Merge pull request #508 from MervinPraison/develop

Develop

.github/workflows/claude.yml

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

.github/workflows/python-package.yml

@@ -19,9 +19,9 @@ jobs:
         python-version: ["3.10", "3.11"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies

.github/workflows/python-publish.yml

@@ -21,9 +21,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: '3.x'
     - name: Install dependencies

.github/workflows/release.yml

@@ -64,7 +64,7 @@ jobs:
         git reset --hard ${{ steps.get_commit.outputs.commit_hash }}
 
     - name: Setup Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: '3.11'
         cache: 'pip'
@@ -422,7 +422,7 @@ jobs:
 
     - name: Upload test artifacts
       if: always()
-      uses: actions/upload-artifact@v3
+      uses: actions/upload-artifact@v4
       with:
         name: praisonai-output-${{ matrix.os }}-${{ matrix.framework }}
         path: test_output/

.github/workflows/test-comprehensive.yml

@@ -0,0 +1,177 @@
+name: Comprehensive Test Suite
+
+on:
+  workflow_dispatch:  # Allow manual triggering
+    inputs:
+      test_type:
+        description: 'Type of tests to run'
+        required: true
+        default: 'all'
+        type: choice
+        options:
+        - all
+        - unit
+        - integration
+        - fast
+        - performance
+        - frameworks
+        - autogen
+        - crewai
+  release:
+    types: [published, prereleased]
+  schedule:
+    # Run comprehensive tests weekly on Sundays at 3 AM UTC
+    - cron: '0 3 * * 0'
+
+jobs:
+  comprehensive-test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.11"]
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+    - name: Install dependencies
+      run: |
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov pytest-benchmark
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+    - name: Run Comprehensive Test Suite
+      run: |
+        # Determine test type from input or default to 'all'
+        TEST_TYPE="${{ github.event.inputs.test_type || 'all' }}"
+        
+        echo "🧪 Running comprehensive test suite (type: $TEST_TYPE)"
+        
+        case $TEST_TYPE in
+          "unit")
+            python tests/test_runner.py --pattern unit
+            ;;
+          "integration")
+            python tests/test_runner.py --pattern integration
+            ;;
+          "fast")
+            python tests/test_runner.py --pattern fast
+            ;;
+          "performance")
+            python tests/test_runner.py --pattern "performance"
+            ;;
+          "frameworks")
+            python tests/test_runner.py --pattern frameworks
+            ;;
+          "autogen")
+            python tests/test_runner.py --pattern autogen
+            ;;
+          "crewai")
+            python tests/test_runner.py --pattern crewai
+            ;;
+          "all"|*)
+            python tests/test_runner.py --pattern all
+            ;;
+        esac
+
+    - name: Generate Comprehensive Test Report
+      if: always()
+      run: |
+        echo "# 📋 Comprehensive Test Report" > comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        echo "**Python Version:** ${{ matrix.python-version }}" >> comprehensive_report.md
+        echo "**Test Type:** ${{ github.event.inputs.test_type || 'all' }}" >> comprehensive_report.md
+        echo "**Trigger:** ${{ github.event_name }}" >> comprehensive_report.md
+        echo "**Date:** $(date -u)" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        
+        echo "## 🧪 Test Categories Covered:" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        echo "### Unit Tests:" >> comprehensive_report.md
+        echo "- ✅ Core agent functionality" >> comprehensive_report.md
+        echo "- ✅ Async operations" >> comprehensive_report.md
+        echo "- ✅ Tool integrations" >> comprehensive_report.md
+        echo "- ✅ UI components" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        
+        echo "### Integration Tests:" >> comprehensive_report.md
+        echo "- ✅ MCP (Model Context Protocol)" >> comprehensive_report.md
+        echo "- ✅ RAG (Retrieval Augmented Generation)" >> comprehensive_report.md
+        echo "- ✅ Base URL API mapping" >> comprehensive_report.md
+        echo "- ✅ Multi-agent workflows" >> comprehensive_report.md
+        echo "- ✅ AutoGen framework integration" >> comprehensive_report.md
+        echo "- ✅ CrewAI framework integration" >> comprehensive_report.md
+        echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
+        echo "- 🖥️ UI frameworks (Gradio, Streamlit)" >> comprehensive_report.md
+        echo "- 📊 Memory and persistence" >> comprehensive_report.md
+        echo "- 🌐 Multi-modal capabilities" >> comprehensive_report.md
+
+        echo "- ✅ AutoGen framework integration" >> comprehensive_report.md
+        echo "- ✅ CrewAI framework integration" >> comprehensive_report.md
+        echo "" >> comprehensive_report.md
+        
+        echo "### Key Features Tested:" >> comprehensive_report.md
+        echo "- 🤖 Agent creation and configuration" >> comprehensive_report.md
+        echo "- 📋 Task management and execution" >> comprehensive_report.md
+        echo "- 🔄 Sync/async workflows" >> comprehensive_report.md
+        echo "- 🛠️ Custom tools and error handling" >> comprehensive_report.md
+        echo "- 🧠 Knowledge bases and RAG" >> comprehensive_report.md
+        echo "- 🔌 MCP server connections" >> comprehensive_report.md
+        echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
+
+    - name: Upload Comprehensive Test Results
+      uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: comprehensive-test-results-python-${{ matrix.python-version }}
+        path: |
+          comprehensive_report.md
+          htmlcov/
+          coverage.xml
+          .coverage
+        retention-days: 30
+
+  test-matrix-summary:
+    runs-on: ubuntu-latest
+    needs: comprehensive-test
+    if: always()
+    
+    steps:
+    - name: Generate Matrix Summary
+      run: |
+        echo "# 🎯 Test Matrix Summary" > matrix_summary.md
+        echo "" >> matrix_summary.md
+        echo "## Python Version Results:" >> matrix_summary.md
+        echo "- Python 3.10: ${{ needs.comprehensive-test.result }}" >> matrix_summary.md  
+        echo "- Python 3.11: ${{ needs.comprehensive-test.result }}" >> matrix_summary.md
+        echo "" >> matrix_summary.md
+        echo "## Overall Status:" >> matrix_summary.md
+        if [ "${{ needs.comprehensive-test.result }}" == "success" ]; then
+          echo "✅ **All tests passed across all Python versions!**" >> matrix_summary.md
+        else
+          echo "❌ **Some tests failed. Check individual job logs for details.**" >> matrix_summary.md
+        fi
+        
+    - name: Upload Matrix Summary
+      uses: actions/upload-artifact@v4
+      with:
+        name: test-matrix-summary
+        path: matrix_summary.md
+        retention-days: 30 

.github/workflows/test-core.yml

@@ -0,0 +1,75 @@
+name: Core Tests
+
+on: 
+  push:
+    branches: [ main, develop ]
+  pull_request:
+    branches: [ main, develop ]
+
+jobs:
+  test-core:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.11]
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+    - name: Install dependencies
+      run: |
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+    - name: Run Unit Tests
+      run: |
+        python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonaiagents --cov-report=term-missing
+
+    - name: Run Integration Tests  
+      run: |
+        python -m pytest tests/integration/ -v --tb=short --disable-warnings
+
+    - name: Run AutoGen Framework Tests
+      run: |
+        echo "🤖 Testing AutoGen Framework Integration..."
+        python tests/test_runner.py --pattern autogen --verbose || echo "⚠️ AutoGen tests completed with issues"
+      continue-on-error: true
+
+    - name: Run CrewAI Framework Tests  
+      run: |
+        echo "⛵ Testing CrewAI Framework Integration..."
+        python tests/test_runner.py --pattern crewai --verbose || echo "⚠️ CrewAI tests completed with issues"
+      continue-on-error: true
+
+    - name: Run Legacy Tests
+      run: |
+        python -m pytest tests/test.py -v --tb=short --disable-warnings
+
+    - name: Upload Coverage Reports
+      if: matrix.python-version == '3.11'
+      uses: actions/upload-artifact@v4
+      with:
+        name: coverage-reports
+        path: |
+          .coverage
+          htmlcov/
+        retention-days: 7

.github/workflows/test-extended.yml

@@ -0,0 +1,156 @@
+name: Extended Tests
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+  schedule:
+    # Run nightly at 2 AM UTC
+    - cron: '0 2 * * *'
+  workflow_dispatch:  # Allow manual triggering
+
+jobs:
+  test-examples:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.11
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+    - name: Install dependencies
+      run: |
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+    - name: Test Key Example Scripts
+      run: |
+        echo "🧪 Testing key example scripts from praisonai-agents..."
+        
+        # Create a timeout function for consistent handling
+        timeout_run() {
+          timeout 30s "$@" || echo "⏱️  $1 test completed/timed out"
+        }
+        
+        # Test basic agent functionality
+        timeout_run python src/praisonai-agents/basic-agents.py
+        
+        # Test async functionality  
+        timeout_run python src/praisonai-agents/async_example.py
+        
+        # Test knowledge/RAG functionality
+        timeout_run python src/praisonai-agents/knowledge-agents.py
+        
+        # Test MCP functionality
+        timeout_run python src/praisonai-agents/mcp-basic.py
+        
+        # Test UI functionality
+        timeout_run python src/praisonai-agents/ui.py
+        
+        echo "✅ Example script testing completed"
+      continue-on-error: true
+
+  performance-test:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.11
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+    - name: Install dependencies
+      run: |
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system pytest pytest-benchmark
+
+    - name: Run Performance Benchmarks
+      run: |
+        echo "🏃 Running performance benchmarks..."
+        python -c "
+        import time
+        import sys
+        import statistics
+        sys.path.insert(0, 'src/praisonai-agents')
+        
+        print('🏃 Testing agent creation performance...')
+        times = []
+        try:
+            from praisonaiagents import Agent
+            for i in range(5):
+                start_time = time.time()
+                agent = Agent(name=f'PerfAgent{i}')
+                times.append(time.time() - start_time)
+            
+            avg_time = statistics.mean(times)
+            print(f'✅ Average agent creation time: {avg_time:.3f}s')
+            print(f'📊 Min: {min(times):.3f}s, Max: {max(times):.3f}s')
+        except Exception as e:
+            print(f'❌ Agent creation benchmark failed: {e}')
+        
+        print('🏃 Testing import performance...')
+        start_time = time.time()
+        try:
+            import praisonaiagents
+            import_time = time.time() - start_time
+            print(f'✅ Import completed in {import_time:.3f}s')
+        except Exception as e:
+            print(f'❌ Import benchmark failed: {e}')
+        
+        print('🏃 Testing memory usage...')
+        try:
+            import psutil
+            import os
+            process = psutil.Process(os.getpid())
+            memory_mb = process.memory_info().rss / 1024 / 1024
+            print(f'📊 Memory usage: {memory_mb:.1f} MB')
+        except ImportError:
+            print('⚠️  psutil not available for memory testing')
+        except Exception as e:
+            print(f'❌ Memory benchmark failed: {e}')
+        "
+      continue-on-error: true
+
+    - name: Generate Performance Report
+      run: |
+        echo "## 📊 Performance Test Results" > performance_report.md
+        echo "" >> performance_report.md
+        echo "### Benchmarks Run:" >> performance_report.md
+        echo "- ⚡ Agent creation speed" >> performance_report.md
+        echo "- 📦 Import performance" >> performance_report.md
+        echo "- 💾 Memory usage" >> performance_report.md
+        echo "- 🧪 Example script execution" >> performance_report.md
+        echo "" >> performance_report.md
+        echo "_Performance results are logged in the CI output above._" >> performance_report.md
+
+    - name: Upload Performance Report
+      uses: actions/upload-artifact@v4
+      with:
+        name: performance-report
+        path: performance_report.md
+        retention-days: 30 

