Merge pull request #611 from MervinPraison/develop

Update PraisonAI version to 2.2.30 across Dockerfiles and related files

.github/workflows/coverage.yml

@@ -0,0 +1,60 @@
+name: Run tests and upload coverage
+
+on: 
+  push:
+    branches: [ main, develop ]
+  pull_request:
+    branches: [ main, develop ]
+
+jobs:
+  test:
+    name: Run tests and collect coverage
+    runs-on: ubuntu-latest
+    
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.11
+
+      - name: Install UV
+        run: |
+          curl -LsSf https://astral.sh/uv/install.sh | sh
+          echo "$HOME/.local/bin" >> $GITHUB_PATH
+
+      - name: Install dependencies
+        run: |
+          cd src/praisonai
+          uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
+          uv pip install --system duckduckgo_search
+          uv pip install --system pytest pytest-cov pytest-asyncio
+          # Install knowledge dependencies from praisonai-agents
+          uv pip install --system "praisonaiagents[knowledge]"
+
+      - name: Set environment variables
+        run: |
+          echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
+          echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
+          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+          echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
+          echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
+
+      - name: Run tests
+        run: |
+          cd src/praisonai
+          pytest --cov=praisonai --cov-branch --cov-report=xml tests/unit/
+
+      - name: Upload results to Codecov
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
+          file: src/praisonai/coverage.xml
+          flags: unit-tests
+          name: unit-tests-coverage
+          fail_ci_if_error: false
+          verbose: true 

.github/workflows/python-package.yml

@@ -65,4 +65,4 @@ jobs:
     #     flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        cd src/praisonai && python -m pytest
+        cd src/praisonai && python tests/test_runner.py --pattern fast

.github/workflows/test-comprehensive.yml

@@ -69,10 +69,10 @@ jobs:
         
         case $TEST_TYPE in
           "unit")
-            cd src/praisonai && python tests/test_runner.py --pattern unit
+            cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch
             ;;
           "integration")
-            cd src/praisonai && python tests/test_runner.py --pattern integration
+            cd src/praisonai && python -m pytest tests/integration/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch
             ;;
           "fast")
             cd src/praisonai && python tests/test_runner.py --pattern fast
@@ -139,6 +139,16 @@ jobs:
         echo "- 🔌 MCP server connections" >> comprehensive_report.md
         echo "- 💬 LLM integrations (OpenAI, Anthropic, etc.)" >> comprehensive_report.md
 
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: src/praisonai/coverage.xml
+        flags: comprehensive-tests
+        name: comprehensive-tests-coverage
+        fail_ci_if_error: false
+        verbose: true
+
     - name: Upload Comprehensive Test Results
       uses: actions/upload-artifact@v4
       if: always()
@@ -146,9 +156,9 @@ jobs:
         name: comprehensive-test-results-python-${{ matrix.python-version }}
         path: |
           comprehensive_report.md
-          htmlcov/
-          coverage.xml
-          .coverage
+          src/praisonai/htmlcov/
+          src/praisonai/coverage.xml
+          src/praisonai/.coverage
         retention-days: 30
 
   test-matrix-summary:

.github/workflows/test-core.yml

@@ -195,7 +195,7 @@ jobs:
 
     - name: Run Unit Tests
       run: |
-        cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=term-missing
+        cd src/praisonai && python -m pytest tests/unit/ -v --tb=short --disable-warnings --cov=praisonai --cov-report=term-missing --cov-report=xml --cov-branch
 
     - name: Run Integration Tests  
       run: |
@@ -263,12 +263,24 @@ jobs:
         export OPENAI_MODEL_NAME="$OPENAI_MODEL_NAME"
         cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
 
-    - name: Upload Coverage Reports
+    - name: Upload coverage reports to Codecov
+      if: matrix.python-version == '3.11'
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: src/praisonai/coverage.xml
+        flags: core-tests
+        name: core-tests-coverage
+        fail_ci_if_error: false
+        verbose: true
+
+    - name: Upload Coverage Reports (Artifacts)
       if: matrix.python-version == '3.11'
       uses: actions/upload-artifact@v4
       with:
         name: coverage-reports
         path: |
-          .coverage
-          htmlcov/
+          src/praisonai/.coverage
+          src/praisonai/htmlcov/
+          src/praisonai/coverage.xml
         retention-days: 7

.github/workflows/unittest.yml

@@ -471,8 +471,8 @@ jobs:
 
     - name: Run Fast Tests
       run: |
-        # Run the fastest, most essential tests
-        cd src/praisonai && python tests/test_runner.py --pattern fast
+        # Run the fastest, most essential tests with coverage
+        cd src/praisonai && python -m pytest tests/unit/test_core_agents.py -v --tb=short --disable-warnings --cov=praisonai --cov-report=xml --cov-branch
 
     - name: Run Real API Tests
       run: |
@@ -491,6 +491,16 @@ jobs:
         cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
       continue-on-error: true
 
+    - name: Upload coverage reports to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: src/praisonai/coverage.xml
+        flags: quick-validation
+        name: quick-validation-coverage
+        fail_ci_if_error: false
+        verbose: true
+
     - name: Restore Root Config Files
       run: |
         echo "🔄 Restoring root configuration files..."

.gitignore

@@ -74,4 +74,5 @@ agents/praisonaiagents/praisonaiagents.egg-info
 .praison
 # Local Netlify folder
 .netlify
-.qodo
+.qodo
+CopilotKit*

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.26" \
+    "praisonai>=2.2.30" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.26" \
+    "praisonai>=2.2.30" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.26" \
+    "praisonai>=2.2.30" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.26" \
+    "praisonai>=2.2.30" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.26`
+- PraisonAI: `>=2.2.30`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.26" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.30" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

examples/cookbooks/Code_Analysis_Agent.ipynb

@@ -0,0 +1,467 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bLeMxwWimvzX"
+      },
+      "source": [
+        "# Code Analysis Agent\n",
+        "Learn how to create AI agents for comprehensive code analysis and quality assessment."
+      ],
+      "id": "bLeMxwWimvzX"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb)\n"
+=======
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/code_analysis_agent.ipynb)\n"
+
+      ],
+      "metadata": {
+        "id": "P3X8PXPyojnL"
+      },
+      "id": "P3X8PXPyojnL"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xEs8bwWfmvza"
+      },
+      "source": [
+        "## Dependencies\n",
+        "Install the required packages."
+      ],
+      "id": "xEs8bwWfmvza"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_RPiU3j6mvzb"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install praisonaiagents gitingest"
+      ],
+      "id": "_RPiU3j6mvzb"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "81-orTkzmvzc"
+      },
+      "source": [
+        "## Set API Key\n",
+        "Set your OpenAI API key as an environment variable in your terminal:"
+      ],
+      "id": "81-orTkzmvzc"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "84iL7rGimvzc"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ['OPENAI_API_KEY'] = 'your_api_key_here'"
+      ],
+      "id": "84iL7rGimvzc"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TV15GFsRmvzc"
+      },
+      "source": [
+        "## Tools and Data Models\n",
+        "Define the agent, task, and output schema."
+      ],
+      "id": "TV15GFsRmvzc"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0Wq_g0pNmvzd"
+      },
+      "outputs": [],
+      "source": [
+        "from praisonaiagents import Agent, Task, PraisonAIAgents\n",
+        "from pydantic import BaseModel\n",
+        "from typing import List, Dict\n",
+        "from gitingest import ingest\n",
+        "\n",
+        "class CodeMetrics(BaseModel):\n",
+        "    category: str\n",
+        "    score: int\n",
+        "    findings: List[str]\n",
+        "\n",
+        "class CodeAnalysisReport(BaseModel):\n",
+        "    overall_quality: int\n",
+        "    code_metrics: List[CodeMetrics]\n",
+        "    architecture_score: int\n",
+        "    maintainability_score: int\n",
+        "    performance_score: int\n",
+        "    security_score: int\n",
+        "    test_coverage: int\n",
+        "    key_strengths: List[str]\n",
+        "    improvement_areas: List[str]\n",
+        "    tech_stack: List[str]\n",
+        "    recommendations: List[str]\n",
+        "    complexity_metrics: Dict[str, int]\n",
+        "    best_practices: List[Dict[str, str]]\n",
+        "    potential_risks: List[str]\n",
+        "    documentation_quality: int"
+      ],
+      "id": "0Wq_g0pNmvzd"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EatrK7-wmvzd"
+      },
+      "source": [
+        "## YAML Prompt Setup\n",
+        "Define the agent and task with expectations."
+      ],
+      "id": "EatrK7-wmvzd"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EdZNSOy-mvze"
+      },
+      "outputs": [],
+      "source": [
+        "code_analyzer = Agent(\n",
+        "    role=\"Code Analysis Expert\",\n",
+        "    goal=\"Provide comprehensive code evaluation and recommendations\",\n",
+        "    backstory=\"\"\"Expert code analyst specializing in architecture review,\n",
+        "    best practices, and technical debt assessment.\"\"\",\n",
+        "    verbose=True\n",
+        ")\n",
+        "\n",
+        "code_analysis_task = Task(\n",
+        "    description=\"\"\"Analyze code repository and provide structured evaluation:\n",
+        "\n",
+        "    1. Overall Quality (0-100)\n",
+        "    2. Core Metrics Analysis:\n",
+        "       - Architecture and Design\n",
+        "       - Code Maintainability\n",
+        "       - Performance Optimization\n",
+        "       - Security Practices\n",
+        "       - Test Coverage\n",
+        "    3. Technical Assessment:\n",
+        "       - Technology Stack Review\n",
+        "       - Code Complexity Analysis\n",
+        "       - Best Practices Adherence\n",
+        "       - Risk Assessment\n",
+        "    4. Recommendations:\n",
+        "       - Key Improvements\n",
+        "       - Architecture Suggestions\n",
+        "       - Security Enhancements\"\"\",\n",
+        "    expected_output=\"Detailed code analysis report with metrics and recommendations\",\n",
+        "    agent=code_analyzer,\n",
+        "    output_pydantic=CodeAnalysisReport\n",
+        ")"
+      ],
+      "id": "EdZNSOy-mvze"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CeLS55ROmvzf"
+      },
+      "source": [
+        "## Main Function\n",
+        "Function to analyze code from GitHub URL or local path."
+      ],
+      "id": "CeLS55ROmvzf"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3Eo6LEOxmvzf"
+      },
+      "outputs": [],
+      "source": [
+        "def analyze_code(code_source: str) -> CodeAnalysisReport:\n",
+        "    \"\"\"\n",
+        "    Analyze code from directory path or GitHub URL\n",
+        "    \"\"\"\n",
+        "    # Ingest code content\n",
+        "    summary, tree, content = ingest(code_source)\n",
+        "\n",
+        "    # Concatenate context into structured format\n",
+        "    context_text = f\"\"\"\n",
+        "    CODE REPOSITORY ANALYSIS\n",
+        "    =======================\n",
+        "\n",
+        "    SUMMARY\n",
+        "    -------\n",
+        "    {summary}\n",
+        "\n",
+        "    REPOSITORY STRUCTURE\n",
+        "    -------------------\n",
+        "    {tree}\n",
+        "\n",
+        "    SOURCE CODE\n",
+        "    -----------\n",
+        "    {content}\n",
+        "    \"\"\"\n",
+        "\n",
+        "    # Initialize and run analysis\n",
+        "    agents = PraisonAIAgents(\n",
+        "        agents=[code_analyzer],\n",
+        "        tasks=[code_analysis_task]\n",
+        "    )\n",
+        "\n",
+        "    return agents.start(context_text)"
+      ],
+      "id": "3Eo6LEOxmvzf"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v25nLA0pmvzf"
+      },
+      "source": [
+        "## Output\n",
+        "Example usage to run the analysis and view results."
+      ],
+      "id": "v25nLA0pmvzf"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "5BzccaeFmvzg",
+        "outputId": "8b4cddae-b6b3-4a92-8a13-30694cd09c52"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ],
+            "text/markdown": "\n### 👤 Agent: Code Analysis Expert\n\n**Role**: Provides comprehensive code evaluation and recommendations  \n**Backstory**: Expert in architecture, best practices, and technical assessment  \n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "─── 📊 AGENT CODE ANALYSIS REPORT ───\n",
+            "{\n",
+            "    \"overall_quality\": 85,\n",
+            "    \"code_metrics\": [\n",
+            "        {\n",
+            "            \"category\": \"Architecture and Design\",\n",
+            "            \"score\": 80,\n",
+            "            \"findings\": [\n",
+            "                \"Modular structure with clear separation of concerns.\",\n",
+            "                \"Use of type annotations improves code readability and maintainability.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Code Maintainability\",\n",
+            "            \"score\": 85,\n",
+            "            \"findings\": [\n",
+            "                \"Consistent use of type hints and NamedTuple for structured data.\",\n",
+            "                \"Logical organization of functions and classes.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Performance Optimization\",\n",
+            "            \"score\": 75,\n",
+            "            \"findings\": [\n",
+            "                \"Potential performance overhead due to repeated sys.stdout.write calls.\",\n",
+            "                \"Efficient use of optional parameters to control execution flow.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Security Practices\",\n",
+            "            \"score\": 80,\n",
+            "            \"findings\": [\n",
+            "                \"No obvious security vulnerabilities in the code.\",\n",
+            "                \"Proper encapsulation of functionality.\"\n",
+            "            ]\n",
+            "        },\n",
+            "        {\n",
+            "            \"category\": \"Test Coverage\",\n",
+            "            \"score\": 70,\n",
+            "            \"findings\": [\n",
+            "                \"Lack of explicit test cases in the provided code.\",\n",
+            "                \"Use of type checking suggests some level of validation.\"\n",
+            "            ]\n",
+            "        }\n",
+            "    ],\n",
+            "    \"architecture_score\": 80,\n",
+            "    \"maintainability_score\": 85,\n",
+            "    \"performance_score\": 75,\n",
+            "    \"security_score\": 80,\n",
+            "    \"test_coverage\": 70,\n",
+            "    \"key_strengths\": [\n",
+            "        \"Strong use of type annotations and typing extensions.\",\n",
+            "        \"Clear separation of CLI argument parsing and business logic.\"\n",
+            "    ],\n",
+            "    \"improvement_areas\": [\n",
+            "        \"Increase test coverage to ensure robustness.\",\n",
+            "        \"Optimize I/O operations to improve performance.\"\n",
+            "    ],\n",
+            "    \"tech_stack\": [\n",
+            "        \"Python\",\n",
+            "        \"argparse\",\n",
+            "        \"typing_extensions\"\n",
+            "    ],\n",
+            "    \"recommendations\": [\n",
+            "        \"Add unit tests to improve reliability.\",\n",
+            "        \"Consider async I/O for improved performance in CLI tools.\"\n",
+            "    ]\n",
+            "}\n"
+          ]
+        }
+      ],
+      "source": [
+        "import json\n",
+        "from IPython.display import display, Markdown\n",
+        "\n",
+        "# Optional: Define agent info\n",
+        "agent_info = \"\"\"\n",
+        "### 👤 Agent: Code Analysis Expert\n",
+        "\n",
+        "**Role**: Provides comprehensive code evaluation and recommendations\n",
+        "**Backstory**: Expert in architecture, best practices, and technical assessment\n",
+        "\"\"\"\n",
+        "\n",
+        "# Analysis Result Data\n",
+        "analysis_result = {\n",
+        "    \"overall_quality\": 85,\n",
+        "    \"code_metrics\": [\n",
+        "        {\n",
+        "            \"category\": \"Architecture and Design\",\n",
+        "            \"score\": 80,\n",
+        "            \"findings\": [\n",
+        "                \"Modular structure with clear separation of concerns.\",\n",
+        "                \"Use of type annotations improves code readability and maintainability.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Code Maintainability\",\n",
+        "            \"score\": 85,\n",
+        "            \"findings\": [\n",
+        "                \"Consistent use of type hints and NamedTuple for structured data.\",\n",
+        "                \"Logical organization of functions and classes.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Performance Optimization\",\n",
+        "            \"score\": 75,\n",
+        "            \"findings\": [\n",
+        "                \"Potential performance overhead due to repeated sys.stdout.write calls.\",\n",
+        "                \"Efficient use of optional parameters to control execution flow.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Security Practices\",\n",
+        "            \"score\": 80,\n",
+        "            \"findings\": [\n",
+        "                \"No obvious security vulnerabilities in the code.\",\n",
+        "                \"Proper encapsulation of functionality.\"\n",
+        "            ]\n",
+        "        },\n",
+        "        {\n",
+        "            \"category\": \"Test Coverage\",\n",
+        "            \"score\": 70,\n",
+        "            \"findings\": [\n",
+        "                \"Lack of explicit test cases in the provided code.\",\n",
+        "                \"Use of type checking suggests some level of validation.\"\n",
+        "            ]\n",
+        "        }\n",
+        "    ],\n",
+        "    \"architecture_score\": 80,\n",
+        "    \"maintainability_score\": 85,\n",
+        "    \"performance_score\": 75,\n",
+        "    \"security_score\": 80,\n",
+        "    \"test_coverage\": 70,\n",
+        "    \"key_strengths\": [\n",
+        "        \"Strong use of type annotations and typing extensions.\",\n",
+        "        \"Clear separation of CLI argument parsing and business logic.\"\n",
+        "    ],\n",
+        "    \"improvement_areas\": [\n",
+        "        \"Increase test coverage to ensure robustness.\",\n",
+        "        \"Optimize I/O operations to improve performance.\"\n",
+        "    ],\n",
+        "    \"tech_stack\": [\"Python\", \"argparse\", \"typing_extensions\"],\n",
+        "    \"recommendations\": [\n",
+        "        \"Add unit tests to improve reliability.\",\n",
+        "        \"Consider async I/O for improved performance in CLI tools.\"\n",
+        "    ]\n",
+        "}\n",
+        "\n",
+        "# Display Agent Info and Analysis Report\n",
+        "display(Markdown(agent_info))\n",
+        "print(\"─── 📊 AGENT CODE ANALYSIS REPORT ───\")\n",
+        "print(json.dumps(analysis_result, indent=4))\n"
+      ],
+      "id": "5BzccaeFmvzg"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "%cd PraisonAI"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mNZjZLedorlu",
+        "outputId": "82c96cdf-e2e5-4a9c-e633-f0fc225ad973"
+      },
+      "id": "mNZjZLedorlu",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/PraisonAI\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+
+}
+=======
+}
+

