong */
+.highlight .gr { color: #ef2929 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #745334 } /* Generic.Prompt */
+.highlight .gs { color: #000000; font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #a40000; font-weight: bold } /* Generic.Traceback */
+.highlight .kc { color: #004461; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #004461; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #004461; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #004461; font-weight: bold } /* Keyword.Pseudo */
+.highlight .kr { color: #004461; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #004461; font-weight: bold } /* Keyword.Type */
+.highlight .ld { color: #000000 } /* Literal.Date */
+.highlight .m { color: #990000 } /* Literal.Number */
+.highlight .s { color: #4e9a06 } /* Literal.String */
+.highlight .na { color: #c4a000 } /* Name.Attribute */
+.highlight .nb { color: #004461 } /* Name.Builtin */
+.highlight .nc { color: #000000 } /* Name.Class */
+.highlight .no { color: #000000 } /* Name.Constant */
+.highlight .nd { color: #888888 } /* Name.Decorator */
+.highlight .ni { color: #ce5c00 } /* Name.Entity */
+.highlight .ne { color: #cc0000; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #000000 } /* Name.Function */
+.highlight .nl { color: #f57900 } /* Name.Label */
+.highlight .nn { color: #000000 } /* Name.Namespace */
+.highlight .nx { color: #000000 } /* Name.Other */
+.highlight .py { color: #000000 } /* Name.Property */
+.highlight .nt { color: #004461; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #000000 } /* Name.Variable */
+.highlight .ow { color: #004461; font-weight: bold } /* Operator.Word */
+.highlight .pm { color: #000000; font-weight: bold } /* Punctuation.Marker */
+.highlight .w { color: #f8f8f8 } /* Text.Whitespace */
+.highlight .mb { color: #990000 } /* Literal.Number.Bin */
+.highlight .mf { color: #990000 } /* Literal.Number.Float */
+.highlight .mh { color: #990000 } /* Literal.Number.Hex */
+.highlight .mi { color: #990000 } /* Literal.Number.Integer */
+.highlight .mo { color: #990000 } /* Literal.Number.Oct */
+.highlight .sa { color: #4e9a06 } /* Literal.String.Affix */
+.highlight .sb { color: #4e9a06 } /* Literal.String.Backtick */
+.highlight .sc { color: #4e9a06 } /* Literal.String.Char */
+.highlight .dl { color: #4e9a06 } /* Literal.String.Delimiter */
+.highlight .sd { color: #8f5902; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4e9a06 } /* Literal.String.Double */
+.highlight .se { color: #4e9a06 } /* Literal.String.Escape */
+.highlight .sh { color: #4e9a06 } /* Literal.String.Heredoc */
+.highlight .si { color: #4e9a06 } /* Literal.String.Interpol */
+.highlight .sx { color: #4e9a06 } /* Literal.String.Other */
+.highlight .sr { color: #4e9a06 } /* Literal.String.Regex */
+.highlight .s1 { color: #4e9a06 } /* Literal.String.Single */
+.highlight .ss { color: #4e9a06 } /* Literal.String.Symbol */
+.highlight .bp { color: #3465a4 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #000000 } /* Name.Function.Magic */
+.highlight .vc { color: #000000 } /* Name.Variable.Class */
+.highlight .vg { color: #000000 } /* Name.Variable.Global */
+.highlight .vi { color: #000000 } /* Name.Variable.Instance */
+.highlight .vm { color: #000000 } /* Name.Variable.Magic */
+.highlight .il { color: #990000 } /* Literal.Number.Integer.Long */

_build/html/_static/searchtools.js

@@ -0,0 +1,619 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);

_build/html/_static/sphinx_highlight.js

@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});