.github/workflows/test-frameworks.yml

@@ -0,0 +1,152 @@
+name: Framework Integration Tests
+
+on:
+  workflow_dispatch:  # Allow manual triggering
+    inputs:
+      framework:
+        description: 'Framework to test'
+        required: true
+        default: 'all'
+        type: choice
+        options:
+        - all
+        - autogen
+        - crewai
+  schedule:
+    # Run framework tests daily at 6 AM UTC
+    - cron: '0 6 * * *'
+  push:
+    paths:
+      - 'tests/integration/autogen/**'
+      - 'tests/integration/crewai/**'
+      - '.github/workflows/test-frameworks.yml'
+
+jobs:
+  framework-tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.11]
+        framework: [autogen, crewai]
+    
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+    - name: Install dependencies
+      run: |
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+    - name: Test ${{ matrix.framework }} Framework
+      run: |
+        echo "🧪 Testing ${{ matrix.framework }} framework integration with Python ${{ matrix.python-version }}"
+        python tests/test_runner.py --pattern ${{ matrix.framework }} --verbose --coverage
+      continue-on-error: false
+
+    - name: Generate Framework Test Report
+      if: always()
+      run: |
+        echo "# 🤖 ${{ matrix.framework }} Framework Test Report" > ${{ matrix.framework }}_report.md
+        echo "" >> ${{ matrix.framework }}_report.md
+        echo "**Framework:** ${{ matrix.framework }}" >> ${{ matrix.framework }}_report.md
+        echo "**Python Version:** ${{ matrix.python-version }}" >> ${{ matrix.framework }}_report.md
+        echo "**Date:** $(date -u)" >> ${{ matrix.framework }}_report.md
+        echo "**Trigger:** ${{ github.event_name }}" >> ${{ matrix.framework }}_report.md
+        echo "" >> ${{ matrix.framework }}_report.md
+        
+        if [ "${{ matrix.framework }}" == "autogen" ]; then
+          echo "## AutoGen Integration Tests" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ AutoGen import verification" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Basic agent creation through PraisonAI" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Conversation flow testing" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Configuration validation" >> ${{ matrix.framework }}_report.md
+        elif [ "${{ matrix.framework }}" == "crewai" ]; then
+          echo "## CrewAI Integration Tests" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ CrewAI import verification" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Basic crew creation through PraisonAI" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Multi-agent workflow testing" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Agent collaboration verification" >> ${{ matrix.framework }}_report.md
+          echo "- ✅ Configuration validation" >> ${{ matrix.framework }}_report.md
+        fi
+        
+        echo "" >> ${{ matrix.framework }}_report.md
+        echo "## Test Commands" >> ${{ matrix.framework }}_report.md
+        echo '```bash' >> ${{ matrix.framework }}_report.md
+        echo "# Run ${{ matrix.framework }} tests locally:" >> ${{ matrix.framework }}_report.md
+        echo "python tests/test_runner.py --pattern ${{ matrix.framework }} --verbose" >> ${{ matrix.framework }}_report.md
+        echo '```' >> ${{ matrix.framework }}_report.md
+
+    - name: Upload Framework Test Results
+      uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: ${{ matrix.framework }}-test-results-python-${{ matrix.python-version }}
+        path: |
+          ${{ matrix.framework }}_report.md
+          htmlcov/
+          coverage.xml
+          .coverage
+        retention-days: 14
+
+  framework-summary:
+    runs-on: ubuntu-latest
+    needs: framework-tests
+    if: always()
+    
+    steps:
+    - name: Generate Framework Summary
+      run: |
+        echo "# 🚀 Framework Integration Test Summary" > framework_summary.md
+        echo "" >> framework_summary.md
+        echo "## Test Results by Framework and Python Version:" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "### AutoGen Framework:" >> framework_summary.md
+        echo "- Python 3.11: ${{ needs.framework-tests.result }}" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "### CrewAI Framework:" >> framework_summary.md
+        echo "- Python 3.11: ${{ needs.framework-tests.result }}" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "## Overall Status:" >> framework_summary.md
+        if [ "${{ needs.framework-tests.result }}" == "success" ]; then
+          echo "✅ **All framework integration tests passed!**" >> framework_summary.md
+        else
+          echo "❌ **Some framework tests failed. Check individual job logs for details.**" >> framework_summary.md
+        fi
+        
+        echo "" >> framework_summary.md
+        echo "## Frameworks Tested:" >> framework_summary.md
+        echo "- **AutoGen**: Microsoft's conversational AI framework" >> framework_summary.md
+        echo "- **CrewAI**: Multi-agent collaboration framework" >> framework_summary.md
+        echo "" >> framework_summary.md
+        echo "## Test Coverage:" >> framework_summary.md
+        echo "- Import verification" >> framework_summary.md
+        echo "- Agent/crew creation" >> framework_summary.md
+        echo "- Workflow execution" >> framework_summary.md
+        echo "- Configuration validation" >> framework_summary.md
+        echo "- Integration with PraisonAI" >> framework_summary.md
+        
+    - name: Upload Framework Summary
+      uses: actions/upload-artifact@v4
+      with:
+        name: framework-test-summary
+        path: framework_summary.md
+        retention-days: 30 

.github/workflows/test-real.yml

@@ -0,0 +1,164 @@
+name: Real End-to-End Tests
+
+# ⚠️ WARNING: This workflow makes real API calls and incurs costs!
+# Only runs when manually triggered to prevent accidental charges
+
+on:
+  workflow_dispatch:  # Manual trigger only
+    inputs:
+      framework:
+        description: 'Framework to test (⚠️ Will incur API costs!)'
+        required: true
+        default: 'none'
+        type: choice
+        options:
+        - none
+        - autogen
+        - crewai
+        - all
+      confirm_costs:
+        description: 'I understand this will make real API calls and may incur costs'
+        required: true
+        type: boolean
+        default: false
+
+jobs:
+  real-tests:
+    runs-on: ubuntu-latest
+    if: ${{ github.event.inputs.confirm_costs == 'true' && github.event.inputs.framework != 'none' }}
+    
+    strategy:
+      matrix:
+        python-version: [3.11]  # Single version to minimize costs
+    
+    steps:
+    - name: 🚨 Cost Warning
+      run: |
+        echo "⚠️  WARNING: This workflow will make real API calls!"
+        echo "💰 This may incur charges on your API accounts"
+        echo "🎯 Framework: ${{ github.event.inputs.framework }}"
+        echo "✅ Cost confirmation: ${{ github.event.inputs.confirm_costs }}"
+        
+    - name: Checkout code
+      uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install UV
+      run: |
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+    - name: Install dependencies
+      run: |
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system pytest pytest-asyncio pytest-cov
+
+    - name: Set environment variables
+      run: |
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV
+        echo "ANTHROPIC_API_KEY=${{ secrets.ANTHROPIC_API_KEY }}" >> $GITHUB_ENV
+        echo "GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }}" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+    - name: Verify API Keys
+      run: |
+        if [ -z "${{ secrets.OPENAI_API_KEY }}" ]; then
+          echo "❌ OPENAI_API_KEY not set in secrets"
+          echo "🔧 Add your API key to repository secrets"
+          exit 1
+        fi
+        echo "✅ API keys configured"
+
+    - name: Run Real AutoGen Tests
+      if: ${{ github.event.inputs.framework == 'autogen' || github.event.inputs.framework == 'all' }}
+      run: |
+        echo "🤖 Running REAL AutoGen tests (⚠️  API costs may apply)"
+        python -m pytest tests/e2e/autogen/ -v -m real --tb=short
+      continue-on-error: false
+
+    - name: Run Real CrewAI Tests
+      if: ${{ github.event.inputs.framework == 'crewai' || github.event.inputs.framework == 'all' }}
+      run: |
+        echo "⛵ Running REAL CrewAI tests (⚠️  API costs may apply)"
+        python -m pytest tests/e2e/crewai/ -v -m real --tb=short
+      continue-on-error: false
+
+    - name: Generate Real Test Report
+      if: always()
+      run: |
+        echo "# 🔥 Real End-to-End Test Report" > real_test_report.md
+        echo "" >> real_test_report.md
+        echo "⚠️  **WARNING: This report represents tests that made real API calls**" >> real_test_report.md
+        echo "" >> real_test_report.md
+        echo "**Framework Tested:** ${{ github.event.inputs.framework }}" >> real_test_report.md
+        echo "**Python Version:** ${{ matrix.python-version }}" >> real_test_report.md
+        echo "**Date:** $(date -u)" >> real_test_report.md
+        echo "**Triggered by:** ${{ github.actor }}" >> real_test_report.md
+        echo "" >> real_test_report.md
+        
+        echo "## 🧪 Test Results" >> real_test_report.md
+        echo "" >> real_test_report.md
+        
+        if [ "${{ github.event.inputs.framework }}" == "autogen" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
+          echo "### AutoGen Real Tests:" >> real_test_report.md
+          echo "- Environment verification" >> real_test_report.md
+          echo "- Agent creation with real API calls" >> real_test_report.md
+          echo "- Configuration validation" >> real_test_report.md
+          echo "" >> real_test_report.md
+        fi
+        
+        if [ "${{ github.event.inputs.framework }}" == "crewai" ] || [ "${{ github.event.inputs.framework }}" == "all" ]; then
+          echo "### CrewAI Real Tests:" >> real_test_report.md
+          echo "- Environment verification" >> real_test_report.md
+          echo "- Crew creation with real API calls" >> real_test_report.md
+          echo "- Multi-agent setup validation" >> real_test_report.md
+          echo "" >> real_test_report.md
+        fi
+        
+        echo "## 💰 Cost Considerations" >> real_test_report.md
+        echo "- These tests made actual API calls to LLM providers" >> real_test_report.md
+        echo "- Costs depend on your API pricing tier" >> real_test_report.md
+        echo "- Tests are designed to be minimal to reduce costs" >> real_test_report.md
+        echo "- Check your API provider dashboard for actual usage" >> real_test_report.md
+        
+        echo "## 📋 Next Steps" >> real_test_report.md
+        echo "- Review test results for any failures" >> real_test_report.md
+        echo "- Check API usage in your provider dashboard" >> real_test_report.md
+        echo "- Use mock tests (tests/integration/) for routine testing" >> real_test_report.md
+
+    - name: Upload Real Test Results
+      uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: real-test-results-${{ github.event.inputs.framework }}-python-${{ matrix.python-version }}
+        path: |
+          real_test_report.md
+        retention-days: 30
+
+  # Safety job that runs when costs not confirmed
+  safety-check:
+    runs-on: ubuntu-latest
+    if: ${{ github.event.inputs.confirm_costs != 'true' || github.event.inputs.framework == 'none' }}
+    
+    steps:
+    - name: 🛡️ Safety Check Failed
+      run: |
+        echo "🚨 Real tests not executed due to safety checks:"
+        echo ""
+        echo "✅ Costs confirmed: ${{ github.event.inputs.confirm_costs }}"
+        echo "✅ Framework selected: ${{ github.event.inputs.framework }}"
+        echo ""
+        echo "To run real tests:"
+        echo "1. Select a framework (autogen, crewai, or all)"
+        echo "2. Check 'I understand this will make real API calls and may incur costs'"
+        echo "3. Ensure API keys are set in repository secrets"
+        echo ""
+        echo "💡 For cost-free testing, use mock tests instead:"
+        echo "   - Run 'python -m pytest tests/integration/' locally"
+        echo "   - Or trigger other workflows that use mock tests"
+        
+        exit 1 

.github/workflows/unittest.yml

@@ -1,36 +1,61 @@
-name: Run specific unittest
+name: Quick Validation Tests
 
 on: [push, pull_request]
 
 jobs:
-  test:
+  quick-test:
     runs-on: ubuntu-latest
-
+    
     steps:
     - name: Checkout code
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
 