examples/cookbooks/Gemma2B_Instruction_Agent.ipynb

@@ -0,0 +1,605 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "PnWDOzZelHlE",
+   "metadata": {
+    "id": "PnWDOzZelHlE"
+   },
+   "source": [
+    "# 🌱 Gemma 2B Instruction Agent\n",
+    "\n",
+    "**Goal:** You will learn how to do data prep, how to train, how to run the model, and how to save it using Google’s `gemma-2b-it` open-source model.\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4qumJb5qs9CF",
+   "metadata": {
+    "id": "4qumJb5qs9CF"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Gemma2B_Instruction_Agent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qdsqiQDosXmA",
+   "metadata": {
+    "id": "qdsqiQDosXmA"
+   },
+   "source": [
+    "#  Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6G4RPDe3lHlH",
+   "metadata": {
+    "id": "6G4RPDe3lHlH"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install transformers accelerate datasets bitsandbytes -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mop0KNO7sdEl",
+   "metadata": {
+    "id": "mop0KNO7sdEl"
+   },
+   "source": [
+    "# Tools & Model Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "spOeRX2UlHlI",
+   "metadata": {
+    "colab": {
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+      "9157d82cca3f45c5b5a666a3d35d6abe"
+     ]
+    },
+    "id": "spOeRX2UlHlI",
+    "outputId": "093b74d1-9ece-4db5-d177-3aff6e2d6e0b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2db67e368d94f579c59b36c6ce7af33",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/47.0k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "269373e971704b9ea2e10e261b1c56d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1ec61e51d4c4b07b301959c4919f2a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1bc8cf2ed6542c49c1940673a662b1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/636 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e1c3ca06b2948f0bcf53a975dd67aec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/838 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c040fa1bbd9f442884776fdf33efb4a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors.index.json:   0%|          | 0.00/24.2k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfa90d76c670436a80f1a4fd0971b171",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9c6d577f113f4f4baaa1f0e001549862",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00001-of-00002.safetensors:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c22264e11c0402fb07f56a0763dc055",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00002-of-00002.safetensors:   0%|          | 0.00/241M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f271e9c78dcf4ab686caac37f18bd6f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf293367470430f84f341ef8711460a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/187 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import login\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer\n",
+    "from datasets import load_dataset\n",
+    "import torch\n",
+    "\n",
+    "login(\"Enter your token here\")\n",
+    "\n",
+    "model_id = \"google/gemma-2-2b-it\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_id,\n",
+    "    device_map=\"auto\",              # Automatically selects GPU if available\n",
+    "    torch_dtype=torch.float16       # Optimized for performance\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "GaykKFKYspMd",
+   "metadata": {
+    "id": "GaykKFKYspMd"
+   },
+   "source": [
+    "# Yaml Prompt Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "iLBZ7WPAlHlJ",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "iLBZ7WPAlHlJ",
+    "outputId": "a99fc3f2-68e0-4af8-cfbd-3c48a865538f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are Qwen, a helpful assistant.\n",
+      "User: What is the capital of France?\n",
+      "Assistant: The capital of France is **Paris**. \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"You are Qwen, a helpful assistant.\\nUser: What is the capital of France?\\nAssistant:\"\n",
+    "inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n",
+    "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+    "print(response)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "XPqtCExSsvhn",
+   "metadata": {
+    "id": "XPqtCExSsvhn"
+   },
+   "source": [
+    "# Use a small sample dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ActfjFx0lHlK",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "f1028b5dcab4460c81b09802ca7ec89c",
+      "ff33dcccaad945e9bf136d0f72e38d74",
+      "eec3a11b0605435d8d1e55092f5e5b85",
+      "d997b7bc993b4aa58cfafa7c59829ad3",
+      "62e2dc508746419ba3c0405a7199c6dc",
+      "2a7fc59019c749aa98d0e970a9b6b168",
+      "78067f66c8544020aa262c0012aa019b",
+      "f5e511b4279747c0a33b10f499a4f718",
+      "10e248a5730c4c09834b4065b6dd7843",
+      "bbd5a106c1de43e497987648899569e0",
+      "118faf3d1ed142038af4783fb3ba86cc"
+     ]
+    },
+    "id": "ActfjFx0lHlK",
+    "outputId": "f257e553-7baa-4f1e-b531-a6b7dc358665"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1028b5dcab4460c81b09802ca7ec89c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map:   0%|          | 0/4 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import Dataset\n",
+    "\n",
+    "sample_data = {\n",
+    "    'text': [\n",
+    "        'The sun is a star at the center of our solar system.',\n",
+    "        'Photosynthesis is the process by which green plants make food.',\n",
+    "        'Water freezes at 0 degrees Celsius.',\n",
+    "        'The Earth revolves around the sun in 365 days.'\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "dataset = Dataset.from_dict(sample_data)\n",
+    "\n",
+    "def tokenize_function(example):\n",
+    "    return tokenizer(example['text'], padding='max_length', truncation=True, max_length=64)\n",
+    "\n",
+    "tokenized_dataset = dataset.map(tokenize_function)\n",
+    "tokenized_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "x7icWpOnszjj",
+   "metadata": {
+    "id": "x7icWpOnszjj"
+   },
+   "source": [
+    "# Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8mM4PAhRlHlL",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8mM4PAhRlHlL",
+    "outputId": "1ba90639-b847-4cb1-df42-3fdd644e4cb8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "user\n",
+      "Explain photosynthesis to a child.\n",
+      "model\n",
+      "Imagine plants are like tiny chefs, and they cook their own food! \n",
+      "\n",
+      "They use sunlight, air, and water to make yummy food called sugar.  \n",
+      "\n",
+      "Here's how it works:\n",
+      "\n",
+      "1. **Sunlight:** Plants have special green stuff called chlorophyll that acts like a solar panel, soaking up the sun's energy.\n",
+      "2. **Air:** Plants take in air through tiny holes in their leaves called stomata.  The air has a gas called carbon dioxide.\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_text = \"Explain photosynthesis to a child.\"\n",
+    "chat = tokenizer.apply_chat_template(\n",
+    "    [{\"role\": \"user\", \"content\": input_text}],\n",
+    "    tokenize=False,\n",
+    "    add_generation_prompt=True\n",
+    ")\n",
+    "inputs = tokenizer(chat, return_tensors=\"pt\").to(model.device)\n",
+    "output = model.generate(**inputs, max_new_tokens=100)\n",
+    "print(tokenizer.decode(output[0], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "Fhkfhbjns2oq",
+   "metadata": {
+    "id": "Fhkfhbjns2oq"
+   },
+   "source": [
+    "# Save Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "OuJEt2-jlHlL",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OuJEt2-jlHlL",
+    "outputId": "f076e950-cce7-4850-edd9-ff1d84a096d6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('gemma-finetuned-demo/tokenizer_config.json',\n",
+       " 'gemma-finetuned-demo/special_tokens_map.json',\n",
+       " 'gemma-finetuned-demo/chat_template.jinja',\n",
+       " 'gemma-finetuned-demo/tokenizer.model',\n",
+       " 'gemma-finetuned-demo/added_tokens.json',\n",
+       " 'gemma-finetuned-demo/tokenizer.json')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.save_pretrained(\"gemma-finetuned-demo\")\n",
+    "tokenizer.save_pretrained(\"gemma-finetuned-demo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "uFK1sCzblHlM",
+   "metadata": {
+    "id": "uFK1sCzblHlM"
+   },
+   "source": [
+    "# Output\n",
+    "`Photosynthesis is how plants eat sunlight! 🌞 They use air, water, and sunlight to make food and grow.`"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/cookbooks/Hackathon_Agent_Notebook.ipynb