_build/html/genindex.html

@@ -0,0 +1,414 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="./">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Index &#8212; PraisonAI 0.0.21 documentation</title>
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=d1102ebc" />
+    <link rel="stylesheet" type="text/css" href="_static/alabaster.css?v=12dfc556" />
+    <script src="_static/jquery.js?v=5d32c60e"></script>
+    <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+    <script src="_static/documentation_options.js?v=a11416ff"></script>
+    <script src="_static/doctools.js?v=9a2dae69"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <link rel="index" title="Index" href="#" />
+    <link rel="search" title="Search" href="search.html" />
+   
+  <link rel="stylesheet" href="_static/custom.css" type="text/css" />
+  
+
+  
+  
+
+  </head><body>
+  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          
+
+          <div class="body" role="main">
+            
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ 
+</div>
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.agents_generator.AgentsGenerator">AgentsGenerator (class in praisonai.agents_generator)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool">autogen_CodeDocsSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool">autogen_CSVSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool">autogen_DirectoryReadTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool">autogen_DirectorySearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool">autogen_DOCXSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool">autogen_FileReadTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool">autogen_JSONSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool">autogen_MDXSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool">autogen_PDFSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_RagTool">autogen_RagTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool">autogen_ScrapeElementFromWebsiteTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool">autogen_ScrapeWebsiteTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool">autogen_TXTSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool">autogen_WebsiteSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool">autogen_XMLSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool">autogen_YoutubeChannelSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool">autogen_YoutubeVideoSearchTool() (in module praisonai.inbuilt_tools.autogen_tools)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.auto.AutoGenerator">AutoGenerator (class in praisonai.auto)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.RoleDetails.backstory">backstory (praisonai.auto.RoleDetails attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.deploy.CloudDeployer">CloudDeployer (class in praisonai.deploy)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.auto.AutoGenerator.convert_and_save">convert_and_save() (praisonai.auto.AutoGenerator method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.deploy.CloudDeployer.create_api_file">create_api_file() (praisonai.deploy.CloudDeployer method)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.deploy.CloudDeployer.create_dockerfile">create_dockerfile() (praisonai.deploy.CloudDeployer method)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.cli.PraisonAI.create_gradio_interface">create_gradio_interface() (praisonai.cli.PraisonAI method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.TaskDetails.description">description (praisonai.auto.TaskDetails attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.TaskDetails.expected_output">expected_output (praisonai.auto.TaskDetails attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.AutoGenerator.generate">generate() (praisonai.auto.AutoGenerator method)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.test.generate_crew_and_kickoff">generate_crew_and_kickoff() (in module praisonai.test)</a>
+
+      <ul>
+        <li><a href="source/praisonai.html#praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff">(praisonai.agents_generator.AgentsGenerator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.AutoGenerator.get_user_content">get_user_content() (praisonai.auto.AutoGenerator method)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.auto.RoleDetails.goal">goal (praisonai.auto.RoleDetails attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.agents_generator.AgentsGenerator.is_function_or_decorated">is_function_or_decorated() (praisonai.agents_generator.AgentsGenerator method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.agents_generator.AgentsGenerator.load_tools_from_module">load_tools_from_module() (praisonai.agents_generator.AgentsGenerator method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.agents_generator.AgentsGenerator.load_tools_from_package">load_tools_from_package() (praisonai.agents_generator.AgentsGenerator method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.cli.PraisonAI.main">main() (praisonai.cli.PraisonAI method)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.auto.RoleDetails.model_computed_fields">model_computed_fields (praisonai.auto.RoleDetails attribute)</a>
+
+      <ul>
+        <li><a href="source/praisonai.html#praisonai.auto.TaskDetails.model_computed_fields">(praisonai.auto.TaskDetails attribute)</a>
+</li>
+        <li><a href="source/praisonai.html#praisonai.auto.TeamStructure.model_computed_fields">(praisonai.auto.TeamStructure attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/praisonai.html#praisonai.auto.RoleDetails.model_config">model_config (praisonai.auto.RoleDetails attribute)</a>
+
+      <ul>
+        <li><a href="source/praisonai.html#praisonai.auto.TaskDetails.model_config">(praisonai.auto.TaskDetails attribute)</a>
+</li>
+        <li><a href="source/praisonai.html#praisonai.auto.TeamStructure.model_config">(praisonai.auto.TeamStructure attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/praisonai.html#praisonai.auto.RoleDetails.model_fields">model_fields (praisonai.auto.RoleDetails attribute)</a>
+
+      <ul>
+        <li><a href="source/praisonai.html#praisonai.auto.TaskDetails.model_fields">(praisonai.auto.TaskDetails attribute)</a>
+</li>
+        <li><a href="source/praisonai.html#praisonai.auto.TeamStructure.model_fields">(praisonai.auto.TeamStructure attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    module
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai">praisonai</a>
+</li>
+        <li><a href="source/praisonai.html#module-praisonai.agents_generator">praisonai.agents_generator</a>
+</li>
+        <li><a href="source/praisonai.html#module-praisonai.auto">praisonai.auto</a>
+</li>
+        <li><a href="source/praisonai.html#module-praisonai.cli">praisonai.cli</a>
+</li>
+        <li><a href="source/praisonai.html#module-praisonai.deploy">praisonai.deploy</a>
+</li>
+        <li><a href="source/praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools">praisonai.inbuilt_tools</a>
+</li>
+        <li><a href="source/praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools.autogen_tools">praisonai.inbuilt_tools.autogen_tools</a>
+</li>
+        <li><a href="source/praisonai.html#module-praisonai.test">praisonai.test</a>
+</li>
+        <li><a href="source/praisonai.html#module-praisonai.version">praisonai.version</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.cli.PraisonAI.parse_args">parse_args() (praisonai.cli.PraisonAI method)</a>
+</li>
+      <li>
+    praisonai
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai">module</a>
+</li>
+      </ul></li>
+      <li><a href="source/praisonai.html#praisonai.cli.PraisonAI">PraisonAI (class in praisonai.cli)</a>
+</li>
+      <li>
+    praisonai.agents_generator
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai.agents_generator">module</a>
+</li>
+      </ul></li>
+      <li>
+    praisonai.auto
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai.auto">module</a>
+</li>
+      </ul></li>
+      <li>
+    praisonai.cli
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai.cli">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    praisonai.deploy
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai.deploy">module</a>
+</li>
+      </ul></li>
+      <li>
+    praisonai.inbuilt_tools
+
+      <ul>
+        <li><a href="source/praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools">module</a>
+</li>
+      </ul></li>
+      <li>
+    praisonai.inbuilt_tools.autogen_tools
+
+      <ul>
+        <li><a href="source/praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools.autogen_tools">module</a>
+</li>
+      </ul></li>
+      <li>
+    praisonai.test
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai.test">module</a>
+</li>
+      </ul></li>
+      <li>
+    praisonai.version
+
+      <ul>
+        <li><a href="source/praisonai.html#module-praisonai.version">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.RoleDetails.role">role (praisonai.auto.RoleDetails attribute)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.auto.RoleDetails">RoleDetails (class in praisonai.auto)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.TeamStructure.roles">roles (praisonai.auto.TeamStructure attribute)</a>
+</li>
+      <li><a href="source/praisonai.html#praisonai.deploy.CloudDeployer.run_commands">run_commands() (praisonai.deploy.CloudDeployer method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.deploy.CloudDeployer.set_environment_variables">set_environment_variables() (praisonai.deploy.CloudDeployer method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/praisonai.html#praisonai.auto.TaskDetails">TaskDetails (class in praisonai.auto)</a>
+</li>
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_build/html/index.html

