Merge pull request #266 from MervinPraison/develop

setting openai_model_name env variable to set

.github/workflows/release.yml

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

.gitignore

@@ -64,4 +64,5 @@ node_modules
 outputs
 agents/praisonaiagents/build
 agents/praisonaiagents/dist
-agents/praisonaiagents/praisonaiagents.egg-info
+agents/praisonaiagents/praisonaiagents.egg-info
+.codegpt

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.18 gunicorn markdown
+RUN pip install flask praisonai==2.0.30 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

Dockerfile.chat

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

Dockerfile.dev

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

Dockerfile.ui

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

README.md

@@ -22,17 +22,30 @@
 
 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
+- 🎨 Interactive UIs
 - 📄 YAML-based Configuration
 - 🛠️ Custom Tool Integration
 - 🔍 Internet Search Capability (using Crawl4AI and Tavily)
-- 👁️ Vision Language Model (VLM) Support
+- 🖼️ Vision Language Model (VLM) Support
 - 🎙️ Real-time Voice Interaction
 
 ## Using No Code
@@ -515,3 +528,55 @@ Praison AI is an open-sourced software licensed under the **[MIT license](https:
 ## License
 
 Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
+
+## Local Docker Development with Live Reload
+
+To facilitate local development with live reload, you can use Docker. Follow the steps below:
+
+1. **Create a `Dockerfile.dev`**:
+    ```dockerfile
+    FROM python:3.11-slim
+
+    WORKDIR /app
+
+    COPY . .
+
+    RUN pip install flask praisonai==2.0.18 watchdog
+
+    EXPOSE 5555
+
+    ENV FLASK_ENV=development
+
+    CMD ["flask", "run", "--host=0.0.0.0"]
+    ```
+
+2. **Create a `docker-compose.yml`**:
+    ```yaml
+    version: '3.8'
+
+    services:
+      app:
+        build:
+          context: .
+          dockerfile: Dockerfile.dev
+        volumes:
+          - .:/app
+        ports:
+          - "5555:5555"
+        environment:
+          FLASK_ENV: development
+        command: flask run --host=0.0.0.0
+
+      watch:
+        image: alpine:latest
+        volumes:
+          - .:/app
+        command: sh -c "apk add --no-cache inotify-tools && while inotifywait -r -e modify,create,delete /app; do kill -HUP 1; done"
+    ```
+
+3. **Run Docker Compose**:
+    ```bash
+    docker-compose up
+    ```
+
+This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.

agents.yaml

@@ -7,12 +7,8 @@ roles:
       information.
     goal: Gather information about Mars and cats
     role: Researcher
-    llm:
-      model: "gpt-4o"
-    reflect_llm:
-      model: "gpt-4o"
-    min_reflect: 2
-    max_reflect: 4
+    min_reflect: 1
+    max_reflect: 2
     tasks:
       research_task:
         description: Research about Mars, its environment, and the feasibility of
@@ -25,8 +21,6 @@ roles:
       stories.
     goal: Create a story concept for a movie about a cat in Mars
     role: Narrative Designer
-    llm:
-      model: "gpt-4o"
     tasks:
       story_concept_development:
         description: Use the research findings to craft a unique story concept for
@@ -39,8 +33,6 @@ roles:
       scripts.
     goal: Write a movie script about a cat in Mars
     role: Scriptwriter
-    llm:
-      model: "gpt-4o"
     tasks:
       scriptwriting_task:
         description: Turn the story concept into a production-ready movie script,
@@ -48,4 +40,4 @@ roles:
         expected_output: Final movie script with dialogue and scene details.
     tools:
     - ''
-dependencies: []
+dependencies: []

agents/async_example.py

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

agents/async_example_full.py

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

agents/async_example_full_multigroups.py

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

agents/example2.py

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

agents/example_callback.py

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

agents/mini_agents_example.py

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

agents/praisonaiagents/__init__.py

@@ -5,6 +5,7 @@ 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,
@@ -16,11 +17,19 @@ from .main import (
     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',
@@ -32,4 +41,7 @@ __all__ = [
     'display_generating',
     'clean_triple_backticks',
     'error_logs',
+    'register_display_callback',
+    'sync_display_callbacks',
+    'async_display_callbacks',
 ] 

agents/praisonaiagents/agent/agent.py

@@ -1,9 +1,12 @@
-import logging
-import json
+import os
 import time
-from typing import List, Optional, Any, Dict, Union, Literal
+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,
@@ -16,6 +19,9 @@ from ..main import (
     error_logs
 )
 
+if TYPE_CHECKING:
+    from ..task.task import Task
+
 class Agent:
     def _generate_tool_definition(self, function_name):
         """
@@ -129,11 +135,12 @@ class Agent:
 
     def __init__(
         self,
-        name: str,
-        role: str,
-        goal: str,
-        backstory: str,
-        llm: Optional[Union[str, Any]] = "gpt-4o",
+        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,
@@ -155,16 +162,35 @@ class Agent:
         knowledge_sources: Optional[List[Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
-        self_reflect: bool = True,
+        self_reflect: Optional[bool] = None,
         max_reflect: int = 3,
         min_reflect: int = 1,
         reflect_llm: Optional[str] = None
     ):
-        self.name = name
-        self.role = role
-        self.goal = goal
-        self.backstory = backstory
-        self.llm = llm
+        # 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
@@ -187,11 +213,32 @@ class Agent:
         self.use_system_prompt = use_system_prompt
         self.chat_history = []
         self.markdown = markdown
-        self.self_reflect = self_reflect
         self.max_reflect = max_reflect
         self.min_reflect = min_reflect
-        self.reflect_llm = reflect_llm
+        # 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):
         """
@@ -536,4 +583,143 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             cleaned = cleaned[len("```"):].strip()
         if cleaned.endswith("```"):
             cleaned = cleaned[:-3].strip()
-        return cleaned 
+        return cleaned 
+
+    async def achat(self, prompt, temperature=0.2, tools=None, output_json=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()}"
+
+            # 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 else "")}
+                ]
+            else:
+                # For multimodal prompts
+                messages = [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt}
+                ]
+                if output_json:
+                    # 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:
+                response = await async_client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    response_format={"type": "json_object"}
+                )
+                result = response.choices[0].message.content
+                # Clean and parse the JSON response
+                cleaned_json = self.clean_json_output(result)
+                try:
+                    parsed = json.loads(cleaned_json)
+                    return output_json(**parsed)
+                except Exception as e:
+                    display_error(f"Error parsing JSON response: {e}")
+                    return None
+            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/agents.py

@@ -10,9 +10,8 @@ from rich.console import Console
 from ..main import display_error, TaskOutput, error_logs, client
 from ..agent.agent import Agent
 from ..task.task import Task
-
-class LoopItems(BaseModel):
-    items: List[Any]
+from ..process.process import Process, LoopItems
+import asyncio
 
 def encode_file_to_base64(file_path: str) -> str:
     """Base64-encode a file."""
@@ -42,7 +41,10 @@ def process_video(video_path: str, seconds_per_frame=2):
     return base64_frames
 
 class PraisonAIAgents:
-    def __init__(self, agents, tasks, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
+    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:
@@ -52,12 +54,41 @@ class PraisonAIAgents:
         self.verbose = verbose
         self.max_retries = max_retries
         self.process = process
-        if not manager_llm:
-            logging.debug("No manager_llm provided. Using OPENAI_MODEL_NAME environment variable or defaulting to 'gpt-4o'")
-        self.manager_llm = manager_llm if manager_llm else os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        
+        # 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):
@@ -84,7 +115,8 @@ class PraisonAIAgents:
             return True
         return len(agent_output.strip()) > 0
 
-    def execute_task(self, task_id):
+    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
@@ -119,9 +151,219 @@ Expected Output: {task.expected_output}.
                 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}
-            """
+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
+            )
+        else:
+            agent_output = await executor_agent.achat(task_prompt, tools=task.tools)
+
+        if agent_output:
+            task_output = TaskOutput(
+                description=task.description,
+                summary=task.description[:10],
+                raw=agent_output,
+                agent=executor_agent.name,
+                output_format="RAW"
+            )
+
+            if task.output_json:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    task_output.json_dict = parsed
+                    task_output.output_format = "JSON"
+                except:
+                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+
+            if task.output_pydantic:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    pyd_obj = task.output_pydantic(**parsed)
+                    task_output.pydantic = pyd_obj
+                    task_output.output_format = "Pydantic"
+                except:
+                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+
+            task.result = task_output
+            return task_output
+        else:
+            task.status = "failed"
+            return None
+
+    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:
@@ -204,19 +446,8 @@ Expected Output: {task.expected_output}.
             task.status = "failed"
             return None
 
-    def save_output_to_file(self, task, task_output):
-        if task.output_file:
-            try:
-                if task.create_directory:
-                    os.makedirs(os.path.dirname(task.output_file), exist_ok=True)
-                with open(task.output_file, "w") as f:
-                    f.write(str(task_output))
-                if self.verbose >= 1:
-                    logging.info(f"Task output saved to {task.output_file}")
-            except Exception as e:
-                display_error(f"Error saving task output to file: {e}")
-
     def run_task(self, task_id):
+        """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
@@ -255,255 +486,25 @@ Expected Output: {task.expected_output}.
             logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
 
     def run_all_tasks(self):
-        """Execute tasks based on execution mode"""
+        """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":
-            # 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
+            for task_id in process.workflow():
                 self.run_task(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
-
         elif self.process == "sequential":
-            # Keep original sequential execution
-            for task_id in self.tasks:
-                if self.tasks[task_id].status != "completed":
-                    self.run_task(task_id)
-
+            for task_id in process.sequential():
+                self.run_task(task_id)
         elif self.process == "hierarchical":
-            # Keep original hierarchical execution
-            logging.debug(f"Starting hierarchical task execution with {len(self.tasks)} tasks")
-            manager_agent = Agent(
-                name="Manager",
-                role="Project manager",
-                goal="Manage the entire flow of tasks and delegate them to the right agent",
-                backstory="Expert project manager to coordinate tasks among agents",
-                llm=self.manager_llm,
-                verbose=self.verbose,
-                markdown=True,
-                self_reflect=False
-            )
-
-            class ManagerInstructions(BaseModel):
-                task_id: int
-                agent_name: str
-                action: str
-
-            manager_task = Task(
-                name="manager_task",
-                description="Decide the order of tasks and which agent executes them",
-                expected_output="All tasks completed successfully",
-                agent=manager_agent
-            )
-            manager_task_id = self.add_task(manager_task)
-            logging.info(f"Created manager task with ID {manager_task_id}")
-
-            completed_count = 0
-            total_tasks = len(self.tasks) - 1
-            logging.info(f"Need to complete {total_tasks} tasks (excluding manager task)")
-
-            while completed_count < total_tasks:
-                tasks_summary = []
-                for tid, tk in self.tasks.items():
-                    if tk.name == "manager_task":
-                        continue
-                    task_info = {
-                        "task_id": tid,
-                        "name": tk.name,
-                        "description": tk.description,
-                        "status": tk.status if tk.status else "not started",
-                        "agent": tk.agent.name if tk.agent else "No agent"
-                    }
-                    tasks_summary.append(task_info)
-                    logging.info(f"Task {tid} status: {task_info}")
-
-                manager_prompt = f"""
-Here is the current status of all tasks except yours (manager_task):
-{tasks_summary}
-
-Provide a JSON with the structure:
-{{
-   "task_id": <int>,
-   "agent_name": "<string>",
-   "action": "<execute or stop>"
-}}
-"""
-
-                try:
-                    logging.info("Requesting manager instructions...")
-                    manager_response = client.beta.chat.completions.parse(
-                        model=self.manager_llm,
-                        messages=[
-                            {"role": "system", "content": manager_task.description},
-                            {"role": "user", "content": manager_prompt}
-                        ],
-                        temperature=0.7,
-                        response_format=ManagerInstructions
-                    )
-                    parsed_instructions = manager_response.choices[0].message.parsed
-                    logging.info(f"Manager instructions: {parsed_instructions}")
-                except Exception as e:
-                    display_error(f"Manager parse error: {e}")
-                    logging.error(f"Manager parse error: {str(e)}", exc_info=True)
-                    break
-
-                selected_task_id = parsed_instructions.task_id
-                selected_agent_name = parsed_instructions.agent_name
-                action = parsed_instructions.action
-
-                logging.info(f"Manager selected task_id={selected_task_id}, agent={selected_agent_name}, action={action}")
-
-                if action.lower() == "stop":
-                    logging.info("Manager decided to stop task execution")
-                    break
-
-                if selected_task_id not in self.tasks:
-                    error_msg = f"Manager selected invalid task id {selected_task_id}"
-                    display_error(error_msg)
-                    logging.error(error_msg)
-                    break
-
-                original_agent = self.tasks[selected_task_id].agent.name if self.tasks[selected_task_id].agent else "None"
-                for a in self.agents:
-                    if a.name == selected_agent_name:
-                        self.tasks[selected_task_id].agent = a
-                        logging.info(f"Changed agent for task {selected_task_id} from {original_agent} to {selected_agent_name}")
-                        break
-
-                if self.tasks[selected_task_id].status != "completed":
-                    logging.info(f"Starting execution of task {selected_task_id}")
-                    self.run_task(selected_task_id)
-                    logging.info(f"Finished execution of task {selected_task_id}, status: {self.tasks[selected_task_id].status}")
-
-                if self.tasks[selected_task_id].status == "completed":
-                    completed_count += 1
-                    logging.info(f"Task {selected_task_id} completed. Total completed: {completed_count}/{total_tasks}")
-
-            self.tasks[manager_task.id].status = "completed"
-            if self.verbose >= 1:
-                logging.info("All tasks completed under manager supervision.")
-            logging.info("Hierarchical task execution finished")
+            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:

agents/praisonaiagents/main.py

@@ -12,6 +12,7 @@ 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()
 
@@ -25,6 +26,50 @@ logging.basicConfig(
 # 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():
@@ -46,21 +91,30 @@ def _clean_display_content(content: str, max_length: int = 20000) -> str:
     return content.strip()
 
 def display_interaction(message, response, markdown=True, generation_time=None, console=None):
-    """Display the interaction between user and assistant."""
+    """Synchronous version of display_interaction."""
     if console is None:
         console = Console()
-    if generation_time:
-        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
-
-    # Handle multimodal content (list)
+    
     if isinstance(message, list):
-        # Extract just the text content from the multimodal message
         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"))
@@ -74,6 +128,11 @@ def display_self_reflection(message: str, console=None):
     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):
@@ -82,6 +141,11 @@ def display_instruction(message: str, console=None):
     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):
@@ -90,6 +154,11 @@ def display_tool_call(message: str, console=None):
     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):
@@ -98,19 +167,138 @@ def display_error(message: str, console=None):
     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"))
-    # Store errors
     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")  # Return empty panel when no content
+        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:

agents/praisonaiagents/process/__init__.py

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

agents/praisonaiagents/process/process.py

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

agents/praisonaiagents/task/task.py

@@ -1,5 +1,6 @@
 import logging
-from typing import List, Optional, Dict, Any, Type, Callable, Union
+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
@@ -19,7 +20,7 @@ class Task:
         output_file: Optional[str] = None,
         output_json: Optional[Type[BaseModel]] = None,
         output_pydantic: Optional[Type[BaseModel]] = None,
-        callback: Optional[Any] = 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,
@@ -61,4 +62,13 @@ class Task:
         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}')" 
+        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

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

agents/praisonaiagents/tools/tools.py

@@ -0,0 +1,40 @@
+"""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

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.14"
+version = "0.0.22"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/uv.lock

@@ -186,7 +186,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.14"
+version = "0.0.22"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

agents/workflow_example_basic.py

@@ -1,8 +1,6 @@
 from praisonaiagents import Agent, Task, PraisonAIAgents
-import json
 from typing import List, Dict
-from pydantic import BaseModel
-from duckduckgo_search import DDGS  # Install with pip if not already available
+from duckduckgo_search import DDGS
 