@@ -0,0 +1,436 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dcpGQid2Jcud"
+      },
+      "source": [
+        "# 📘 Hackathon Project Video Evaluator using PraisonAIAgents"
+      ],
+      "id": "dcpGQid2Jcud"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Hackathon_Agent_Notebook.ipynb)\n"
+      ],
+      "metadata": {
+        "id": "9A8PW-_1NyK4"
+      },
+      "id": "9A8PW-_1NyK4"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Dependencies"
+      ],
+      "metadata": {
+        "id": "B-RlKMC3NWZL"
+      },
+      "id": "B-RlKMC3NWZL"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ItoyCaVhJcug"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install praisonaiagents opencv-python moviepy"
+      ],
+      "id": "ItoyCaVhJcug"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Set OpenAI API Key for Authentication"
+      ],
+      "metadata": {
+        "id": "x4dBCoN-Nf4h"
+      },
+      "id": "x4dBCoN-Nf4h"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "os.environ[\"OPENAI_API_KEY\"] = \"Enter your api key\"\n",
+        "print(\"OpenAI API key has been set successfully!\")"
+      ],
+      "metadata": {
+        "id": "wz9hEZf1Ncv2"
+      },
+      "id": "wz9hEZf1Ncv2",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Setup: Imports and Agent Frameworks"
+      ],
+      "metadata": {
+        "id": "L8UiiaAiNtRQ"
+      },
+      "id": "L8UiiaAiNtRQ"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eU6iiDynJcuh"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import json\n",
+        "import re\n",
+        "import cv2\n",
+        "import numpy as np\n",
+        "from typing import List\n",
+        "from pydantic import BaseModel\n",
+        "from praisonaiagents import Agent, Task, PraisonAIAgents"
+      ],
+      "id": "eU6iiDynJcuh"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jIBUGHAbJcui"
+      },
+      "source": [
+        "## 🛠️ Tools and Data Models"
+      ],
+      "id": "jIBUGHAbJcui"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3wGOzkZcJcuj"
+      },
+      "outputs": [],
+      "source": [
+        "class ProjectEvaluation(BaseModel):\n",
+        "    innovation_score: int\n",
+        "    technical_complexity: int\n",
+        "    presentation_quality: int\n",
+        "    user_experience: int\n",
+        "    completeness: int\n",
+        "    overall_score: int\n",
+        "    key_strengths: List[str]\n",
+        "    areas_for_improvement: List[str]\n",
+        "    notable_features: List[str]\n",
+        "    technical_highlights: List[str]\n",
+        "    recommendations: List[str]\n",
+        "    market_potential: str\n",
+        "    scalability_assessment: str"
+      ],
+      "id": "3wGOzkZcJcuj"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "idRWbOQNJcuj"
+      },
+      "source": [
+        "## 🎞️ Video Frame Extraction"
+      ],
+      "id": "idRWbOQNJcuj"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rlhm_jzfJcuj"
+      },
+      "outputs": [],
+      "source": [
+        "def extract_frames(video_path: str, num_frames: int = 5) -> List[str]:\n",
+        "    if not os.path.exists(video_path):\n",
+        "        raise FileNotFoundError(f\"Video not found: {video_path}\")\n",
+        "    frames_dir = os.path.join(os.path.dirname(video_path), \"frames\")\n",
+        "    os.makedirs(frames_dir, exist_ok=True)\n",
+        "    cap = cv2.VideoCapture(video_path)\n",
+        "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
+        "    interval = total_frames // (num_frames + 1)\n",
+        "    paths = []\n",
+        "    for i in range(1, num_frames + 1):\n",
+        "        cap.set(cv2.CAP_PROP_POS_FRAMES, i * interval)\n",
+        "        ret, frame = cap.read()\n",
+        "        if not ret: break\n",
+        "        path = os.path.join(frames_dir, f\"frame_{i}.jpg\")\n",
+        "        cv2.imwrite(path, frame)\n",
+        "        paths.append(path)\n",
+        "    cap.release()\n",
+        "    return paths"
+      ],
+      "id": "Rlhm_jzfJcuj"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "97Bap7zKJcuk"
+      },
+      "source": [
+        "## 🤖 Define the AI Agent"
+      ],
+      "id": "97Bap7zKJcuk"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KLME0GcsJcuk"
+      },
+      "outputs": [],
+      "source": [
+        "hackathon_judge = Agent(\n",
+        "    name=\"HackathonJudge\",\n",
+        "    role=\"Technical Project Evaluator\",\n",
+        "    goal=\"Evaluate hackathon projects through video demonstrations\",\n",
+        "    backstory=\"\"\"You are an expert hackathon judge. You give fair scores and detailed feedback\n",
+        "    on technical complexity, innovation, presentation, and UX.\"\"\",\n",
+        "    llm=\"gpt-4o-mini\",\n",
+        "    self_reflect=False\n",
+        ")"
+      ],
+      "id": "KLME0GcsJcuk"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HG5dmlzhJcul"
+      },
+      "source": [
+        "## 🚀 Main Evaluation Function"
+      ],
+      "id": "HG5dmlzhJcul"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6ioAIkG4Jcul"
+      },
+      "outputs": [],
+      "source": [
+        "def evaluate_project(video_path: str) -> ProjectEvaluation:\n",
+        "    frame_paths = extract_frames(video_path)\n",
+        "    task = Task(\n",
+        "        name=\"project_evaluation\",\n",
+        "        description=\"\"\"Analyze hackathon video frames and score: innovation, complexity, UX,\n",
+        "        presentation, completeness. Provide strengths, improvements, highlights, market potential,\n",
+        "        and recommendations.\"\"\",\n",
+        "        expected_output=\"JSON feedback with scores and suggestions\",\n",
+        "        agent=hackathon_judge,\n",
+        "        output_pydantic=ProjectEvaluation,\n",
+        "        images=frame_paths\n",
+        "    )\n",
+        "    agents = PraisonAIAgents(\n",
+        "        agents=[hackathon_judge],\n",
+        "        tasks=[task],\n",
+        "        process=\"sequential\",\n",
+        "        verbose=True\n",
+        "    )\n",
+        "\n",
+        "    try:\n",
+        "        response = agents.start()\n",
+        "        if isinstance(response, dict) and 'task_results' in response:\n",
+        "            output = response['task_results'][0]\n",
+        "            if hasattr(output, 'pydantic'):\n",
+        "                return output.pydantic\n",
+        "            elif hasattr(output, 'raw'):\n",
+        "                raw = output.raw.strip()\n",
+        "                json_text = re.search(r'```(?:json)?\\s*([\\s\\S]+?)\\s*```', raw)\n",
+        "                raw = json_text.group(1).strip() if json_text else raw\n",
+        "                return ProjectEvaluation(**json.loads(raw))\n",
+        "        elif isinstance(response, str):\n",
+        "            return ProjectEvaluation(**json.loads(response))\n",
+        "        return ProjectEvaluation(**response)\n",
+        "    finally:\n",
+        "        for path in frame_paths:\n",
+        "            os.remove(path)\n",
+        "        try:\n",
+        "            os.rmdir(os.path.join(os.path.dirname(video_path), \"frames\"))\n",
+        "        except: pass"
+      ],
+      "id": "6ioAIkG4Jcul"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VAwiZiJ1Jcum"
+      },
+      "source": [
+        "## ▶️ Run the Evaluation"
+      ],
+      "id": "VAwiZiJ1Jcum"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Jf9_efNPJcum"
+      },
+      "outputs": [],
+      "source": [
+        "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...YOUR_API_KEY...\"\n",
+        "\n",
+        "video_file = \"presentation.mp4\"\n",
+        "try:\n",
+        "    result = evaluate_project(video_file)\n",
+        "    print(\"\\n🔍 Hackathon Project Evaluation\")\n",
+        "    print(\"=\" * 40)\n",
+        "    print(f\"Overall Score: {result.overall_score}/100\")\n",
+        "    print(f\"Innovation: {result.innovation_score}/100\")\n",
+        "    print(f\"Technical Complexity: {result.technical_complexity}/100\")\n",
+        "    print(f\"Presentation Quality: {result.presentation_quality}/100\")\n",
+        "    print(f\"User Experience: {result.user_experience}/100\")\n",
+        "    print(f\"Completeness: {result.completeness}/100\")\n",
+        "    print(\"\\n✅ Key Strengths:\")\n",
+        "    for s in result.key_strengths: print(f\"- {s}\")\n",
+        "    print(\"\\n⚠️ Areas for Improvement:\")\n",
+        "    for a in result.areas_for_improvement: print(f\"- {a}\")\n",
+        "    print(\"\\n🛠️ Technical Highlights:\")\n",
+        "    for t in result.technical_highlights: print(f\"- {t}\")\n",
+        "    print(\"\\n📌 Recommendations:\")\n",
+        "    for r in result.recommendations: print(f\"- {r}\")\n",
+        "    print(f\"\\n📈 Market Potential: {result.market_potential}\")\n",
+        "    print(f\"\\n📊 Scalability Assessment: {result.scalability_assessment}\")\n",
+        "except FileNotFoundError:\n",
+        "    print(\"❗ 'presentation.mp4' not found. Please add it to the current directory.\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ Evaluation Error: {e}\")"
+      ],
+      "id": "Jf9_efNPJcum"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "output = \"\"\"\n",
+        "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+        "│                                                                                                                              │\n",
+        "│  👤 Agent: HackathonJudge                                                                                                    │\n",
+        "│  Role: Technical Project Evaluator                                                                                           │\n",
+        "│                                                                                                                              │\n",
+        "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+        "\n",
+        "╭───────────────────────────────────────────────────── Generating... 6.6s ─────────────────────────────────────────────────────╮\n",
+        "│                                                                                                                              │\n",
+        "│  {                                                                                                                           │\n",
+        "│    \"innovation_score\": 30,                                                                                                   │\n",
+        "│    \"technical_complexity\": 20,                                                                                               │\n",
+        "│    \"presentation_quality\": 25,                                                                                               │\n",
+        "│    \"user_experience\": 35,                                                                                                    │\n",
+        "│    \"completeness\": 20,                                                                                                       │\n",
+        "│    \"overall_score\": 24,                                                                                                      │\n",
+        "│    \"key_strengths\": [                                                                                                        │\n",
+        "│      \"Simple concept\",                                                                                                       │\n",
+        "│      \"Clear visual representation\"                                                                                           │\n",
+        "│    ],                                                                                                                        │\n",
+        "│    \"areas_for_improvement\": [                                                                                                │\n",
+        "│      \"Enhance technical features\",                                                                                           │\n",
+        "│      \"Improve presentation clarity\",                                                                                         │\n",
+        "│      \"Add user interaction elements\"                                                                                         │\n",
+        "│    ],                                                                                                                        │\n",
+        "│    \"notable_features\": [                                                                                                     │\n",
+        "│      \"Basic visual interface\"                                                                                                │\n",
+        "│    ],                                                                                                                        │\n",
+        "│    \"technical_highlights\": [                                                                                                 │\n",
+        "│      \"Use of basic graphics\"                                                                                                 │\n",
+        "│    ],                                                                                                                        │\n",
+        "│    \"recommendations\": [                                                                                                      │\n",
+        "│      \"Incorporate user feedback mechanisms\",                                                                                 │\n",
+        "│      \"Develop a more robust backend\",                                                                                        │\n",
+        "│      \"Enhance visual aesthetics\"                                                                                             │\n",
+        "│    ],                                                                                                                        │\n",
+        "│    \"market_potential\": \"Limited due to simplicity\",                                                                          │\n",
+        "│    \"scalability_assessment\": \"Low, requires significant development\"                                                         │\n",
+        "│  }                                                                                                                           │\n",
+        "│                                                                                                                              │\n",
+        "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+        "\"\"\"\n",
+        "print(output)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "T-wYaKzkKGNK",
+        "outputId": "ebeaf7e3-6362-4d1c-f9d1-cc3504d733a2"
+      },
+      "id": "T-wYaKzkKGNK",
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                                              │\n",
+            "│  👤 Agent: HackathonJudge                                                                                                    │\n",
+            "│  Role: Technical Project Evaluator                                                                                           │\n",
+            "│                                                                                                                              │\n",
+            "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "╭───────────────────────────────────────────────────── Generating... 6.6s ─────────────────────────────────────────────────────╮\n",
+            "│                                                                                                                              │\n",
+            "│  {                                                                                                                           │\n",
+            "│    \"innovation_score\": 30,                                                                                                   │\n",
+            "│    \"technical_complexity\": 20,                                                                                               │\n",
+            "│    \"presentation_quality\": 25,                                                                                               │\n",
+            "│    \"user_experience\": 35,                                                                                                    │\n",
+            "│    \"completeness\": 20,                                                                                                       │\n",
+            "│    \"overall_score\": 24,                                                                                                      │\n",
+            "│    \"key_strengths\": [                                                                                                        │\n",
+            "│      \"Simple concept\",                                                                                                       │\n",
+            "│      \"Clear visual representation\"                                                                                           │\n",
+            "│    ],                                                                                                                        │\n",
+            "│    \"areas_for_improvement\": [                                                                                                │\n",
+            "│      \"Enhance technical features\",                                                                                           │\n",
+            "│      \"Improve presentation clarity\",                                                                                         │\n",
+            "│      \"Add user interaction elements\"                                                                                         │\n",
+            "│    ],                                                                                                                        │\n",
+            "│    \"notable_features\": [                                                                                                     │\n",
+            "│      \"Basic visual interface\"                                                                                                │\n",
+            "│    ],                                                                                                                        │\n",
+            "│    \"technical_highlights\": [                                                                                                 │\n",
+            "│      \"Use of basic graphics\"                                                                                                 │\n",
+            "│    ],                                                                                                                        │\n",
+            "│    \"recommendations\": [                                                                                                      │\n",
+            "│      \"Incorporate user feedback mechanisms\",                                                                                 │\n",
+            "│      \"Develop a more robust backend\",                                                                                        │\n",
+            "│      \"Enhance visual aesthetics\"                                                                                             │\n",
+            "│    ],                                                                                                                        │\n",
+            "│    \"market_potential\": \"Limited due to simplicity\",                                                                          │\n",
+            "│    \"scalability_assessment\": \"Low, requires significant development\"                                                         │\n",
+            "│  }                                                                                                                           │\n",
+            "│                                                                                                                              │\n",
+            "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.11"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb

@@ -0,0 +1,401 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l7oSysosTfJj"
+      },
+      "source": [
+        "# Predictive Maintenance Workflow using PraisonAIAgents\n",
+        "\n",
+        "This notebook demonstrates how to build a predictive maintenance workflow using multiple AI agents."
+      ],
+      "id": "l7oSysosTfJj"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi-Agent_Workflow.ipynb)"
+      ],
+      "metadata": {
+        "id": "LH0_JjO7Y1G2"
+      },
+      "id": "LH0_JjO7Y1G2"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Dependencies"
+      ],
+      "metadata": {
+        "id": "ei-yNZSKXgtJ"
+      },
+      "id": "ei-yNZSKXgtJ"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fnkuN8sTTfJq"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install praisonaiagents"
+      ],
+      "id": "fnkuN8sTTfJq"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Set your OpenAI API key"
+      ],
+      "metadata": {
+        "id": "7PUSTQHwX5ia"
+      },
+      "id": "7PUSTQHwX5ia"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "33HivD-LTfJu"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ['OPENAI_API_KEY'] = 'enter your api key'"
+      ],
+      "id": "33HivD-LTfJu"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import required modules"
+      ],
+      "metadata": {
+        "id": "D6we7t4nYESB"
+      },
+      "id": "D6we7t4nYESB"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "03z7Pi22TfJw"
+      },
+      "outputs": [],
+      "source": [
+        "from praisonaiagents import Agent, Task, PraisonAIAgents\n",
+        "import time\n",
+        "from typing import Dict, List\n",
+        "import asyncio"
+      ],
+      "id": "03z7Pi22TfJw"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Define helper functions"
+      ],
+      "metadata": {
+        "id": "TzZt0jbjYGgw"
+      },
+      "id": "TzZt0jbjYGgw"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2oMzDsp3TfJw"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "def collect_sensor_data():\n",
+        "    return {\n",
+        "        \"temperature\": 75 + (int(time.time()) % 20),\n",
+        "        \"vibration\": 0.5 + (int(time.time()) % 10) / 10,\n",
+        "        \"pressure\": 100 + (int(time.time()) % 50),\n",
+        "        \"noise_level\": 60 + (int(time.time()) % 30)\n",
+        "    }\n",
+        "\n",
+        "def analyze_performance():\n",
+        "    return {\n",
+        "        \"efficiency\": 0.8 + (int(time.time()) % 20) / 100,\n",
+        "        \"uptime\": 0.95 + (int(time.time()) % 5) / 100,\n",
+        "        \"output_quality\": 0.9 + (int(time.time()) % 10) / 100\n",
+        "    }\n",
+        "\n",
+        "def detect_anomalies(sensor_data: Dict, performance: Dict):\n",
+        "    anomalies = []\n",
+        "    if sensor_data[\"temperature\"] > 90:\n",
+        "        anomalies.append({\"type\": \"temperature_high\", \"severity\": \"critical\"})\n",
+        "    if sensor_data[\"vibration\"] > 1.2:\n",
+        "        anomalies.append({\"type\": \"vibration_excess\", \"severity\": \"warning\"})\n",
+        "    if performance[\"efficiency\"] < 0.85:\n",
+        "        anomalies.append({\"type\": \"efficiency_low\", \"severity\": \"warning\"})\n",
+        "    return anomalies\n",
+        "\n",
+        "def predict_failures(anomalies: List[Dict]):\n",
+        "    predictions = []\n",
+        "    severity_scores = {\"critical\": 0.9, \"warning\": 0.6}\n",
+        "    for anomaly in anomalies:\n",
+        "        predictions.append({\n",
+        "            \"component\": anomaly[\"type\"].split(\"_\")[0],\n",
+        "            \"probability\": severity_scores[anomaly[\"severity\"]],\n",
+        "            \"timeframe\": \"24_hours\" if anomaly[\"severity\"] == \"critical\" else \"7_days\"\n",
+        "        })\n",
+        "    return predictions\n",
+        "\n",
+        "def schedule_maintenance(predictions: List[Dict]):\n",
+        "    schedule = []\n",
+        "    for pred in predictions:\n",
+        "        schedule.append({\n",
+        "            \"component\": pred[\"component\"],\n",
+        "            \"priority\": \"immediate\" if pred[\"timeframe\"] == \"24_hours\" else \"planned\",\n",
+        "            \"estimated_duration\": \"2_hours\",\n",
+        "            \"required_parts\": [\"replacement_\" + pred[\"component\"]]\n",
+        "        })\n",
+        "    return schedule"
+      ],
+      "id": "2oMzDsp3TfJw"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Define agents and tasks"
+      ],
+      "metadata": {
+        "id": "-7qMHyY5YIph"
+      },
+      "id": "-7qMHyY5YIph"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4ZYbYzjNTfJy"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "sensor_monitor = Agent(\"Sensor Monitor\", \"Data Collection\", \"Collect sensor data\", \"Monitor and collect sensor readings\", [collect_sensor_data])\n",
+        "performance_analyzer = Agent(\"Performance Analyzer\", \"Performance Analysis\", \"Analyze equipment performance\", \"Analyze operational metrics\", [analyze_performance])\n",
+        "anomaly_detector = Agent(\"Anomaly Detector\", \"Anomaly Detection\", \"Detect operational anomalies\", \"Identify abnormal patterns\", [detect_anomalies])\n",
+        "failure_predictor = Agent(\"Failure Predictor\", \"Failure Prediction\", \"Predict potential failures\", \"Predict equipment failures\", [predict_failures])\n",
+        "maintenance_scheduler = Agent(\"Maintenance Scheduler\", \"Maintenance Planning\", \"Schedule maintenance activities\", \"Plan and schedule maintenance\", [schedule_maintenance])\n",
+        "\n",
+        "sensor_task = Task(\"collect_data\", \"Collect sensor data\", \"Sensor readings\", sensor_monitor, is_start=True, next_tasks=[\"analyze_performance\"], async_execution=True)\n",
+        "performance_task = Task(\"analyze_performance\", \"Analyze performance metrics\", \"Performance analysis\", performance_analyzer, next_tasks=[\"detect_anomalies\"], async_execution=True)\n",
+        "anomaly_task = Task(\"detect_anomalies\", \"Detect operational anomalies\", \"Detected anomalies\", anomaly_detector, next_tasks=[\"predict_failures\"], context=[sensor_task, performance_task])\n",
+        "prediction_task = Task(\"predict_failures\", \"Predict potential failures\", \"Failure predictions\", failure_predictor, next_tasks=[\"schedule_maintenance\"], task_type=\"decision\", condition={\"critical\": [\"schedule_maintenance\"], \"warning\": [\"schedule_maintenance\"], \"normal\": \"\"})\n",
+        "scheduling_task = Task(\"schedule_maintenance\", \"Schedule maintenance activities\", \"Maintenance schedule\", maintenance_scheduler, context=[prediction_task])"
+      ],
+      "id": "4ZYbYzjNTfJy"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Create and run the workflow"
+      ],
+      "metadata": {
+        "id": "XOdIxJKiYKuD"
+      },
+      "id": "XOdIxJKiYKuD"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KYu_YTVDTfJz"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "workflow = PraisonAIAgents(\n",
+        "    agents=[sensor_monitor, performance_analyzer, anomaly_detector, failure_predictor, maintenance_scheduler],\n",
+        "    tasks=[sensor_task, performance_task, anomaly_task, prediction_task, scheduling_task],\n",
+        "    process=\"workflow\",\n",
+        "    verbose=True\n",
+        ")\n",
+        "\n",
+        "async def main():\n",
+        "    print(\"\\nStarting Predictive Maintenance Workflow...\")\n",
+        "    print(\"=\" * 50)\n",
+        "    results = await workflow.astart()\n",
+        "    print(\"\\nMaintenance Planning Results:\")\n",
+        "    print(\"=\" * 50)\n",
+        "    for task_id, result in results[\"task_results\"].items():\n",
+        "        if result:\n",
+        "            print(f\"\\nTask: {task_id}\\nResult: {result.raw}\\n{'-'*50}\")\n",
+        "\n",
+        "await main()"
+      ],
+      "id": "KYu_YTVDTfJz"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\"\"\n",
+        "[Starting Predictive Maintenance Workflow...\n",
+        "==================================================\n",
+        "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+        "│                                                                                                                                              │\n",
+        "│  👤 Agent: Sensor Monitor                                                                                                                    │\n",
+        "│  Role: Data Collection                                                                                                                       │\n",
+        "│  Tools: collect_sensor_data                                                                                                                  │\n",
+        "│                                                                                                                                              │\n",
+        "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+        "\n",
+        "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+        "│                                                                                                                                              │\n",
+        "│  👤 Agent: Performance Analyzer                                                                                                              │\n",
+        "│  Role: Performance Analysis                                                                                                                  │\n",
+        "│  Tools: analyze_performance                                                                                                                  │\n",
+        "│                                                                                                                                              │\n",
+        "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+        "\n",
+        "[20:01:26] INFO     [20:01:26] process.py:429 INFO Task schedule_maintenance has no next tasks, ending workflow                   process.py:429\n",
+        "\n",
+        "Maintenance Planning Results:\n",
+        "==================================================\n",
+        "\n",
+        "Task: 0\n",
+        "Result: The sensor readings you have collected are as follows:\n",
+        "\n",
+        "- **Temperature**: 86°F\n",
+        "- **Vibration**: 0.6 (units not specified, but typically measured in g-forces or mm/s)\n",
+        "- **Pressure**: 101 (units not specified, but typically measured in kPa or psi)\n",
+        "- **Noise Level**: 81 dB\n",
+        "\n",
+        "Here's a brief analysis of these readings:\n",
+        "\n",
+        "1. **Temperature**: At 86°F, the temperature is relatively warm. Depending on the context (e.g., industrial equipment, environmental monitoring), this could be within normal operating conditions or might require cooling measures if it's above the optimal range.\n",
+        "\n",
+        "2. **Vibration**: A vibration level of 0.6 is generally low, but the significance depends on the type of equipment being monitored. For precision machinery, even small vibrations can be critical, whereas for more robust equipment, this might be negligible.\n",
+        "\n",
+        "3. **Pressure**: A pressure reading of 101 is often within normal ranges for many systems, but without specific units or context, it's hard to determine if this is optimal or requires adjustment.\n",
+        "\n",
+        "4. **Noise Level**: At 81 dB, the noise level is relatively high. Prolonged exposure to noise levels above 85 dB can be harmful to hearing, so if this is a workplace environment, it might be necessary to implement noise reduction measures or provide hearing protection.\n",
+        "\n",
+        "Overall, these readings should be compared against the specific operational thresholds and safety standards relevant to the equipment or environment being monitored. If any values are outside of acceptable ranges, further investigation or corrective actions may be needed.\n",
+        "--------------------------------------------------\n",
+        "\n",
+        "Task: 1\n",
+        "Result: Based on the provided operational metrics, here's an analysis of the equipment performance:\n",
+        "\n",
+        "1. **Efficiency (94%)**:\n",
+        "   - The equipment is operating at a high efficiency level, with 94% of the input being effectively converted into useful output. This suggests\n",
+        "that the equipment is well-maintained and optimized for performance. However, there is still a 6% margin for improvement, which could be addressed by identifying and minimizing any inefficiencies in the process.\n",
+        "\n",
+        "2. **Uptime (99%)**:\n",
+        "   - The equipment has an excellent uptime rate of 99%, indicating that it is available and operational almost all the time. This is a strong indicator of reliability and suggests that downtime due to maintenance or unexpected failures is minimal. Maintaining this level of uptime should\n",
+        "be a priority, as it directly impacts productivity and operational continuity.\n",
+        "\n",
+        "3. **Output Quality (94%)**:\n",
+        "   - The output quality is also at 94%, which is a positive sign that the equipment is producing high-quality products or results. However, similar to efficiency, there is room for improvement. Efforts could be made to identify any factors that might be affecting quality, such as calibration issues, material inconsistencies, or process deviations.\n",
+        "\n",
+        "**Overall Assessment**:\n",
+        "The equipment is performing well across all key metrics, with high efficiency, uptime, and output quality. To further enhance performance, focus should be placed on fine-tuning processes to close the small gaps in efficiency and quality. Regular maintenance, monitoring, and process optimization can help sustain and potentially improve these metrics.\n",
+        "--------------------------------------------------]\n",
+        "\"\"\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4hrEJ5S6XpJ7",
+        "outputId": "899e677d-19d5-4e0a-d9ab-ebc945aeee1b"
+      },
+      "id": "4hrEJ5S6XpJ7",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "[Starting Predictive Maintenance Workflow...\n",
+            "==================================================\n",
+            "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                                                              │\n",
+            "│  👤 Agent: Sensor Monitor                                                                                                                    │\n",
+            "│  Role: Data Collection                                                                                                                       │\n",
+            "│  Tools: collect_sensor_data                                                                                                                  │\n",
+            "│                                                                                                                                              │\n",
+            "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+            "│                                                                                                                                              │\n",
+            "│  👤 Agent: Performance Analyzer                                                                                                              │\n",
+            "│  Role: Performance Analysis                                                                                                                  │\n",
+            "│  Tools: analyze_performance                                                                                                                  │\n",
+            "│                                                                                                                                              │\n",
+            "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+            "\n",
+            "[20:01:26] INFO     [20:01:26] process.py:429 INFO Task schedule_maintenance has no next tasks, ending workflow                   process.py:429\n",
+            "\n",
+            "Maintenance Planning Results:\n",
+            "==================================================\n",
+            "\n",
+            "Task: 0\n",
+            "Result: The sensor readings you have collected are as follows:\n",
+            "\n",
+            "- **Temperature**: 86°F\n",
+            "- **Vibration**: 0.6 (units not specified, but typically measured in g-forces or mm/s)\n",
+            "- **Pressure**: 101 (units not specified, but typically measured in kPa or psi)\n",
+            "- **Noise Level**: 81 dB\n",
+            "\n",
+            "Here's a brief analysis of these readings:\n",
+            "\n",
+            "1. **Temperature**: At 86°F, the temperature is relatively warm. Depending on the context (e.g., industrial equipment, environmental monitoring), this could be within normal operating conditions or might require cooling measures if it's above the optimal range.\n",
+            "\n",
+            "2. **Vibration**: A vibration level of 0.6 is generally low, but the significance depends on the type of equipment being monitored. For precision machinery, even small vibrations can be critical, whereas for more robust equipment, this might be negligible.\n",
+            "\n",
+            "3. **Pressure**: A pressure reading of 101 is often within normal ranges for many systems, but without specific units or context, it's hard to determine if this is optimal or requires adjustment.\n",
+            "\n",
+            "4. **Noise Level**: At 81 dB, the noise level is relatively high. Prolonged exposure to noise levels above 85 dB can be harmful to hearing, so if this is a workplace environment, it might be necessary to implement noise reduction measures or provide hearing protection.\n",
+            "\n",
+            "Overall, these readings should be compared against the specific operational thresholds and safety standards relevant to the equipment or environment being monitored. If any values are outside of acceptable ranges, further investigation or corrective actions may be needed.\n",
+            "--------------------------------------------------\n",
+            "\n",
+            "Task: 1\n",
+            "Result: Based on the provided operational metrics, here's an analysis of the equipment performance:\n",
+            "\n",
+            "1. **Efficiency (94%)**:\n",
+            "   - The equipment is operating at a high efficiency level, with 94% of the input being effectively converted into useful output. This suggests \n",
+            "that the equipment is well-maintained and optimized for performance. However, there is still a 6% margin for improvement, which could be addressed by identifying and minimizing any inefficiencies in the process.\n",
+            "\n",
+            "2. **Uptime (99%)**:\n",
+            "   - The equipment has an excellent uptime rate of 99%, indicating that it is available and operational almost all the time. This is a strong indicator of reliability and suggests that downtime due to maintenance or unexpected failures is minimal. Maintaining this level of uptime should \n",
+            "be a priority, as it directly impacts productivity and operational continuity.\n",
+            "\n",
+            "3. **Output Quality (94%)**:\n",
+            "   - The output quality is also at 94%, which is a positive sign that the equipment is producing high-quality products or results. However, similar to efficiency, there is room for improvement. Efforts could be made to identify any factors that might be affecting quality, such as calibration issues, material inconsistencies, or process deviations.\n",
+            "\n",
+            "**Overall Assessment**:\n",
+            "The equipment is performing well across all key metrics, with high efficiency, uptime, and output quality. To further enhance performance, focus should be placed on fine-tuning processes to close the small gaps in efficiency and quality. Regular maintenance, monitoring, and process optimization can help sustain and potentially improve these metrics.\n",
+            "--------------------------------------------------]\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}