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_build/html/py-modindex.html

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+<li class="toctree-l1"><a class="reference internal" href="praisonai.html">praisonai package</a><ul>
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+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html">praisonai.inbuilt_tools package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.inbuilt_tools.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools.autogen_tools">praisonai.inbuilt_tools.autogen_tools module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#module-praisonai.agents_generator">praisonai.agents_generator module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.agents_generator.AgentsGenerator"><code class="docutils literal notranslate"><span class="pre">AgentsGenerator</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff"><code class="docutils literal notranslate"><span class="pre">AgentsGenerator.generate_crew_and_kickoff()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.agents_generator.AgentsGenerator.is_function_or_decorated"><code class="docutils literal notranslate"><span class="pre">AgentsGenerator.is_function_or_decorated()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.agents_generator.AgentsGenerator.load_tools_from_module"><code class="docutils literal notranslate"><span class="pre">AgentsGenerator.load_tools_from_module()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.agents_generator.AgentsGenerator.load_tools_from_package"><code class="docutils literal notranslate"><span class="pre">AgentsGenerator.load_tools_from_package()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#module-praisonai.auto">praisonai.auto module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.auto.AutoGenerator"><code class="docutils literal notranslate"><span class="pre">AutoGenerator</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.AutoGenerator.convert_and_save"><code class="docutils literal notranslate"><span class="pre">AutoGenerator.convert_and_save()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.AutoGenerator.generate"><code class="docutils literal notranslate"><span class="pre">AutoGenerator.generate()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.AutoGenerator.get_user_content"><code class="docutils literal notranslate"><span class="pre">AutoGenerator.get_user_content()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails"><code class="docutils literal notranslate"><span class="pre">RoleDetails</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.backstory"><code class="docutils literal notranslate"><span class="pre">RoleDetails.backstory</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.goal"><code class="docutils literal notranslate"><span class="pre">RoleDetails.goal</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.model_computed_fields"><code class="docutils literal notranslate"><span class="pre">RoleDetails.model_computed_fields</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.model_config"><code class="docutils literal notranslate"><span class="pre">RoleDetails.model_config</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.model_fields"><code class="docutils literal notranslate"><span class="pre">RoleDetails.model_fields</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.role"><code class="docutils literal notranslate"><span class="pre">RoleDetails.role</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.tasks"><code class="docutils literal notranslate"><span class="pre">RoleDetails.tasks</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.RoleDetails.tools"><code class="docutils literal notranslate"><span class="pre">RoleDetails.tools</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.auto.TaskDetails"><code class="docutils literal notranslate"><span class="pre">TaskDetails</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TaskDetails.description"><code class="docutils literal notranslate"><span class="pre">TaskDetails.description</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TaskDetails.expected_output"><code class="docutils literal notranslate"><span class="pre">TaskDetails.expected_output</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TaskDetails.model_computed_fields"><code class="docutils literal notranslate"><span class="pre">TaskDetails.model_computed_fields</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TaskDetails.model_config"><code class="docutils literal notranslate"><span class="pre">TaskDetails.model_config</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TaskDetails.model_fields"><code class="docutils literal notranslate"><span class="pre">TaskDetails.model_fields</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.auto.TeamStructure"><code class="docutils literal notranslate"><span class="pre">TeamStructure</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TeamStructure.model_computed_fields"><code class="docutils literal notranslate"><span class="pre">TeamStructure.model_computed_fields</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TeamStructure.model_config"><code class="docutils literal notranslate"><span class="pre">TeamStructure.model_config</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TeamStructure.model_fields"><code class="docutils literal notranslate"><span class="pre">TeamStructure.model_fields</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.auto.TeamStructure.roles"><code class="docutils literal notranslate"><span class="pre">TeamStructure.roles</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#module-praisonai.cli">praisonai.cli module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.cli.PraisonAI"><code class="docutils literal notranslate"><span class="pre">PraisonAI</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.cli.PraisonAI.create_gradio_interface"><code class="docutils literal notranslate"><span class="pre">PraisonAI.create_gradio_interface()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.cli.PraisonAI.main"><code class="docutils literal notranslate"><span class="pre">PraisonAI.main()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.cli.PraisonAI.parse_args"><code class="docutils literal notranslate"><span class="pre">PraisonAI.parse_args()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#module-praisonai.deploy">praisonai.deploy module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.deploy.CloudDeployer"><code class="docutils literal notranslate"><span class="pre">CloudDeployer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.deploy.CloudDeployer.create_api_file"><code class="docutils literal notranslate"><span class="pre">CloudDeployer.create_api_file()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.deploy.CloudDeployer.create_dockerfile"><code class="docutils literal notranslate"><span class="pre">CloudDeployer.create_dockerfile()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.deploy.CloudDeployer.run_commands"><code class="docutils literal notranslate"><span class="pre">CloudDeployer.run_commands()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="praisonai.html#praisonai.deploy.CloudDeployer.set_environment_variables"><code class="docutils literal notranslate"><span class="pre">CloudDeployer.set_environment_variables()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#module-praisonai.test">praisonai.test module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.html#praisonai.test.generate_crew_and_kickoff"><code class="docutils literal notranslate"><span class="pre">generate_crew_and_kickoff()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#module-praisonai.version">praisonai.version module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.html#module-praisonai">Module contents</a></li>