 # 1. Tool
 def internet_search_tool(query: str) -> List[Dict]:

chainlit.md

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

docker-compose.yml

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

docs/api-reference/index.mdx

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

docs/api.mdx

@@ -1,6 +1,7 @@
 ---
 title: "API Reference"
 description: "Complete API reference for PraisonAI, including core modules, installation options, and framework-specific features"
+icon: "code"
 ---
 
 ## Core Modules

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.18 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.30 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

docs/api/praisonaiagents/agent/agent.mdx

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

docs/api/praisonaiagents/agents/agents.mdx

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

docs/api/praisonaiagents/index.mdx

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

docs/api/praisonaiagents/process/process.mdx

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

docs/api/praisonaiagents/task/task.mdx

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

docs/auto.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Auto Generation Mode"
 description: "Guide to PraisonAI's automatic agent generation feature, supporting both CrewAI and AutoGen frameworks for automated task execution"
+icon: "robot"
 ---
 
 PraisonAI supports automatic agent generation with PraisonAI Agents, CrewAI and AutoGen frameworks.

docs/call.mdx

@@ -1,6 +1,7 @@
 ---
 title: "PraisonAI Call"
 description: "Guide to PraisonAI's voice-based interaction feature enabling AI customer service through phone calls, including setup and tool integration"
+icon: "phone"
 ---
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/m1cwrUG2iAk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

docs/code/installation.mdx

@@ -0,0 +1,69 @@
+---
+title: Installation
+description: Set up PraisonAI in your development environment
+icon: download
+---
+
+<Note>
+  **Requirements**
+  
+  - Python 3.10 or higher
+  - pip package manager
+  - Virtual environment (recommended)
+</Note>
+
+# Installing PraisonAI
+
+Follow these steps to set up PraisonAI in your development environment.
+
+<Steps>
+  <Step title="Create Virtual Environment (Optional)">
+    First, create and activate a virtual environment:
+    <CodeGroup>
+    ```bash Mac/Linux
+    python -m venv praisonai-env
+    source praisonai-env/bin/activate
+    ```
+
+    ```bash Windows
+    python -m venv praisonai-env
+    .\praisonai-env\Scripts\activate
+    ```
+    </CodeGroup>
+  </Step>
+
+  <Step title="Install PraisonAI">
+    Install the core package and tools:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    You can also use other LLM providers like Anthropic, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+</Steps>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Quick Start Guide"
+    icon="bolt"
+    href="/code/quickstart"
+  >
+    Build your first AI agent
+  </Card>
+  <Card
+    title="API Reference"
+    icon="code"
+    href="/api/praisonaiagents/index"
+  >
+    Explore the API documentation
+  </Card>
+</CardGroup> 

docs/code/introduction.mdx

@@ -0,0 +1,188 @@
+---
+title: "Introduction"
+description: "Welcome to PraisonAI - The Next Generation AI Agent Framework"
+icon: "book-open"
+---
+
+<Note>
+  PraisonAI is a powerful framework for building and deploying AI agents that can understand, reason, and execute complex tasks autonomously.
+</Note>
+
+# What is PraisonAI?
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Process fill:#5C80BC,stroke:#7C90A0,color:#fff
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[" "]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+        
+        style Task1 fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AgentIcon1 fill:#5C80BC,stroke:#7C90A0,color:#fff
+        style Tools1 fill:#2D3047,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Agent2[" "]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+        
+        style Task2 fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AgentIcon2 fill:#5C80BC,stroke:#7C90A0,color:#fff
+        style Tools2 fill:#2D3047,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Style for agent containers
+    style Agent1 fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Agent2 fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+
+## Core Components
+
+<CardGroup cols={2}>
+  <Card title="Agents" icon="user-robot">
+    Autonomous AI entities that can understand context and execute tasks
+  </Card>
+  <Card title="Tasks" icon="list-check">
+    Units of work that agents can execute independently or collaboratively
+  </Card>
+  <Card title="Process" icon="diagram-project">
+    Workflow management for coordinating agent activities
+  </Card>
+  <Card title="Tools" icon="toolbox">
+    Extensible capabilities that agents can leverage
+  </Card>
+</CardGroup>
+
+## Key Features
+
+<Steps>
+  <Step title="Autonomous Agents">
+    Create intelligent agents that can:
+    - Understand natural language instructions
+    - Make decisions based on context
+    - Execute complex tasks autonomously
+  </Step>
+  <Step title="Flexible Architecture">
+    Build with:
+    - Modular components
+    - Extensible tools
+    - Customizable workflows
+  </Step>
+  <Step title="Advanced Capabilities">
+    Leverage:
+    - Multi-agent collaboration
+    - Memory and context management
+    - Tool integration
+  </Step>
+</Steps>
+
+## Use Cases
+
+<CardGroup cols={2}>
+  <Card title="Customer Service" icon="headset">
+    Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.
+  </Card>
+  <Card title="Data Analysis" icon="chart-line">
+    Create agents that can process, analyze, and derive insights from complex datasets.
+  </Card>
+  <Card title="Content Creation" icon="pen-nib">
+    Deploy agents that can generate, edit, and optimize content across various formats.
+  </Card>
+  <Card title="Process Automation" icon="gears">
+    Automate complex workflows with intelligent agents that can coordinate and execute tasks.
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+<Warning>
+  Before proceeding, ensure you have Python 3.9+ installed on your system.
+</Warning>
+
+### Quick Setup
+
+<CodeGroup>
+  ```bash pip
+  pip install praisonai
+  ```
+</CodeGroup>
+
+### Basic Example
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create an agent
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI",
+    backstory="You are an expert at a technology research group",
+    verbose=True,
+    llm="gpt-4o"
+)
+
+# Define a task
+task = Task(
+    name="research_task",
+    description="Analyze 2024's AI advancements",
+    expected_output="A detailed report",
+    agent=researcher
+)
+
+# Run the agents
+agents = PraisonAIAgents(
+    agents=[researcher],
+    tasks=[task],
+    verbose=False
+)
+
+result = agents.start()
+```
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Installation"
+    icon="download"
+    href="/code/installation"
+  >
+    Complete installation guide and setup instructions
+  </Card>
+  <Card
+    title="Quick Start"
+    icon="play"
+    href="/code/quickstart"
+  >
+    Build your first AI agent in minutes
+  </Card>
+</CardGroup>
+
+<Tip>
+  Join our community on [Discord](https://www.youtube.com/redirect?event=channel_description&redir_token=QUFFLUhqbUpiZE00YlF1MjB1LVNIeTFxTnV6MWRRc18wZ3xBQ3Jtc0ttQVZ0aFJ4RXRDanlBQU1Vd3lHU3BBcml3bzhoek1pTmlkZkFkWjlNVDBpRkxZd1dTOWhPc20xbHJOb1NibmNYeTEydklJT0pZOVJFQW93MmJOQXU0WkJNVzNsd29SZ3FBNFVwMTFGNThNSHBWVUhjSQ&q=https%3A%2F%2Fdiscord.gg%2FnNZu5gGT59) to connect with other developers and get help!
+</Tip> 

docs/code/quickstart.mdx

@@ -0,0 +1,195 @@
+---
+title: Quick Start
+description: Build your first AI agent with PraisonAI
+icon: bolt
+---
+
+<Steps>
+
+  <Step title="Install PraisonAI">
+    Install the core package:
+    ```bash Terminal
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Configure Environment">
+    ```bash Terminal
+    export OPENAI_API_KEY=your_openai_key
+    ```
+    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
+    You can also use other LLM providers like Anthropic, Google, etc. Please refer to the [Models](/models) for more information.
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+## Code Example
+
+<CodeGroup>
+  ```python Basic
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Advanced
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+  </Step>
+
+  <Step title="Run the Program">
+    Execute your script:
+    ```bash Terminal
+    python app.py
+    ```
+
+    You should see:
+    - Agent initialization
+    - Agents progress
+    - Final results
+    - Generated report
+  </Step>
+</Steps>
+
+## Advanced Usage
+
+### Creating Custom Tool
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+
+# 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",
+)
+
+# 4. Start Agents
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task],
+    process="sequential"
+)
+
+agents.start()
+```
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Core Concepts"
+    icon="book"
+    href="/concepts/agents"
+  >
+    Learn about agents, tasks, and processes
+  </Card>
+  <Card
+    title="API Reference"
+    icon="code"
+    href="/api/praisonaiagents/index"
+  >
+    Explore detailed API documentation
+  </Card>
+</CardGroup> 