examples/cookbooks/Qwen2_5_InstructionAgent.ipynb

@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "MLutIRWTYLqq",
+   "metadata": {
+    "id": "MLutIRWTYLqq"
+   },
+   "source": [
+    "# Simple Chat with Qwen2.5 (0.5B) using Hugging Face Transformers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2LoeTPnYQOW",
+   "metadata": {
+    "id": "a2LoeTPnYQOW"
+   },
+   "source": [
+    "This beginner-friendly notebook demonstrates how to use the Qwen2.5-0.5B-Instruct model for simple chat-based generation. It includes installing dependencies, setting up authentication via Hugging Face token, generating a response using a chat prompt, and displaying the output. The notebook is organized into 5 clean sections for easy understanding."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "WlfJBFucY9gi",
+   "metadata": {
+    "id": "WlfJBFucY9gi"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57059c85",
+   "metadata": {
+    "id": "57059c85"
+   },
+   "source": [
+    "# 🔧 Dependencies\n",
+    "Install necessary Python packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1cdaae47",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1cdaae47",
+    "outputId": "5b140b09-5c16-4849-f370-f8b5abcc127a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m58.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m58.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m64.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers --quiet\n",
+    "!pip install accelerate --quiet\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a65a2fa",
+   "metadata": {
+    "id": "9a65a2fa"
+   },
+   "source": [
+    "# 🛠️ Tools\n",
+    "Import required tools and libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8a4bc30",
+   "metadata": {
+    "id": "a8a4bc30"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "import torch\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ad2bec4",
+   "metadata": {
+    "id": "1ad2bec4"
+   },
+   "source": [
+    "# ⚙️ YAML Prompt (Token Authentication)\n",
+    "Use Hugging Face token to access the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cd030b94",
+   "metadata": {
+    "id": "cd030b94"
+   },
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login(token=\"Enter your huggingface token\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7b1a32d",
+   "metadata": {
+    "id": "b7b1a32d"
+   },
+   "source": [
+    "# 🚀 Main\n",
+    "Load model, prepare prompt and generate response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ef73b9ea",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 365,
+     "referenced_widgets": [
+      "a175e2b1fa8c4733afe1ddc78095be58",
+      "f5aaf63d7be04927a33e5cdd3655a003",
+      "7af74b8c184b4e72a2dba769b9caa40f",
+      "22b1c604d91648479cf01198db25a863",
+      "faaeb1c3ab914a6db9ff9b8ea06a440f",
+      "138860a12c6347fba11437eb098d31b5",
+      "d2f5fe459d544fa6adc92debab823ee6",
+      "468e8e7bee5c4198b121ac1aa677153d",
+      "068991c9ac44496bab06ccaa7d030722",
+      "44436151662f41ef81f6a4005add51de",
+      "c774c78c4fce4825872a76bb1e5fde33",
+      "4a7d0a84994c4b628974f631ecb95425",
+      "4adb654ea143456eb3b4244c3030b39f",
+      "d8dbdbc9327f4799a07767b59db59c47",
+      "669ea95c4cb642b580aa176ae2e89f9f",
+      "0bc8529b03364f83adf1110b5c1378ec",
+      "2eb8007145ef4c3caf7f5a9d1fd299e3",
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+      "c346fba7badb4c4596f6d6e8fa62224b",
+      "39dd29002d7b46848a29dfeb7405d9f9",
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+      "7d8c99f622c34229894024c9772aed84",
+      "1e6e892b153c4c05b7de806ec674215f",
+      "85ac6a67bb0944b5bb8713fcf302da45",
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+      "c686586c431f4c22b0f0c698a9b213b6",
+      "239180fd12624e3a90188270b55ee218",
+      "e04653090c7e454d9c5e520e7f313588",
+      "992c7a4b44aa4feb8e13714fa922d705",
+      "a0a238c78d2842d680be448a2cb20157",
+      "6ab5ebed63674b5e87327a1623eba7f0",
+      "a49887aa0fcd4b91a0d5d978b29c1e0a",
+      "d9b2e4142dd14264b90829a5a4116613",
+      "154b27bb2ba44d10bde5c96929b65cc9",
+      "f0bf6206c31f49f2ad1ce97b99963011",
+      "1762735cfbcf4706b1c0686d3c2926a0",
+      "d5e3440785794f0f9ad4fd0650c2f682",
+      "08019bdd70364fabbc222dca66db0bb1",
+      "6f0b3a92d47c42dd815a670bb1c0f42c",
+      "54554545ad5a4f39b649f3acff6d5cba",
+      "bfd8f4ee8c414349a3695975e79c332e",
+      "332fc944eef14a9c82f71a68a3857dea",
+      "3b0b899fcfae451ba8ec3d8da176fcec",
+      "8383df3f07bd427287ad05726737cc67",
+      "67c267d7d1ec424593775bf8129e8ac9",
+      "85a50e2cb406403988639e6d6ecf9dc1",
+      "7251ed20382c4a60bae8549f68971043",
+      "44fbc377fd6a4c5a910c00dcceb143af",
+      "2d6d273941f04c13af598c53e067aae7"
+     ]
+    },
+    "id": "ef73b9ea",
+    "outputId": "34512fa7-527c-45cb-f3c7-b42d37e74980"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+      "You will be able to reuse this secret in all of your notebooks.\n",
+      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a175e2b1fa8c4733afe1ddc78095be58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/7.30k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a7d0a84994c4b628974f631ecb95425",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ad8e2e2200c4277ba10dd8e9ac83b90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6a31c3cf026450088596db5917c3e4a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/7.03M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "955225e4206a4ee5a4284579935db920",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/659 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e04653090c7e454d9c5e520e7f313588",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/988M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6f0b3a92d47c42dd815a670bb1c0f42c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/242 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_name = \"Qwen/Qwen2.5-0.5B-Instruct\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_name,\n",
+    "    torch_dtype=\"auto\",\n",
+    "    device_map=\"auto\"\n",
+    ")\n",
+    "\n",
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": \"You are Qwen, a helpful assistant.\"},\n",
+    "    {\"role\": \"user\", \"content\": \"Explain photosynthesis in simple words.\"}\n",
+    "]\n",
+    "\n",
+    "text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
+    "inputs = tokenizer(text, return_tensors=\"pt\").to(model.device)\n",
+    "\n",
+    "outputs = model.generate(**inputs, max_new_tokens=100)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf70c765",
+   "metadata": {
+    "id": "bf70c765"
+   },
+   "source": [
+    "# 📤 Output\n",
+    "Print the model response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d8f70a7b",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "d8f70a7b",
+    "outputId": "559e9358-c2b7-4410-c1a6-1624640dfeae"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "system\n",
+      "You are Qwen, a helpful assistant.\n",
+      "user\n",
+      "Explain photosynthesis in simple words.\n",
+      "assistant\n",
+      "Photosynthesis is the process by which plants, algae, and some bacteria convert light energy from the sun into chemical energy stored in glucose molecules, using carbon dioxide and water as reactants. This process occurs in chloroplasts within plant cells and involves two main stages: the light-dependent reactions and the light-independent reactions (also known as the Calvin cycle).\n",
+      "\n",
+      "In the light-dependent reactions, light energy is captured by pigments like chlorophyll, causing electrons to be excited and transferred to oxygen through a series\n"
+     ]
+    }
+   ],
+   "source": [
+    "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+    "print(response)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb

@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3622c575",
+   "metadata": {
+    "id": "3622c575"
+   },
+   "source": [
+    "# TinyLlama Agent Demo\n",
+    "\n",
+    "This notebook demonstrates how to create a simple AI agent using the TinyLlama (1.1B) model.\n",
+    "\n",
+    "**Requirements:**\n",
+    "- `transformers`\n",
+    "- `accelerate`\n",
+    "- `torch`\n",
+    "\n",
+    "We will load the model, prepare a basic conversational interface, and test it with a simple prompt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "qS0M9pRhTGpf",
+   "metadata": {
+    "id": "qS0M9pRhTGpf"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tuw0IDpBS0ei",
+   "metadata": {
+    "id": "tuw0IDpBS0ei"
+   },
+   "source": [
+    "# Install necessary packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0d06cae9",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0d06cae9",
+    "outputId": "3e1d8fb7-19be-455b-dc35-d0ab19146669"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m45.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25h"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -q transformers accelerate torch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "RFhYLkO1S5F9",
+   "metadata": {
+    "id": "RFhYLkO1S5F9"
+   },
+   "source": [
+    "# Load TinyLlama model and tokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "415af398",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 241,
+     "referenced_widgets": [
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+      "faa4f285eb224bbcbed649bd479e571f",
+      "6e684d35885a4706b8add6834cd521ad",
+      "f9c55ada3386460183e02559defec8a4",
+      "9d64cbe1605f4f358923754c5f8b6424",
+      "6008ed7eca09485195eb37c36acfbe79"
+     ]
+    },
+    "id": "415af398",
+    "outputId": "28de1886-e83f-49bf-b05f-cd19439605c5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dae7d635257948149c74e65956f614e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/1.29k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d347fa0c0fc849559b66c8f116c86317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7451ec9bd091441c852d075b47bd1ca5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e282c1fcae842cc87c93ecfc1763654",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/551 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e78dca0cf1314d01a5d5126e448e9d87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/608 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b391081de4444859ac3213aac99cc33a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/2.20G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1b07183320d42af88f985bd1abf054f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "import torch\n",
+    "\n",
+    "model_name = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map=\"auto\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "N0bRTd_DS8aL",
+   "metadata": {
+    "id": "N0bRTd_DS8aL"
+   },
+   "source": [
+    "# Define a function to generate responses from the agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1b0483e1",
+   "metadata": {
+    "id": "1b0483e1"
+   },
+   "outputs": [],
+   "source": [
+    "def generate_response(prompt, max_length=256):\n",
+    "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
+    "    outputs = model.generate(**inputs, max_new_tokens=max_length)\n",
+    "    return tokenizer.decode(outputs[0], skip_special_tokens=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vbHKNxc-S_Bk",
+   "metadata": {
+    "id": "vbHKNxc-S_Bk"
+   },
+   "source": [
+    "# Example usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "298c9811",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "298c9811",
+    "outputId": "6e412608-735c-4604-8a5d-a66b98dd9df0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You are a helpful assistant. What is the capital of France?\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = \"You are a helpful assistant. What is the capital of France?\"\n",
+    "response = generate_response(prompt)\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/praisonai-agents/.cursorrules

@@ -5,146 +5,4 @@
 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. 
-
-
-Below is a **detailed technical overview** of the issues that have been coming up in the workflow execution (specifically around loops, resetting tasks, and continuing on to subsequent tasks). It includes:
-
-1. **How the workflow currently flows**  
-2. **What logic marks a task as “completed”**  
-3. **What issues arose in `loop` tasks**  
-4. **Why the workflow can end up “stuck” or “looping indefinitely”**  
-
----
-
-## 1. Overall Workflow Flow
-
-### a. Building relationships among tasks
-
-- At startup, the code iterates through all `tasks`.  
-- For each `task`:
-  - It looks at `task.next_tasks`, and for each `next_task_name`:
-    - Finds the corresponding `Task` object
-    - Appends the current task’s name to the found `next_task`'s `previous_tasks` list.  
-- This means if Task A has `next_tasks=["B"]`, then Task B’s `previous_tasks` will include `"A"`.
-
-### b. Finding and starting with a “start task”
-
-- The workflow code tries to locate a task with `is_start=True`.  
-- If no such task is found, it uses the first item in the tasks dictionary instead.  
-- That “start task” is what the workflow tries to run first.
-
-### c. Execution loop in the method (e.g., `workflow()` or `aworkflow()`)
-
-- There is a `while current_task:` loop that processes tasks in sequence, or conditionally, based on `task.condition`.
-- Each time a task runs (if non-loop), it yields the `task_id` or triggers an agent to run. Once the agent finishes (with or without a final result), the workflow picks up again to see what to do next:
-  - If the `task` is a `loop` type, the code tries to create or manage sub-tasks for each row/line in an “input_file.”
-  - If the `task` is a normal (“decision” or “task” or “some-other-type”), it just executes once, sets `status="completed"`, and the code moves on.
-
-### d. Condition-based branching
-
-- If a task has a result that includes a “decision” (like `{"decision":"more"}` or `"done"`), the code checks `task.condition`. For example:
-  ```python
-  condition = {
-    "more": "generate_task",
-    "done": "evaluate_total_questions"
-  }
-  ```
-- If the result’s decision is `"done"`, it jumps to the task named `"evaluate_total_questions"`.  
-- If the result’s decision is `"more"`, it jumps right back to `"generate_task"`.  
-- If no condition matches, it can fallback to the first item in `task.next_tasks`.
-
-### e. Marking tasks as “completed”
-
-- After a task’s execution (like a typical “non-loop” task), the code sets `task.status = "completed"`.
-- Then the code has a snippet that says:
-  ```python
-  if self.tasks[task_id].status == "completed":
-      # Possibly reset to "not started" so we can re-run if needed
-  ```
-- By default, the system tries to “reset” tasks to `"not started"`, **unless** it is a loop task or a subtask of a loop.  
-
----
-
-## 2. How a “completed” task is marked
-
-Generally, tasks are marked `status="completed"` in two primary ways:
-
-1. **Non-Loop Execution**  
-   A normal task (like a “decision” or “task”) is executed once the code calls the agent, the agent returns a final result, and the system sets `task.status = "completed"`.
-2. **Loop Execution**  
-   A loop-type task is *programmatically* set to `status="completed"` when all of its sub-tasks have finished. That is:
-   - The code checks: “Have all child tasks of this loop finished?”  
-   - If `True`, the loop task is set to `completed`.
-
----
-
-## 3. Issues Specifically in `loop` Tasks
-
-### a. Re-Entering the Loop
-
-- Before, the same snippet that “resets completed tasks to ‘not started’ so they can re-run if needed” **also** tried to reset loop tasks or their subtasks.  
-- If a loop task got reset to `"not started"`, the code would eventually pick it back up again, leading to repeated creation of sub-tasks (or repeated attempts to re-run them).
-- This caused an **infinite loop** or repeating the same steps in the workflow, never truly exiting the loop stage.
-
-### b. Subtasks Not Marked or Overwritten
-
-- Another tricky scenario: If sub-tasks themselves got reset, the parent loop would see them as “not started” again, and might wait for them to “complete,” or might re-run them. That can lead to indefinite re-running of sub-tasks.
-
-### c. Not Proceeding to Next (e.g., “upload_to_huggingface”)
-
-- If the loop kept “restarting,” the workflow never ended up hitting the next tasks. For example, if your workflow is:
-  1. `generate_task`  
-  2. `evaluate_total_questions`  
-  3. `generate_cot` (loop)  
-  4. `upload_to_huggingface`
-- The system might get stuck in step #3 indefinitely (the sub-tasks keep getting reset, so it never actually transitions to step #4).
-
-### d. Condition Logic vs. Next Tasks
-
-- Another subtlety: If the loop tasks had a `condition` that pointed them back to a prior step, it might cause unintentional re-entry. Typically, you only want loop tasks to proceed once, **unless** you explicitly want to re-visit. But if it’s a data ingestion process, you usually want to do it once, then move on to the next step.
-
----
-
-## 4. Why the System Can End Up Stuck or “Looping Indefinitely”
-
-1. **Reset Mechanism**  
-   - The code tries to “reset tasks to ‘not started’ once they complete,” so they can be re-run in some dynamic multi-run scenario.  
-   - But that same logic can cause loop tasks to revert back to “not started” the moment they end. The system sees “Oh, a task is ‘not started’? Let’s run it!” and you’re in a cycle.
-
-2. **No Condition for Exit**  
-   - If the loop has a condition that leads back to a prior step (like `"more" -> generate_cot`), it can keep re-running.
-
-3. **Subtasks Not Marked**  
-   - If the subtask or the loop tries to “reactivate” each other, it never exits.
-
----
-
-## Summary of the “Core Problem”
-
-1. We want to keep the resetting mechanism for **non-loop tasks** – because in some advanced workflows, we like re-running them from a different path or after some condition.  
-2. But we want **loop tasks** to remain `"completed"` once all sub-tasks are done, so the code can seamlessly proceed to the next major step.  
-3. Before the fix, loop tasks or their sub-tasks got reset. This triggered the system to re-enter the loop, re-run the sub-tasks, etc., causing an infinite loop and preventing the workflow from reaching tasks like “upload_to_huggingface.”
-
----
-
-## Technical Highlights to Pass On
-
-- **In the reset snippet**:  
-  ```python
-  if self.tasks[task_id].status == "completed":
-      # never reset if loop or subtask-of-loop
-      # else reset to "not started"
-  ```
-  This is crucial to skipping re-runs on loop tasks.  
-- **Ensure** that once a loop’s sub-tasks are all “completed,” the loop’s status is set to “completed,” and it transitions to the next major tasks (like `upload_to_huggingface`).  
-- **Check** if the loop’s condition is correct. If you want a single pass, do not implement a condition that leads back to the same loop.  
-- Also, you can check you do not have “overlapping conditions” that cause re-entry.
-
----
-
-### Conclusion
-
-**Hence,** the main challenge is that the reset logic (meant to let normal tasks be re-run) conflicts with a loop task’s one-pass usage. Once you avoid resetting the loop tasks or sub-tasks, you can finish them once, mark them “completed,” and properly proceed to the next stage.
-
-Dont remove any logging or debug statements, as it will help you to understand the flow of the code.
+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. 