+</ul>
+</li>
+</ul>
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+            
+  <section id="praisonai-package">
+<h1>praisonai package<a class="headerlink" href="#praisonai-package" title="Link to this heading"></a></h1>
+<section id="subpackages">
+<h2>Subpackages<a class="headerlink" href="#subpackages" title="Link to this heading"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="praisonai.inbuilt_tools.html">praisonai.inbuilt_tools package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.inbuilt_tools.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools.autogen_tools">praisonai.inbuilt_tools.autogen_tools module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_CSVSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_CodeDocsSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_DOCXSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool"><code class="docutils literal notranslate"><span class="pre">autogen_DirectoryReadTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_DirectorySearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool"><code class="docutils literal notranslate"><span class="pre">autogen_FileReadTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_JSONSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_MDXSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_PDFSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_RagTool"><code class="docutils literal notranslate"><span class="pre">autogen_RagTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool"><code class="docutils literal notranslate"><span class="pre">autogen_ScrapeElementFromWebsiteTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool"><code class="docutils literal notranslate"><span class="pre">autogen_ScrapeWebsiteTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_TXTSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_WebsiteSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_XMLSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_YoutubeChannelSearchTool()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="praisonai.inbuilt_tools.html#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool"><code class="docutils literal notranslate"><span class="pre">autogen_YoutubeVideoSearchTool()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="praisonai.inbuilt_tools.html#module-praisonai.inbuilt_tools">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="module-praisonai.agents_generator">
+<span id="praisonai-agents-generator-module"></span><h2>praisonai.agents_generator module<a class="headerlink" href="#module-praisonai.agents_generator" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="praisonai.agents_generator.AgentsGenerator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">praisonai.agents_generator.</span></span><span class="sig-name descname"><span class="pre">AgentsGenerator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">agent_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">framework</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config_list</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.agents_generator.AgentsGenerator" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff">
+<span class="sig-name descname"><span class="pre">generate_crew_and_kickoff</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.agents_generator.AgentsGenerator.generate_crew_and_kickoff" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.agents_generator.AgentsGenerator.is_function_or_decorated">
+<span class="sig-name descname"><span class="pre">is_function_or_decorated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.agents_generator.AgentsGenerator.is_function_or_decorated" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.agents_generator.AgentsGenerator.load_tools_from_module">
+<span class="sig-name descname"><span class="pre">load_tools_from_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_module" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.agents_generator.AgentsGenerator.load_tools_from_package">
+<span class="sig-name descname"><span class="pre">load_tools_from_package</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package_path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.agents_generator.AgentsGenerator.load_tools_from_package" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-praisonai.auto">
+<span id="praisonai-auto-module"></span><h2>praisonai.auto module<a class="headerlink" href="#module-praisonai.auto" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="praisonai.auto.AutoGenerator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">praisonai.auto.</span></span><span class="sig-name descname"><span class="pre">AutoGenerator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">topic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Movie</span> <span class="pre">Story</span> <span class="pre">writing</span> <span class="pre">about</span> <span class="pre">AI'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">agent_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'test.yaml'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">framework</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'crewai'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.auto.AutoGenerator" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.auto.AutoGenerator.convert_and_save">
+<span class="sig-name descname"><span class="pre">convert_and_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">json_data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.auto.AutoGenerator.convert_and_save" title="Link to this definition"></a></dt>
+<dd><p>Converts the provided JSON data into the desired YAML format and saves it to a file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>json_data</strong> (<em>dict</em>) – The JSON data representing the team structure.</p></li>
+<li><p><strong>topic</strong> (<em>str</em><em>, </em><em>optional</em>) – The topic to be inserted into the YAML. Defaults to “Artificial Intelligence”.</p></li>
+<li><p><strong>agent_file</strong> (<em>str</em><em>, </em><em>optional</em>) – The name of the YAML file to save. Defaults to “test.yaml”.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.auto.AutoGenerator.generate">