docs/concepts/agents.mdx

@@ -0,0 +1,235 @@
+---
+title: "Agents"
+description: "Understanding Agents in PraisonAI"
+icon: "user"
+---
+
+<Note>
+  Agents are the core building blocks of PraisonAI. Each agent is an autonomous AI entity with specific roles, goals, and capabilities.
+</Note>
+
+# Understanding Agents
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Styling for main elements
+    style Start fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Output fill:#2D3047,stroke:#7C90A0,color:#fff
+    style Process fill:#5C80BC,stroke:#7C90A0,color:#fff
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[" "]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+        
+        style Task1 fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AgentIcon1 fill:#5C80BC,stroke:#7C90A0,color:#fff
+        style Tools1 fill:#2D3047,stroke:#7C90A0,color:#fff
+    end
+    
+    subgraph Agent2[" "]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+        
+        style Task2 fill:#2D3047,stroke:#7C90A0,color:#fff
+        style AgentIcon2 fill:#5C80BC,stroke:#7C90A0,color:#fff
+        style Tools2 fill:#2D3047,stroke:#7C90A0,color:#fff
+    end
+    
+    %% Style for agent containers
+    style Agent1 fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    style Agent2 fill:#1B1F3B,stroke:#7C90A0,color:#fff
+    
+    %% Global styles
+    classDef default fill:#2D3047,stroke:#7C90A0,color:#fff
+    linkStyle default stroke:#7C90A0,stroke-width:2px
+```
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Role & Goal" icon="target">
+    Defines the agent's purpose and objectives through role definition and specific goals
+  </Card>
+  <Card title="Capabilities" icon="toolbox">
+    Tools and functions available to the agent for task execution
+  </Card>
+  <Card title="Memory" icon="brain">
+    Context retention and learning capabilities across interactions
+  </Card>
+  <Card title="Language Model" icon="microchip">
+    The underlying AI model powering the agent's intelligence
+  </Card>
+</CardGroup>
+
+## Component Details
+
+### Role and Goal
+<Tip>
+  Clear role and goal definitions are crucial for optimal agent performance.
+</Tip>
+
+| Component | Description | Example |
+|:----------|:------------|:--------|
+| **Role** | Agent's function and expertise | Research Analyst, Code Developer |
+| **Goal** | Specific objectives to achieve | Analyze market trends, Generate reports |
+| **Backstory** | Contextual background | Expert with 10 years of experience |
+
+### Capabilities
+<CodeGroup>
+  ```python Basic
+  agent = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+  ```
+</CodeGroup>
+
+## Agent Types
+
+<Steps>
+  <Step title="Basic Agent">
+    - Single-purpose focus
+    - Direct user interaction
+    - Limited tool set
+  </Step>
+  <Step title="Specialized Agent">
+    - Domain expertise
+    - Advanced capabilities
+    - Custom tools
+  </Step>
+  <Step title="Collaborative Agent">
+    - Team interaction
+    - Task delegation
+    - Shared context
+  </Step>
+</Steps>
+
+## Best Practices
+
+<Warning>
+  Always implement proper error handling and resource management in your agent configurations.
+</Warning>
+
+### Agent Design
+1. Clear role definition
+2. Specific, measurable goals
+3. Relevant tool selection
+4. Appropriate memory configuration
+
+### Agent Interaction
+1. Clear communication protocols
+2. Defined delegation rules
+3. Error handling mechanisms
+4. Resource management
+
+## Advanced Features
+
+<CardGroup cols={2}>
+  <Card title="Memory Management" icon="brain">
+    - Short-term conversation memory
+    - Long-term knowledge retention
+    - Context preservation
+  </Card>
+  <Card title="Tool Integration" icon="plug">
+    - Custom tool development
+    - External API integration
+    - Resource access control
+  </Card>
+</CardGroup>
+
+## Async Support
+Agents now support asynchronous operations through the following methods:
+
+- `achat`: Async version of the chat method
+- `astart`: Async version of start method
+- `aexecute_task`: Async version of execute_task method
+- `arun_task`: Async version of run_task method
+- `arun_all_tasks`: Async version of run_all_tasks method
+
+### Example Usage:
+
+```python
+import asyncio
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+async def main():
+    # Create an async agent
+    async_agent = Agent(
+        name="AsyncAgent",
+        role="Async Task Specialist",
+        goal="Perform async operations",
+        backstory="Expert in async operations",
+        tools=[async_search_tool],  # Your async tool
+        verbose=True
+    )
+
+    # Create an async task
+    async_task = Task(
+        description="Perform async operation",
+        expected_output="Async result",
+        agent=async_agent,
+        async_execution=True  # Enable async execution
+    )
+
+    # Create and start agents with async support
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=[async_task],
+        verbose=True
+    )
+    
+    # Start async execution
+    result = await agents.astart()
+    print(result)
+
+# Run the async main function
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Key Features:
+- Full async/await support
+- Parallel task execution
+- Async tool integration
+- Async callback support
+- Mixed sync/async operations
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Create Your First Agent"
+    icon="code"
+    href="/code/quickstart"
+  >
+    Follow our quickstart guide to create your first agent
+  </Card>
+  <Card
+    title="API Reference"
+    icon="book"
+    href="/api/praisonaiagents/agent/agent"
+  >
+    Explore the complete Agent API documentation
+  </Card>
+</CardGroup> 

docs/concepts/process.mdx

@@ -0,0 +1,220 @@
+---
+title: "Process"
+description: "Understanding Process Types in PraisonAI"
+icon: "diagram-project"
+---
+
+# Understanding Process Types
+
+Process types in PraisonAI define how tasks are executed and how agents collaborate. Each process type offers different patterns for task execution and agent coordination.
+
+## Process Types Overview
+
+Three types of processes are implemented in PraisonAI:
+
+<Frame>
+  <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <div className="flex items-center mb-4">
+        <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-lg">
+          <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M4 6h16M4 12h16m-7 6h7" />
+          </svg>
+        </div>
+        <h3 className="ml-3 text-lg font-semibold">Sequential Process</h3>
+      </div>
+      <p>Linear task execution in a predefined order</p>
+    </div>
+
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <div className="flex items-center mb-4">
+        <div className="p-2 bg-green-100 dark:bg-green-900 rounded-lg">
+          <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M12 6V4m0 2a2 2 0 100 4m0-4a2 2 0 110 4m-6 8a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4m6 6v10m6-2a2 2 0 100-4m0 4a2 2 0 110-4m0 4v2m0-6V4" />
+          </svg>
+        </div>
+        <h3 className="ml-3 text-lg font-semibold">Hierarchical Process</h3>
+      </div>
+      <p>Manager-coordinated task execution with dynamic assignment</p>
+    </div>
+
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <div className="flex items-center mb-4">
+        <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-lg">
+          <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M9 19v-6a2 2 0 00-2-2H5a2 2 0 00-2 2v6a2 2 0 002 2h2a2 2 0 002-2zm0 0V9a2 2 0 012-2h2a2 2 0 012 2v10m-6 0a2 2 0 002 2h2a2 2 0 002-2m0 0V5a2 2 0 012-2h2a2 2 0 012 2v14a2 2 0 01-2 2h-2a2 2 0 01-2-2z" />
+          </svg>
+        </div>
+        <h3 className="ml-3 text-lg font-semibold">Workflow Process</h3>
+      </div>
+      <p>Complex task flows with conditional execution and state management</p>
+    </div>
+  </div>
+</Frame>
+
+### Sequential Process
+The simplest form of task execution where tasks are performed one after another.
+
+<Frame>
+  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
+    #### Characteristics
+    - Linear execution flow
+    - Predictable order
+    - Simple dependency management
+    - Direct task progression
+
+    #### Usage
+    ```python
+    agents = PraisonAIAgents(
+        agents=[agent1, agent2],
+        tasks=[task1, task2, task3],
+        process="sequential"
+    )
+    ```
+  </div>
+</Frame>
+
+### Hierarchical Process
+Uses a manager agent to coordinate task execution and agent assignments.
+
+<Frame>
+  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
+    #### Characteristics
+    - Manager-driven coordination
+    - Dynamic task assignment
+    - Flexible execution order
+    - Intelligent resource allocation
+
+    #### Configuration
+    ```python
+    agents = PraisonAIAgents(
+        agents=[manager_agent, worker_agent1, worker_agent2],
+        tasks=[task1, task2],
+        process="hierarchical",
+        manager_llm="gpt-4o"
+    )
+    ```
+  </div>
+</Frame>
+
+### Workflow Process
+Advanced process type supporting complex task relationships and conditional execution.
+
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/yWTH44QPl2A"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+<br />
+
+
+<Frame>
+  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
+    #### Features
+    - Task dependencies
+    - Conditional branching
+    - Loop handling
+    - Context sharing
+    - State management
+
+    #### Implementation
+    ```python
+    task_workflow = {
+        "start_task": {
+            "next": ["decision_task"],
+            "type": "task"
+        },
+        "decision_task": {
+            "type": "decision",
+            "conditions": {
+                "success": ["process_task"],
+                "failure": ["error_task"]
+            }
+        }
+    }
+    ```
+  </div>
+</Frame>
+
+## Advanced Features
+
+### State Management
+- Task state tracking
+- Context preservation
+- Data flow control
+
+### Error Handling
+- Graceful failure recovery
+- Alternative path execution
+- Error reporting
+
+### Monitoring
+- Progress tracking
+- Performance metrics
+- Resource utilization
+
+### Integration
+- External system coordination
+- API synchronization
+- Event handling 
+
+## Async Process Execution
+All process types (sequential, workflow, hierarchical) support async execution through async generators. This enables efficient handling of async tasks and parallel processing.
+
+### Async Process Methods
+- `asequential`: Async version of sequential process
+- `aworkflow`: Async version of workflow process
+- `ahierarchical`: Async version of hierarchical process
+
+### Example Usage:
+
+```python
+import asyncio
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+async def main():
+    # Create agents and tasks
+    agents = [async_agent1, async_agent2]
+    tasks = [async_task1, async_task2]
+
+    # Initialize PraisonAIAgents with async workflow
+    agents = PraisonAIAgents(
+        agents=agents,
+        tasks=tasks,
+        process="workflow"  # or "sequential" or "hierarchical"
+    )
+
+    # Start async execution
+    result = await agents.astart()
+    print(result)
+
+# Run the async workflow
+asyncio.run(main())
+```
+
+### Async Process Features:
+- Parallel task execution in workflow
+- Async task delegation in hierarchical process
+- Non-blocking task execution
+- Efficient resource utilization
+- Mixed sync/async task support
+
+### Process-Specific Async Behavior:
+1. **Sequential Process**:
+   - Tasks are executed in order
+   - Async tasks don't block other tasks
+   - Maintains sequential order while allowing async execution
+
+2. **Workflow Process**:
+   - Parallel execution of independent tasks
+   - Async branching and merging
+   - Dynamic task scheduling
+
+3. **Hierarchical Process**:
+   - Async manager delegation
+   - Parallel subtask execution
+   - Dynamic task assignment 

docs/concepts/tasks.mdx

@@ -0,0 +1,317 @@
+---
+title: "Tasks"
+description: "Understanding Tasks in PraisonAI"
+icon: "list-check"
+---
+
+<Note>
+  Tasks are the fundamental units of work in PraisonAI, defining what needs to be done, how it should be done, and who should do it.
+</Note>
+
+# Understanding Tasks
+
+<Frame>
+  <div className="p-6 bg-white dark:bg-gray-800 rounded-lg">
+    <div className="grid grid-cols-1 gap-6">
+      {/* Task Flow Steps */}
+      <div className="flex justify-between items-center">
+        {/* Task Definition */}
+        <div className="flex-1 p-4 border-2 border-blue-500 rounded-lg text-center">
+          <div className="flex items-center justify-center mb-2">
+            <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-full">
+              <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2" />
+              </svg>
+            </div>
+          </div>
+          <h4 className="font-semibold">Task Definition</h4>
+          <p className="text-sm mt-1">Description & Requirements</p>
+        </div>
+
+        {/* Arrow 1 */}
+        <div className="flex-none px-4">
+          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
+          </svg>
+        </div>
+
+        {/* Agent Assignment */}
+        <div className="flex-1 p-4 border-2 border-green-500 rounded-lg text-center">
+          <div className="flex items-center justify-center mb-2">
+            <div className="p-2 bg-green-100 dark:bg-green-900 rounded-full">
+              <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M16 7a4 4 0 11-8 0 4 4 0 018 0zM12 14a7 7 0 00-7 7h14a7 7 0 00-7-7z" />
+              </svg>
+            </div>
+          </div>
+          <h4 className="font-semibold">Agent Assignment</h4>
+          <p className="text-sm mt-1">Matching & Delegation</p>
+        </div>
+
+        {/* Arrow 2 */}
+        <div className="flex-none px-4">
+          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
+          </svg>
+        </div>
+
+        {/* Execution */}
+        <div className="flex-1 p-4 border-2 border-purple-500 rounded-lg text-center">
+          <div className="flex items-center justify-center mb-2">
+            <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-full">
+              <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M13 10V3L4 14h7v7l9-11h-7z" />
+              </svg>
+            </div>
+          </div>
+          <h4 className="font-semibold">Execution</h4>
+          <p className="text-sm mt-1">Processing & Tools</p>
+        </div>
+
+        {/* Arrow 3 */}
+        <div className="flex-none px-4">
+          <svg className="w-6 h-6 text-gray-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M17 8l4 4m0 0l-4 4m4-4H3" />
+          </svg>
+        </div>
+
+        {/* Output */}
+        <div className="flex-1 p-4 border-2 border-yellow-500 rounded-lg text-center">
+          <div className="flex items-center justify-center mb-2">
+            <div className="p-2 bg-yellow-100 dark:bg-yellow-900 rounded-full">
+              <svg className="w-6 h-6 text-yellow-600 dark:text-yellow-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+                <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M8 9l3 3-3 3m5 0h3M5 20h14a2 2 0 002-2V6a2 2 0 00-2-2H5a2 2 0 00-2 2v12a2 2 0 002 2z" />
+              </svg>
+            </div>
+          </div>
+          <h4 className="font-semibold">Output</h4>
+          <p className="text-sm mt-1">Results & Validation</p>
+        </div>
+      </div>
+
+      {/* Task States */}
+      <div className="grid grid-cols-4 gap-4 mt-4">
+        <div className="p-3 border border-gray-200 rounded-lg bg-blue-50 dark:bg-blue-900">
+          <div className="flex items-center justify-center">
+            <span className="font-semibold">Pending</span>
+          </div>
+        </div>
+        <div className="p-3 border border-gray-200 rounded-lg bg-yellow-50 dark:bg-yellow-900">
+          <div className="flex items-center justify-center">