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: 3.11
 
-    - name: Install Poetry
+    - name: Install UV
       run: |
-        curl -sSL https://install.python-poetry.org | python3 -
-        export PATH="$HOME/.local/bin:$PATH"
+        curl -LsSf https://astral.sh/uv/install.sh | sh
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
 
     - name: Install dependencies
       run: |
-        poetry install
-        poetry run python -m pip install duckduckgo_search
+        uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+        uv pip install --system duckduckgo_search
+        uv pip install --system pytest pytest-asyncio pytest-cov
 
     - name: Set environment variables
       run: |
-        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV
-        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME }}" >> $GITHUB_ENV
+        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
 
-    - name: Run specific unittest
+    - name: Debug API Key Status
       run: |
-        poetry run python -m unittest tests.test.TestExamples
+        echo "🔍 Checking API key availability..."
+        if [ -n "${{ secrets.OPENAI_API_KEY }}" ]; then
+          echo "✅ GitHub secret OPENAI_API_KEY is available"
+          echo "🔑 API key starts with: $(echo "$OPENAI_API_KEY" | cut -c1-7)..."
+        else
+          echo "⚠️ GitHub secret OPENAI_API_KEY is NOT set - using fallback"
+          echo "🔑 Using fallback key: sk-test-key..."
+        fi
+        echo "🌐 API Base: $OPENAI_API_BASE"
+        echo "🤖 Model: $OPENAI_MODEL_NAME"
+        echo "📊 Environment Check:"
+        echo "  - OPENAI_API_KEY length: ${#OPENAI_API_KEY}"
+        echo "  - OPENAI_API_BASE: $OPENAI_API_BASE"
+        echo "  - OPENAI_MODEL_NAME: $OPENAI_MODEL_NAME"
+
+    - name: Run Fast Tests
+      run: |
+        # Run the fastest, most essential tests
+        python tests/test_runner.py --pattern fast
+
+    - name: Run Legacy Example Tests
+      run: |
+        python -m pytest tests/test.py -v --tb=short --disable-warnings
+      continue-on-error: true

.gitignore

@@ -1,3 +1,4 @@
+workspace.code-workspace
 .vscode
 crewai
 .cache
@@ -51,6 +52,8 @@ agentops.log
 
 # crewAI
 crewAI
+!tests/integration/crewai
+!tests/e2e/crewai
 
 # virtualenv
 .venv
@@ -65,4 +68,7 @@ outputs
 agents/praisonaiagents/build
 agents/praisonaiagents/dist
 agents/praisonaiagents/praisonaiagents.egg-info
-.codegpt
+.codegpt
+.praison
+# Local Netlify folder
+.netlify

README.md

@@ -20,7 +20,7 @@
 
 </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.
+PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
 
 <div align="center">
   <a href="https://docs.praison.ai">
@@ -37,35 +37,24 @@ PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application
 - 🧠 Reasoning AI Agents
 - 👁️ Multi Modal AI Agents
 - 🤝 Multi Agent Collaboration
-- ⚡ AI Agent Workflow
-- 🔄 Use CrewAI or AutoGen Framework
+- 🎭 AI Agent Workflow
+- 📚 Add Custom Knowledge
+- 🧠 Agents with Short and Long Term Memory
+- 📄 Chat with PDF Agents
+- 💻 Code Interpreter Agents
+- 📚 RAG Agents
+- 🤔 Async & Parallel Processing
+- 🔄 Auto Agents
+- 🔢 Math Agents
+- 🎯 Structured Output Agents
+- 🔗 LangChain Integrated Agents
+- 📞 Callback Agents
+- 🤏 Mini AI Agents
+- 🛠️ 100+ Custom Tools
+- 📄 YAML Configuration
 - 💯 100+ LLM Support
-- 💻 Chat with ENTIRE Codebase
-- 🎨 Interactive UIs
-- 📄 YAML-based Configuration
-- 🛠️ Custom Tool Integration
-- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
-- 🖼️ Vision Language Model (VLM) Support
-- 🎙️ Real-time Voice Interaction
 
-## 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
+## Using Python Code
 
 Light weight package dedicated for coding:
 ```bash
@@ -76,58 +65,13 @@ pip install praisonaiagents
 export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
 ```
 
+### 1. Single Agent
+
 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()
+from praisonaiagents import Agent
+agent = Agent(instructions="Your are a helpful AI assistant")
+agent.start("Write a movie script about a robot in Mars")
 ```
 
 Run:
@@ -135,6 +79,396 @@ Run:
 python app.py
 ```
 
+### 2. Multi Agents
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Using No Code
+
+### Auto Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
+
+## Using JavaScript Code
+
+```bash
+npm install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
+
+```javascript
+const { Agent } = require('praisonai');
+const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
+agent.start('Write a movie script about a robot in Mars');
+```
+
+![PraisonAI CLI Demo](docs/demo/praisonai-cli-demo.gif)
+
+## AI Agents Flow
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[ ]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+    end
+    
+    subgraph Agent2[ ]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+    end
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
+```
+
+## AI Agents with Tools
+
+Create AI agents that can use tools to interact with external systems and perform actions.
+
+```mermaid
+flowchart TB
+    subgraph Tools
+        direction TB
+        T3[Internet Search]
+        T1[Code Execution]
+        T2[Formatting]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    T3 --> A1
+    T1 --> A2
+    T2 --> A3
+
+    style Tools fill:#189AB4,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Memory
+
+Create AI agents with memory capabilities for maintaining context and information across tasks.
+
+```mermaid
+flowchart TB
+    subgraph Memory
+        direction TB
+        STM[Short Term]
+        LTM[Long Term]
+    end
+
+    subgraph Store
+        direction TB
+        DB[(Vector DB)]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    Memory <--> Store
+    Store <--> A1
+    Store <--> A2
+    Store <--> A3
+
+    style Memory fill:#189AB4,color:#fff
+    style Store fill:#2E8B57,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Different Processes
+
+### Sequential Process
+
+The simplest form of task execution where tasks are performed one after another.
+
+```mermaid
+graph LR
+    Input[Input] --> A1
+    subgraph Agents
+        direction LR
+        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
+    end
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class A1,A2,A3 process
+    class Agents transparent
+```
+
+### Hierarchical Process
+
+Uses a manager agent to coordinate task execution and agent assignments.
+
+```mermaid
+graph TB
+    Input[Input] --> Manager
+    
+    subgraph Agents
+        Manager[Manager Agent]
+        
+        subgraph Workers
+            direction LR
+            W1[Worker 1]
+            W2[Worker 2]
+            W3[Worker 3]
+        end
+        
+        Manager --> W1
+        Manager --> W2
+        Manager --> W3
+    end
+    
+    W1 --> Manager
+    W2 --> Manager
+    W3 --> Manager
+    Manager --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Manager,W1,W2,W3 process
+    class Agents,Workers transparent
+```
+
+### Workflow Process
+
+Advanced process type supporting complex task relationships and conditional execution.
+
+```mermaid
+graph LR
+    Input[Input] --> Start
+    
+    subgraph Workflow
+        direction LR
+        Start[Start] --> C1{Condition}
+        C1 --> |Yes| A1[Agent 1]
+        C1 --> |No| A2[Agent 2]
+        A1 --> Join
+        A2 --> Join
+        Join --> A3[Agent 3]
+    end
+    
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Start,A1,A2,A3,Join process
+    class C1 decision
+    class Workflow transparent
+```
+
+#### Agentic Routing Workflow
+
+Create AI agents that can dynamically route tasks to specialized LLM instances.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Out[Out]
+    LLM2 --> Out
+    LLM3 --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Orchestrator Worker
+
+Create AI agents that orchestrate and distribute tasks among specialized workers.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Synthesizer[Synthesizer]
+    LLM2 --> Synthesizer
+    LLM3 --> Synthesizer
+    Synthesizer --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Autonomous Workflow
+
+Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.
+
+```mermaid
+flowchart LR
+    Human[Human] <--> LLM[LLM Call]
+    LLM -->|ACTION| Environment[Environment]
+    Environment -->|FEEDBACK| LLM
+    LLM --> Stop[Stop]
+    
+    style Human fill:#8B0000,color:#fff
+    style LLM fill:#2E8B57,color:#fff
+    style Environment fill:#8B0000,color:#fff
+    style Stop fill:#333,color:#fff
+```
+
+#### Agentic Parallelization
+
+Create AI agents that can execute tasks in parallel for improved performance.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM2[LLM Call 2]
+    In --> LLM1[LLM Call 1]
+    In --> LLM3[LLM Call 3]
+    LLM1 --> Aggregator[Aggregator]
+    LLM2 --> Aggregator
+    LLM3 --> Aggregator
+    Aggregator --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Aggregator fill:#fff,color:#000
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Prompt Chaining
+
+Create AI agents with sequential prompt chaining for complex workflows.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
+    Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
+    Gate -->|Fail| Exit[Exit]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+    style Exit fill:#8B0000,color:#fff
+```
+
+#### Agentic Evaluator Optimizer
+
+Create AI agents that can generate and optimize solutions through iterative feedback.
+
+```mermaid
+flowchart LR
+    In[In] --> Generator[LLM Call Generator] 
+    Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
+    Evaluator -->|REJECTED + FEEDBACK| Generator
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Evaluator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Repetitive Agents
+
+Create AI agents that can efficiently handle repetitive tasks through automated loops.
+
+```mermaid
+flowchart LR
+    In[Input] --> LoopAgent[("Looping Agent")]
+    LoopAgent --> Task[Task]
+    Task --> |Next iteration| LoopAgent
+    Task --> |Done| Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style LoopAgent fill:#2E8B57,color:#fff,shape:circle
+    style Task fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Adding Models
+
+<div align="center">
+  <a href="https://docs.praison.ai/models">
+    <p align="center">
+      <img src="https://img.shields.io/badge/%F0%9F%93%9A_Models-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Models" />
+    </p>
+  </a>
+</div>
+
 ## Ollama Integration
 ```bash
 export OPENAI_BASE_URL=http://localhost:11434/v1
@@ -147,267 +481,16 @@ export OPENAI_API_KEY=xxxxxxxxxxx
 export OPENAI_BASE_URL=https://api.groq.com/openai/v1
 ```
 