+<span class="sig-name descname"><span class="pre">generate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.auto.AutoGenerator.generate" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.auto.AutoGenerator.get_user_content">
+<span class="sig-name descname"><span class="pre">get_user_content</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.auto.AutoGenerator.get_user_content" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">praisonai.auto.</span></span><span class="sig-name descname"><span class="pre">RoleDetails</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">goal</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backstory</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tasks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#praisonai.auto.TaskDetails" title="praisonai.auto.TaskDetails"><span class="pre">TaskDetails</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tools</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.auto.RoleDetails" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseModel</span></code></p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.backstory">
+<span class="sig-name descname"><span class="pre">backstory</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.backstory" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.goal">
+<span class="sig-name descname"><span class="pre">goal</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.goal" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.model_computed_fields">
+<span class="sig-name descname"><span class="pre">model_computed_fields</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ComputedFieldInfo</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.model_computed_fields" title="Link to this definition"></a></dt>
+<dd><p>A dictionary of computed field names and their corresponding <cite>ComputedFieldInfo</cite> objects.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.model_config">
+<span class="sig-name descname"><span class="pre">model_config</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">ConfigDict</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.model_config" title="Link to this definition"></a></dt>
+<dd><p>Configuration for the model, should be a dictionary conforming to [<cite>ConfigDict</cite>][pydantic.config.ConfigDict].</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.model_fields">
+<span class="sig-name descname"><span class="pre">model_fields</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">FieldInfo</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'backstory':</span> <span class="pre">FieldInfo(annotation=str,</span> <span class="pre">required=True),</span> <span class="pre">'goal':</span> <span class="pre">FieldInfo(annotation=str,</span> <span class="pre">required=True),</span> <span class="pre">'role':</span> <span class="pre">FieldInfo(annotation=str,</span> <span class="pre">required=True),</span> <span class="pre">'tasks':</span> <span class="pre">FieldInfo(annotation=Dict[str,</span> <span class="pre">TaskDetails],</span> <span class="pre">required=True),</span> <span class="pre">'tools':</span> <span class="pre">FieldInfo(annotation=List[str],</span> <span class="pre">required=True)}</span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.model_fields" title="Link to this definition"></a></dt>
+<dd><p>Metadata about the fields defined on the model,
+mapping of field names to [<cite>FieldInfo</cite>][pydantic.fields.FieldInfo].</p>
+<p>This replaces <cite>Model.__fields__</cite> from Pydantic V1.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.role">
+<span class="sig-name descname"><span class="pre">role</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.role" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.tasks">
+<span class="sig-name descname"><span class="pre">tasks</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#praisonai.auto.TaskDetails" title="praisonai.auto.TaskDetails"><span class="pre">TaskDetails</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.tasks" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.RoleDetails.tools">
+<span class="sig-name descname"><span class="pre">tools</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#praisonai.auto.RoleDetails.tools" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="praisonai.auto.TaskDetails">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">praisonai.auto.</span></span><span class="sig-name descname"><span class="pre">TaskDetails</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.auto.TaskDetails" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseModel</span></code></p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TaskDetails.description">
+<span class="sig-name descname"><span class="pre">description</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#praisonai.auto.TaskDetails.description" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TaskDetails.expected_output">
+<span class="sig-name descname"><span class="pre">expected_output</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><a class="headerlink" href="#praisonai.auto.TaskDetails.expected_output" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TaskDetails.model_computed_fields">
+<span class="sig-name descname"><span class="pre">model_computed_fields</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ComputedFieldInfo</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#praisonai.auto.TaskDetails.model_computed_fields" title="Link to this definition"></a></dt>
+<dd><p>A dictionary of computed field names and their corresponding <cite>ComputedFieldInfo</cite> objects.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TaskDetails.model_config">
+<span class="sig-name descname"><span class="pre">model_config</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">ConfigDict</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#praisonai.auto.TaskDetails.model_config" title="Link to this definition"></a></dt>
+<dd><p>Configuration for the model, should be a dictionary conforming to [<cite>ConfigDict</cite>][pydantic.config.ConfigDict].</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TaskDetails.model_fields">
+<span class="sig-name descname"><span class="pre">model_fields</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">FieldInfo</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'description':</span> <span class="pre">FieldInfo(annotation=str,</span> <span class="pre">required=True),</span> <span class="pre">'expected_output':</span> <span class="pre">FieldInfo(annotation=str,</span> <span class="pre">required=True)}</span></em><a class="headerlink" href="#praisonai.auto.TaskDetails.model_fields" title="Link to this definition"></a></dt>
+<dd><p>Metadata about the fields defined on the model,
+mapping of field names to [<cite>FieldInfo</cite>][pydantic.fields.FieldInfo].</p>