+            <span className="font-semibold">In Progress</span>
+          </div>
+        </div>
+        <div className="p-3 border border-gray-200 rounded-lg bg-green-50 dark:bg-green-900">
+          <div className="flex items-center justify-center">
+            <span className="font-semibold">Completed</span>
+          </div>
+        </div>
+        <div className="p-3 border border-gray-200 rounded-lg bg-red-50 dark:bg-red-900">
+          <div className="flex items-center justify-center">
+            <span className="font-semibold">Failed</span>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</Frame>
+
+## Core Components
+
+<CardGroup cols={2}>
+  <Card title="Task Definition" icon="file-lines">
+    Clear description of work to be done and expected outputs
+  </Card>
+  <Card title="Agent Assignment" icon="user-check">
+    Matching tasks with capable agents
+  </Card>
+  <Card title="Tool Access" icon="toolbox">
+    Resources available for task completion
+  </Card>
+  <Card title="Output Handling" icon="file-export">
+    Managing and formatting task results
+  </Card>
+</CardGroup>
+
+## Task Configuration
+
+<CodeGroup>
+  ```python Basic
+  task = Task(
+      description="Research AI trends",
+      expected_output="Summary report",
+      agent=research_agent
+  )
+  ```
+  ```python Advanced
+  task = Task(
+      description="Analyze market data",
+      expected_output="Detailed analysis",
+      agent=analyst_agent,
+      tools=[AnalysisTool()],
+      output_file="analysis.md",
+      async_execution=True
+  )
+  ```
+</CodeGroup>
+
+## Task Types
+
+<Steps>
+  <Step title="Basic Task">
+    Simple, single-operation tasks with clear inputs and outputs
+  </Step>
+  <Step title="Decision Task">
+    Tasks involving choices and conditional paths
+    ```python
+    decision_task = Task(
+        type="decision",
+        conditions={
+            "success": ["next_task"],
+            "failure": ["error_task"]
+        }
+    )
+    ```
+  </Step>
+  <Step title="Loop Task">
+    Repetitive operations over collections
+    ```python
+    loop_task = Task(
+        type="loop",
+        items=data_list,
+        operation="process_item"
+    )
+    ```
+  </Step>
+</Steps>
+
+## Task Relationships
+
+<Warning>
+  Properly managing task dependencies is crucial for complex workflows. Always ensure proper context sharing and error handling.
+</Warning>
+
+### Context Sharing
+```python
+task_a = Task(name="research")
+task_b = Task(
+    name="analyze",
+    context=[task_a]  # Uses task_a's output
+)
+```
+
+### Task Dependencies
+
+| Relationship | Description | Example |
+|:-------------|:------------|:--------|
+| **Sequential** | Tasks run one after another | Research → Analysis → Report |
+| **Parallel** | Independent tasks run simultaneously | Data Collection + Processing |
+| **Conditional** | Tasks depend on previous results | Success → Next Task, Failure → Retry |
+
+## Advanced Features
+
+<CardGroup cols={2}>
+  <Card title="Output Handling" icon="file-code">
+    - Multiple output formats
+    - Structured data validation
+    - File system integration
+  </Card>
+  <Card title="Task Control" icon="sliders">
+    - Execution flow control
+    - Error recovery
+    - Progress tracking
+  </Card>
+</CardGroup>
+
+## Best Practices
+
+<Tip>
+  Design tasks to be atomic and focused. Each task should have a single, clear responsibility.
+</Tip>
+
+1. Clear task descriptions
+2. Well-defined success criteria
+3. Appropriate tool selection
+4. Error handling consideration
+5. Resource management
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card
+    title="Create Tasks"
+    icon="plus"
+    href="/code/quickstart"
+  >
+    Learn how to create and configure tasks
+  </Card>
+  <Card
+    title="Task API"
+    icon="code"
+    href="/api/praisonaiagents/task/task"
+  >
+    Explore the complete Task API documentation
+  </Card>
+</CardGroup>
+
+## Async Task Execution
+Tasks can be executed asynchronously by setting `async_execution=True`. This is particularly useful for tasks that involve I/O operations or when you want to run multiple tasks in parallel.
+
+### Async Task Configuration
+```python
+async_task = Task(
+    description="Perform async operation",
+    expected_output="Async result",
+    agent=async_agent,
+    async_execution=True,  # Enable async execution
+    callback=async_callback  # Optional async callback
+)
+```
+
+### Async Callbacks
+Tasks support both synchronous and asynchronous callbacks:
+
+```python
+async def async_callback(output: TaskOutput):
+    await asyncio.sleep(1)  # Simulate async operation
+    print(f"Processed output: {output.raw}")
+
+# Task with async callback
+task = Task(
+    description="Task with async callback",
+    expected_output="Result",
+    agent=agent,
+    async_execution=True,
+    callback=async_callback
+)
+```
+
+### Mixing Sync and Async Tasks
+You can mix synchronous and asynchronous tasks in the same workflow:
+
+```python
+# Sync task
+sync_task = Task(
+    description="Synchronous operation",
+    expected_output="Sync result",
+    agent=sync_agent,
+    async_execution=False
+)
+
+# Async task
+async_task = Task(
+    description="Asynchronous operation",
+    expected_output="Async result",
+    agent=async_agent,
+    async_execution=True
+)
+
+# Create agents with mixed tasks
+agents = PraisonAIAgents(
+    agents=[sync_agent, async_agent],
+    tasks=[sync_task, async_task]
+)
+
+# Run asynchronously
+result = await agents.astart()
+``` 

docs/concepts/tools.mdx

@@ -0,0 +1,335 @@
+---
+title: "Tools"
+description: "Understanding Tools in PraisonAI"
+icon: "screwdriver-wrench"
+---
+
+# Understanding Tools
+
+Tools in PraisonAI are functions that agents can use to interact with external systems, perform computations, or access data. They extend the capabilities of agents beyond just language processing.
+
+## Tools Overview
+
+<Frame>
+  <div className="grid grid-cols-1 md:grid-cols-3 gap-4">
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <div className="flex items-center mb-4">
+        <div className="p-2 bg-blue-100 dark:bg-blue-900 rounded-lg">
+          <svg className="w-6 h-6 text-blue-600 dark:text-blue-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M21 21l-6-6m2-5a7 7 0 11-14 0 7 7 0 0114 0z" />
+          </svg>
+        </div>
+        <h3 className="ml-3 text-lg font-semibold">Search Tools</h3>
+      </div>
+      <p>Tools for searching and retrieving information from various sources</p>
+    </div>
+
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <div className="flex items-center mb-4">
+        <div className="p-2 bg-green-100 dark:bg-green-900 rounded-lg">
+          <svg className="w-6 h-6 text-green-600 dark:text-green-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M8 7v8a2 2 0 002 2h6M8 7V5a2 2 0 012-2h4.586a1 1 0 01.707.293l4.414 4.414a1 1 0 01.293.707V15a2 2 0 01-2 2h-2M8 7H6a2 2 0 00-2 2v10a2 2 0 002 2h8a2 2 0 002-2v-2" />
+          </svg>
+        </div>
+        <h3 className="ml-3 text-lg font-semibold">File Tools</h3>
+      </div>
+      <p>Tools for reading, writing, and manipulating files</p>
+    </div>
+
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <div className="flex items-center mb-4">
+        <div className="p-2 bg-purple-100 dark:bg-purple-900 rounded-lg">
+          <svg className="w-6 h-6 text-purple-600 dark:text-purple-300" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+            <path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M3 6l3 1m0 0l-3 9a5.002 5.002 0 006.001 0M6 7l3 9M6 7l6-2m6 2l3-1m-3 1l-3 9a5.002 5.002 0 006.001 0M18 7l3 9m-3-9l-6-2m0-2v2m0 16V5m0 16H9m3 0h3" />
+          </svg>
+        </div>
+        <h3 className="ml-3 text-lg font-semibold">API Tools</h3>
+      </div>
+      <p>Tools for interacting with external APIs and services</p>
+    </div>
+  </div>
+</Frame>
+
+## Creating Custom Tool
+<Steps>
+<Step>
+Create any function that you want to use as a tool, that performs a specific task.
+```python
+from duckduckgo_search import DDGS
+from typing import List, Dict
+
+# Tool Implementation
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform Internet Search using DuckDuckGo
+    
+    Args:
+        query (str): The search query string
+        
+    Returns:
+        List[Dict]: List of search results containing title, URL, and snippet
+    """
+    results = []
+    ddgs = DDGS()
+    for result in ddgs.text(keywords=query, max_results=5):
+        results.append({
+            "title": result.get("title", ""),
+            "url": result.get("href", ""),
+            "snippet": result.get("body", "")
+        })
+    return results
+```
+</Step>
+<Step>
+Assign the tool to an agent
+```python
+    data_agent = Agent(
+        name="DataCollector",
+        role="Search Specialist",
+        goal="Perform internet searches to collect relevant information.",
+        backstory="Expert in finding and organising internet data.",
+        tools=[internet_search_tool], ## Add the tool to the agent i.e the function name
+    )
+```
+</Step>
+</Steps>
+
+<Card title="That's it!">
+<Check>You have created a custom tool and assigned it to an agent.</Check>
+</Card>
+
+## Implementing Tools Full Code Example
+
+Tools are implemented as Python functions with type hints and docstrings. Here's a complete example:
+
+
+    ```python
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+    from duckduckgo_search import DDGS
+    from typing import List, Dict
+
+    # 1. Tool Implementation
+    def internet_search_tool(query: str) -> List[Dict]:
+        """
+        Perform Internet Search using DuckDuckGo
+        
+        Args:
+            query (str): The search query string
+            
+        Returns:
+            List[Dict]: List of search results containing title, URL, and snippet
+        """
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    # 2. Assign the tool to an agent
+    data_agent = Agent(
+        name="DataCollector",
+        role="Search Specialist",
+        goal="Perform internet searches to collect relevant information.",
+        backstory="Expert in finding and organising internet data.",
+        tools=[internet_search_tool],
+        self_reflect=False
+    )
+
+    # 3. Task Definition
+    collect_task = Task(
+        description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+        expected_output="List of search results with titles, URLs, and snippets.",
+        agent=data_agent,
+        name="collect_data",
+    )
+
+    # 4. Start Agents
+    agents = PraisonAIAgents(
+        agents=[data_agent],
+        tasks=[collect_task],
+        process="sequential"
+    )
+
+    agents.start()
+    ```
+
+
+
+
+## Advanced Tool Features
+
+### Tool Configuration
+<Frame>
+  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
+    ```python
+    def configured_tool(
+        query: str,
+        max_results: int = 5,
+        timeout: int = 10
+    ) -> List[Dict]:
+        """
+        Example of a configurable tool
+        
+        Args:
+            query (str): Search query
+            max_results (int): Maximum number of results
+            timeout (int): Request timeout in seconds
+            
+        Returns:
+            List[Dict]: Search results
+        """
+        # Tool implementation
+        pass
+    ```
+  </div>
+</Frame>
+
+### Tool Chaining
+<Frame>
+  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
+    ```python
+    def chain_tools(input_data: str) -> Dict:
+        """
+        Example of chaining multiple tools
+        
+        Args:
+            input_data (str): Input data
+            
+        Returns:
+            Dict: Processed results
+        """
+        # 1. Search for data
+        search_results = internet_search_tool(input_data)
+        
+        # 2. Process results
+        processed_data = process_tool(search_results)
+        
+        # 3. Format output
+        return format_tool(processed_data)
+    ```
+  </div>
+</Frame>
+
+### Tool Categories
+
+1. **Data Collection Tools**
+   - Web scraping
+   - API integration
+   - Database queries
+
+2. **Processing Tools**
+   - Data transformation
+   - Text analysis
+   - Image processing
+
+3. **Output Tools**
+   - File generation
+   - Report creation
+   - Data visualization
+
+## Tool Integration
+
+### Adding Tools to Agents
+<Frame>
+  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
+    ```python
+    # Multiple tools
+    agent = Agent(
+        name="MultiTool Agent",
+        tools=[
+            internet_search_tool,
+            file_processing_tool,
+            api_integration_tool
+        ]
+    )
+    ```
+  </div>
+</Frame>
+
+### Tool Dependencies
+<Frame>
+  <div className="p-4 bg-white dark:bg-gray-800 rounded-lg">
+    ```python
+    # Tool with dependencies
+    def advanced_tool(data: Dict) -> Dict:
+        """
+        Tool that depends on external libraries
+        
+        Args:
+            data (Dict): Input data
+            
+        Returns:
+            Dict: Processed data
+        """
+        try:
+            import required_library
+            # Tool implementation
+            return processed_result
+        except ImportError:
+            raise Exception("Required library not installed")
+    ```
+  </div>
+</Frame>
+
+## Tool Guidelines
+  <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <h3 className="text-lg font-semibold mb-3">Best Practices</h3>
+      1. **Type Hints**
+         - Use Python type hints
+         - Define clear input/output types
+         - Document complex types
+
+      2. **Documentation**
+         - Write clear docstrings
+         - Explain parameters
+         - Provide usage examples
+
+      3. **Error Handling**
+         - Handle exceptions gracefully
+         - Return meaningful errors
+         - Validate inputs
+    </div>
+
+    <div className="p-4 border rounded-lg bg-white dark:bg-gray-800">
+      <h3 className="text-lg font-semibold mb-3">Tool Types</h3>
+      1. **Search Tools**
+         - Web search
+         - Database queries
+         - Document search
+
+      2. **File Tools**
+         - Read/write operations
+         - File conversion
+         - Data extraction
+
+      3. **API Tools**
+         - REST API calls
+         - GraphQL queries
+         - Service integration
+    </div>
+  </div>
+
+## Best Practices Summary
+
+1. **Design Principles**
+   - Single responsibility
+   - Clear interfaces
+   - Proper documentation
+   - Error handling
+
+2. **Performance**
+   - Efficient processing
+   - Resource management
+   - Caching when appropriate
+   - Asynchronous operations
+
+3. **Security**
+   - Input validation
+   - Rate limiting
+   - API key management
+   - Error masking

docs/contributing.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Contributing"
 description: "Guide for contributing to PraisonAI through GitHub, including forking, cloning, and submitting pull requests"
+icon: "code-branch"
 ---
 
 - Fork on GitHub: Use the "Fork" button on the repository page.

docs/deploy.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Deploy"
 description: "Guide for deploying PraisonAI to cloud platforms, with detailed instructions for Google Cloud deployment"
+icon: "cloud"
 ---
 
 ## Google Cloud

docs/developers/agents-playbook.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Agents Playbook"
 description: "Examples and templates for creating PraisonAI agent playbooks, from simple to detailed configurations with multiple roles and tasks"
+icon: "book"
 ---
 
 # Agents Playbook 

docs/developers/googlecolab-tools.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Google Colab Tools"
 description: "Guide for implementing and using custom tools in PraisonAI with Google Colab, including examples of internet search and data processing tools"
+icon: "wrench"
 ---
 