src/praisonai-agents/CLAUDE.md

@@ -0,0 +1,237 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+PraisonAI Agents is a hierarchical AI agent framework for completing complex tasks with self-reflection capabilities. It supports multi-agent collaboration, tool integration, and various execution patterns (sequential, hierarchical, parallel).
+
+## Development Commands
+
+### Installation and Setup
+```bash
+# Install core package
+pip install -e .
+
+# Install with specific features
+pip install -e .[all]          # All features
+pip install -e .[memory]       # Memory capabilities
+pip install -e .[knowledge]    # Document processing
+pip install -e .[mcp]          # MCP server support
+pip install -e .[llm]          # Extended LLM support
+pip install -e .[api]          # API server capabilities
+```
+
+### Testing
+```bash
+# Run individual test examples (no formal test runner configured)
+python tests/basic-agents.py
+python tests/async_example.py
+python tests/knowledge-agents.py
+
+# Test specific features
+python tests/mcp-agents.py           # MCP integration
+python tests/memory_example.py      # Memory functionality
+python tests/tools_example.py       # Tool system
+```
+
+### Running Examples
+```bash
+# Basic agent usage
+python tests/single-agent.py
+
+# Multi-agent workflows
+python tests/multi-agents-api.py
+
+# Async operations
+python tests/async_example_full.py
+
+# MCP server examples
+python tests/mcp-sse-direct-server.py  # Start MCP server
+python tests/mcp-sse-direct-client.py  # Connect to server
+```
+
+## Core Architecture
+
+### Agent System (`praisonaiagents/agent/`)
+- **Agent**: Core agent class with LLM integration, tool calling, and self-reflection
+- **ImageAgent**: Specialized multimodal agent for image processing
+- Self-reflection with configurable min/max iterations (default: 1-3)
+- Delegation support for hierarchical agent structures
+
+### Multi-Agent Orchestration (`praisonaiagents/agents/`)
+- **PraisonAIAgents**: Main orchestrator for managing multiple agents and tasks
+- **AutoAgents**: Automatic agent creation and management
+- Process types: `sequential`, `hierarchical`, `parallel`
+- Context passing between agents and task dependency management
+
+### Task System (`praisonaiagents/task/`)
+- **Task**: Core task definition with context, callbacks, and output specifications
+- Supports file output, JSON/Pydantic structured output, async execution
+- Conditional logic with `condition` parameter for task flow control
+- Context passing via `context` parameter for task dependencies
+- **Guardrails**: Built-in validation and safety mechanisms for task outputs
+  - Function-based guardrails for custom validation logic
+  - LLM-based guardrails using natural language descriptions
+  - Automatic retry with configurable `max_retries` parameter
+  - Compatible with CrewAI guardrail patterns
+
+### LLM Integration (`praisonaiagents/llm/`)
+- Unified wrapper for multiple LLM providers via LiteLLM
+- Supports OpenAI, Anthropic, Gemini, DeepSeek, local models (Ollama)
+- Context length management and tool calling capabilities
+- Set via `llm` parameter on agents or global `OPENAI_API_KEY`/`ANTHROPIC_API_KEY`
+
+### Tool System (`praisonaiagents/tools/`)
+Two implementation patterns:
+1. **Function-based**: Simple tools using `@tool` decorator
+2. **Class-based**: Complex tools inheriting from `BaseTool`
+
+Built-in tools include: DuckDuckGo search, file operations, calculator, Wikipedia, arXiv, data analysis tools, shell execution.
+
+### Memory & Knowledge Systems
+- **Memory** (`praisonaiagents/memory/`): Multi-layered memory with RAG support
+  - Types: short-term, long-term, entity, user memory
+  - Providers: ChromaDB, Mem0, custom implementations
+- **Knowledge** (`praisonaiagents/knowledge/`): Document processing with chunking
+  - Chunking strategies via `chonkie` library
+  - Embedding and retrieval capabilities
+
+### MCP (Model Context Protocol) Integration
+- **MCP Server**: Server-side tool protocol for distributed execution
+- **SSE Support**: Server-sent events for real-time communication
+- Tool discovery and dynamic registration
+
+## Development Patterns
+
+### Agent Creation
+```python
+agent = Agent(
+    name="Agent Name",
+    role="Agent Role",
+    goal="Agent Goal",
+    backstory="Agent Background",
+    llm="gpt-4o-mini",  # or other LLM
+    self_reflect=True,  # Enable self-reflection
+    min_reflect=1,      # Minimum reflection iterations
+    max_reflect=3,      # Maximum reflection iterations
+    tools=[tool1, tool2],  # Optional tools
+    guardrail=validate_function,  # Agent-level guardrail (function or string)
+    max_guardrail_retries=3  # Retry limit for guardrail failures
+)
+```
+
+### Task Definition
+```python
+task = Task(
+    name="task_name",
+    description="Task description",
+    expected_output="Expected output format",
+    agent=agent,
+    context=[previous_task],  # Task dependencies
+    output_pydantic=ResponseModel,  # Structured output
+    condition="condition_function"  # Conditional execution
+)
+```
+
+### Guardrails Usage
+
+#### Task-Level Guardrails
+```python
+from typing import Tuple, Any
+
+# Function-based guardrail
+def validate_output(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """Custom validation function."""
+    if "error" in task_output.raw.lower():
+        return False, "Output contains errors"
+    if len(task_output.raw) < 10:
+        return False, "Output is too short"
+    return True, task_output
+
+task = Task(
+    description="Write a professional email",
+    expected_output="A well-formatted email",
+    agent=agent,
+    guardrail=validate_output,  # Function-based guardrail
+    max_retries=3  # Retry up to 3 times if guardrail fails
+)
+
+# LLM-based guardrail
+task = Task(
+    description="Generate marketing copy",
+    expected_output="Professional marketing content",
+    agent=agent,
+    guardrail="Ensure the content is professional, engaging, and free of errors",  # String description
+    max_retries=2
+)
+```
+
+#### Agent-Level Guardrails
+```python
+# Agent guardrails apply to ALL outputs from that agent
+def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """Ensure professional tone in all agent responses."""
+    content = task_output.raw.lower()
+    casual_words = ['yo', 'dude', 'awesome', 'cool']
+    for word in casual_words:
+        if word in content:
+            return False, f"Unprofessional language detected: {word}"
+    return True, task_output
+
+# Agent with function-based guardrail
+agent = Agent(
+    name="BusinessWriter",
+    instructions="You are a professional business writer",
+    guardrail=validate_professional_tone,  # Function guardrail
+    max_guardrail_retries=3
+)
+
+# Agent with LLM-based guardrail
+agent = Agent(
+    name="ContentWriter", 
+    instructions="You are a content writer",
+    guardrail="Ensure all responses are professional, accurate, and appropriate for business use",  # String guardrail
+    max_guardrail_retries=2
+)
+```
+
+### Multi-Agent Workflow
+```python
+workflow = PraisonAIAgents(
+    agents=[agent1, agent2],
+    tasks=[task1, task2],
+    process="sequential",  # or "hierarchical", "parallel"
+    verbose=True,
+    manager_agent=manager_agent  # For hierarchical process
+)
+result = workflow.start()
+```
+
+### Async Support
+All major components support async execution:
+```python
+result = await workflow.astart()
+result = await agent.aexecute(task)
+```
+
+## Key Dependencies
+
+- **Core**: `pydantic`, `rich`, `openai`, `mcp`
+- **Memory**: `chromadb`, `mem0ai`
+- **Knowledge**: `markitdown`, `chonkie`
+- **LLM**: `litellm` for unified provider access
+- **API**: `fastapi`, `uvicorn` for server capabilities
+
+## Error Handling
+
+- Global error logging via `error_logs` list
+- Callback system for real-time error reporting
+- Context length exception handling with automatic retry
+- Graceful degradation for optional dependencies
+
+## Testing Strategy
+
+The project uses example-driven testing with 100+ test files in `tests/` directory. Each test file demonstrates specific usage patterns and serves as both test and documentation. Run individual examples to test functionality rather than using a formal test runner.
+
+Use conda activate praisonai-agents to activate the environment.

src/praisonai-agents/agent_guardrails_example.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+"""
+Agent-level guardrails example.
+
+This example demonstrates how to use guardrails at the Agent level,
+which will apply to all tasks executed by that agent.
+"""
+
+from typing import Tuple, Any
+from praisonaiagents import Agent, TaskOutput
+
+def validate_content_length(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """
+    Validate that task output content meets minimum length requirement.
+    
+    Args:
+        task_output: The task output to validate
+        
+    Returns:
+        Tuple of (success, result_or_error_message)
+    """
+    if len(task_output.raw) < 50:
+        return False, "Content too short - must be at least 50 characters"
+    return True, task_output
+
+def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """
+    Validate that the content has a professional tone.
+    
+    Args:
+        task_output: The task output to validate
+        
+    Returns:
+        Tuple of (success, result_or_error_message)
+    """
+    content = task_output.raw.lower()
+    unprofessional_words = ['yo', 'dude', 'awesome', 'cool', 'lol']
+    
+    for word in unprofessional_words:
+        if word in content:
+            return False, f"Content contains unprofessional word: '{word}'"
+    
+    return True, task_output
+
+def main():
+    """Demonstrate Agent-level guardrails with function-based and LLM-based validation."""
+    
+    print("=== Agent Guardrail Examples ===\n")
+    
+    # Example 1: Function-based guardrail
+    print("1. Function-based guardrail (content length validation):")
+    agent1 = Agent(
+        name="ContentWriter",
+        instructions="You are a professional content writer who creates detailed responses",
+        guardrail=validate_content_length,
+        max_guardrail_retries=2
+    )
+    
+    try:
+        result1 = agent1.start("Write a brief welcome message")
+        print(f"Result: {result1}\n")
+    except Exception as e:
+        print(f"Error: {e}\n")
+    
+    # Example 2: LLM-based guardrail (string description)
+    print("2. LLM-based guardrail (professional tone validation):")
+    agent2 = Agent(
+        name="BusinessWriter", 
+        instructions="You are a business communication expert",
+        guardrail="Ensure the content is professional, formal, and suitable for business communication. No casual language or slang.",
+        max_guardrail_retries=3
+    )
+    
+    try:
+        result2 = agent2.start("Write a welcome message for new employees")
+        print(f"Result: {result2}\n")
+    except Exception as e:
+        print(f"Error: {e}\n")
+    
+    # Example 3: Multiple agents with different guardrails
+    print("3. Professional tone function-based guardrail:")
+    agent3 = Agent(
+        name="ProfessionalWriter",
+        instructions="Write professional business content",
+        guardrail=validate_professional_tone,
+        max_guardrail_retries=2
+    )
+    
+    try:
+        result3 = agent3.start("Write a casual greeting message")
+        print(f"Result: {result3}\n")
+    except Exception as e:
+        print(f"Error: {e}\n")
+    
+    print("=== Agent Guardrails Demonstration Complete ===")
+
+if __name__ == "__main__":
+    main()

src/praisonai-agents/debug_comparison.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+
+import sys
+import traceback
+from praisonaiagents import Agent, MCP
+
+def test_agent_direct():
+    """Test gpt-4o-mini (agent.py path)"""
+    print("=" * 50)
+    print("TESTING: gpt-4o-mini (agent.py direct calls)")
+    print("=" * 50)
+    
+    try:
+        agent = Agent(
+            instructions="""You are a helpful assistant that can break down complex problems.
+            Use the available tools when relevant to perform step-by-step analysis.""",
+            llm="gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+        )
+        
+        print("✅ Agent created successfully")
+        print(f"✅ Agent LLM: {getattr(agent, 'llm', 'Not set')}")
+        print(f"✅ Agent using custom LLM: {getattr(agent, '_using_custom_llm', False)}")
+        
+        result = agent.start("What are 3 steps to make coffee?")
+        print("✅ Agent execution completed successfully")
+        return True, result
+        
+    except Exception as e:
+        print(f"❌ Error in agent direct: {e}")
+        traceback.print_exc()
+        return False, str(e)
+
+def test_llm_class():
+    """Test openai/gpt-4o-mini (llm.py path)"""
+    print("\n" + "=" * 50)
+    print("TESTING: openai/gpt-4o-mini (llm.py LiteLLM)")
+    print("=" * 50)
+    
+    try:
+        agent = Agent(
+            instructions="""You are a helpful assistant that can break down complex problems.
+            Use the available tools when relevant to perform step-by-step analysis.""",
+            llm="openai/gpt-4o-mini",
+            tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+        )
+        
+        print("✅ Agent created successfully")
+        print(f"✅ Agent LLM instance: {getattr(agent, 'llm_instance', 'Not set')}")
+        print(f"✅ Agent using custom LLM: {getattr(agent, '_using_custom_llm', False)}")
+        
+        result = agent.start("What are 3 steps to make coffee?")
+        print("✅ Agent execution completed successfully")
+        return True, result
+        
+    except Exception as e:
+        print(f"❌ Error in llm class: {e}")
+        traceback.print_exc()
+        return False, str(e)
+
+if __name__ == "__main__":
+    print("🔍 DEBUGGING: Comparing both LLM approaches\n")
+    
+    # Test agent direct
+    success1, result1 = test_agent_direct()
+    
+    # Test LLM class
+    success2, result2 = test_llm_class()
+    
+    print("\n" + "=" * 50)
+    print("FINAL RESULTS")
+    print("=" * 50)
+    
+    if success1:
+        print("✅ gpt-4o-mini (agent.py) - SUCCESS")
+    else:
+        print("❌ gpt-4o-mini (agent.py) - FAILED")
+        print(f"   Error: {result1}")
+    
+    if success2:
+        print("✅ openai/gpt-4o-mini (llm.py) - SUCCESS")
+    else:
+        print("❌ openai/gpt-4o-mini (llm.py) - FAILED")
+        print(f"   Error: {result2}")
+    
+    if success1 and success2:
+        print("\n🎉 BOTH FORMATS WORK CORRECTLY!")
+        print("📝 The issue mentioned might be resolved or was a different problem.")
+    elif success1 and not success2:
+        print("\n⚠️  CONFIRMED: LLM class path has issues")
+        print("📝 Need to debug the LLM class implementation")
+    elif success2 and not success1:
+        print("\n⚠️  CONFIRMED: Agent direct path has issues")
+        print("📝 Need to debug the agent direct implementation")
+    else:
+        print("\n💥 BOTH PATHS FAILED - Something is fundamentally wrong")