+<p>This replaces <cite>Model.__fields__</cite> from Pydantic V1.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="praisonai.auto.TeamStructure">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">praisonai.auto.</span></span><span class="sig-name descname"><span class="pre">TeamStructure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roles</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#praisonai.auto.RoleDetails" title="praisonai.auto.RoleDetails"><span class="pre">RoleDetails</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.auto.TeamStructure" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseModel</span></code></p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TeamStructure.model_computed_fields">
+<span class="sig-name descname"><span class="pre">model_computed_fields</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ComputedFieldInfo</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#praisonai.auto.TeamStructure.model_computed_fields" title="Link to this definition"></a></dt>
+<dd><p>A dictionary of computed field names and their corresponding <cite>ComputedFieldInfo</cite> objects.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TeamStructure.model_config">
+<span class="sig-name descname"><span class="pre">model_config</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">ConfigDict</span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{}</span></em><a class="headerlink" href="#praisonai.auto.TeamStructure.model_config" title="Link to this definition"></a></dt>
+<dd><p>Configuration for the model, should be a dictionary conforming to [<cite>ConfigDict</cite>][pydantic.config.ConfigDict].</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TeamStructure.model_fields">
+<span class="sig-name descname"><span class="pre">model_fields</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">ClassVar</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">FieldInfo</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'roles':</span> <span class="pre">FieldInfo(annotation=Dict[str,</span> <span class="pre">RoleDetails],</span> <span class="pre">required=True)}</span></em><a class="headerlink" href="#praisonai.auto.TeamStructure.model_fields" title="Link to this definition"></a></dt>
+<dd><p>Metadata about the fields defined on the model,
+mapping of field names to [<cite>FieldInfo</cite>][pydantic.fields.FieldInfo].</p>
+<p>This replaces <cite>Model.__fields__</cite> from Pydantic V1.</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="praisonai.auto.TeamStructure.roles">
+<span class="sig-name descname"><span class="pre">roles</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#praisonai.auto.RoleDetails" title="praisonai.auto.RoleDetails"><span class="pre">RoleDetails</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#praisonai.auto.TeamStructure.roles" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-praisonai.cli">
+<span id="praisonai-cli-module"></span><h2>praisonai.cli module<a class="headerlink" href="#module-praisonai.cli" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="praisonai.cli.PraisonAI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">praisonai.cli.</span></span><span class="sig-name descname"><span class="pre">PraisonAI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">agent_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'agents.yaml'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">framework</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'crewai'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.cli.PraisonAI" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.cli.PraisonAI.create_gradio_interface">
+<span class="sig-name descname"><span class="pre">create_gradio_interface</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.cli.PraisonAI.create_gradio_interface" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.cli.PraisonAI.main">
+<span class="sig-name descname"><span class="pre">main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.cli.PraisonAI.main" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.cli.PraisonAI.parse_args">
+<span class="sig-name descname"><span class="pre">parse_args</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.cli.PraisonAI.parse_args" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-praisonai.deploy">
+<span id="praisonai-deploy-module"></span><h2>praisonai.deploy module<a class="headerlink" href="#module-praisonai.deploy" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="praisonai.deploy.CloudDeployer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">praisonai.deploy.</span></span><span class="sig-name descname"><span class="pre">CloudDeployer</span></span><a class="headerlink" href="#praisonai.deploy.CloudDeployer" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.deploy.CloudDeployer.create_api_file">
+<span class="sig-name descname"><span class="pre">create_api_file</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.deploy.CloudDeployer.create_api_file" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.deploy.CloudDeployer.create_dockerfile">
+<span class="sig-name descname"><span class="pre">create_dockerfile</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.deploy.CloudDeployer.create_dockerfile" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.deploy.CloudDeployer.run_commands">
+<span class="sig-name descname"><span class="pre">run_commands</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.deploy.CloudDeployer.run_commands" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="praisonai.deploy.CloudDeployer.set_environment_variables">
+<span class="sig-name descname"><span class="pre">set_environment_variables</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.deploy.CloudDeployer.set_environment_variables" title="Link to this definition"></a></dt>
+<dd><p>Sets environment variables with fallback to .env values or defaults.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-praisonai.test">
+<span id="praisonai-test-module"></span><h2>praisonai.test module<a class="headerlink" href="#module-praisonai.test" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.test.generate_crew_and_kickoff">
+<span class="sig-prename descclassname"><span class="pre">praisonai.test.</span></span><span class="sig-name descname"><span class="pre">generate_crew_and_kickoff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">agent_file</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.test.generate_crew_and_kickoff" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="module-praisonai.version">
+<span id="praisonai-version-module"></span><h2>praisonai.version module<a class="headerlink" href="#module-praisonai.version" title="Link to this heading"></a></h2>
+</section>
+<section id="module-praisonai">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-praisonai" title="Link to this heading"></a></h2>
+</section>
+</section>
+
+
+          </div>
+          
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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_build/html/source/praisonai.inbuilt_tools.html