 ```

docs/developers/googlecolab.mdx

@@ -1,8 +1,22 @@
 ---
 title: "Google Colab Integration"
 description: "Guide for using PraisonAI in Google Colab environments, with example code and configurations for agent-based tasks"
+icon: "google"
 ---
 
+<CardGroup cols={2}>
+  <Card title="Basic PraisonAI" icon="book">
+    <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>
+  </Card>
+  <Card title="PraisonAI with Tools" icon="screwdriver-wrench">
+    <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>
+  </Card>
+</CardGroup>
+
 ```
 jupyter:
   accelerator: GPU

docs/developers/test.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Test"
 description: "Instructions for running and writing tests for PraisonAI components, including unit tests and test automation"
+icon: "vial"
 ---
 
 # Test

docs/developers/wrapper-tools.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Integrate with Tools"
 description: "Guide for integrating external tools and APIs with PraisonAI, including examples of custom tool creation and implementation"
+icon: "plug"
 ---
 
 # Integrate with Tools

docs/developers/wrapper.mdx

@@ -1,6 +1,7 @@
 ---
 title: "PraisonAI Package Integration"
 description: "Guide for integrating the PraisonAI package into your Python projects, including YAML configuration and different execution modes"
+icon: "box"
 ---
 
 # Include praisonai package in your project

docs/features/async.mdx

@@ -0,0 +1,494 @@
+---
+title: "Async AI Agents"
+description: "Async AI Agents allow you to run AI tasks asynchronously, improving performance and efficiency in your applications."
+icon: "clock"
+---
+
+<Note>
+  **Requirements**
+  
+  - Python 3.10 or higher
+  - Basic understanding of async/await in Python
+</Note>
+
+## Quick Start
+
+<Steps>
+  <Step title="Install Package">
+    First, install the PraisonAI Agents package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Set API Key">
+    Set your OpenAI API key as an environment variable:
+    ```bash
+    export OPENAI_API_KEY=your_api_key_here
+    ```
+  </Step>
+
+  <Step title="Create Project">
+    Create a new file `app.py` with the basic setup:
+    ```python
+    import asyncio
+    from praisonaiagents import Agent, Task, PraisonAIAgents
+
+    # Create an agent
+    agent = Agent(
+        name="AsyncAgent",
+        role="Assistant",
+        goal="Help with tasks",
+        backstory="Expert in async operations"
+    )
+
+    # Create a task
+    task = Task(
+        name="hello_task",
+        description="Say hello and introduce yourself",
+        agent=agent,
+        async_execution=True,
+        expected_output="Answer to the question"
+    )
+
+    # Create agents manager
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        process="sequential"
+    )
+
+    # Main async function
+    async def main():
+        result = await agents.astart()
+        print(result)
+
+    # Run the program
+    if __name__ == "__main__":
+        asyncio.run(main())
+
+    ```
+  </Step>
+
+  <Step title="Run the Program">
+    Run your async AI agent:
+    ```bash
+    python app.py
+    ```
+  </Step>
+</Steps>
+
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/VhVQfgo00LE"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
+## Understanding Async Execution
+
+<Card title="What is Asynchronous Execution?" icon="question">
+  Async execution lets your program continue running while waiting for operations to complete. This is ideal for:
+  - Making multiple AI API calls
+  - Processing large datasets
+  - Handling multiple tasks simultaneously
+  - Maintaining responsive applications
+</Card>
+
+<CodeGroup>
+  ```python Sync
+  # Synchronous execution (blocks until complete)
+  result = agent.chat("Hello")
+  ```
+
+  ```python Async
+  # Asynchronous execution (non-blocking)
+  result = await agent.achat("Hello")
+  ```
+</CodeGroup>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Async Tasks" icon="tasks">
+    Create tasks that run asynchronously with built-in error handling and retries.
+  </Card>
+  <Card title="Parallel Processing" icon="arrows-split-up-and-left">
+    Run multiple tasks in parallel using asyncio.gather().
+  </Card>
+  <Card title="Mixed Mode" icon="shuffle">
+    Mix sync and async tasks in the same workflow seamlessly.
+  </Card>
+  <Card title="Resource Management" icon="gear">
+    Built-in resource management and performance optimization.
+  </Card>
+</CardGroup>
+
+## Advanced Usage
+
+### Async Tasks with Callbacks and Tools Example Code
+
+```python
+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 async tool
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, str]]
+    total_results: int
+
+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
+        }
+
+# 2. 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}")
+
+# 3. 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
+)
+
+# 4. 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_pydantic=SearchResult
+)
+
+# 5. 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_pydantic=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
+    )
+    
+    # Create a single PraisonAIAgents instance with both agents
+    agents = PraisonAIAgents(
+        agents=[async_agent, summary_agent],  # Include both agents
+        tasks=parallel_tasks + [summary_task],  # Include all tasks
+        verbose=1,
+        process="sequential"  # Tasks will run in sequence, with parallel tasks running first
+    )
+    
+    # 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:
+        # 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())
+
+```
+
+### 1. Async Tasks with Callbacks
+
+<CodeGroup>
+  ```python Task
+  # Create an async task with callback
+  async_task = Task(
+      name="weather_task",
+      description="Check weather conditions",
+      agent=agent,
+      async_execution=True,
+      callback=async_callback
+  )
+  ```
+
+  ```python Callback
+  async def async_callback(output):
+      await process_result(output)
+      await save_to_database(output)
+  ```
+</CodeGroup>
+
+### 2. Parallel Task Processing
+
+<Warning>
+  Be mindful of rate limits and resource usage when processing tasks in parallel.
+</Warning>
+
+```python
+async def process_multiple_tasks():
+    tasks = [
+        Task(
+            name=f"task_{i}",
+            description=f"Process item {i}",
+            async_execution=True
+        ) for i in range(5)
+    ]
+    
+    results = await asyncio.gather(
+        *[agent.achat(task.description) for task in tasks]
+    )
+    return results
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Error Handling">
+    ```python
+    async def safe_async_operation():
+        try:
+            result = await agent.achat("Process this")
+            return result
+        except Exception as e:
+            logging.error(f"Error: {e}")
+            return None
+    ```
+  </Accordion>
+
+  <Accordion title="Resource Management">
+    ```python
+    async def efficient_processing():
+        semaphore = asyncio.Semaphore(5)
+        async with semaphore:
+            result = await agent.achat("Process within limits")
+        return result
+    ```
+  </Accordion>
+
+  <Accordion title="Performance Tips">
+    - Use `asyncio.gather()` for parallel operations
+    - Implement proper error handling
+    - Monitor memory usage
+    - Use timeouts for long-running operations
+  </Accordion>
+</AccordionGroup>
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="ChatCompletion Error" icon="triangle-exclamation">
+    If you see "ChatCompletion can't be used in await expression":
+    - Use AsyncOpenAI() client
+    - Ensure proper async context
+  </Card>
+
+  <Card title="Event Loop Error" icon="circle-exclamation">
+    If you get "Event loop is closed":
+    - Use asyncio.run() for main entry
+    - Check async context
+  </Card>
+</CardGroup>
+
+## API Reference
+
+### Async Methods
+
+<ResponseField name="achat()" type="async">
+  Async version of chat method
+</ResponseField>
+
+<ResponseField name="aexecute_tool()" type="async">
+  Async tool execution method
+</ResponseField>
+
+<ResponseField name="astart()" type="async">
+  Async task execution starter
+</ResponseField>
+
+### Task Properties
+
+<ResponseField name="async_execution" type="boolean">
+  Enable/disable async mode for tasks
+</ResponseField>
+
+<ResponseField name="callback" type="function">
+  Set sync or async callback function
+</ResponseField>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Examples" icon="code" href="./examples">
+    Check out more examples in our examples directory
+  </Card>
+  <Card title="Advanced Topics" icon="book" href="./advanced">
+    Learn about advanced async patterns and optimizations
+  </Card>
+</CardGroup>
+
+<Note>
+  Remember to handle errors properly, manage resources efficiently, and test thoroughly in async contexts.
+</Note>

docs/features/callbacks.mdx

@@ -0,0 +1,783 @@
+---
+title: "AI Agents with Callbacks"
+sidebarTitle: "Callbacks"
+description: "Learn how to implement callbacks to monitor and log AI agent interactions, errors, and task completions."
+icon: "bell"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of Python functions
+</Note>
+
+## Quick Start
+
+<Steps>
+  <Step title="Install Package">
+    First, install the PraisonAI Agents package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Required Functions">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import (
+        register_display_callback,
+        Agent, 
+        Task, 
+        PraisonAIAgents
+    )
+    ```
+  </Step>
+
+  <Step title="Create a Simple Callback">
+    Define your first callback function:
+    ```python
+    def simple_callback(message=None, response=None, **kwargs):
+        print(f"Received message: {message}")
+        print(f"Got response: {response}")
+    
+    # Register as synchronous callback
+    register_display_callback('interaction', simple_callback, is_async=False)
+
+    # For async callbacks
+    async def async_simple_callback(message=None, response=None, **kwargs):
+        await asyncio.sleep(0)  # Non-blocking pause
+        print(f"Received message: {message}")
+        print(f"Got response: {response}")
+    
+    # Register as async callback
+    register_display_callback('interaction', async_simple_callback, is_async=True)
+    ```
+  </Step>
+
+  <Step title="Use in Your Code">
+    Create and run an agent with callbacks enabled:
+    ```python
+    # Create an agent
+    agent = Agent(
+        name="MyAgent",
+        role="Assistant",
+        goal="Help with tasks",
+        backstory="I am a helpful assistant",
+        verbose=True  # Enable verbose mode to see callbacks in action
+    )
+
+    # Create a task
+    task = Task(
+        name="simple_task",
+        description="Say hello",
+        agent=agent,
+        expected_output="A greeting"
+    )
+
+    # Run the agent
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task]
+    )
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+<Note>
+  PraisonAI Agents supports both synchronous and asynchronous callbacks. When registering a callback, specify `is_async=True` for async callbacks. Both sync and async callbacks for the same display type can be executed simultaneously.
+</Note>
+
+## Understanding Callbacks
+
+<Card title="What are Callbacks?" icon="question">
+  Callbacks are functions that get called automatically when specific events occur in your AI agents:
+  - Interactions between user and agent
+  - Error messages
+  - Tool calls
+  - Self-reflection moments
+  - Task completion
+  - Generation progress
+</Card>
+
+## Available Callback Types
+
+<CardGroup cols={2}>
+  <Card title="Interaction Callback" icon="comments">
+    Triggered when the agent interacts with users
+  </Card>
+  <Card title="Error Callback" icon="triangle-exclamation">
+    Called when errors occur
+  </Card>
+  <Card title="Tool Call Callback" icon="wrench">
+    Activated when tools are used
+  </Card>
+  <Card title="Self Reflection Callback" icon="brain">
+    Triggered during agent self-reflection
+  </Card>
+  <Card title="Instruction Callback" icon="list-check">
+    Called when instructions are processed
+  </Card>
+  <Card title="Generating Callback" icon="spinner">
+    Activated during content generation
+  </Card>
+</CardGroup>
+
+## Basic Implementation
+
+### 1. Simple Logging Callback
+
+<CodeGroup>
+```python Basic
+import logging
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+
+def log_callback(message=None, **kwargs):
+    logging.info(f"Agent message: {message}")
+
+# Register synchronous callback
+register_display_callback('interaction', log_callback, is_async=False)
+
+# Register asynchronous callback
+async def async_log_callback(message=None, **kwargs):
+    await asyncio.sleep(0)
+    logging.info(f"Agent message: {message}")
+
+# Register as async callback
+register_display_callback('interaction', async_log_callback, is_async=True)
+```
+
+```python Advanced
+import logging
+from datetime import datetime
+
+# Setup logging with file output
+logging.basicConfig(
+    filename='ai_interactions.log',
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+
+def detailed_callback(message=None, response=None, **kwargs):
+    logging.info(f"""
+    Time: {datetime.now()}
+    Message: {message}
+    Response: {response}
+    Additional Info: {kwargs}
+    """)
+
+register_display_callback('interaction', detailed_callback)
+```
+</CodeGroup>
+
+### 2. Multiple Callback Types
+
+```python
+# Error callback
+def error_callback(message=None):
+    logging.error(f"Error occurred: {message}")
+
+# Tool call callback
+def tool_callback(message=None):
+    logging.info(f"Tool called: {message}")
+
+# Register multiple callbacks
+register_display_callback('error', error_callback)
+register_display_callback('tool_call', tool_callback)
+```
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Error Handling">
+    Always include error handling in your callbacks:
+    ```python
+    def safe_callback(message=None, **kwargs):
+        try:
+            # Process the callback
+            logging.info(message)
+        except Exception as e:
+            logging.error(f"Callback error: {e}")
+    ```
+  </Accordion>
+
+  <Accordion title="Performance">
+    Keep callbacks lightweight:
+    ```python
+    # Good - Quick logging
+    def good_callback(message=None):
+        logging.info(message)
+
+    # Bad - Heavy processing
+    def bad_callback(message=None):
+        time.sleep(1)  # Delays agent execution
+        process_heavy_task(message)
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Complete Example
+
+Here's a full implementation showing all callback types and proper logging:
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents, register_display_callback
+import logging
+from datetime import datetime
+
+# Setup logging
+logging.basicConfig(
+    filename='ai_interactions.log',
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+# Interaction callback
+def interaction_callback(message=None, response=None, markdown=None, generation_time=None):
+    logging.info(f"""
+    === INTERACTION ===
+    Time: {datetime.now()}
+    Generation Time: {generation_time}s
+    Message: {message}
+    Response: {response}
+    Markdown: {markdown}
+    """)
+
+# Error callback
+def error_callback(message=None):
+    logging.error(f"""
+    === ERROR ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+# Tool call callback
+def tool_call_callback(message=None):
+    logging.info(f"""
+    === TOOL CALL ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+# Register callbacks
+register_display_callback('interaction', interaction_callback)
+register_display_callback('error', error_callback)
+register_display_callback('tool_call', tool_call_callback)
+
+agent = Agent(
+    name="CallbackAgent",
+    role="Assistant",
+    goal="Demonstrate callbacks",
+    backstory="I am a helpful assistant",
+    verbose=True
+)
+
+task = Task(
+    name="callback_task",
+    description="Show how callbacks work",
+    agent=agent,
+    expected_output="Demonstration complete"
+)
+
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    verbose=True
+)
+
+agents.start()
+```
+
+## Advanced Examples
+
+- All callback types
+- Comprehensive logging
+- Task callbacks
+- Tool integration
+- Multiple agents
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs, register_display_callback
+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()
+
+## Async Callbacks
+
+Async callbacks allow you to handle events asynchronously, which is particularly useful for long-running operations or when dealing with multiple agents simultaneously.
+
+### Basic Async Callback Implementation
+
+```python
+import asyncio
+from praisonaiagents import register_display_callback
+
+async def async_interaction_callback(message=None, response=None, **kwargs):
+    """Async callback for handling interactions"""
+    await asyncio.sleep(0)  # Non-blocking pause
+    print(f"Async processing - Message: {message}")
+    print(f"Response: {response}")
+
+# Register the async callback
+register_display_callback('interaction', async_interaction_callback)
+```
+
+### Complete Async Example
+
+```python
+import asyncio
+from praisonaiagents import Agent, Task, PraisonAIAgents, register_display_callback
+import logging
+from datetime import datetime
+
+# Setup async logging
+async def setup_async_logging():
+    logging.basicConfig(
+        filename='async_ai_interactions.log',
+        level=logging.INFO,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    )
+
+# Async callbacks
+async def async_interaction_callback(message=None, response=None, markdown=None, generation_time=None):
+    await asyncio.sleep(0)
+    logging.info(f"""
+    === ASYNC INTERACTION ===
+    Time: {datetime.now()}
+    Generation Time: {generation_time}s
+    Message: {message}
+    Response: {response}
+    """)
+
+async def async_error_callback(message=None):
+    await asyncio.sleep(0)
+    logging.error(f"""
+    === ASYNC ERROR ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+# Register async callbacks
+register_display_callback('interaction', async_interaction_callback, is_async=True)
+register_display_callback('error', async_error_callback, is_async=True)
+
+# Create async task callback
+async def async_task_callback(output):
+    await asyncio.sleep(0)
+    logging.info(f"""
+    === ASYNC TASK COMPLETED ===
+    Time: {datetime.now()}
+    Description: {output.description}
+    Agent: {output.agent}
+    Output: {output.raw[:200]}...
+    """)
+
+async def main():
+    await setup_async_logging()
+    
+    # Create agent with async capabilities
+    agent = Agent(
+        name="AsyncAgent",
+        role="Assistant",
+        goal="Demonstrate async callbacks",
+        backstory="I am an async-capable assistant",
+        verbose=True
+    )
+
+    # Create task with async callback
+    task = Task(
+        name="async_task",
+        description="Demonstrate async callbacks",
+        agent=agent,
+        expected_output="Async demonstration complete",
+        callback=async_task_callback
+    )
+
+    # Create and run agents with async support
+    agents = PraisonAIAgents(
+        agents=[agent],
+        tasks=[task],
+        verbose=True
+    )
+
+    await agents.start_async()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+### Available Async Functions
+
+PraisonAI Agents provides several async versions of display functions, prefixed with 'a'. Here's the complete list:
+
+<CardGroup cols={2}>
+  <Card title="adisplay_interaction" icon="comments">
+    ```python
+    async def adisplay_interaction(
+        message, 
+        response, 
+        markdown=True, 
+        generation_time=None, 
+        console=None
+    )
+    ```
+    Async version of display_interaction for showing conversations.
+  </Card>
+  
+  <Card title="adisplay_self_reflection" icon="brain">
+    ```python
+    async def adisplay_self_reflection(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for displaying agent's self-reflection moments.
+  </Card>
+
+  <Card title="adisplay_instruction" icon="list-check">
+    ```python
+    async def adisplay_instruction(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for showing instructions.
+  </Card>
+
+  <Card title="adisplay_tool_call" icon="wrench">
+    ```python
+    async def adisplay_tool_call(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for displaying tool calls.
+  </Card>
+
+  <Card title="adisplay_error" icon="triangle-exclamation">
+    ```python
+    async def adisplay_error(
+        message: str, 
+        console=None
+    )
+    ```
+    Async version for error messages.
+  </Card>
+
+  <Card title="adisplay_generating" icon="spinner">
+    ```python
+    async def adisplay_generating(
+        content: str = "", 
+        start_time: Optional[float] = None
+    )
+    ```
+    Async version for showing generation progress.
+  </Card>
+</CardGroup>
+
+#### Example Usage
+
+```python
+import asyncio
+from praisonaiagents import (
+    adisplay_interaction,
+    adisplay_error,
+    adisplay_tool_call
+)
+
+async def main():
+    # Display an interaction
+    await adisplay_interaction(
+        message="What's the weather?",
+        response="Let me check that for you.",
+        generation_time=0.5
+    )
+
+    # Display a tool call
+    await adisplay_tool_call(
+        "Calling weather API for location data..."
+    )
+
+    # Handle an error
+    try:
+        raise Exception("API connection failed")
+    except Exception as e:
+        await adisplay_error(str(e))
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+### Best Practices for Async Callbacks
+
+<AccordionGroup>
+  <Accordion title="Error Handling">
+    Implement proper error handling in async callbacks:
+    ```python
+    async def safe_async_callback(message=None, **kwargs):
+        try:
+            await asyncio.sleep(0)
+            # Process the callback
+            await process_message(message)
+        except Exception as e:
+            logging.error(f"Async callback error: {e}")
+    ```
+  </Accordion>
+
+  <Accordion title="Performance">
+    Optimize async callbacks for better performance:
+    ```python
+    async def optimized_async_callback(message=None, **kwargs):
+        # Use asyncio.gather for concurrent operations
+        results = await asyncio.gather(
+            process_message(message),
+            update_status(),
+            log_interaction(message)
+        )
+        return results
+    ```
+  </Accordion>
+</AccordionGroup>
+
+### Key Considerations
+
+<CardGroup cols={2}>
+  <Card title="Concurrency" icon="arrows-split-up-and-left">
+    - Use asyncio.gather for parallel operations
+    - Avoid blocking calls in async callbacks
+    - Handle concurrent access to shared resources
+  </Card>
+  <Card title="Resource Management" icon="server">
+    - Implement proper cleanup in async contexts
+    - Use async context managers when appropriate
+    - Monitor memory usage in long-running callbacks
+  </Card>
+</CardGroup>
+
+<Note>
+  When using async callbacks, ensure all dependent operations are also async-compatible to prevent blocking the event loop.
+</Note>
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Callback Not Firing" icon="bug">
+    - Verify callback registration
+    - Check verbose mode is enabled
+    - Ensure correct callback name
+  </Card>
+  <Card title="Logging Issues" icon="file-lines">
+    - Check file permissions
+    - Verify logging configuration
+    - Monitor log file size
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Async Callbacks" icon="bolt" href="./async">
+    Learn about asynchronous callbacks
+  </Card>
+  <Card title="Advanced Patterns" icon="code" href="./advanced">
+    Explore advanced callback patterns
+  </Card>
+</CardGroup>
+
+<Note>
+  Remember to handle callbacks efficiently and implement proper error handling to ensure smooth agent operations.
+</Note>

docs/features/mini.mdx

@@ -0,0 +1,248 @@
+---
+title: "Mini AI Agents"
+description: "Learn how to create simple yet powerful AI agents in just a few lines of code."
+icon: "robot"
+---
+
+<Note>
+  **Prerequisites**
+  - Python 3.10 or higher
+  - PraisonAI Agents package installed
+  - Basic understanding of Python
+</Note>
+
+## Quick Start
+
+Create multiple AI agents that can work together in just a few lines of code!
+
+<Steps>
+  <Step title="Install Package">
+    First, install the PraisonAI Agents package:
+    ```bash
+    pip install praisonaiagents
+    ```
+  </Step>
+
+  <Step title="Import Required Components">
+    Import the necessary components:
+    ```python
+    from praisonaiagents import Agent, Agents
+    ```
+  </Step>
+
+  <Step title="Create Your Agents">
+    Create specialized agents with specific roles:
+    ```python 
+    summarise_agent = Agent(instructions="Summarise what is Photosynthesis")
+    ```
+  </Step>
+
+  <Step title="Start Your Agents">
+    Initialize and run your agents:
+    ```python
+    agents = Agents(agents=[summarise_agent])
+    agents.start()
+    ```
+  </Step>
+</Steps>
+
+## Understanding Mini AI Agents
+
+<Card title="What are Mini AI Agents?" icon="question">
+  Mini AI Agents are simplified yet powerful AI agents that can:
+  - Work together to accomplish tasks
+  - Use tools like internet search
+  - Process and summarize information
+  - Execute tasks sequentially
+</Card>
+
+## Key Components
+
+<CardGroup cols={2}>
+  <Card title="Agent" icon="user-robot">
+    Individual AI agents with specific roles and capabilities
+    ```python
+    Agent(instructions="Your agent's role description")
+    ```
+  </Card>
+  <Card title="Tools" icon="wrench">
+    Built-in tools that agents can use
+    ```python
+    tools=[Tools.internet_search]
+    ```
+  </Card>
+  <Card title="Agents Manager" icon="users">
+    Coordinates multiple agents
+    ```python
+    Agents(agents=[agent1, agent2])
+    ```
+  </Card>
+  <Card title="Sequential Flow" icon="arrow-right">
+    Agents work in sequence, passing results to each other
+  </Card>
+</CardGroup>
+
+## Available Tools
+
+<CardGroup cols={2}>
+  <Card title="Internet Search" icon="search">
+    ```python
+    Tools.internet_search
+    ```
+    Search the internet using DuckDuckGo
+    <Note>
+      Requires: `pip install duckduckgo_search`
+    </Note>
+  </Card>
+</CardGroup>
+
+## Examples
+
+### Basic Research and Summary
+
+```terminal
+pip install praisonaiagents duckduckgo_search
+```
+
+```python
+from praisonaiagents import Agent, Agents, Tools
+
+# Create a research agent
+research_agent = Agent(
+    instructions="You are a research agent to search internet about AI 2024",
+    tools=[Tools.internet_search]
+)
+
+# Create a summarize agent
+summarise_agent = Agent(
+    instructions="You are a summarize agent to summarise research agent's findings"
+)
+
+# Initialize and run agents
+agents = Agents(agents=[research_agent, summarise_agent])
+agents.start()
+```
+
+### Custom Instructions
+
+<CodeGroup>
+```python Basic
+# Simple instructions
+research_agent = Agent(
+    instructions="Research about climate change"
+)
+```
+
+```python Advanced
+# Detailed instructions
+research_agent = Agent(
+    instructions="""
+    You are a research agent focused on gathering information about:
+    1. Latest AI developments in 2024
+    2. Major breakthroughs in machine learning
+    3. New AI applications in industry
+    
+    Provide detailed and accurate information from reliable sources.
+    """
+)
+```
+</CodeGroup>
+
+## Best Practices
+
+<AccordionGroup>
+  <Accordion title="Agent Instructions">
+    Write clear and specific instructions:
+    ```python
+    # Good
+    Agent(instructions="Research and analyze the latest AI developments in 2024")
+    
+    # Too vague
+    Agent(instructions="Research AI")
+    ```
+  </Accordion>
+
+  <Accordion title="Tool Usage">
+    Provide tools only to agents that need them:
+    ```python
+    # Research agent needs search
+    research_agent = Agent(
+        instructions="Research AI",
+        tools=[Tools.internet_search]
+    )
+    
+    # Summary agent doesn't need search
+    summary_agent = Agent(
+        instructions="Summarize findings"
+    )
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Common Patterns
+
+### Research and Analysis
+```python
+# Research agent
+researcher = Agent(
+    instructions="Research latest developments in quantum computing",
+    tools=[Tools.internet_search]
+)
+
+# Analysis agent
+analyst = Agent(
+    instructions="Analyze and explain the research findings in simple terms"
+)
+
+agents = Agents(agents=[researcher, analyst])
+```
+
+### Information Processing
+```python
+# Data collector
+collector = Agent(
+    instructions="Collect information about renewable energy",
+    tools=[Tools.internet_search]
+)
+
+# Summarizer
+summarizer = Agent(
+    instructions="Create a concise summary of the collected information"
+)
+
+# Report writer
+writer = Agent(
+    instructions="Write a detailed report based on the summary"
+)
+
+agents = Agents(agents=[collector, summarizer, writer])
+```
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Tool Not Available" icon="triangle-exclamation">
+    If using `Tools.internet_search`, install required package:
+    ```bash
+    pip install duckduckgo_search
+    ```
+  </Card>
+  <Card title="Agent Communication" icon="comments">
+    Ensure agent instructions are clear and complementary
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="Advanced Agents" icon="graduation-cap" href="./advanced">
+    Learn about advanced agent configurations
+  </Card>
+  <Card title="Custom Tools" icon="screwdriver-wrench" href="./tools">
+    Create your own agent tools
+  </Card>
+</CardGroup>
+
+<Note>
+  Mini AI Agents are designed to be simple yet powerful. They're perfect for quick prototypes and straightforward automation tasks.
+</Note>