src/praisonai-agents/guardrail_agent_example.py

@@ -0,0 +1,14 @@
+from praisonaiagents import Agent
+
+def validate_content(data):
+    if len(str(data)) < 50:
+        return False, "Content too short"
+    return True, data
+
+agent = Agent(
+    instructions="You are a writer",
+    guardrail=validate_content,
+    max_guardrail_retries=1
+)
+
+agent.start("Write a welcome message with 5 words")

src/praisonai-agents/guardrail_agents_example.py

@@ -0,0 +1,21 @@
+from praisonaiagents import Agent, Task, TaskOutput, PraisonAIAgents
+from typing import Tuple, Any
+
+def validate_content(task_output: TaskOutput) -> Tuple[bool, Any]:
+    if len(task_output.raw) < 50:
+        return False, "Content too short"
+    return True, task_output
+
+agent = Agent(
+    instructions="You are a writer",
+)
+
+task = Task(
+    description="Write a welcome message",
+    guardrail=validate_content,
+    agent=agent
+)
+
+praison_agents = PraisonAIAgents(agents=[agent], tasks=[task])
+
+praison_agents.start()

src/praisonai-agents/guardrails_example.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""
+Example demonstrating guardrails functionality in PraisonAI Agents.
+
+This example shows both function-based and LLM-based guardrails
+for validating task outputs.
+"""
+
+import sys
+import os
+from typing import Tuple, Any
+
+from praisonaiagents import Agent, Task, TaskOutput
+
+
+def email_validator(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """
+    Function-based guardrail to validate email content.
+    
+    Args:
+        task_output: The task output to validate
+        
+    Returns:
+        Tuple of (success, result_or_error)
+    """
+    content = task_output.raw.lower()
+    
+    # Check for required email components
+    if "subject:" not in content:
+        return False, "Email must include a subject line"
+    
+    if "dear" not in content and "hello" not in content:
+        return False, "Email must include a proper greeting"
+    
+    if len(content) < 50:
+        return False, "Email content is too short"
+        
+    if "error" in content or "problem" in content:
+        return False, "Email should not mention errors or problems"
+        
+    return True, task_output
+
+
+def main():
+    """Run the guardrails example."""
+    print("PraisonAI Agents - Guardrails Example")
+    print("=====================================\n")
+    
+    # Create an agent
+    agent = Agent(
+        name="Email Assistant",
+        role="Professional Email Writer",
+        goal="Write clear, professional emails",
+        backstory="I am an AI assistant specialized in writing professional emails"
+    )
+    
+    print("1. Testing Function-based Guardrail")
+    print("------------------------------------")
+    
+    # Create task with function-based guardrail
+    task_with_function_guardrail = Task(
+        description="Write a professional email to a client about project completion",
+        expected_output="A well-formatted professional email",
+        agent=agent,
+        guardrail=email_validator,  # Function-based guardrail
+        max_retries=2
+    )
+    
+    print(f"Task created with function guardrail: {email_validator.__name__}")
+    print(f"Max retries: {task_with_function_guardrail.max_retries}")
+    
+    # Simulate a task output that should pass
+    good_output = TaskOutput(
+        description="Email task",
+        raw="""Subject: Project Completion Update
+
+Dear Client,
+
+I am pleased to inform you that your project has been completed successfully. 
+All deliverables have been reviewed and are ready for your review. 
+Please let me know if you have any questions.
+
+Best regards,
+Project Team""",
+        agent="Email Assistant"
+    )
+    
+    result = task_with_function_guardrail._process_guardrail(good_output)
+    print(f"Good email result: {'PASSED' if result.success else 'FAILED'}")
+    if not result.success:
+        print(f"Error: {result.error}")
+    
+    # Simulate a task output that should fail
+    bad_output = TaskOutput(
+        description="Email task",
+        raw="Hi there, there was an error with your project.",
+        agent="Email Assistant"
+    )
+    
+    result = task_with_function_guardrail._process_guardrail(bad_output)
+    print(f"Bad email result: {'PASSED' if result.success else 'FAILED'}")
+    if not result.success:
+        print(f"Error: {result.error}")
+    
+    print("\n2. Testing String-based LLM Guardrail")
+    print("-------------------------------------")
+    
+    # Create task with string-based guardrail
+    task_with_llm_guardrail = Task(
+        description="Write a marketing email for a new product launch",
+        expected_output="Engaging marketing content",
+        agent=agent,
+        guardrail="Ensure the content is professional, engaging, includes a clear call-to-action, and is free of errors",
+        max_retries=3
+    )
+    
+    print("Task created with LLM-based guardrail")
+    print("Guardrail description: 'Ensure the content is professional, engaging, includes a clear call-to-action, and is free of errors'")
+    print(f"Max retries: {task_with_llm_guardrail.max_retries}")
+    
+    print("\n3. Backward Compatibility")
+    print("-------------------------")
+    
+    # Create task without guardrail (backward compatible)
+    task_without_guardrail = Task(
+        description="Write a simple thank you note",
+        expected_output="A brief thank you message",
+        agent=agent
+    )
+    
+    print("Task created without guardrail (backward compatible)")
+    print(f"Guardrail function: {task_without_guardrail._guardrail_fn}")
+    
+    # Test that it doesn't break existing functionality
+    simple_output = TaskOutput(
+        description="Thank you task",
+        raw="Thank you for your business!",
+        agent="Email Assistant"
+    )
+    
+    result = task_without_guardrail._process_guardrail(simple_output)
+    print(f"No guardrail result: {'PASSED' if result.success else 'FAILED'}")
+    
+    print("\n✅ Guardrails example completed successfully!")
+    print("\nKey Features Demonstrated:")
+    print("- Function-based guardrails with custom validation logic")
+    print("- String-based LLM guardrails using natural language")
+    print("- Configurable retry mechanism")
+    print("- Backward compatibility with existing tasks")
+    print("- Integration with TaskOutput validation")
+
+
+if __name__ == "__main__":
+    main()

src/praisonai-agents/praisonaiagents/__init__.py

@@ -12,6 +12,8 @@ from .knowledge.knowledge import Knowledge
 from .knowledge.chunking import Chunking
 from .mcp.mcp import MCP
 from .session import Session
+from .memory.memory import Memory
+from .guardrails import GuardrailResult, LLMGuardrail
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -42,6 +44,7 @@ __all__ = [
     'ReflectionOutput',
     'AutoAgents',
     'Session',
+    'Memory',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',
@@ -55,5 +58,7 @@ __all__ = [
     'async_display_callbacks',
     'Knowledge',
     'Chunking',
-    'MCP'
+    'MCP',
+    'GuardrailResult',
+    'LLMGuardrail'
 ] 

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -3,7 +3,7 @@ import time
 import json
 import logging
 import asyncio
-from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING
+from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING, Callable, Tuple
 from rich.console import Console
 from rich.live import Live
 from openai import AsyncOpenAI
@@ -32,6 +32,7 @@ _shared_apps = {}  # Dict of port -> FastAPI app
 
 if TYPE_CHECKING:
     from ..task.task import Task
+    from ..main import TaskOutput
 
 @dataclass
 class ChatCompletionMessage:
@@ -368,7 +369,9 @@ class Agent:
         min_reflect: int = 1,
         reflect_llm: Optional[str] = None,
         user_id: Optional[str] = None,
-        reasoning_steps: bool = False
+        reasoning_steps: bool = False,
+        guardrail: Optional[Union[Callable[['TaskOutput'], Tuple[bool, Any]], str]] = None,
+        max_guardrail_retries: int = 3
     ):
         # Add check at start if memory is requested
         if memory is not None:
@@ -483,6 +486,12 @@ Your Goal: {self.goal}
         # Store user_id
         self.user_id = user_id or "praison"
         self.reasoning_steps = reasoning_steps
+        
+        # Initialize guardrail settings
+        self.guardrail = guardrail
+        self.max_guardrail_retries = max_guardrail_retries
+        self._guardrail_fn = None
+        self._setup_guardrail()
 
         # Check if knowledge parameter has any values
         if not knowledge:
@@ -512,6 +521,149 @@ Your Goal: {self.goal}
         except Exception as e:
             logging.error(f"Error processing knowledge item: {knowledge_item}, error: {e}")
 
+    def _setup_guardrail(self):
+        """Setup the guardrail function based on the provided guardrail parameter."""
+        if self.guardrail is None:
+            self._guardrail_fn = None
+            return
+            
+        if callable(self.guardrail):
+            # Validate function signature
+            sig = inspect.signature(self.guardrail)
+            positional_args = [
+                param for param in sig.parameters.values()
+                if param.default is inspect.Parameter.empty
+            ]
+            if len(positional_args) != 1:
+                raise ValueError("Agent guardrail function must accept exactly one parameter (TaskOutput)")
+            
+            # Check return annotation if present
+            from typing import get_args, get_origin
+            return_annotation = sig.return_annotation
+            if return_annotation != inspect.Signature.empty:
+                return_annotation_args = get_args(return_annotation)
+                if not (
+                    get_origin(return_annotation) is tuple
+                    and len(return_annotation_args) == 2
+                    and return_annotation_args[0] is bool
+                    and (
+                        return_annotation_args[1] is Any
+                        or return_annotation_args[1] is str
+                        or str(return_annotation_args[1]).endswith('TaskOutput')
+                        or str(return_annotation_args[1]).startswith('typing.Union')
+                    )
+                ):
+                    raise ValueError(
+                        "If return type is annotated, it must be Tuple[bool, Any] or Tuple[bool, Union[str, TaskOutput]]"
+                    )
+            
+            self._guardrail_fn = self.guardrail
+        elif isinstance(self.guardrail, str):
+            # Create LLM-based guardrail
+            from ..guardrails import LLMGuardrail
+            llm = getattr(self, 'llm', None) or getattr(self, 'llm_instance', None)
+            self._guardrail_fn = LLMGuardrail(description=self.guardrail, llm=llm)
+        else:
+            raise ValueError("Agent guardrail must be either a callable or a string description")
+
+    def _process_guardrail(self, task_output):
+        """Process the guardrail validation for a task output.
+        
+        Args:
+            task_output: The task output to validate
+            
+        Returns:
+            GuardrailResult: The result of the guardrail validation
+        """
+        from ..guardrails import GuardrailResult
+        
+        if not self._guardrail_fn:
+            return GuardrailResult(success=True, result=task_output)
+        
+        try:
+            # Call the guardrail function
+            result = self._guardrail_fn(task_output)
+            
+            # Convert the result to a GuardrailResult
+            return GuardrailResult.from_tuple(result)
+            
+        except Exception as e:
+            logging.error(f"Agent {self.name}: Error in guardrail validation: {e}")
+            # On error, return failure
+            return GuardrailResult(
+                success=False,
+                result=None,
+                error=f"Agent guardrail validation error: {str(e)}"
+            )
+
+    def _apply_guardrail_with_retry(self, response_text, prompt, temperature=0.2, tools=None):
+        """Apply guardrail validation with retry logic.
+        
+        Args:
+            response_text: The response to validate
+            prompt: Original prompt for regeneration if needed
+            temperature: Temperature for regeneration
+            tools: Tools for regeneration
+            
+        Returns:
+            str: The validated response text or None if validation fails after retries
+        """
+        if not self._guardrail_fn:
+            return response_text
+            
+        from ..main import TaskOutput
+        
+        retry_count = 0
+        current_response = response_text
+        
+        while retry_count <= self.max_guardrail_retries:
+            # Create TaskOutput object
+            task_output = TaskOutput(
+                description="Agent response output",
+                raw=current_response,
+                agent=self.name
+            )
+            
+            # Process guardrail
+            guardrail_result = self._process_guardrail(task_output)
+            
+            if guardrail_result.success:
+                logging.info(f"Agent {self.name}: Guardrail validation passed")
+                # Return the potentially modified result
+                if guardrail_result.result and hasattr(guardrail_result.result, 'raw'):
+                    return guardrail_result.result.raw
+                elif guardrail_result.result:
+                    return str(guardrail_result.result)
+                else:
+                    return current_response
+            
+            # Guardrail failed
+            if retry_count >= self.max_guardrail_retries:
+                raise Exception(
+                    f"Agent {self.name} response failed guardrail validation after {self.max_guardrail_retries} retries. "
+                    f"Last error: {guardrail_result.error}"
+                )
+            
+            retry_count += 1
+            logging.warning(f"Agent {self.name}: Guardrail validation failed (retry {retry_count}/{self.max_guardrail_retries}): {guardrail_result.error}")
+            
+            # Regenerate response for retry
+            try:
+                retry_prompt = f"{prompt}\n\nNote: Previous response failed validation due to: {guardrail_result.error}. Please provide an improved response."
+                response = self._chat_completion([{"role": "user", "content": retry_prompt}], temperature, tools)
+                if response and response.choices:
+                    current_response = response.choices[0].message.content.strip()
+                else:
+                    raise Exception("Failed to generate retry response")
+            except Exception as e:
+                logging.error(f"Agent {self.name}: Error during guardrail retry: {e}")
+                # If we can't regenerate, fail the guardrail
+                raise Exception(
+                    f"Agent {self.name} guardrail retry failed: {e}"
+                )
+        
+        return current_response
+
     def generate_task(self) -> 'Task':
         """Generate a Task object from the agent's instructions"""
         from ..task.task import Task
@@ -788,69 +940,92 @@ Your Goal: {self.goal}
                     )
             else:
                 # Use the standard OpenAI client approach
-                if stream:
-                    # Process as streaming response with formatted tools
-                    final_response = self._process_stream_response(
-                        messages, 
-                        temperature, 
-                        start_time, 
-                        formatted_tools=formatted_tools if formatted_tools else None,
-                        reasoning_steps=reasoning_steps
-                    )
-                else:
-                    # Process as regular non-streaming response
-                    final_response = client.chat.completions.create(
-                        model=self.llm,
-                        messages=messages,
-                        temperature=temperature,
-                        tools=formatted_tools if formatted_tools else None,
-                        stream=False
-                    )
+                # Continue tool execution loop until no more tool calls are needed
+                max_iterations = 10  # Prevent infinite loops
+                iteration_count = 0
+                
+                while iteration_count < max_iterations:
+                    if stream:
+                        # Process as streaming response with formatted tools
+                        final_response = self._process_stream_response(
+                            messages, 
+                            temperature, 
+                            start_time, 
+                            formatted_tools=formatted_tools if formatted_tools else None,
+                            reasoning_steps=reasoning_steps
+                        )
+                    else:
+                        # Process as regular non-streaming response
+                        final_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(final_response.choices[0].message, 'tool_calls', None)
+                    tool_calls = getattr(final_response.choices[0].message, 'tool_calls', None)
 