-## Logging
-```bash
-export LOGLEVEL=info
-```
-
-Advanced logging:
-```bash
-export LOGLEVEL=debug
-```
-
-<div align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="docs/images/architecture-dark.png" />
-    <source media="(prefers-color-scheme: light)" srcset="docs/images/architecture-light.png" />
-    <img alt="PraisonAI Architecture" src="docs/images/architecture-light.png" />
-  </picture>
-</div>
-
-## Different User Interfaces:
-
-| Interface | Description | URL |
-|---|---|---|
-| **UI** | Multi Agents such as CrewAI or AutoGen | [https://docs.praison.ai/ui/ui](https://docs.praison.ai/ui/ui) |
-| **Chat** | Chat with 100+ LLMs, single AI Agent | [https://docs.praison.ai/ui/chat](https://docs.praison.ai/ui/chat) |
-| **Code** | Chat with entire Codebase, single AI Agent | [https://docs.praison.ai/ui/code](https://docs.praison.ai/ui/code) |
-| **Realtime** | Real-time voice interaction with AI | [https://docs.praison.ai/ui/realtime](https://docs.praison.ai/ui/realtime) |
-
-| Other Features | Description | Docs |
-|---|---|---|
-| **Train** | Fine-tune LLMs using your custom data | [https://docs.praison.ai/train](https://docs.praison.ai/train) |
-
-
-## Google Colab Multi Agents
-
-|               | Cookbook        | Open in Colab                                                                                                                                                                                                                                  |
-| ------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Basic         | PraisonAI       | <a target="_blank" href="https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-googlecolab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>       |
-| Include Tools | PraisonAI Tools | <a target="_blank" href="https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-tools-googlecolab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a> |
-
-## Installation Options
-
-### Using pip
-```bash
-pip install praisonai
-```
-
-### Framework-specific Installation
-```bash
-# Install with CrewAI support
-pip install "praisonai[crewai]"
-
-# Install with AutoGen support
-pip install "praisonai[autogen]"
-
-# Install with both frameworks
-pip install "praisonai[crewai,autogen]"
-```
-
-### UI and Additional Features
-```bash
-# Install UI support
-pip install "praisonai[ui]"
-
-# Install Chat interface
-pip install "praisonai[chat]"
-
-# Install Code interface
-pip install "praisonai[code]"
-
-# Install Realtime voice interaction
-pip install "praisonai[realtime]"
-
-# Install Call feature
-pip install "praisonai[call]"
-```
-
-## Quick Start
-
-```bash
-# Set your OpenAI API key
-export OPENAI_API_KEY="Enter your API key"
-
-# Initialize with CrewAI (default)
-praisonai --init "create a movie script about dog in moon"
-
-# Or initialize with AutoGen
-praisonai --framework autogen --init "create a movie script about dog in moon"
-
-# Run the agents
-praisonai
-```
-
-## Full Automatic Mode
-
-```bash
-# With CrewAI (default)
-praisonai --auto "create a movie script about Dog in Moon"
-
-# With AutoGen
-praisonai --framework autogen --auto "create a movie script about Dog in Moon"
-```
-
-## Framework-specific Features
-
-### CrewAI
-When installing with `pip install "praisonai[crewai]"`, you get:
-- CrewAI framework support
-- PraisonAI tools integration
-- Task delegation capabilities
-- Sequential and parallel task execution
-
-### AutoGen
-When installing with `pip install "praisonai[autogen]"`, you get:
-- AutoGen framework support
-- PraisonAI tools integration
-- Multi-agent conversation capabilities
-- Code execution environment
-
-
-
-## TL;DR Multi Agents
-
-```bash
-pip install praisonai
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init create a movie script about dog in moon
-praisonai
-```
-
-## Table of Contents
-
-- [Installation](#installation)
-- [Initialise](#initialise)
-- [Run](#run)
-- [Full Automatic Mode](#full-automatic-mode)
-- [User Interface](#user-interface)
-- [Praison AI Chat](#praison-ai-chat)
-- [Create Custom Tools](#create-custom-tools)
-- [Agents Playbook](#agents-playbook)
-- [Include praisonai package in your project](#include-praisonai-package-in-your-project)
-- [Commands to Install Dev Dependencies](#commands-to-install-dependencies)
-- [Other Models](#other-models)
-- [Contributing](#contributing)
-- [Star History](#star-history)
-
-## Installation Multi Agents
-
-```bash
-pip install praisonai
-```
-
-## Initialise
-
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
-
-Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
-
-Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
-
-```bash
-praisonai --init create a movie script about dog in moon
-```
-
-This will automatically create agents.yaml file in the current directory.
-
-### To initialise with a specific agent framework (Optional):
-
-```bash
-praisonai --framework autogen --init create movie script about cat in mars
-```
-
-## Run
-
-```bash
-praisonai
-```
-
-or
-
-```bash
-python -m praisonai
-```
-
-### Specify the agent framework (Optional):
-
-```bash
-praisonai --framework autogen
-```
-
-### Full Automatic Mode
-
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```
-
-## User Interface
-
-## PraisonAI User Interfaces:
-
-| Interface | Description                                | URL                                                                   |
-| --------- | ------------------------------------------ | --------------------------------------------------------------------- |
-| **UI**    | Multi Agents such as CrewAI or AutoGen     | [https://docs.praisonai.com/ui/ui](https://docs.praison.ai/ui/ui)     |
-| **Chat**  | Chat with 100+ LLMs, single AI Agent       | [https://docs.praisonai.com/ui/chat](https://docs.praison.ai/ui/chat) |
-| **Code**  | Chat with entire Codebase, single AI Agent | [https://docs.praisonai.com/ui/code](https://docs.praison.ai/ui/code) |
-
-```bash
-pip install -U "praisonai[ui]"
-export OPENAI_API_KEY="Enter your API key"
-chainlit create-secret
-export CHAINLIT_AUTH_SECRET=xxxxxxxx
-praisonai ui
-```
-
-or
-
-```
-python -m praisonai ui
-```
-
-## Praison AI Chat
-
-- https://docs.praison.ai/chat/
-
-```bash
-pip install "praisonai[chat]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai chat
-```
-
-### Internet Search
-
-Praison AI Chat and Praison AI Code now includes internet search capabilities using Crawl4AI and Tavily, allowing you to retrieve up-to-date information during your conversations.
-
-### Vision Language Model Support
-
-You can now upload images and ask questions based on them using Vision Language Models. This feature enables visual understanding and analysis within your chat sessions.
-
-## Praison AI Code
-
-```bash
-pip install "praisonai[code]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai code
-```
-
-### Internet Search
-
-Praison AI Code also includes internet search functionality, enabling you to find relevant code snippets and programming information online.
-
-## Create Custom Tools
-
-- https://docs.praison.ai/tools/custom/
+## No Code Options
 
 ## Agents Playbook
 
 ### Simple Playbook Example
 
+Create `agents.yaml` file and add the code below:
+
 ```yaml
-framework: crewai
+framework: praisonai
 topic: Artificial Intelligence
 roles:
   screenwriter:
@@ -420,62 +503,25 @@ roles:
         expected_output: "Complete script ready for production."
 ```
 
+*To run the playbook:*
+```bash
+praisonai agents.yaml
+```
+
 ## Use 100+ Models
 
 - https://docs.praison.ai/models/
+<div align="center">
+  <a href="https://docs.praison.ai">
+    <p align="center">
+      <img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
+    </p>
+  </a>
+</div>
 
-## Include praisonai package in your project
+## Development:
 
-- https://docs.praison.ai/developers/wrapper
-- https://docs.praison.ai/developers/wrapper-tools/
-
-## Option 1: Using RAW YAML
-
-```python
-from praisonai import PraisonAI
-
-# Example agent_yaml content
-agent_yaml = """
-framework: "crewai"
-topic: "Space Exploration"
-
-roles:
-  astronomer:
-    role: "Space Researcher"
-    goal: "Discover new insights about {topic}"
-    backstory: "You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos."
-    tasks:
-      investigate_exoplanets:
-        description: "Research and compile information about exoplanets discovered in the last decade."
-        expected_output: "A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth."
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)
-```
-
-## Option 2: Using separate agents.yaml file
-
-Note: Please create agents.yaml file before hand.
-
-```python
-from praisonai import PraisonAI
-
-def basic(): # Basic Mode
-    praisonai = PraisonAI(agent_file="agents.yaml")
-    praisonai.run()
-
-if __name__ == "__main__":
-    basic()
-```
-
-## Commands to Install Dependencies:
+Below is used for development only.
 