docs/features/reasoning.mdx

@@ -0,0 +1,239 @@
+---
+title: "Reasoning AI Agents"
+sidebarTitle: "Reasoning"
+description: "Reasoning enables AI agents to think logically, solve complex problems autonomously, and make human-like decisions through multi-step logical reasoning."
+icon: "brain-circuit"
+---
+
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/KNDVWGN3TpM"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
+## Overview
+
+Reasoning in PraisonAI agents enables:
+- Autonomous problem solving
+- Multi-step logical reasoning
+- Human-like decision making
+- Deep comprehension of tasks
+- Error handling with alternative solutions
+
+## Usage
+
+Reasoning is controlled through reflection cycles using `min_reflect` and `max_reflect` parameters:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="SystemOps",
+    role="System Administrator",
+    goal="Manage and monitor system operations",
+    backstory="You are an expert system administrator",
+    min_reflect=6,  # Minimum reflection cycles
+    max_reflect=10  # Maximum reflection cycles
+)
+```
+
+## Benefits
+
+- **Deep Comprehension**: Instead of rule-based responses, agents understand tasks deeply
+- **Error Recovery**: Agents can reason through errors and find alternative solutions
+- **Step-by-Step Processing**: Complex tasks are broken down and processed systematically
+- **Quality Control**: Multiple reflection cycles ensure high-quality outputs
+- **Adaptive Problem Solving**: Agents can adjust their approach based on reasoning
+
+## Example
+
+Here's a complete example showing reasoning in action:
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import subprocess
+import os
+
+def run_terminal_command(command: str):
+    """
+    Run a terminal command and return its output.
+    """
+    try:
+        result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        print(f"Command output: {result}")
+        return {"stdout": result.stdout, "stderr": result.stderr}
+    except subprocess.CalledProcessError as e:
+        return {"error": str(e), "stdout": e.stdout, "stderr": e.stderr}
+
+def save_to_file(file_path: str, content: str):
+    """
+    Save the given content to the specified file path. Create the folder/file if it doesn't exist.
+    """
+    # Ensure the directory exists
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    
+    # Write the content to the file
+    with open(file_path, 'w') as file:
+        file.write(content)
+
+    return file_path
+
+# Create System Operations Agent
+system_ops_agent = Agent(
+    name="SystemOps",
+    role="System Operations Specialist",
+    goal="Execute and manage complex system operations and commands",
+    backstory="""You are an expert system administrator with deep knowledge of Unix/Linux systems.
+    You excel at running complex system commands, managing processes, and handling system operations.
+    You always validate commands before execution and ensure they are safe to run.""",
+    min_reflect=6,
+    max_reflect=10,
+    tools=[run_terminal_command, save_to_file],
+    llm="gpt-4o-mini"
+)
+
+# Create a complex task that tests various system operations
+task = Task(
+    name="system_analysis_task",
+    description="""Perform a comprehensive system analysis by executing the following operations in sequence:
+    1. Get system information (OS, kernel version)
+    2. List 5 running processes and sort them by CPU usage
+    3. Check disk space usage and list directories over 1GB
+    4. Display current system load and memory usage
+    5. List 5 network connections
+    6. Create a summary report with all findings in a text file called system_report.txt
+    
+    Do it step by step. One task at a time. 
+    Save only the Summary report in the file.
+    Use appropriate commands for each step and ensure proper error handling.""",
+    expected_output="A comprehensive system report containing all requested information saved in system_report.txt",
+    agent=system_ops_agent,
+)
+
+agents = PraisonAIAgents(
+    agents=[system_ops_agent],
+    tasks=[task],
+    process="sequential"
+)
+
+result = agents.start()
+```
+
+## e2b Tool Integration Example
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
+import json
+from e2b_code_interpreter import Sandbox
+
+def code_interpret(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})
+
+# 1) Create Agents
+web_scraper_agent = Agent(
+    name="WebScraper",
+    role="Web Scraper",
+    goal="Extract URLs from https://quotes.toscrape.com/",
+    backstory="An expert in data extraction from websites, adept at navigating and retrieving detailed information.",
+    tools=[code_interpret],
+    llm="gpt-4o"
+)
+
+url_data_extractor_agent = Agent(
+    name="URLDataExtractor",
+    role="URL Data Extractor",
+    goal="Crawl each URL for data extraction",
+    backstory="Specializes in crawling websites to gather comprehensive data, ensuring nothing is missed from each link.",
+    tools=[code_interpret],
+    llm="gpt-4o"
+)
+
+blog_writer_agent = Agent(
+    name="BlogWriter",
+    role="Creative Content Writer",
+    goal="Create engaging and insightful blog posts from provided data",
+    backstory="An experienced content creator and storyteller with a knack for weaving compelling narratives. Skilled at analyzing quotes and creating meaningful connections that resonate with readers.",
+    tools=[code_interpret],
+    llm="gpt-4o"
+)
+
+# 2) Create Tasks
+task_url_extraction = Task(
+    name="url_extraction_task",
+    description="""Use code_interpret to run Python code that fetches https://quotes.toscrape.com/
+    and extracts 5 URLs from the page, then outputs them as a list. Only first 5 urls""",
+    expected_output="A list of URLs extracted from the source page.",
+    agent=web_scraper_agent,
+    output_file="urls.txt" 
+)
+
+task_data_extraction = Task(
+    name="data_extraction_task",
+    description="""Take the URLs from url_extraction_task, crawl each URL and extract all pertinent
+    data using code_interpret to run Python code, then output raw txt, not the html.""",
+    expected_output="Raw data collected from each crawled URL.",
+    agent=url_data_extractor_agent,
+    context=[task_url_extraction]
+)
+
+blog_writing_task = Task(
+    name="blog_writing_task",
+    description="""Write an engaging blog post using the extracted quotes data. The blog post should:
+    1. Have a catchy title
+    2. Include relevant quotes from the data
+    3. Provide commentary and insights about the quotes
+    4. Be well-structured with introduction, body, and conclusion
+    5. Be approximately 500 words long""",
+    expected_output="A well-written blog post incorporating the extracted quotes with analysis",
+    agent=blog_writer_agent,
+    context=[task_data_extraction],
+    output_file="blog_post.txt"  
+)
+
+# 3) Create and run Agents manager
+agents_manager = PraisonAIAgents(
+    agents=[web_scraper_agent, url_data_extractor_agent, blog_writer_agent],
+    tasks=[task_url_extraction, task_data_extraction, blog_writing_task],
+    verbose=True,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
+
+result = agents_manager.start()
+```
+
+## Integration with Tools
+
+Reasoning works particularly well with tools that require complex decision-making:
+
+- **Code Interpreter**: For dynamic code generation and execution
+- **System Operations**: For multi-step system management tasks
+- **Data Analysis**: For complex data processing workflows
+- **Web Scraping**: For intelligent data extraction and processing
+
+## Performance Considerations
+
+- Higher reflection cycles mean more thorough reasoning but longer processing time
+- `min_reflect` ensures minimum quality standards
+- `max_reflect` prevents excessive processing
+- Balance based on task complexity and quality requirements

docs/features/selfreflection.mdx

@@ -0,0 +1,102 @@
+---
+title: "Self Reflection AI Agents"
+sidebarTitle: "Self-Reflection"
+description: "Self-reflection is a powerful feature in PraisonAI that enables agents to evaluate and improve their own responses before delivering them. This leads to higher quality outputs and more reliable task completion."
+icon: "brain"
+---
+
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/vLXobEN2Vc8"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
+## Overview
+
+When an agent with self-reflection enabled processes a task:
+1. It generates an initial response using its tools and capabilities
+2. The agent then reflects on its own response, evaluating:
+   - Completeness
+   - Accuracy
+   - Relevance
+   - Quality
+3. Based on this reflection, the agent may revise and improve its response
+4. This cycle can repeat until the agent is satisfied with the quality
+
+## Usage
+
+Self-reflection is enabled by default for all agents. You can explicitly control it using the `self_reflect` parameter:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI",
+    backstory="You are an expert at analyzing tech trends",
+    self_reflect=True  # Enable self-reflection (default)
+)
+```
+
+## Benefits
+
+- **Higher Quality Outputs**: Agents can catch and correct potential issues before delivering responses
+- **More Reliable Task Completion**: Self-reflection helps ensure all aspects of a task are properly addressed
+- **Conscious Decision Making**: Agents evaluate their own work, leading to more thoughtful responses
+- **Improved Accuracy**: Multiple reflection cycles help refine and perfect the output
+
+## Integration with Processes
+
+Self-reflection works seamlessly with both sequential and hierarchical processes:
+
+- **Sequential**: Each agent self-reflects on their individual tasks
+- **Hierarchical**: Agents self-reflect before reporting to the manager agent, ensuring high-quality inputs for decision-making
+
+## Example
+
+Here's a complete example showing self-reflection in action:
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI",
+    backstory="You are an expert at analyzing tech trends",
+    self_reflect=True,
+    llm="gpt-4o"
+)
+
+task = Task(
+    name="research_task",
+    description="Analyze 2024's AI advancements",
+    expected_output="A detailed report on AI trends",
+    agent=researcher
+)
+
+agents = PraisonAIAgents(
+    agents=[researcher],
+    tasks=[task],
+    process="sequential"
+)
+
+result = agents.start()
+```
+
+In this example, the researcher agent will:
+1. Research AI advancements
+2. Generate an initial report
+3. Self-reflect on the report's quality
+4. Make improvements if needed
+5. Deliver the final, refined output
+
+## Performance Impact
+
+While self-reflection adds additional processing time, the improvement in output quality often justifies the extra computation. 
+The number of reflection cycles need to be manually optimized to balance quality and performance.

docs/firecrawl.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Firecrawl PraisonAI Integration"
 description: "Guide for integrating Firecrawl web scraping capabilities with PraisonAI, including custom tool implementation for web page content extraction"
+icon: "spider"
 ---
 
 # Firecrawl PraisonAI Integration

docs/framework/autogen.mdx

@@ -1,6 +1,7 @@
 ---
 title: "AutoGen with PraisonAI"
 description: "Guide for using AutoGen framework with PraisonAI, including installation, setup, and multi-agent conversation capabilities"
+icon: "robot"
 ---
 
 # AutoGen with PraisonAI

docs/framework/crewai.mdx

@@ -1,6 +1,7 @@
 ---
 title: "CrewAI with PraisonAI"
 description: "Guide for using CrewAI framework with PraisonAI, including installation, setup, and execution of agent-based tasks"
+icon: "users"
 ---
 
 # CrewAI with PraisonAI

docs/framework/multimodalagents.mdx

@@ -1,8 +1,19 @@
 ---
 title: "Multimodal AI Agents"
 description: "Guide for creating and using multimodal AI agents in PraisonAI for processing images, videos, and other media types"
+icon: "images"
 ---
 
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/hjAWmUT1qqY"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>
+
 # Multimodal AI Agents
 
 PraisonAI supports multimodal AI agents capable of processing various types of media including images and videos. This enables you to create agents that can understand and analyze visual content alongside text.

docs/framework/praisonaiagents.mdx

@@ -1,6 +1,7 @@
 ---
 title: "PraisonAI Agents"
 description: "Guide for using PraisonAI Agents framework, a lightweight package for creating and managing AI agents with advanced capabilities"
+icon: "users-gear"
 ---
 
 A lightweight package dedicated to creating and managing AI agents with advanced capabilities.

docs/home.mdx

@@ -1,131 +1,327 @@
 ---
 title: "Praison AI"
-description: "Home page for PraisonAI documentation, featuring installation options, user interfaces, and quick start guides for multi-agent LLM systems"
+sidebarTitle: "Home"
+description: "Comprehensive guide to PraisonAI - an AI Agents Framework with Self Reflection, combining PraisonAI Agents, AutoGen, and CrewAI for building multi-agent LLM systems"
+icon: "house"
+openGraph:
+  url: "/"
+seo:
+  canonical: "/"
 ---
-
-# Home
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/Fn1lQjC0GO0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="/logo/dark.png" />
-    <source media="(prefers-color-scheme: light)" srcset="/logo/light.png" />
-    <img alt="PraisonAI Logo" src="/logo/light.png" style={{display: "block", margin: "auto"}} />
-  </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>
-
-# Praison AI
-
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
-
-<div align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="images/architecture-dark.png" />
-    <source media="(prefers-color-scheme: light)" srcset="images/architecture-light.png" />
-    <img alt="PraisonAI Architecture" src="images/architecture-light.png" />
-  </picture>
+<Note>
+  Welcome to PraisonAI - Your comprehensive solution for building and managing multi-agent LLM systems with self-reflection capabilities.
+</Note>
+<div className="flex justify-start w-full max-w-xs sm:max-w-sm md:max-w-md">
+  <img 
+    src="images/praisonai-logo-black.png"
+    alt="PraisonAI Logo"
+    className="w-full h-auto rounded-lg shadow-md block dark:hidden"
+    width="400"
+    height="100"
+  />
+  <img 
+    src="images/praisonai-logo-light.png"
+    alt="PraisonAI Logo"
+    className="w-full h-auto rounded-lg shadow-md hidden dark:block"
+    width="400"
+    height="100"
+  />
+</div>
+<div className="flex flex-wrap gap-4 my-8">
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/stars/MervinPraison/PraisonAI?style=social" alt="GitHub Stars" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/forks/MervinPraison/PraisonAI?style=social" alt="GitHub Forks" />
+  </div>
 </div>
 
-## Different User Interfaces:
+<Tip>
+  PraisonAI combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration.
+</Tip>
 
-| 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) |
+## Key Features
 
-## 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> |
+<CardGroup cols={2}>
+  <Card title="AI Agents Creation" icon="robot">
+    Automated creation and management of AI agents with self-reflection capabilities
+  </Card>
+  <Card title="Framework Integration" icon="puzzle-piece">
+    Seamless integration with CrewAI and AutoGen frameworks
+  </Card>
+  <Card title="LLM Support" icon="brain">
+    Support for 100+ Language Learning Models
+  </Card>
+  <Card title="Code Integration" icon="code">
+    Chat with your entire codebase using advanced context understanding
+  </Card>
+  <Card title="Interactive UI" icon="desktop">
+    Rich, interactive user interfaces for better control and monitoring
+  </Card>
+  <Card title="Configuration" icon="gear">
+    YAML-based configuration for easy setup and customization
+  </Card>
+  <Card title="Tool Integration" icon="screwdriver-wrench">
+    Custom tool integration for extended functionality
+  </Card>
+  <Card title="Search Capability" icon="magnifying-glass">
+    Internet search using Crawl4AI and Tavily
+  </Card>
+</CardGroup>
+<br />
+<div className="relative w-full aspect-video overflow-hidden rounded-lg shadow-lg mb-8">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full border-0"
+    src="https://www.youtube.com/embed/Fn1lQjC0GO0"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+    style={{
+      maxWidth: '100vw',
+      margin: '0 auto'
+    }}
+  ></iframe>
+</div>
 