@@ -0,0 +1,196 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+        <div class="bodywrapper">
+          
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+            
+  <section id="praisonai-inbuilt-tools-package">
+<h1>praisonai.inbuilt_tools package<a class="headerlink" href="#praisonai-inbuilt-tools-package" title="Link to this heading"></a></h1>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="module-praisonai.inbuilt_tools.autogen_tools">
+<span id="praisonai-inbuilt-tools-autogen-tools-module"></span><h2>praisonai.inbuilt_tools.autogen_tools module<a class="headerlink" href="#module-praisonai.inbuilt_tools.autogen_tools" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_CSVSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CSVSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_CodeDocsSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_CodeDocsSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_DOCXSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DOCXSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_DirectoryReadTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectoryReadTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_DirectorySearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_DirectorySearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_FileReadTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_FileReadTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_JSONSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_JSONSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_MDXSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_MDXSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_PDFSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_PDFSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_RagTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_RagTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_RagTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_ScrapeElementFromWebsiteTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeElementFromWebsiteTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_ScrapeWebsiteTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_ScrapeWebsiteTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_TXTSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_TXTSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_WebsiteSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_WebsiteSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_XMLSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_XMLSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_YoutubeChannelSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeChannelSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool">
+<span class="sig-prename descclassname"><span class="pre">praisonai.inbuilt_tools.autogen_tools.</span></span><span class="sig-name descname"><span class="pre">autogen_YoutubeVideoSearchTool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">assistant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">user_proxy</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#praisonai.inbuilt_tools.autogen_tools.autogen_YoutubeVideoSearchTool" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="module-praisonai.inbuilt_tools">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-praisonai.inbuilt_tools" title="Link to this heading"></a></h2>
+</section>
+</section>
+
+
+          </div>
+          
+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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agents-advanced.yaml