 ### Using uv
 ```bash
@@ -500,18 +546,34 @@ uv pip install -r pyproject.toml --extra "crewai,autogen"
 - Submit a pull request via GitHub's web interface.
 - Await feedback from project maintainers.
 
+## Other Features
+
+- 🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
+- 💻 Chat with ENTIRE Codebase
+- 🎨 Interactive UIs
+- 📄 YAML-based Configuration
+- 🛠️ Custom Tool Integration
+- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
+- 🖼️ Vision Language Model (VLM) Support
+- 🎙️ Real-time Voice Interaction
+
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
 
-## License
-
-Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
-
 ## Video Tutorials
 
 | Topic | Video |
 |-------|--------|
+| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
+| Reasoning Data Generating Agent | [![Reasoning Data](https://img.youtube.com/vi/fUT332Y2zA8/0.jpg)](https://www.youtube.com/watch?v=fUT332Y2zA8) |
+| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
+| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
+| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
+| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
+| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
+| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
+| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
 | Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
 | Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
 | Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
@@ -524,59 +586,5 @@ Praison AI is an open-sourced software licensed under the **[MIT license](https:
 | Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
 | Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
 | Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
+| Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |
 
-## License
-
-Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
-
-## 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

@@ -1,6 +1,7 @@
 framework: praisonai
 process: sequential
 topic: create movie script about cat in mars
+memory: false
 roles:
   researcher:
     backstory: Skilled in research, with a focus on gathering accurate and relevant

agents/.cursorrules

@@ -1,8 +0,0 @@
-1. Make minimal code changes as possible
-2. if you are creating function, make sure to name it in more easy, user friendly , optimised manner. 
-3. Never copy the exact code if presented with a code from other location or file or codebase, just make it optimised
-4. Always Follow the principle LESS IS MORE
-5. Make it minimal change as possible
-6. Firstly try not to make any modification to the existing code as possible
-7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
-8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 

agents/memory_simple.py

@@ -1,35 +0,0 @@
-from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import duckduckgo
-
-research_agent = Agent(
-    role="Research Analyst",
-    goal="Research and document key information about topics",
-    backstory="Expert at analyzing and storing information in memory",
-    llm="gpt-4o-mini",
-    tools=[duckduckgo]
-)
-
-blog_agent = Agent(
-    role="Blog Writer",
-    goal="Write a blog post about the research",
-    backstory="Expert at writing blog posts",
-    llm="gpt-4o-mini"
-)
-
-research_task = Task(
-    description="Research and document key information about topics",
-    agent=research_agent
-)
-
-blog_task = Task(
-    description="Write a blog post about the research",
-    agent=blog_agent
-)
-
-agents = PraisonAIAgents(
-    agents=[research_agent, blog_agent],
-    tasks=[research_task, blog_task],
-    memory=True
-)   
-
-agents.start()

agents/praisonaiagents/agent/__init__.py

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

agents/praisonaiagents/agent/agent.py

@@ -1,740 +0,0 @@
-import os
-import time
-import json
-import logging
-import asyncio
-from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING
-from rich.console import Console
-from rich.live import Live
-from openai import AsyncOpenAI
-from ..main import (
-    display_error,
-    display_tool_call,
-    display_instruction,
-    display_interaction,
-    display_generating,
-    display_self_reflection,
-    ReflectionOutput,
-    client,
-    error_logs
-)
-import inspect
-
-if TYPE_CHECKING:
-    from ..task.task import Task
-
-class Agent:
-    def _generate_tool_definition(self, function_name):
-        """
-        Generate a tool definition from a function name by inspecting the function.
-        """
-        logging.debug(f"Attempting to generate tool definition for: {function_name}")
-        
-        # First try to get the tool definition if it exists
-        tool_def_name = f"{function_name}_definition"
-        tool_def = globals().get(tool_def_name)
-        logging.debug(f"Looking for {tool_def_name} in globals: {tool_def is not None}")
-        
-        if not tool_def:
-            import __main__
-            tool_def = getattr(__main__, tool_def_name, None)
-            logging.debug(f"Looking for {tool_def_name} in __main__: {tool_def is not None}")
-        
-        if tool_def:
-            logging.debug(f"Found tool definition: {tool_def}")
-            return tool_def
-
-        # Try to find the function in the agent's tools list first
-        func = None
-        for tool in self.tools:
-            if callable(tool) and getattr(tool, '__name__', '') == function_name:
-                func = tool
-                break
-        
-        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
-        
-        # If not found in tools, try globals and main
-        if not func:
-            func = globals().get(function_name)
-            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
-            
-            if not func:
-                import __main__
-                func = getattr(__main__, function_name, None)
-                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
-
-        if not func or not callable(func):
-            logging.debug(f"Function {function_name} not found or not callable")
-            return None
-
-        import inspect
-        # Langchain tools
-        if inspect.isclass(func) and hasattr(func, 'run'):
-            original_func = func
-            func = func.run
-            function_name = original_func.__name__
-
-        sig = inspect.signature(func)
-        logging.debug(f"Function signature: {sig}")
-        
-        # Skip self, *args, **kwargs, so they don't get passed in arguments
-        parameters_list = []
-        for name, param in sig.parameters.items():
-            if name == "self":
-                continue
-            if param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
-                continue
-            parameters_list.append((name, param))
-
-        parameters = {
-            "type": "object",
-            "properties": {},
-            "required": []
-        }
-        
-        # Parse docstring for parameter descriptions
-        docstring = inspect.getdoc(func)
-        logging.debug(f"Function docstring: {docstring}")
-        
-        param_descriptions = {}
-        if docstring:
-            import re
-            param_section = re.split(r'\s*Args:\s*', docstring)
-            logging.debug(f"Param section split: {param_section}")
-            if len(param_section) > 1:
-                param_lines = param_section[1].split('\n')
-                for line in param_lines:
-                    line = line.strip()
-                    if line and ':' in line:
-                        param_name, param_desc = line.split(':', 1)
-                        param_descriptions[param_name.strip()] = param_desc.strip()
-        
-        logging.debug(f"Parameter descriptions: {param_descriptions}")
-
-        for name, param in parameters_list:
-            param_type = "string"  # Default type
-            if param.annotation != inspect.Parameter.empty:
-                if param.annotation == int:
-                    param_type = "integer"
-                elif param.annotation == float:
-                    param_type = "number"
-                elif param.annotation == bool:
-                    param_type = "boolean"
-                elif param.annotation == list:
-                    param_type = "array"
-                elif param.annotation == dict:
-                    param_type = "object"
-            
-            param_info = {"type": param_type}
-            if name in param_descriptions:
-                param_info["description"] = param_descriptions[name]
-            
-            parameters["properties"][name] = param_info
-            if param.default == inspect.Parameter.empty:
-                parameters["required"].append(name)
-        
-        logging.debug(f"Generated parameters: {parameters}")
-
-        # Extract description from docstring
-        description = docstring.split('\n')[0] if docstring else f"Function {function_name}"
-        
-        tool_def = {
-            "type": "function",
-            "function": {
-                "name": function_name,
-                "description": description,
-                "parameters": parameters
-            }
-        }
-        logging.debug(f"Generated tool definition: {tool_def}")
-        return tool_def
-
-    def __init__(
-        self,
-        name: Optional[str] = None,
-        role: Optional[str] = None,
-        goal: Optional[str] = None,
-        backstory: Optional[str] = None,
-        instructions: Optional[str] = None,
-        llm: Optional[Union[str, Any]] = None,
-        tools: Optional[List[Any]] = None,
-        function_calling_llm: Optional[Any] = None,
-        max_iter: int = 20,
-        max_rpm: Optional[int] = None,
-        max_execution_time: Optional[int] = None,
-        memory: Optional[Any] = None,
-        verbose: bool = True,
-        allow_delegation: bool = False,
-        step_callback: Optional[Any] = None,
-        cache: bool = True,
-        system_template: Optional[str] = None,
-        prompt_template: Optional[str] = None,
-        response_template: Optional[str] = None,
-        allow_code_execution: Optional[bool] = False,
-        max_retry_limit: int = 2,
-        respect_context_window: bool = True,
-        code_execution_mode: Literal["safe", "unsafe"] = "safe",
-        embedder_config: Optional[Dict[str, Any]] = None,
-        knowledge_sources: Optional[List[Any]] = None,
-        use_system_prompt: Optional[bool] = True,
-        markdown: bool = True,
-        self_reflect: bool = False,
-        max_reflect: int = 3,
-        min_reflect: int = 1,
-        reflect_llm: Optional[str] = None
-    ):
-        # Handle backward compatibility for required fields
-        if all(x is None for x in [name, role, goal, backstory, instructions]):
-            raise ValueError("At least one of name, role, goal, backstory, or instructions must be provided")
-
-        # If instructions are provided, use them to set role, goal, and backstory
-        if instructions:
-            self.name = name or "Agent"
-            self.role = role or "Assistant"
-            self.goal = goal or instructions
-            self.backstory = backstory or instructions
-            # Set self_reflect to False by default for instruction-based agents
-            self.self_reflect = False if self_reflect is None else self_reflect
-        else:
-            # Use provided values or defaults
-            self.name = name or "Agent"
-            self.role = role or "Assistant"
-            self.goal = goal or "Help the user with their tasks"
-            self.backstory = backstory or "I am an AI assistant"
-            # Default to True for traditional agents if not specified
-            self.self_reflect = True if self_reflect is None else self_reflect
-        
-        self.instructions = instructions
-        # Check for model name in environment variable if not provided
-        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
-        self.tools = tools if tools else []  # Store original tools
-        self.function_calling_llm = function_calling_llm
-        self.max_iter = max_iter
-        self.max_rpm = max_rpm
-        self.max_execution_time = max_execution_time
-        self.memory = memory
-        self.verbose = verbose
-        self.allow_delegation = allow_delegation
-        self.step_callback = step_callback
-        self.cache = cache
-        self.system_template = system_template
-        self.prompt_template = prompt_template
-        self.response_template = response_template
-        self.allow_code_execution = allow_code_execution
-        self.max_retry_limit = max_retry_limit
-        self.respect_context_window = respect_context_window
-        self.code_execution_mode = code_execution_mode
-        self.embedder_config = embedder_config
-        self.knowledge_sources = knowledge_sources
-        self.use_system_prompt = use_system_prompt
-        self.chat_history = []
-        self.markdown = markdown
-        self.max_reflect = max_reflect
-        self.min_reflect = min_reflect
-        # Use the same model selection logic for reflect_llm
-        self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
-        self.console = Console()  # Create a single console instance for the agent
-        
-        # Initialize system prompt
-        self.system_prompt = f"""{self.backstory}\n
-Your Role: {self.role}\n
-Your Goal: {self.goal}
-        """
-
-    def generate_task(self) -> 'Task':
-        """Generate a Task object from the agent's instructions"""
-        from ..task.task import Task
-        
-        description = self.instructions if self.instructions else f"Execute task as {self.role} with goal: {self.goal}"
-        expected_output = "Complete the assigned task successfully"
-        
-        return Task(
-            name=self.name,
-            description=description,
-            expected_output=expected_output,
-            agent=self,
-            tools=self.tools
-        )
-
-    def execute_tool(self, function_name, arguments):
-        """
-        Execute a tool dynamically based on the function name and arguments.
-        """
-        logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
-
-        # Try to find the function in the agent's tools list first
-        func = None
-        for tool in self.tools:
-            if (callable(tool) and getattr(tool, '__name__', '') == function_name) or \
-               (inspect.isclass(tool) and tool.__name__ == function_name):
-                func = tool
-                break
-        
-        if func is None:
-            # If not found in tools, try globals and main
-            func = globals().get(function_name)
-            if not func:
-                import __main__
-                func = getattr(__main__, function_name, None)
-
-        if func:
-            try:
-                # If it's a class with run method, instantiate and call run
-                if inspect.isclass(func) and hasattr(func, 'run'):
-                    instance = func()
-                    # Extract only the parameters that run() expects
-                    run_params = {k: v for k, v in arguments.items() 
-                                if k in inspect.signature(instance.run).parameters 
-                                and k != 'self'}
-                    return instance.run(**run_params)
-                # Otherwise treat as regular function
-                elif callable(func):
-                    return func(**arguments)
-            except Exception as e:
-                error_msg = str(e)
-                logging.error(f"Error executing tool {function_name}: {error_msg}")
-                return {"error": error_msg}
-        
-        error_msg = f"Tool '{function_name}' is not callable"
-        logging.error(error_msg)
-        return {"error": error_msg}
-
-    def clear_history(self):
-        self.chat_history = []
-
-    def __str__(self):
-        return f"Agent(name='{self.name}', role='{self.role}', goal='{self.goal}')"
-
-    def _chat_completion(self, messages, temperature=0.2, tools=None, stream=True):
-        start_time = time.time()
-        logging.debug(f"{self.name} sending messages to LLM: {messages}")
-
-        formatted_tools = []
-        if tools is None:
-            tools = self.tools
-        if tools:
-            for tool in tools:
-                if isinstance(tool, str):
-                    # Generate tool definition for string tool names
-                    tool_def = self._generate_tool_definition(tool)
-                    if tool_def:
-                        formatted_tools.append(tool_def)
-                    else:
-                        logging.warning(f"Could not generate definition for tool: {tool}")
-                elif isinstance(tool, dict):
-                    formatted_tools.append(tool)
-                elif hasattr(tool, "to_openai_tool"):
-                    formatted_tools.append(tool.to_openai_tool())
-                elif callable(tool):
-                    formatted_tools.append(self._generate_tool_definition(tool.__name__))
-                else:
-                    logging.warning(f"Tool {tool} not recognized")
-
-        try:
-            initial_response = client.chat.completions.create(
-                model=self.llm,
-                messages=messages,
-                temperature=temperature,
-                tools=formatted_tools if formatted_tools else None,
-                stream=False
-            )
-
-            tool_calls = getattr(initial_response.choices[0].message, 'tool_calls', None)
-
-            if tool_calls:
-                messages.append({
-                    "role": "assistant",
-                    "content": initial_response.choices[0].message.content,
-                    "tool_calls": tool_calls
-                })
-
-                for tool_call in tool_calls:
-                    function_name = tool_call.function.name
-                    arguments = json.loads(tool_call.function.arguments)
-
-                    if self.verbose:
-                        display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}")
-
-                    tool_result = self.execute_tool(function_name, arguments)
-                    results_str = json.dumps(tool_result) if tool_result else "Function returned an empty output"
-
-                    if self.verbose:
-                        display_tool_call(f"Function '{function_name}' returned: {results_str}")
-
-                    messages.append({
-                        "role": "tool",
-                        "tool_call_id": tool_call.id,
-                        "content": results_str
-                    })
-
-            if stream:
-                response_stream = client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature,
-                    stream=True
-                )
-                full_response_text = ""
-                
-                # Create Live display with proper configuration
-                with Live(
-                    display_generating("", start_time),
-                    console=self.console,
-                    refresh_per_second=4,
-                    transient=False,  # Changed to False to preserve output
-                    vertical_overflow="ellipsis",
-                    auto_refresh=True
-                ) as live:
-                    for chunk in response_stream:
-                        if chunk.choices[0].delta.content:
-                            full_response_text += chunk.choices[0].delta.content
-                            live.update(display_generating(full_response_text, start_time))
-                
-                # Clear the last generating display with a blank line
-                self.console.print()
-                
-                final_response = client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature,
-                    stream=False
-                )
-                return final_response
-            else:
-                if tool_calls:
-                    final_response = client.chat.completions.create(
-                        model=self.llm,
-                        messages=messages,