 ## Installation Options
 
-### Basic Installation
-```bash
-pip install praisonai
-```
+<Tabs>
+  <Tab title="No Code">
+    <Steps>
+      <Step title="Install Package">
+        ```bash
+        pip install praisonai
+        ```
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Auto Mode">
+        ```bash
+        praisonai --auto create a movie script about Robots in Mars
+        ```
 
-### Framework-specific Installation
-```bash
-# Install with CrewAI support
-pip install "praisonai[crewai]"
+        This will automatically create required agents and complete the task
 
-# Install with AutoGen support
-pip install "praisonai[autogen]"
+      </Step>
+    </Steps>
+  </Tab>
+  <Tab title="Code">
+    <Steps>
+      <Step title="Install Package">
+        ```bash
+        pip install praisonaiagents
+        ```
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Create File">
+        Create `app.py` file
 
-# Install with both frameworks
-pip install "praisonai[crewai,autogen]"
-```
+        ## Code Example
 
-### UI and Additional Features
-```bash
-pip install "praisonai[ui]"        # UI support
-pip install "praisonai[chat]"      # Chat interface
-pip install "praisonai[code]"      # Code interface
-pip install "praisonai[realtime]"  # Realtime voice interaction
-pip install "praisonai[call]"      # Call feature
-```
+<CodeGroup>
+  ```python Basic
+  from praisonaiagents import Agent, Task, PraisonAIAgents
 
-## TL;DR Multi Agents
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
 
-```bash
-pip install praisonai
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init create a movie script about dog in moon
-praisonai
-```
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
 
-## Initialise
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
 
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
+  result = agents.start()
+  ```
 
-Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
+  ```python Advanced
+  from praisonaiagents import Agent, Task, PraisonAIAgents
 
-Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+      </Step>
+      <Step title="Run Script">
+        ```bash
+        python app.py
+        ```
+      </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+## Integration Options
+
+<AccordionGroup>
+  <Accordion title="Ollama Integration">
+    ```bash
+    export OPENAI_BASE_URL=http://localhost:11434/v1
+    ```
+  </Accordion>
+  <Accordion title="Groq Integration">
+    ```bash
+    export OPENAI_API_KEY=xxxxxxxxxxx
+    export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+    ```
+  </Accordion>
+  <Accordion title="Logging Configuration">
+    ```bash
+    # Basic logging
+    export LOGLEVEL=info
     
-```bash
-praisonai --init create a movie script about dog in moon
-```
-This will automatically create agents.yaml file in the current directory.
+    # Advanced logging
+    export LOGLEVEL=debug
+    ```
+  </Accordion>
+</AccordionGroup>
 
-### To initialse with a specific agent framework (Optional):
+## Use Cases
 
-```bash
-praisonai --framework autogen --init create movie script about cat in mars
-```
+<CardGroup cols={2}>
+  <Card title="Customer Service" icon="headset">
+    Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.
+  </Card>
+  <Card title="Data Analysis" icon="chart-line">
+    Create agents that can process, analyze, and derive insights from complex datasets.
+  </Card>
+  <Card title="Content Creation" icon="pen-nib">
+    Deploy agents that can generate, edit, and optimize content across various formats.
+  </Card>
+  <Card title="Process Automation" icon="gears">
+    Automate complex workflows with intelligent agents that can coordinate and execute tasks.
+  </Card>
+</CardGroup>
 
-## Run
+## Praison AI Package Overall Features
 
-```bash
-praisonai
-```
+<Frame caption="PraisonAI Features Overview">
+  <div className="w-full max-w-4xl mx-auto">
+    <img 
+      src="images/architecture-light.png"
+      alt="PraisonAI Features"
+      className="w-full h-auto rounded-lg shadow-lg block dark:hidden"
+      width="1200"
+      height="800"
+    />
+    <img 
+      src="images/architecture-dark.png"
+      alt="PraisonAI Features"
+      className="w-full h-auto rounded-lg shadow-lg hidden dark:block"
+      width="1200"
+      height="800"
+    />
+  </div>
+</Frame>
 
-or 
-    
-```bash
-python -m praisonai
-```
+## Features
 
-### Specify the agent framework (Optional):
+<CardGroup cols={3}>
+  <Card title="Self-Reflection" icon="brain-circuit" href="/features/selfreflection">
+    Agents that evaluate and improve their own responses for higher accuracy
+  </Card>
+  <Card title="Reasoning" icon="gears" href="/features/reasoning">
+    Multi-step logical reasoning and autonomous problem solving
+  </Card>
+  <Card title="CrewAI Framework" icon="users-gear" href="/framework/crewai">
+    Build collaborative AI teams with CrewAI integration
+  </Card>
+  <Card title="AutoGen Framework" icon="robot" href="/framework/autogen">
+    Create autonomous agent networks using AutoGen
+  </Card>
+  <Card title="Multimodal Agents" icon="icons" href="/framework/multimodalagents">
+    Work with agents that can process text, images, and other data types
+  </Card>
+  <Card title="Train" icon="graduation-cap" href="/train">
+    Train and fine-tune your LLMs for specific tasks and domains. Then use it as an AI Agent.
+  </Card>
+</CardGroup>
 
-```bash
-praisonai --framework autogen
-```
+## User Interfaces
 
-### Full Automatic Mode
-
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```
+<CardGroup cols={3}>
+  <Card title="Multi Agents UI" icon="users" href="/ui/ui">
+    Work with CrewAI or AutoGen multi-agent systems
+  </Card>
+  <Card title="Chat Interface" icon="comments" href="/ui/chat">
+    Chat with 100+ LLMs using a single AI Agent
+  </Card>
+  <Card title="Code Interface" icon="code" href="/ui/code">
+    Interact with your entire codebase
+  </Card>
+</CardGroup>

docs/index.mdx

@@ -1,9 +1,73 @@
 ---
 title: "Praison AI"
+sidebarTitle: "Home"
 description: "Comprehensive guide to PraisonAI - an AI Agents Framework with Self Reflection, combining PraisonAI Agents, AutoGen, and CrewAI for building multi-agent LLM systems"
+icon: "house"
+openGraph:
+  url: "/"
+seo:
+  canonical: "/"
 ---
+<div className="flex justify-start w-full max-w-xs sm:max-w-sm md:max-w-md">
+  <img 
+    src="images/praisonai-logo-black.png"
+    alt="PraisonAI Logo"
+    className="w-full h-auto rounded-lg shadow-md block dark:hidden"
+    width="400"
+    height="100"
+  />
+  <img 
+    src="images/praisonai-logo-light.png"
+    alt="PraisonAI Logo"
+    className="w-full h-auto rounded-lg shadow-md hidden dark:block"
+    width="400"
+    height="100"
+  />
+</div>
+<div style={{ display: 'flex', flexWrap: 'wrap', gap: '1rem' }}>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/stars/MervinPraison/PraisonAI?style=social" alt="GitHub Stars" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/forks/MervinPraison/PraisonAI?style=social" alt="GitHub Forks" />
+  </div>
+</div>
+<a href="https://trendshift.io/repositories/9130" target="_blank">
+  <img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison/PraisonAI | Trendshift" />
+</a>
+## Key Features
 
-<div className="relative w-full aspect-video overflow-hidden">
+<CardGroup cols={2}>
+  <Card title="AI Agents Creation" icon="robot">
+    Automated creation and management of AI agents with self-reflection capabilities
+  </Card>
+  <Card title="Framework Integration" icon="puzzle-piece">
+    Seamless integration with CrewAI and AutoGen frameworks
+  </Card>
+  <Card title="LLM Support" icon="brain">
+    Support for 100+ Language Learning Models
+  </Card>
+  <Card title="Code Integration" icon="code">
+    Chat with your entire codebase using advanced context understanding
+  </Card>
+  <Card title="Interactive UI" icon="desktop">
+    Rich, interactive user interfaces for better control and monitoring
+  </Card>
+  <Card title="Configuration" icon="gear">
+    YAML-based configuration for easy setup and customization
+  </Card>
+  <Card title="Tool Integration" icon="screwdriver-wrench">
+    Custom tool integration for extended functionality
+  </Card>
+  <Card title="Search Capability" icon="magnifying-glass">
+    Internet search using Crawl4AI and Tavily
+  </Card>
+</CardGroup>
+<br />
+<div className="relative w-full aspect-video overflow-hidden rounded-lg shadow-lg mb-8">
   <iframe
     className="absolute top-0 left-0 w-full h-full border-0"
     src="https://www.youtube.com/embed/Fn1lQjC0GO0"
@@ -17,228 +81,261 @@ description: "Comprehensive guide to PraisonAI - an AI Agents Framework with Sel
   ></iframe>
 </div>
 
-<div className="flex justify-start w-full max-w-xs sm:max-w-sm md:max-w-md">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="images/praisonai-logo-black.png" />
-    <source media="(prefers-color-scheme: light)" srcset="images/praisonai-logo-light.png" />
+## Installation Options
+
+<Tabs>
+  <Tab title="No Code">
+    <Tip>
+        PraisonAI combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration.
+    </Tip>
+    <Steps>
+      <Step title="Install Package">
+        ```bash
+        pip install praisonai
+        ```
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Auto Mode">
+        ```bash
+        praisonai --auto create a movie script about Robots in Mars
+        ```
+
+        This will automatically create required agents and complete the task
+
+      </Step>
+    </Steps>
+  </Tab>
+  <Tab title="Code">
+    <Steps>
+      <Step title="Install Package">
+        ```bash
+        pip install praisonaiagents
+        ```
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Create File">
+        Create `app.py` file
+
+        ## Code Example
+
+<CodeGroup>
+  ```python Basic
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create an agent
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o"
+  )
+
+  # Define a task
+  task = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  # Run the agents
+  agents = PraisonAIAgents(
+      agents=[researcher],
+      tasks=[task],
+      verbose=False
+  )
+
+  result = agents.start()
+  ```
+
+  ```python Advanced
+  from praisonaiagents import Agent, Task, PraisonAIAgents
+
+  # Create multiple agents
+  researcher = Agent(
+      name="Researcher",
+      role="Senior Research Analyst",
+      goal="Uncover cutting-edge developments in AI",
+      backstory="You are an expert at a technology research group",
+      verbose=True,
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  writer = Agent(
+      name="Writer",
+      role="Tech Content Strategist",
+      goal="Craft compelling content on tech advancements",
+      backstory="You are a content strategist",
+      llm="gpt-4o",
+      markdown=True
+  )
+
+  # Define multiple tasks
+  task1 = Task(
+      name="research_task",
+      description="Analyze 2024's AI advancements",
+      expected_output="A detailed report",
+      agent=researcher
+  )
+
+  task2 = Task(
+      name="writing_task",
+      description="Create a blog post about AI advancements",
+      expected_output="A blog post",
+      agent=writer
+  )
+
+  # Run with hierarchical process
+  agents = PraisonAIAgents(
+      agents=[researcher, writer],
+      tasks=[task1, task2],
+      verbose=False,
+      process="hierarchical",
+      manager_llm="gpt-4o"
+  )
+
+  result = agents.start()
+  ```
+</CodeGroup>
+      </Step>
+      <Step title="Run Script">
+        ```bash
+        python app.py
+        ```
+      </Step>
+    </Steps>
+  </Tab>
+</Tabs>
+
+## Integration Options
+
+<AccordionGroup>
+  <Accordion title="Ollama Integration">
+    ```bash
+    export OPENAI_BASE_URL=http://localhost:11434/v1
+    ```
+  </Accordion>
+  <Accordion title="Groq Integration">
+    ```bash
+    export OPENAI_API_KEY=xxxxxxxxxxx
+    export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+    ```
+  </Accordion>
+  <Accordion title="Logging Configuration">
+    ```bash
+    # Basic logging
+    export LOGLEVEL=info
+    
+    # Advanced logging
+    export LOGLEVEL=debug
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Use Cases
+
+<CardGroup cols={2}>
+  <Card title="Customer Service" icon="headset">
+    Build intelligent support agents that can handle customer inquiries and resolve issues autonomously.
+  </Card>
+  <Card title="Data Analysis" icon="chart-line">
+    Create agents that can process, analyze, and derive insights from complex datasets.
+  </Card>
+  <Card title="Content Creation" icon="pen-nib">
+    Deploy agents that can generate, edit, and optimize content across various formats.
+  </Card>
+  <Card title="Process Automation" icon="gears">
+    Automate complex workflows with intelligent agents that can coordinate and execute tasks.
+  </Card>
+</CardGroup>
+
+## Praison AI Package Overall Features
+
+<Frame caption="PraisonAI Features Overview">
+  <div className="w-full max-w-4xl mx-auto">
     <img 
-      src="images/praisonai-logo-light.png"
-      alt="PraisonAI Logo"
-      className="w-full h-auto"
-      width="400"
-      height="100"
-    />
-  </picture>
-</div>
-
-<div className="flex flex-wrap gap-4 my-4">
-  <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>
-  <a href="https://github.com/MervinPraison/PraisonAI">
-    <img src="https://img.shields.io/github/stars/MervinPraison/PraisonAI?style=social" alt="GitHub Stars" />
-  </a>
-  <a href="https://github.com/MervinPraison/PraisonAI">
-    <img src="https://img.shields.io/github/forks/MervinPraison/PraisonAI?style=social" alt="GitHub Forks" />
-  </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.
-
-## Key Features
-
-- 🤖 Automated AI Agents Creation
-- 🔄 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 className="w-full max-w-4xl mx-auto">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="images/architecture-light.png" />
-    <source media="(prefers-color-scheme: light)" srcset="images/architecture-dark.png" />
-    <img 
-      src="images/architecture-dark.png"
-      alt="PraisonAI Architecture"
-      className="w-full h-auto"
+      src="images/architecture-light.png"
+      alt="PraisonAI Features"
+      className="w-full h-auto rounded-lg shadow-lg block dark:hidden"
       width="1200"
       height="800"
     />
-  </picture>
-</div>
+    <img 
+      src="images/architecture-dark.png"
+      alt="PraisonAI Features"
+      className="w-full h-auto rounded-lg shadow-lg hidden dark:block"
+      width="1200"
+      height="800"
+    />
+  </div>
+</Frame>
 
+## Features
 
-## Different User Interfaces:
+<CardGroup cols={3}>
+  <Card title="Self-Reflection" icon="brain-circuit" href="/features/selfreflection">
+    Agents that evaluate and improve their own responses for higher accuracy
+  </Card>
+  <Card title="Reasoning" icon="gears" href="/features/reasoning">
+    Multi-step logical reasoning and autonomous problem solving
+  </Card>
+  <Card title="CrewAI Framework" icon="users-gear" href="/framework/crewai">
+    Build collaborative AI teams with CrewAI integration
+  </Card>
+  <Card title="AutoGen Framework" icon="robot" href="/framework/autogen">
+    Create autonomous agent networks using AutoGen
+  </Card>
+  <Card title="Multimodal Agents" icon="icons" href="/framework/multimodalagents">
+    Work with agents that can process text, images, and other data types
+  </Card>
+  <Card title="Train" icon="graduation-cap" href="/train">
+    Train and fine-tune your LLMs for specific tasks and domains. Then use it as an AI Agent.
+  </Card>
+</CardGroup>
 
-| 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) |
+## User Interfaces
 
-## Google Colab Multi Agents
+<CardGroup cols={3}>
+  <Card title="Multi Agents UI" icon="users" href="/ui/ui">
+    Work with CrewAI or AutoGen multi-agent systems
+  </Card>
+  <Card title="Chat Interface" icon="comments" href="/ui/chat">
+    Chat with 100+ LLMs using a single AI Agent
+  </Card>
+  <Card title="Code Interface" icon="code" href="/ui/code">
+    Interact with your entire codebase
+  </Card>
+</CardGroup>
 