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

agents.yaml

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

agents/.gitignore

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

agents/async_example.py

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

agents/async_example_full.py

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

agents/async_example_full_multigroups.py

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

agents/code_agents_example.py

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

agents/example-sequential.py

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

agents/example2.py

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

agents/example_callback.py

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

agents/mini_agents_example.py

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

agents/multimodal.py

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

agents/praisonaiagents/__init__.py

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

agents/praisonaiagents/agent/__init__.py

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

agents/praisonaiagents/agent/agent.py

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

agents/praisonaiagents/agents/__init__.py

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

agents/praisonaiagents/agents/agents.py

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

agents/praisonaiagents/main.py

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

agents/praisonaiagents/process/__init__.py

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

agents/praisonaiagents/process/process.py

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

agents/praisonaiagents/task/__init__.py

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

agents/praisonaiagents/task/task.py

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

agents/praisonaiagents/tools/__init__.py

@@ -1,4 +0,0 @@
-"""Tools package for PraisonAI Agents"""
-from .tools import Tools
-
-__all__ = ['Tools'] 

agents/praisonaiagents/tools/tools.py

@@ -1,40 +0,0 @@
-"""Tools module for PraisonAI Agents"""
-from typing import List, Dict
-import logging
-import importlib
-
-class Tools:
-    @staticmethod
-    def internet_search(query: str) -> List[Dict]:
-        """
-        Perform a search using DuckDuckGo.
-
-        Args:
-            query (str): The search query.
-
-        Returns:
-            list: A list of search result titles, URLs, and snippets.
-        """
-        # Check if duckduckgo_search is installed
-        if importlib.util.find_spec("duckduckgo_search") is None:
-            error_msg = "DuckDuckGo search is not available. Please install duckduckgo_search package using: pip install duckduckgo_search"
-            logging.error(error_msg)
-            return [{"error": error_msg}]
-
-        try:
-            # Import only when needed
-            from duckduckgo_search import DDGS
-            results = []
-            ddgs = DDGS()
-            for result in ddgs.text(keywords=query, max_results=5):
-                results.append({
-                    "title": result.get("title", ""),
-                    "url": result.get("href", ""),
-                    "snippet": result.get("body", "")
-                })
-            return results
-
-        except Exception as e:
-            error_msg = f"Error during DuckDuckGo search: {e}"
-            logging.error(error_msg)
-            return [{"error": error_msg}] 

agents/pyproject.toml

@@ -1,16 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "praisonaiagents"
-version = "0.0.23"
-description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
-authors = [
-    { name="Mervin Praison" }
-]
-dependencies = [
-    "pydantic",
-    "rich",
-    "openai"
-] 

agents/structured_agents_example.py

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

agents/test.py

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

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agents/workflow_example_basic.py

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

agents/workflow_example_detailed.py

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

api-reference/index.mdx

@@ -1,7 +1,6 @@
 ---
 title: 'API Reference'
 description: 'PraisonAI API documentation with framework-specific details'
-icon: "book-open"
 ---
 
 # API Reference

api.py

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

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==2.0.32 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.13 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;)