-                        temperature=temperature,
-                        stream=False
-                    )
-                    return final_response
-                else:
-                    return initial_response
-
-        except Exception as e:
-            display_error(f"Error in chat completion: {e}")
-            return None
-
-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
-        if self.use_system_prompt:
-            system_prompt = f"""{self.backstory}\n
-Your Role: {self.role}\n
-Your Goal: {self.goal}
-            """
-            if output_json:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
-            elif output_pydantic:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
-        else:
-            system_prompt = None
-
-        messages = []
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-        messages.extend(self.chat_history)
-
-        # Modify prompt if output_json or output_pydantic is specified
-        original_prompt = prompt
-        if output_json or output_pydantic:
-            if isinstance(prompt, str):
-                prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
-            elif isinstance(prompt, list):
-                # For multimodal prompts, append to the text content
-                for item in prompt:
-                    if item["type"] == "text":
-                        item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
-                        break
-
-        if isinstance(prompt, list):
-            # If we receive a multimodal prompt list, place it directly in the user message
-            messages.append({"role": "user", "content": prompt})
-        else:
-            messages.append({"role": "user", "content": prompt})
-
-        final_response_text = None
-        reflection_count = 0
-        start_time = time.time()
-
-        while True:
-            try:
-                if self.verbose:
-                    # Handle both string and list prompts for instruction display
-                    display_text = prompt
-                    if isinstance(prompt, list):
-                        # Extract text content from multimodal prompt
-                        display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
-                    
-                    if display_text and str(display_text).strip():
-                        display_instruction(f"Agent {self.name} is processing prompt: {display_text}", console=self.console)
-
-                response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
-                if not response:
-                    return None
-
-                tool_calls = getattr(response.choices[0].message, 'tool_calls', None)
-                response_text = response.choices[0].message.content.strip()
-
-                if tool_calls:
-                    messages.append({
-                        "role": "assistant",
-                        "content": response_text,
-                        "tool_calls": tool_calls
-                    })
-                    
-                    for tool_call in tool_calls:
-                        function_name = tool_call.function.name
-                        arguments = json.loads(tool_call.function.arguments)
-
-                        if self.verbose:
-                            display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}", console=self.console)
-
-                        tool_result = self.execute_tool(function_name, arguments)
-
-                        if tool_result:
-                            if self.verbose:
-                                display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=self.console)
-                            messages.append({
-                                "role": "tool",
-                                "tool_call_id": tool_call.id,
-                                "content": json.dumps(tool_result)
-                            })
-                        else:
-                            messages.append({
-                                "role": "tool",
-                                "tool_call_id": tool_call.id,
-                                "content": "Function returned an empty output"
-                            })
-                        
-                    response = self._chat_completion(messages, temperature=temperature)
-                    if not response:
-                        return None
-                    response_text = response.choices[0].message.content.strip()
-
-                # Handle output_json or output_pydantic if specified
-                if output_json or output_pydantic:
-                    # Add to chat history and return raw response
-                    self.chat_history.append({"role": "user", "content": original_prompt})
-                    self.chat_history.append({"role": "assistant", "content": response_text})
-                    if self.verbose:
-                        display_interaction(original_prompt, response_text, markdown=self.markdown, 
-                                         generation_time=time.time() - start_time, console=self.console)
-                    return response_text
-
-                if not self.self_reflect:
-                    self.chat_history.append({"role": "user", "content": original_prompt})
-                    self.chat_history.append({"role": "assistant", "content": response_text})
-                    if self.verbose:
-                        logging.info(f"Agent {self.name} final response: {response_text}")
-                    display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
-                    return response_text
-
-                reflection_prompt = f"""
-Reflect on your previous response: '{response_text}'.
-Identify any flaws, improvements, or actions.
-Provide a "satisfactory" status ('yes' or 'no').
-Output MUST be JSON with 'reflection' and 'satisfactory'.
-                """
-                logging.debug(f"{self.name} reflection attempt {reflection_count+1}, sending prompt: {reflection_prompt}")
-                messages.append({"role": "user", "content": reflection_prompt})
-
-                try:
-                    reflection_response = client.beta.chat.completions.parse(
-                        model=self.reflect_llm if self.reflect_llm else self.llm,
-                        messages=messages,
-                        temperature=temperature,
-                        response_format=ReflectionOutput
-                    )
-
-                    reflection_output = reflection_response.choices[0].message.parsed
-
-                    if self.verbose:
-                        display_self_reflection(f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
-
-                    messages.append({"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"})
-
-                    # Only consider satisfactory after minimum reflections
-                    if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
-                        if self.verbose:
-                            display_self_reflection("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
-                        self.chat_history.append({"role": "user", "content": prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
-                        return response_text
-
-                    # Check if we've hit max reflections
-                    if reflection_count >= self.max_reflect - 1:
-                        if self.verbose:
-                            display_self_reflection("Maximum reflection count reached, returning current response", console=self.console)
-                        self.chat_history.append({"role": "user", "content": prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
-                        return response_text
-
-                    logging.debug(f"{self.name} reflection count {reflection_count + 1}, continuing reflection process")
-                    messages.append({"role": "user", "content": "Now regenerate your response using the reflection you made"})
-                    response = self._chat_completion(messages, temperature=temperature, tools=None, stream=True)
-                    response_text = response.choices[0].message.content.strip()
-                    reflection_count += 1
-                    continue  # Continue the loop for more reflections
-
-                except Exception as e:
-                    display_error(f"Error in parsing self-reflection json {e}. Retrying", console=self.console)
-                    logging.error("Reflection parsing failed.", exc_info=True)
-                    messages.append({"role": "assistant", "content": f"Self Reflection failed."})
-                    reflection_count += 1
-                    continue  # Continue even after error to try again
-                
-            except Exception as e:
-                display_error(f"Error in chat: {e}", console=self.console)
-                return None 
-
-    def clean_json_output(self, output: str) -> str:
-        """Clean and extract JSON from response text."""
-        cleaned = output.strip()
-        # Remove markdown code blocks if present
-        if cleaned.startswith("```json"):
-            cleaned = cleaned[len("```json"):].strip()
-        if cleaned.startswith("```"):
-            cleaned = cleaned[len("```"):].strip()
-        if cleaned.endswith("```"):
-            cleaned = cleaned[:-3].strip()
-        return cleaned 
-
-    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
-        """Async version of chat method"""
-        try:
-            # Build system prompt
-            system_prompt = self.system_prompt
-            if output_json:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
-            elif output_pydantic:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
-
-            # Build messages
-            if isinstance(prompt, str):
-                messages = [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if (output_json or output_pydantic) else "")}
-                ]
-            else:
-                # For multimodal prompts
-                messages = [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt}
-                ]
-                if output_json or output_pydantic:
-                    # Add JSON instruction to text content
-                    for item in messages[-1]["content"]:
-                        if item["type"] == "text":
-                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
-                            break
-
-            # Format tools if provided
-            formatted_tools = []
-            if tools:
-                for tool in tools:
-                    if isinstance(tool, str):
-                        tool_def = self._generate_tool_definition(tool)
-                        if tool_def:
-                            formatted_tools.append(tool_def)
-                    elif isinstance(tool, dict):
-                        formatted_tools.append(tool)
-                    elif hasattr(tool, "to_openai_tool"):
-                        formatted_tools.append(tool.to_openai_tool())
-                    elif callable(tool):
-                        formatted_tools.append(self._generate_tool_definition(tool.__name__))
-
-            # Create async OpenAI client
-            async_client = AsyncOpenAI()
-
-            # Make the API call based on the type of request
-            if tools:
-                response = await async_client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature,
-                    tools=formatted_tools
-                )
-                return await self._achat_completion(response, tools)
-            elif output_json or output_pydantic:
-                response = await async_client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature,
-                    response_format={"type": "json_object"}
-                )
-                # Return the raw response
-                return response.choices[0].message.content
-            else:
-                response = await async_client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature
-                )
-                return response.choices[0].message.content
-        except Exception as e:
-            display_error(f"Error in chat completion: {e}")
-            return None
-
-    async def _achat_completion(self, response, tools):
-        """Async version of _chat_completion method"""
-        try:
-            message = response.choices[0].message
-            if not hasattr(message, 'tool_calls') or not message.tool_calls:
-                return message.content
-
-            results = []
-            for tool_call in message.tool_calls:
-                try:
-                    function_name = tool_call.function.name
-                    arguments = json.loads(tool_call.function.arguments)
-                    
-                    # Find the matching tool
-                    tool = next((t for t in tools if t.__name__ == function_name), None)
-                    if not tool:
-                        display_error(f"Tool {function_name} not found")
-                        continue
-                    
-                    # Check if the tool is async
-                    if asyncio.iscoroutinefunction(tool):
-                        result = await tool(**arguments)
-                    else:
-                        # Run sync function in executor to avoid blocking
-                        loop = asyncio.get_event_loop()
-                        result = await loop.run_in_executor(None, lambda: tool(**arguments))
-                    
-                    results.append(result)
-                except Exception as e:
-                    display_error(f"Error executing tool {function_name}: {e}")
-                    results.append(None)
-
-            # If we have results, format them into a response
-            if results:
-                formatted_results = "\n".join([str(r) for r in results if r is not None])
-                if formatted_results:
-                    messages = [
-                        {"role": "system", "content": self.system_prompt},
-                        {"role": "assistant", "content": "Here are the tool results:"},
-                        {"role": "user", "content": formatted_results + "\nPlease process these results and provide a final response."}
-                    ]
-                    try:
-                        async_client = AsyncOpenAI()
-                        final_response = await async_client.chat.completions.create(
-                            model=self.llm,
-                            messages=messages,
-                            temperature=0.2
-                        )
-                        return final_response.choices[0].message.content
-                    except Exception as e:
-                        display_error(f"Error in final chat completion: {e}")
-                        return formatted_results
-                return formatted_results
-            return None
-        except Exception as e:
-            display_error(f"Error in _achat_completion: {e}")
-            return None 
-
-    def run(self):
-        """Alias for start() method"""
-        return self.start() 

agents/praisonaiagents/agents/agents.py

@@ -1,702 +0,0 @@
-import os
-import time
-import json
-import logging
-from typing import Any, Dict, Optional, List
-from pydantic import BaseModel
-from rich.text import Text
-from rich.panel import Panel
-from rich.console import Console
-from ..main import display_error, TaskOutput, error_logs, client
-from ..agent.agent import Agent
-from ..task.task import Task
-from ..process.process import Process, LoopItems
-import asyncio
-
-# Set up logger
-logger = logging.getLogger(__name__)
-
-def encode_file_to_base64(file_path: str) -> str:
-    """Base64-encode a file."""
-    import base64
-    with open(file_path, "rb") as f:
-        return base64.b64encode(f.read()).decode("utf-8")
-
-def process_video(video_path: str, seconds_per_frame=2):
-    """Split video into frames (base64-encoded)."""
-    import cv2
-    import base64
-    base64_frames = []
-    video = cv2.VideoCapture(video_path)
-    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = video.get(cv2.CAP_PROP_FPS)
-    frames_to_skip = int(fps * seconds_per_frame)
-    curr_frame = 0
-    while curr_frame < total_frames:
-        video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
-        success, frame = video.read()
-        if not success:
-            break
-        _, buffer = cv2.imencode(".jpg", frame)
-        base64_frames.append(base64.b64encode(buffer).decode("utf-8"))
-        curr_frame += frames_to_skip
-    video.release()
-    return base64_frames
-
-class PraisonAIAgents:
-    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None):
-        if not agents:
-            raise ValueError("At least one agent must be provided")
-            
-        self.agents = agents
-        self.tasks = {}
-        if max_retries < 3:
-            max_retries = 3
-        self.completion_checker = completion_checker if completion_checker else self.default_completion_checker
-        self.task_id_counter = 0
-        self.verbose = verbose
-        self.max_retries = max_retries
-        self.process = process
-        
-        # Check for manager_llm in environment variable if not provided
-        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
-        
-        # Set logger level based on verbose
-        if verbose >= 5:
-            logger.setLevel(logging.INFO)
-        else:
-            logger.setLevel(logging.WARNING)
-            
-        # Also set third-party loggers to WARNING
-        logging.getLogger('chromadb').setLevel(logging.WARNING)
-        logging.getLogger('openai').setLevel(logging.WARNING)
-        logging.getLogger('httpx').setLevel(logging.WARNING)
-        logging.getLogger('httpcore').setLevel(logging.WARNING)
-
-        if self.verbose:
-            logger.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)
-            logger.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")
-            logger.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])
-            logger.info("Set up sequential flow with automatic context passing")
-        
-        self._state = {}  # Add state storage at PraisonAIAgents level
-        
-        # Initialize memory system
-        self.shared_memory = None
-        if memory:
-            try:
-                from ..memory.memory import Memory
-                
-                # Get memory config from parameter or first task
-                mem_cfg = memory_config
-                if not mem_cfg:
-                    mem_cfg = next((t.config.get('memory_config') for t in tasks if hasattr(t, 'config') and t.config), None)
-                    
-                # Set default memory config if none provided
-                if not mem_cfg:
-                    mem_cfg = {
-                        "provider": "rag",
-                        "use_embedding": True,
-                        "storage": {
-                            "type": "sqlite",
-                            "path": "./.praison/memory.db"
-                        },
-                        "rag_db_path": "./.praison/chroma_db"
-                    }
-                
-                # Add embedder config if provided
-                if embedder:
-                    if isinstance(embedder, dict):
-                        mem_cfg = mem_cfg or {}
-                        mem_cfg["embedder"] = embedder
-                    else:
-                        # Handle direct embedder function
-                        mem_cfg = mem_cfg or {}
-                        mem_cfg["embedder_function"] = embedder
-
-                if mem_cfg:
-                    # Pass verbose level to Memory
-                    self.shared_memory = Memory(config=mem_cfg, verbose=verbose)
-                    if verbose >= 5:
-                        logger.info("Initialized shared memory for PraisonAIAgents")
-                    
-                    # Distribute memory to tasks
-                    for task in tasks:
-                        if not task.memory:
-                            task.memory = self.shared_memory
-                            if verbose >= 5:
-                                logger.info(f"Assigned shared memory to task {task.id}")
-                            
-            except Exception as e:
-                logger.error(f"Failed to initialize shared memory: {e}")
-            
-        # Update tasks with shared memory
-        if self.shared_memory:
-            for task in tasks:
-                if not task.memory:
-                    task.memory = self.shared_memory
-                    logger.info(f"Assigned shared memory to task {task.id}")
-
-    def add_task(self, task):
-        task_id = self.task_id_counter
-        task.id = task_id
-        self.tasks[task_id] = task
-        self.task_id_counter += 1
-        return task_id
-
-    def clean_json_output(self, output: str) -> str:
-        cleaned = output.strip()
-        if cleaned.startswith("```json"):
-            cleaned = cleaned[len("```json"):].strip()
-        if cleaned.startswith("```"):
-            cleaned = cleaned[len("```"):].strip()
-        if cleaned.endswith("```"):
-            cleaned = cleaned[:-3].strip()
-        return cleaned
-
-    def default_completion_checker(self, task, agent_output):
-        if task.output_json and task.result and task.result.json_dict:
-            return True
-        if task.output_pydantic and task.result and task.result.pydantic:
-            return True
-        return len(agent_output.strip()) > 0
-
-    async def aexecute_task(self, task_id):
-        """Async version of execute_task method"""
-        if task_id not in self.tasks:
-            display_error(f"Error: Task with ID {task_id} does not exist")
-            return
-        task = self.tasks[task_id]
-        
-        # Only import multimodal dependencies if task has images
-        if task.images and task.status == "not started":
-            try:
-                import cv2
-                import base64
-                from moviepy import VideoFileClip
-            except ImportError as e:
-                display_error(f"Error: Missing required dependencies for image/video processing: {e}")
-                display_error("Please install with: pip install opencv-python moviepy")
-                task.status = "failed"
-                return None
-
-        if task.status == "not started":
-            task.status = "in progress"
-
-        executor_agent = task.agent
-
-        task_prompt = f"""
-You need to do the following task: {task.description}.
-Expected Output: {task.expected_output}.
-"""
-        if task.context:
-            context_results = ""
-            for context_task in task.context:
-                if context_task.result:
-                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
-                else:
-                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
-            task_prompt += f"""
-Here are the results of previous tasks that might be useful:\n
-{context_results}
-"""
-        task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
-
-        if self.verbose >= 2:
-            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
-        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
-
-        if task.images:
-            def _get_multimodal_message(text_prompt, images):
-                content = [{"type": "text", "text": text_prompt}]
-
-                for img in images:
-                    # If local file path for a valid image
-                    if os.path.exists(img):
-                        ext = os.path.splitext(img)[1].lower()
-                        # If it's a .mp4, convert to frames
-                        if ext == ".mp4":
-                            frames = process_video(img, seconds_per_frame=1)
-                            content.append({"type": "text", "text": "These are frames from the video."})
-                            for f in frames:
-                                content.append({
-                                    "type": "image_url",
-                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
-                                })
-                        else:
-                            encoded = encode_file_to_base64(img)
-                            content.append({
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
-                                }
-                            })
-                    else:
-                        # Treat as a remote URL
-                        content.append({
-                            "type": "image_url",
-                            "image_url": {"url": img}
-                        })
-                return content
-
-            agent_output = await executor_agent.achat(
-                _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools,
-                output_json=task.output_json,
-                output_pydantic=task.output_pydantic
-            )
-        else:
-            agent_output = await executor_agent.achat(
-                task_prompt,
-                tools=task.tools,
-                output_json=task.output_json,
-                output_pydantic=task.output_pydantic
-            )
-
-        if agent_output:
-            task_output = TaskOutput(
-                description=task.description,
-                summary=task.description[:10],
-                raw=agent_output,
-                agent=executor_agent.name,
-                output_format="RAW"
-            )
-
-            if task.output_json:
-                cleaned = self.clean_json_output(agent_output)
-                try:
-                    parsed = json.loads(cleaned)
-                    task_output.json_dict = parsed
-                    task_output.output_format = "JSON"
-                except:
-                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
-                    logger.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:
-                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
-                    logger.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":
-            logger.info(f"Task with ID {task_id} is already completed")
-            return
-
-        retries = 0
-        while task.status != "completed" and retries < self.max_retries:
-            logger.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"
-                    # Run execute_callback for memory operations
-                    try:
-                        await task.execute_callback(task_output)
-                    except Exception as e:
-                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
-                        logger.exception(e)
-                    
-                    # Run task callback if exists
-                    if task.callback:
-                        try:
-                            if asyncio.iscoroutinefunction(task.callback):
-                                await task.callback(task_output)
-                            else:
-                                task.callback(task_output)
-                        except Exception as e:
-                            logger.error(f"Error executing task callback for task {task_id}: {e}")
-                            logger.exception(e)
-                            
-                    self.save_output_to_file(task, task_output)
-                    if self.verbose >= 1:
-                        logger.info(f"Task {task_id} completed successfully.")
-                else:
-                    task.status = "in progress"
-                    if self.verbose >= 1:
-                        logger.info(f"Task {task_id} not completed, retrying")
-                    await asyncio.sleep(1)
-                    retries += 1
-            else:
-                if task.status == "failed":
-                    logger.info("Task is failed, resetting to in-progress for another try...")
-                    task.status = "in progress"
-                else:
-                    logger.info("Invalid Task status")
-                    break
-
-        if retries == self.max_retries and task.status != "completed":
-            logger.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:
-                    logger.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]
-        
-        logger.info(f"Starting execution of task {task_id}")
-        logger.info(f"Task config: {task.config}")
-        
-        # Initialize memory before task execution
-        if not task.memory:
-            task.memory = task.initialize_memory()
-        
-        logger.info(f"Task memory status: {'Initialized' if task.memory else 'Not initialized'}")
-        
-        # 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}
-"""
-        # Add memory context if available
-        if task.memory:
-            try:
-                memory_context = task.memory.build_context_for_task(task.description)
-                if memory_context:
-                    task_prompt += f"\n\nRelevant memory context:\n{memory_context}"
-            except Exception as e:
-                logger.error(f"Error getting memory context: {e}")
-
-        task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
-
-        if self.verbose >= 2:
-            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
-        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
-
-        if task.images:
-            def _get_multimodal_message(text_prompt, images):
-                content = [{"type": "text", "text": text_prompt}]
-
-                for img in images:
-                    # If local file path for a valid image
-                    if os.path.exists(img):
-                        ext = os.path.splitext(img)[1].lower()
-                        # If it's a .mp4, convert to frames
-                        if ext == ".mp4":
-                            frames = process_video(img, seconds_per_frame=1)
-                            content.append({"type": "text", "text": "These are frames from the video."})
-                            for f in frames:
-                                content.append({
-                                    "type": "image_url",
-                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
-                                })
-                        else:
-                            encoded = encode_file_to_base64(img)
-                            content.append({
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
-                                }
-                            })
-                    else:
-                        # Treat as a remote URL
-                        content.append({
-                            "type": "image_url",
-                            "image_url": {"url": img}
-                        })
-                return content
-
-            agent_output = executor_agent.chat(
-                _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools,
-                output_json=task.output_json,
-                output_pydantic=task.output_pydantic
-            )
-        else:
-            agent_output = executor_agent.chat(
-                task_prompt,
-                tools=task.tools,
-                output_json=task.output_json,
-                output_pydantic=task.output_pydantic
-            )
-
-        if agent_output:
-            # Store the response in memory
-            if task.memory:
-                try:
-                    task.store_in_memory(
-                        content=agent_output,
-                        agent_name=executor_agent.name,
-                        task_id=task_id
-                    )
-                except Exception as e:
-                    logger.error(f"Failed to store agent output in memory: {e}")
-
-            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:
-                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
-                    logger.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:
-                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
-                    logger.debug(f"Output that failed Pydantic parsing: {agent_output}")
-
-            task.result = task_output
-            return task_output
-        else:
-            task.status = "failed"
-            return None
-
-    def run_task(self, task_id):
-        """Synchronous version of run_task method"""
-        if task_id not in self.tasks:
-            display_error(f"Error: Task with ID {task_id} does not exist")
-            return
-        task = self.tasks[task_id]
-        if task.status == "completed":
-            logger.info(f"Task with ID {task_id} is already completed")
-            return
-
-        retries = 0
-        while task.status != "completed" and retries < self.max_retries:
-            logger.debug(f"Attempt {retries+1} for task {task_id}")
-            if task.status in ["not started", "in progress"]:
-                task_output = self.execute_task(task_id)
-                if task_output and self.completion_checker(task, task_output.raw):
-                    task.status = "completed"
-                    # Run execute_callback for memory operations
-                    try:
-                        loop = asyncio.get_event_loop()
-                        loop.run_until_complete(task.execute_callback(task_output))
-                    except Exception as e:
-                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
-                        logger.exception(e)
-                    
-                    # Run task callback if exists
-                    if task.callback:
-                        try:
-                            if asyncio.iscoroutinefunction(task.callback):
-                                loop.run_until_complete(task.callback(task_output))
-                            else:
-                                task.callback(task_output)
-                        except Exception as e:
-                            logger.error(f"Error executing task callback for task {task_id}: {e}")
-                            logger.exception(e)
-                            
-                    self.save_output_to_file(task, task_output)
-                    if self.verbose >= 1:
-                        logger.info(f"Task {task_id} completed successfully.")
-                else:
-                    task.status = "in progress"
-                    if self.verbose >= 1:
-                        logger.info(f"Task {task_id} not completed, retrying")
-                    time.sleep(1)
-                    retries += 1
-            else:
-                if task.status == "failed":
-                    logger.info("Task is failed, resetting to in-progress for another try...")
-                    task.status = "in progress"
-                else:
-                    logger.info("Invalid Task status")
-                    break
-
-        if retries == self.max_retries and task.status != "completed":
-            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
-
-    def run_all_tasks(self):
-        """Synchronous version of run_all_tasks method"""
-        process = Process(
-            tasks=self.tasks,
-            agents=self.agents,
-            manager_llm=self.manager_llm,
-            verbose=self.verbose
-        )
-        
-        if self.process == "workflow":
-            for task_id in process.workflow():
-                self.run_task(task_id)
-        elif self.process == "sequential":
-            for task_id in process.sequential():
-                self.run_task(task_id)
-        elif self.process == "hierarchical":
-            for task_id in process.hierarchical():
-                if isinstance(task_id, Task):
-                    task_id = self.add_task(task_id)
-                self.run_task(task_id)
-
-    def get_task_status(self, task_id):
-        if task_id in self.tasks:
-            return self.tasks[task_id].status
-        return None
-
-    def get_all_tasks_status(self):
-        return {task_id: self.tasks[task_id].status for task_id in self.tasks}
-
-    def get_task_result(self, task_id):
-        if task_id in self.tasks:
-            return self.tasks[task_id].result
-        return None
-
-    def get_task_details(self, task_id):
-        if task_id in self.tasks:
-            return str(self.tasks[task_id])
-        return None
-
-    def get_agent_details(self, agent_name):
-        agent = [task.agent for task in self.tasks.values() if task.agent and task.agent.name == agent_name]
-        if agent:
-            return str(agent[0])
-        return None
-
-    def start(self):
-        self.run_all_tasks()
-        return {
-            "task_status": self.get_all_tasks_status(),
-            "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
-        } 
-
-    def set_state(self, key: str, value: Any) -> None:
-        """Set a state value"""
-        self._state[key] = value
-
-    def get_state(self, key: str, default: Any = None) -> Any:
-        """Get a state value"""
-        return self._state.get(key, default)
-
-    def update_state(self, updates: Dict) -> None:
-        """Update multiple state values"""
-        self._state.update(updates)
-
-    def clear_state(self) -> None:
-        """Clear all state values"""
-        self._state.clear() 

agents/praisonaiagents/process/process.py

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

agents/pyproject.toml

@@ -1,21 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "praisonaiagents"
-version = "0.0.30"
-description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
-authors = [
-    { name="Mervin Praison" }
-]
-dependencies = [
-    "pydantic",
-    "rich",
-    "openai"
-]
-
-[project.optional-dependencies]
-memory = [
-    "chromadb>=0.6.0"
-] 

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.38 gunicorn markdown
+RUN pip install flask praisonai==2.2.9 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docker/Dockerfile.chat

@@ -13,7 +13,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN pip install --no-cache-dir \
     praisonaiagents>=0.0.4 \
     praisonai_tools \
-    "praisonai==2.0.18" \
+    "praisonai==2.2.9" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -15,7 +15,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN pip install --no-cache-dir \
     praisonaiagents>=0.0.4 \
     praisonai_tools \
-    "praisonai==2.0.18" \
+    "praisonai==2.2.9" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -13,7 +13,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN pip install --no-cache-dir \
     praisonaiagents>=0.0.4 \
     praisonai_tools \
-    "praisonai==2.0.18" \
+    "praisonai==2.2.9" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docs/agents/agents.mdx

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

docs/agents/data-analyst.mdx

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

docs/agents/finance.mdx

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

docs/agents/image-to-text.mdx

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

docs/agents/image.mdx

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

docs/agents/markdown.mdx

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

docs/agents/planning.mdx

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

docs/agents/programming.mdx

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

docs/agents/recommendation.mdx

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

docs/agents/research.mdx

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

docs/agents/shopping.mdx

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

docs/agents/single.mdx

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

docs/agents/video.mdx

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

docs/agents/websearch.mdx

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

docs/agents/wikipedia.mdx

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

docs/api-reference/index.mdx

@@ -30,7 +30,7 @@ Features:
 - Built-in tools
 - Structured workflows
 
-### AutoGen Integration
+### AG2 (Formerly AutoGen) Integration
 Requires installation with:
 ```bash
 pip install "praisonai[autogen]"