-|               | 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> |
+<Note>
+  Welcome to PraisonAI - Your comprehensive solution for building and managing multi-agent LLM systems with self-reflection capabilities.
+</Note>
 
-## Install
-
-| PraisonAI | PraisonAI Code | PraisonAI Chat |
-| --- | --- | --- |
-| `pip install praisonai` | `pip install "praisonai[code]"` | `pip install "praisonai[chat]"` |
-
-## 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
-```
-
-## Prerequisite:
-### Export API KEY
-
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
-
-## Installation
-
-```bash
-pip install praisonai
-```
-
-## Automatically Create Agents to Perform a Task
-
-```bash
-praisonai --init create a movie script about dog in moon
-```
-## Run
-```bash
-praisonai
-```
+<div style={{ display: 'flex', flexWrap: 'wrap', gap: '1rem' }}>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/stars/MervinPraison/PraisonAI?style=social" alt="GitHub Stars" />
+  </div>
+  <div className="hover:opacity-80 transition-opacity">
+    <img src="https://img.shields.io/github/forks/MervinPraison/PraisonAI?style=social" alt="GitHub Forks" />
+  </div>
+</div>

docs/initialise.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Initialise"
 description: "Instructions for initializing PraisonAI with API keys and creating the initial agents configuration file"
+icon: "power-off"
 ---
 
 ```bash

docs/installation.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Installation"
 description: "Guide for installing PraisonAI and its various components including framework support and additional features"
+icon: "download"
 ---
 
 ## Basic Installation

docs/introduction.mdx

@@ -1,26 +1,76 @@
 ---
 title: 'Introduction'
 description: 'Get started with PraisonAI, an intelligent AI-powered tool'
+icon: "hand-wave"
 ---
 
-Welcome to PraisonAI documentation! PraisonAI is a low-code, centralized framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+<Note>
+  PraisonAI is a low-code, centralized framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications.
+</Note>
 
-## Key Features
+# Welcome to PraisonAI
 
-- Leverages both AutoGen and CrewAI
-- Simplifies multi-agent system creation
-- Emphasizes ease of use and customization
-- Supports human-agent interaction
+<Frame caption="PraisonAI Framework Overview">
+  ![PraisonAI Overview](/images/praisonai-overview.png)
+</Frame>
+
+## Core Features
+
+<CardGroup cols={2}>
+  <Card title="Multi-Agent Systems" icon="users">
+    Create and orchestrate sophisticated multi-agent systems with ease
+  </Card>
+  <Card title="Framework Integration" icon="puzzle-piece">
+    Seamlessly integrates AutoGen and CrewAI capabilities
+  </Card>
+  <Card title="Low-Code Design" icon="code">
+    Build complex AI systems with minimal coding required
+  </Card>
+  <Card title="Human-AI Interaction" icon="people-arrows">
+    Enhanced support for human-agent collaboration
+  </Card>
+</CardGroup>
 
 ## Getting Started
 
-To get started with PraisonAI, check out our [Installation](./installation) guide.
+<Steps>
+  <Step title="Installation">
+    Follow our [Installation Guide](./installation) to set up PraisonAI
+  </Step>
+  <Step title="Quick Overview">
+    Check out our [TL;DR](./tldr) for a rapid introduction
+  </Step>
+  <Step title="First Project">
+    Create your first project following our [Quick Start](./quickstart) guide
+  </Step>
+</Steps>
 
-For a quick overview, see our [TL;DR](./tldr) page.
+## Key Components
+
+| Component | Description | Benefits |
+|:----------|:------------|:---------|
+| **UI Tools** | Intuitive interface components | Easy system interaction and monitoring |
+| **Model Integration** | Support for various LLM models | Flexibility in AI model selection |
+| **Tool System** | Extensible tool framework | Custom capability integration |
+| **API Layer** | Comprehensive API access | Full programmatic control |
 
 ## Explore Further
 
-- [User Interface](./ui/ui)
-- [Models](./models)
-- [Tools](./tools)
-- [API Reference](./api-reference)
+<CardGroup cols={2}>
+  <Card title="User Interface" icon="desktop" href="./ui/ui">
+    Learn about our intuitive UI components
+  </Card>
+  <Card title="Models" icon="robot" href="./models">
+    Explore supported AI models
+  </Card>
+  <Card title="Tools" icon="wrench" href="./tools">
+    Discover available tools and integrations
+  </Card>
+  <Card title="API Reference" icon="code" href="./api-reference">
+    Access comprehensive API documentation
+  </Card>
+</CardGroup>
+
+<Tip>
+  Start with our no-code interface to learn the basics, then transition to code-based development for more advanced use cases.
+</Tip>

docs/mint.json

@@ -1,15 +1,29 @@
 {
+  "$schema": "https://mintlify.com/schema.json",
   "name": "PraisonAI Documentation",
   "logo": {
     "light": "/images/praisonai-logo-large-dark.png",
-    "dark": "/images/praisonai-logo-large-light.png"
+    "dark": "/images/praisonai-logo-large-light.png",
+    "href": "/"
   },
+  "layout": "sidenav",
   "favicon": "/images/praisonai-android-chrome-192x192.png",
+  "sidebar": {
+    "items": "border"
+  },
   "colors": {
     "primary": "#0069ED",
     "light": "#4D9CFF",
     "dark": "#0050B4"
   },
+  "api": {
+    "auth": {
+      "method": "bearer"
+    }
+  },
+  "background": {
+    "style": "windows"
+  },
   "mobileBreakpoint": 768,
   "viewport": {
     "width": "device-width",
@@ -17,14 +31,53 @@
     "maximumScale": 1,
     "userScalable": false
   },
-  "topbarLinks": [],
+  "topbarLinks": [
+    {
+      "name": "Community",
+      "url": "https://github.com/MervinPraison/PraisonAI/discussions"
+    }
+  ],
   "topbarCtaButton": {
     "type": "github",
     "url": "https://github.com/MervinPraison/PraisonAI"
   },
+  "codeBlock": {
+    "mode": "auto"
+  },
+  "feedback": {
+    "thumbsRating": true,
+    "suggestEdit": true
+  },
+  "redirects": [
+    {
+      "source": "/index",
+      "destination": "/"
+    }
+  ],
   "navigation": [
     {
-      "group": "Getting Started",
+      "group": "",
+      "pages": ["index"]
+    },
+    {
+      "group": "Getting Started (Code)",
+      "pages": [
+        "code/introduction",
+        "code/installation",
+        "code/quickstart"
+      ]
+    },
+    {
+      "group": "Core Concepts",
+      "pages": [
+        "concepts/agents",
+        "concepts/tasks",
+        "concepts/process",
+        "concepts/tools"
+      ]
+    },
+    {
+      "group": "Getting Started (No Code)",
       "pages": [
         "introduction",
         "tldr",
@@ -44,11 +97,16 @@
       ]
     },
     {
-      "group": "Core Concepts",
+      "group": "Features",
       "pages": [
+        "features/selfreflection",
+        "features/reasoning",
+        "framework/multimodalagents",
+        "features/async",
+        "features/mini",
+        "features/callbacks",
         "train",
         "framework/praisonaiagents",
-        "framework/multimodalagents",
         "framework/crewai",
         "framework/autogen"
       ]
@@ -58,18 +116,18 @@
       "pages": [
         "models",
         "models/openai",
+        "models/ollama",
         "models/groq",
         "models/google",
+        "models/openrouter",
         "models/anthropic",
         "models/cohere",
         "models/mistral",
-        "models/openrouter",
-        "models/ollama",
         "models/other"
       ]
     },
     {
-      "group": "Tools",
+      "group": "Tools (No Code)",
       "pages": [
         "tools",
         "tools/custom",
@@ -104,12 +162,48 @@
     },
     {
       "group": "API Reference",
-      "pages": ["api-reference/index"]
+      "pages": [
+        "api/praisonaiagents/index",
+        "api/praisonaiagents/agent/agent",
+        "api/praisonaiagents/agents/agents",
+        "api/praisonaiagents/task/task",
+        "api/praisonaiagents/process/process"
+      ]
     }
   ],
   "footerSocials": {
     "github": "https://github.com/MervinPraison/PraisonAI",
     "x": "https://x.com/MervinPraison",
     "youtube": "https://youtube.com/@MervinPraison"
+  },
+  "footer": {
+    "links": [
+      {
+        "title": "Resources",
+        "links": [
+          {
+            "label": "GitHub",
+            "url": "https://github.com/MervinPraison/PraisonAI"
+          },
+          {
+            "label": "Documentation",
+            "url": "https://docs.praisonai.com"
+          }
+        ]
+      },
+      {
+        "title": "Community",
+        "links": [
+          {
+            "label": "Discussions",
+            "url": "https://github.com/MervinPraison/PraisonAI/discussions"
+          },
+          {
+            "label": "Twitter",
+            "url": "https://x.com/MervinPraison"
+          }
+        ]
+      }
+    ]
   }
 }

docs/models.mdx

@@ -1,8 +1,50 @@
 ---
 title: "Models in PraisonAI"
 description: "Overview of supported language models in PraisonAI, including OpenAI, Groq, Google Gemini, Anthropic Claude, and configuration examples"
+icon: "brain"
 ---
 
+## Supported Models for Code
+
+<AccordionGroup>
+  <Accordion title="Ollama Integration" defaultOpen>
+    ```bash
+    export OPENAI_BASE_URL=http://localhost:11434/v1
+    ```
+  </Accordion>
+  <Accordion title="Groq Integration" defaultOpen>
+    ```bash
+    export OPENAI_API_KEY=xxxxxxxxxxx
+    export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+    ```
+  </Accordion>
+  <Accordion title="Google Gemini" defaultOpen> 
+    ```bash
+    export OPENAI_API_KEY=xxxxxxxxxxx
+    export OPENAI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/
+    ```
+  </Accordion>
+  <Accordion title="Jan AI Integration" defaultOpen>
+    ```bash
+    export OPENAI_BASE_URL=http://localhost:1337/v1
+    ```
+  </Accordion>
+  <Accordion title="LM Studio Integration" defaultOpen>
+    ```bash
+    export OPENAI_BASE_URL=http://localhost:1234/v1
+    ```
+  </Accordion>
+  <Accordion title="OpenRouter Integration" defaultOpen>
+    ```bash
+    export OPENAI_API_KEY=xxxxxxxxxxx
+    export OPENAI_BASE_URL=https://openrouter.ai/api/v1
+    ```
+  </Accordion>
+
+</AccordionGroup>
+
+## Supported Models for No Code
+
 | PraisonAI Chat | PraisonAI Code | PraisonAI (Multi-Agents) |
 | --- | --- | --- |
 | [Litellm](https://litellm.vercel.app/docs/providers) | [Litellm](https://litellm.vercel.app/docs/providers) | Below Models |

docs/models/anthropic.mdx

@@ -1,10 +1,13 @@
 ---
 title: "Anthropic"
 description: "Instructions for integrating Anthropic's Claude models with PraisonAI, including API setup and agent configuration"
+icon: "robot"
 ---
 
 # Add Anthropic to Praison AI
 
+## No Code
+
 ```bash
 pip install langchain-anthropic
 ```

docs/models/cohere.mdx

@@ -1,10 +1,13 @@
 ---
 title: "Cohere"
 description: "Guide for integrating Cohere's language models with PraisonAI, including API setup and configuration examples"
+icon: "brain"
 ---
 
 # Add COHERE to Praison AI
 
+## No Code
+
 ```bash
 pip install langchain-cohere
 ```

docs/models/google.mdx

@@ -1,10 +1,23 @@
 ---
 title: "Google Gemini"
 description: "Instructions for integrating Google's Gemini models with PraisonAI, including API setup and configuration details"
+icon: "google"
 ---
 
 # Add Google Gemini to Praison AI
 
+## Code
+
+<AccordionGroup>
+  <Accordion title="Google Gemini" defaultOpen> 
+    ```bash
+    export OPENAI_API_KEY=xxxxxxxxxxx
+    export OPENAI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/
+    ```
+  </Accordion>
+</AccordionGroup>
+## No Code
+
 ```bash
 pip install langchain-google-genai
 ```

docs/models/groq.mdx

@@ -1,10 +1,24 @@
 ---
 title: "Groq"
 description: "Guide for integrating Groq's high-performance language models with PraisonAI, including API setup and configuration"
+icon: "bolt"
 ---
 
 # Add GROQ to Praison AI
 
+## Code
+
+<AccordionGroup>
+  <Accordion title="Groq Integration" defaultOpen>
+    ```bash
+    export OPENAI_API_KEY=xxxxxxxxxxx
+    export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## No Code 
+
 ```bash
 export GROQ_API_KEY=xxxxxxxxxx
 ```

docs/models/mistral.mdx

@@ -1,10 +1,13 @@
 ---
 title: "Mistral"
 description: "Guide for integrating Mistral AI's language models with PraisonAI, including API configuration and usage instructions"
+icon: "wind"
 ---
 
 # Add Mistral to Praison AI
 
+## No Code 
+
 Note: If you want to use Mistral via Ollama, please refer to Ollama document. This is for using Mistral from https://mistral.ai 
 
 ## Using Single Agent

docs/models/ollama.mdx

@@ -1,10 +1,23 @@
 ---
 title: "Ollama"
 description: "Instructions for integrating Ollama's open-source language models with PraisonAI, including configuration and usage examples"
+icon: "terminal"
 ---
 
 # Add Ollama to Praison AI
 
+## Code
+
+<AccordionGroup>
+  <Accordion title="Ollama Integration" defaultOpen>
+    ```bash
+    export OPENAI_BASE_URL=http://localhost:11434/v1
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## No Code
+
 ### agents.yaml file
 
 ```yaml

docs/models/openai.mdx

@@ -1,6 +1,7 @@
 ---
 title: "OpenAI ChatGPT"
 description: "Guide for integrating OpenAI's ChatGPT models with PraisonAI, including configuration for both single and multi-agent setups"
+icon: "sparkles"
 ---
 
 # Add OPENAI ChatGPT to Praison AI

docs/models/openrouter.mdx

@@ -1,6 +1,7 @@
 ---
 title: "OpenRouter"
 description: "Guide for integrating OpenRouter's unified API access to various language models with PraisonAI, including setup and configuration"
+icon: "router"
 ---
 
 # Add OpenRouter to Praison AI

docs/models/other.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Other Models"
 description: "Configuration examples for various other language models with PraisonAI, including Ollama, FastChat, LM Studio, and Mistral API"
+icon: "ellipsis"
 ---
 
 ```bash

docs/monitoring/agentops.mdx

@@ -1,6 +1,7 @@
 ---
 title: "AgentOps PraisonAI Monitoring"
 description: "Guide for setting up and using AgentOps monitoring with PraisonAI, including installation and dashboard configuration"
+icon: "chart-line"
 ---
 
 # AgentOps PraisonAI Monitoring

docs/public/fantasy.svg

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docs/public/game.svg

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docs/public/movie.svg

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docs/public/thriller.svg

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docs/run.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Run"
 description: "Instructions for running PraisonAI from the command line, including framework selection and automatic mode"
+icon: "play"
 ---
 
 ```bash

docs/tldr.mdx

@@ -1,6 +1,7 @@
 ---
 title: "TL;DR"
 description: "Quick start guide for PraisonAI with CrewAI, AutoGen, and UI setup instructions"
+icon: "bolt"
 ---
 
 ## Quick Start with PraisonAI Agents

docs/tools.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Tools"
 description: "Overview of PraisonAI's built-in tools for data search, analysis, and web scraping capabilities"
+icon: "toolbox"
 ---
 
 <div className="relative w-full aspect-video">

docs/tools/composio.mdx

@@ -1,6 +1,7 @@
 ---
 title: "Composio PraisonAI Integration"
 description: "Guide for integrating Composio's extensive tool ecosystem with PraisonAI agents, enabling access to GitHub, Gmail, and other external services"
+icon: "puzzle-piece"
 ---
 
 # Composio PraisonAI Integration