docs/api.mdx

@@ -23,7 +23,7 @@ from praisonai.auto import AutoGenerator
 ### praisonai.agents_generator
 Framework-specific agent generation and orchestration:
 - CrewAI support (requires `praisonai[crewai]`)
-- AutoGen support (requires `praisonai[autogen]`)
+- AG2 (Formerly AutoGen) support (requires `praisonai[autogen]`)
 
 ```python
 from praisonai.agents_generator import AgentsGenerator
@@ -51,7 +51,7 @@ pip install praisonai
 
 # Framework-specific installations
 pip install "praisonai[crewai]"    # Install with CrewAI support
-pip install "praisonai[autogen]"   # Install with AutoGen support
+pip install "praisonai[autogen]"   # Install with AG2 support
 pip install "praisonai[crewai,autogen]"  # Install both frameworks
 
 # Additional features
@@ -71,9 +71,9 @@ When installing with `pip install "praisonai[crewai]"`, you get:
 - Task delegation capabilities
 - Sequential and parallel task execution
 
-### AutoGen
+### AG2 (Formerly AutoGen)
 When installing with `pip install "praisonai[autogen]"`, you get:
-- AutoGen framework support
+- AG2 framework support
 - PraisonAI tools integration
 - Multi-agent conversation capabilities
 - Code execution environment

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.38 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.2.9 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/concepts/agents.mdx

@@ -4,11 +4,8 @@ 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
+Agents are the core building blocks of PraisonAI. Each agent is an autonomous AI entity with specific roles, goals, and capabilities.
 
 ```mermaid
 graph LR
@@ -19,45 +16,34 @@ graph LR
     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[" "]
+    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[" "]
+    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
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
 ```
 
 ## Key Components
@@ -126,86 +112,22 @@ graph LR
   <Step title="Create Agent">
     Create `app.py`:
 <CodeGroup>
-  ```python Single Agent
-  from praisonaiagents import Agent, Task, PraisonAIAgents
+```python Single Agent
+from praisonaiagents import Agent
 
-  # 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"
-  )
+agent = Agent(instructions="Your are a helpful AI assistant")
+agent.start("Write a movie script about a robot in Mars")
+```
 
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
+```python Multi Agents
+from praisonaiagents import Agent, PraisonAIAgents
 
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False
-  )
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
 
-  result = agents.start()
-  ```
-
-  ```python Multiple Agents
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create multiple agents
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```
 </CodeGroup>
   </Step>
 

docs/concepts/knowledge.mdx

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

docs/concepts/memory.mdx

@@ -5,6 +5,48 @@ description: "Learn how to create AI agents with memory capabilities for maintai
 icon: "brain"
 ---
 
+```mermaid
+flowchart TB
+    subgraph Memory
+        direction TB
+        STM[Short Term]
+        LTM[Long Term]
+    end
+
+    subgraph Store
+        direction TB
+        DB[(Vector DB)]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    Memory <--> Store
+    Store <--> A1
+    Store <--> A2
+    Store <--> A3
+
+    style Memory fill:#189AB4,color:#fff
+    style Store fill:#2E8B57,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+| Feature | [Knowledge](/concepts/knowledge) | [Memory](/concepts/memory) |
+|---------|--------------------------------|---------------------------|
+| When Used | Pre-loaded before agent execution | Created and updated during runtime |
+| Purpose | Provide static reference information | Store dynamic context and interactions |
+| Storage | Read-only knowledge base | Read-write memory store |
+| Persistence | Permanent until explicitly changed | Can be temporary (STM) or persistent (LTM) |
+| Updates | Manual updates through knowledge files | Automatic updates during agent execution |
+
 ## Quick Start
 
 <Tabs>
@@ -396,16 +438,6 @@ PraisonAI's memory system includes several types of memory:
     - Semantic search capabilities with RAG
     - Quality-based storage decisions
   </Card>
-  <Card title="Entity Memory" icon="diagram-project">
-    - Store information about specific entities
-    - Track relationships and attributes
-    - Specialized search for entity information
-  </Card>
-  <Card title="User Memory" icon="user">
-    - Store user-specific information
-    - Personalized context for each user
-    - Persistent across sessions
-  </Card>
 </CardGroup>
 
 ### Memory Quality Control
@@ -437,5 +469,4 @@ results = agents.memory.search_long_term(
     query="search query",
     min_quality=0.8,     # Only return high-quality matches
     limit=5              # Maximum number of results
-)
-```
+)

docs/concepts/process.mdx

@@ -36,6 +36,24 @@ Process types in PraisonAI define how tasks are executed and how agents collabor
 
 ## Sequential Process
 
+```mermaid
+graph LR
+    Input[Input] --> A1
+    subgraph Agents
+        direction LR
+        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
+    end
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class A1,A2,A3 process
+    class Agents transparent
+```
+
 The simplest form of task execution where tasks are performed one after another.
 
 <Card>
@@ -57,6 +75,39 @@ The simplest form of task execution where tasks are performed one after another.
 
 ## Hierarchical Process
 
+```mermaid
+graph TB
+    Input[Input] --> Manager
+    
+    subgraph Agents
+        Manager[Manager Agent]
+        
+        subgraph Workers
+            direction LR
+            W1[Worker 1]
+            W2[Worker 2]
+            W3[Worker 3]
+        end
+        
+        Manager --> W1
+        Manager --> W2
+        Manager --> W3
+    end
+    
+    W1 --> Manager
+    W2 --> Manager
+    W3 --> Manager
+    Manager --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Manager,W1,W2,W3 process
+    class Agents,Workers transparent
+```
+
 Uses a manager agent to coordinate task execution and agent assignments.
 
 <Card>
@@ -79,6 +130,33 @@ Uses a manager agent to coordinate task execution and agent assignments.
 
 ## Workflow Process
 
+```mermaid
+graph LR
+    Input[Input] --> Start
+    
+    subgraph Workflow
+        direction LR
+        Start[Start] --> C1{Condition}
+        C1 --> |Yes| A1[Agent 1]
+        C1 --> |No| A2[Agent 2]
+        A1 --> Join
+        A2 --> Join
+        Join --> A3[Agent 3]
+    end
+    
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Start,A1,A2,A3,Join process
+    class C1 decision
+    class Workflow transparent
+```
+
 Advanced process type supporting complex task relationships and conditional execution.
 
 <Card>

docs/concepts/tasks.mdx

@@ -4,9 +4,36 @@ 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
+Tasks are units of work that agents execute. Each task has a clear goal, input requirements, and expected outputs.
+
+```mermaid
+graph LR
+    Input[Input] ---> Task
+    
+    subgraph Task
+        direction LR
+        Role[Role]
+        Goal[Goal]
+        Story[Backstory]
+        Expected[Expected Output]
+        
+        Role --> Goal
+        Goal --> Story
+        Story --> Expected
+    end
+    
+    Task ---> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Role,Goal,Story,Expected process
+    class Task transparent
+```
 
 ## Core Components
 

docs/concepts/tools.mdx

@@ -1,12 +1,49 @@
 ---
-title: "Tools"
-description: "Understanding Tools in PraisonAI"
+title: "AI Agents with Tools"
+sidebarTitle: "Tools"
+description: "Learn how to create AI agents that can use tools to interact with external systems and perform actions."
 icon: "screwdriver-wrench"
 ---
 
-# Understanding Tools
+```mermaid
+flowchart TB
+    subgraph Tools
+        direction TB
+        T3[Internet Search]
+        T1[Code Execution]
+        T2[Formatting]
+    end
 
-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.
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    T3 --> A1
+    T1 --> A2
+    T2 --> A3
+
+    style Tools fill:#189AB4,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+| Feature | [Knowledge](/concepts/knowledge) | [Tools](/concepts/tools) |
+|---------|--------------------------------|---------------------------|
+| Purpose | Static reference information | Dynamic interaction capabilities |
+| Access | Read-only reference | Execute actions and commands |
+| Updates | Manual through files | Real-time through tool calls |
+| Storage | Knowledge base | Assigned to specific agents |
+| Persistence | Permanent until changed | Available during agent execution |
+
+## Quick Start
+
+Tools are functions that agents can use to interact with external systems and perform actions. They are essential for creating agents that can do more than just process text.
 
 ## Creating Custom Tool
 <Steps>

docs/course/agents/01-introduction.mdx

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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