-            if tool_calls:
-                messages.append({
-                    "role": "assistant", 
-                    "content": final_response.choices[0].message.content,
-                    "tool_calls": tool_calls
-                })
+                    if tool_calls:
+                        messages.append({
+                            "role": "assistant", 
+                            "content": final_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)
+                        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}")
+                            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"
+                            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}")
+                            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
-                    })
+                            messages.append({
+                                "role": "tool",
+                                "tool_call_id": tool_call.id,
+                                "content": results_str
+                            })
 
-                # Get final response after tool calls
-                if stream:
-                    final_response = self._process_stream_response(
-                        messages, 
-                        temperature, 
-                        start_time,
-                        formatted_tools=formatted_tools if formatted_tools else None,
-                        reasoning_steps=reasoning_steps
-                    )
-                else:
-                    final_response = client.chat.completions.create(
-                        model=self.llm,
-                        messages=messages,
-                        temperature=temperature,
-                        stream=False
-                    )
+                        # Check if we should continue (for tools like sequential thinking)
+                        should_continue = False
+                        for tool_call in tool_calls:
+                            function_name = tool_call.function.name
+                            arguments = json.loads(tool_call.function.arguments)
+                            
+                            # For sequential thinking tool, check if nextThoughtNeeded is True
+                            if function_name == "sequentialthinking" and arguments.get("nextThoughtNeeded", False):
+                                should_continue = True
+                                break
+
+                        if not should_continue:
+                            # Get final response after tool calls
+                            if stream:
+                                final_response = self._process_stream_response(
+                                    messages, 
+                                    temperature, 
+                                    start_time,
+                                    formatted_tools=formatted_tools if formatted_tools else None,
+                                    reasoning_steps=reasoning_steps
+                                )
+                            else:
+                                final_response = client.chat.completions.create(
+                                    model=self.llm,
+                                    messages=messages,
+                                    temperature=temperature,
+                                    stream=False
+                                )
+                            break
+                        
+                        iteration_count += 1
+                    else:
+                        # No tool calls, we're done
+                        break
 
             return final_response
 
@@ -944,7 +1119,13 @@ Your Goal: {self.goal}
                     total_time = time.time() - start_time
                     logging.debug(f"Agent.chat completed in {total_time:.2f} seconds")
 
-                return response_text
+                # Apply guardrail validation for custom LLM response
+                try:
+                    validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools)
+                    return validated_response
+                except Exception as e:
+                    logging.error(f"Agent {self.name}: Guardrail validation failed for custom LLM: {e}")
+                    return None
             except Exception as e:
                 display_error(f"Error in LLM chat: {e}")
                 return None
@@ -1032,8 +1213,20 @@ Your Goal: {self.goal}
                         display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                         # Return only reasoning content if reasoning_steps is True
                         if reasoning_steps and hasattr(response.choices[0].message, 'reasoning_content'):
-                            return response.choices[0].message.reasoning_content
-                        return response_text
+                            # Apply guardrail to reasoning content
+                            try:
+                                validated_reasoning = self._apply_guardrail_with_retry(response.choices[0].message.reasoning_content, original_prompt, temperature, tools)
+                                return validated_reasoning
+                            except Exception as e:
+                                logging.error(f"Agent {self.name}: Guardrail validation failed for reasoning content: {e}")
+                                return None
+                        # Apply guardrail to regular response
+                        try:
+                            validated_response = self._apply_guardrail_with_retry(response_text, original_prompt, temperature, tools)
+                            return validated_response
+                        except Exception as e:
+                            logging.error(f"Agent {self.name}: Guardrail validation failed: {e}")
+                            return None
 
                     reflection_prompt = f"""
 Reflect on your previous response: '{response_text}'.
@@ -1066,7 +1259,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             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
+                            # Apply guardrail validation after satisfactory reflection
+                            try:
+                                validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools)
+                                return validated_response
+                            except Exception as e:
+                                logging.error(f"Agent {self.name}: Guardrail validation failed after reflection: {e}")
+                                return None
 
                         # Check if we've hit max reflections
                         if reflection_count >= self.max_reflect - 1:
@@ -1075,7 +1274,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             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
+                            # Apply guardrail validation after max reflections
+                            try:
+                                validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools)
+                                return validated_response
+                            except Exception as e:
+                                logging.error(f"Agent {self.name}: Guardrail validation failed after max reflections: {e}")
+                                return None
 
                         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"})
@@ -1099,8 +1304,16 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
             total_time = time.time() - start_time
             logging.debug(f"Agent.chat completed in {total_time:.2f} seconds")
-            
-        return response_text
+        
+        # Apply guardrail validation before returning    
+        try:
+            validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools)
+            return validated_response
+        except Exception as e:
+            logging.error(f"Agent {self.name}: Guardrail validation failed: {e}")
+            if self.verbose:
+                display_error(f"Guardrail validation failed: {e}", console=self.console)
+            return None
 
     def clean_json_output(self, output: str) -> str:
         """Clean and extract JSON from response text."""
@@ -1670,6 +1883,7 @@ Your Goal: {self.goal}
                 import threading
                 import time
                 import inspect
+                import asyncio  # Import asyncio in the MCP scope
                 # logging is already imported at the module level
                 
             except ImportError as e:

src/praisonai-agents/praisonaiagents/guardrails/__init__.py

@@ -0,0 +1,11 @@
+"""
+Guardrails module for PraisonAI Agents.
+
+This module provides validation and safety mechanisms for task outputs,
+including both function-based and LLM-based guardrails.
+"""
+
+from .guardrail_result import GuardrailResult
+from .llm_guardrail import LLMGuardrail
+
+__all__ = ["GuardrailResult", "LLMGuardrail"]

src/praisonai-agents/praisonaiagents/guardrails/guardrail_result.py

@@ -0,0 +1,43 @@
+"""
+Guardrail result classes for PraisonAI Agents.
+
+This module provides the result types for guardrail validation,
+following the same pattern as CrewAI for consistency.
+"""
+
+from typing import Any, Tuple, Union
+from pydantic import BaseModel, Field
+from ..main import TaskOutput
+
+
+class GuardrailResult(BaseModel):
+    """Result of a guardrail validation."""
+    
+    success: bool = Field(description="Whether the guardrail check passed")
+    result: Union[str, TaskOutput, None] = Field(description="The result if modified, or None if unchanged")
+    error: str = Field(default="", description="Error message if validation failed")
+    
+    @classmethod
+    def from_tuple(cls, result: Tuple[bool, Any]) -> "GuardrailResult":
+        """Create a GuardrailResult from a tuple returned by a guardrail function.
+        
+        Args:
+            result: Tuple of (success, result_or_error)
+            
+        Returns:
+            GuardrailResult: The structured result
+        """
+        success, data = result
+        
+        if success:
+            return cls(
+                success=True,
+                result=data,
+                error=""
+            )
+        else:
+            return cls(
+                success=False,
+                result=None,
+                error=str(data) if data else "Guardrail validation failed"
+            )

src/praisonai-agents/praisonaiagents/guardrails/llm_guardrail.py

@@ -0,0 +1,88 @@
+"""
+LLM-based guardrail implementation for PraisonAI Agents.
+
+This module provides LLM-powered guardrails that can validate task outputs
+using natural language descriptions, similar to CrewAI's implementation.
+"""
+
+import logging
+from typing import Any, Tuple, Union, Optional
+from pydantic import BaseModel
+from ..main import TaskOutput
+
+
+class LLMGuardrail:
+    """An LLM-powered guardrail that validates task outputs using natural language."""
+    
+    def __init__(self, description: str, llm: Any = None):
+        """Initialize the LLM guardrail.
+        
+        Args:
+            description: Natural language description of what to validate
+            llm: The LLM instance to use for validation
+        """
+        self.description = description
+        self.llm = llm
+        self.logger = logging.getLogger(__name__)
+    
+    def __call__(self, task_output: TaskOutput) -> Tuple[bool, Union[str, TaskOutput]]:
+        """Validate the task output using the LLM.
+        
+        Args:
+            task_output: The task output to validate
+            
+        Returns:
+            Tuple of (success, result) where result is the output or error message
+        """
+        try:
+            if not self.llm:
+                self.logger.warning("No LLM provided for guardrail validation")
+                return True, task_output
+            
+            # Create validation prompt
+            validation_prompt = f"""
+You are a quality assurance validator. Your task is to evaluate the following output against specific criteria.
+
+Validation Criteria: {self.description}
+
+Output to Validate:
+{task_output.raw}
+
+Please evaluate if this output meets the criteria. Respond with:
+1. "PASS" if the output meets all criteria
+2. "FAIL: [specific reason]" if the output does not meet criteria
+
+Your response:"""
+
+            # Get LLM response
+            if hasattr(self.llm, 'chat'):
+                # For Agent's LLM interface
+                response = self.llm.chat(validation_prompt, temperature=0.1)
+            elif hasattr(self.llm, 'get_response'):
+                # For custom LLM instances
+                response = self.llm.get_response(validation_prompt, temperature=0.1)
+            elif callable(self.llm):
+                # For simple callable LLMs
+                response = self.llm(validation_prompt)
+            else:
+                self.logger.error(f"Unsupported LLM type: {type(self.llm)}")
+                return True, task_output
+            
+            # Parse response
+            response = str(response).strip()
+            
+            if response.upper().startswith("PASS"):
+                return True, task_output
+            elif response.upper().startswith("FAIL"):
+                # Extract the reason
+                reason = response[5:].strip(": ")
+                return False, f"Guardrail validation failed: {reason}"
+            else:
+                # Unclear response, log and pass through
+                self.logger.warning(f"Unclear guardrail response: {response}")
+                return True, task_output
+                
+        except Exception as e:
+            self.logger.error(f"Error in LLM guardrail validation: {str(e)}")
+            # On error, pass through the original output
+            return True, task_output

src/praisonai-agents/praisonaiagents/llm/llm.py

@@ -413,24 +413,30 @@ class LLM:
             start_time = time.time()
             reflection_count = 0
 
-            while True:
-                try:
-                    if verbose:
-                        display_text = prompt
-                        if isinstance(prompt, list):
-                            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 {agent_name} is processing prompt: {display_text}",
-                                console=console,
-                                agent_name=agent_name,
-                                agent_role=agent_role,
-                                agent_tools=agent_tools
-                            )
+            # Display initial instruction once
+            if verbose:
+                display_text = prompt
+                if isinstance(prompt, list):
+                    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 {agent_name} is processing prompt: {display_text}",
+                        console=console,
+                        agent_name=agent_name,
+                        agent_role=agent_role,
+                        agent_tools=agent_tools
+                    )
 
+            # Sequential tool calling loop - similar to agent.py
+            max_iterations = 10  # Prevent infinite loops
+            iteration_count = 0
+            final_response_text = ""
+
+            while iteration_count < max_iterations:
+                try:
                     # Get response from LiteLLM
-                    start_time = time.time()
+                    current_time = time.time()
 
                     # If reasoning_steps is True, do a single non-streaming call
                     if reasoning_steps:
@@ -445,6 +451,7 @@ class LLM:
                         )
                         reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
                         response_text = resp["choices"][0]["message"]["content"]
+                        final_response = resp
                         
                         # Optionally display reasoning if present
                         if verbose and reasoning_content:
@@ -452,7 +459,7 @@ class LLM:
                                 original_prompt,
                                 f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
                                 markdown=markdown,
-                                generation_time=time.time() - start_time,
+                                generation_time=time.time() - current_time,
                                 console=console
                             )
                         else:
@@ -460,14 +467,14 @@ class LLM:
                                 original_prompt,
                                 response_text,
                                 markdown=markdown,
-                                generation_time=time.time() - start_time,
+                                generation_time=time.time() - current_time,
                                 console=console
                             )
                     
                     # Otherwise do the existing streaming approach
                     else:
                         if verbose:
-                            with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                            with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
                                 response_text = ""
                                 for chunk in litellm.completion(
                                     **self._build_completion_params(
@@ -481,7 +488,7 @@ class LLM:
                                     if chunk and chunk.choices and chunk.choices[0].delta.content:
                                         content = chunk.choices[0].delta.content
                                         response_text += content
-                                        live.update(display_generating(response_text, start_time))
+                                        live.update(display_generating(response_text, current_time))
                         else:
                             # Non-verbose mode, just collect the response
                             response_text = ""
@@ -499,20 +506,20 @@ class LLM:
 
                         response_text = response_text.strip()
 
-                    # Get final completion to check for tool calls
-                    final_response = litellm.completion(
-                        **self._build_completion_params(
-                            messages=messages,
-                            tools=formatted_tools,
-                            temperature=temperature,
-                            stream=False,  # No streaming for tool call check
-                            **kwargs
+                        # Get final completion to check for tool calls
+                        final_response = litellm.completion(
+                            **self._build_completion_params(
+                                messages=messages,
+                                tools=formatted_tools,
+                                temperature=temperature,
+                                stream=False,  # No streaming for tool call check
+                                **kwargs
+                            )
                         )
-                    )
                     
                     tool_calls = final_response["choices"][0]["message"].get("tool_calls")
                     
-                    # Handle tool calls
+                    # Handle tool calls - Sequential tool calling logic
                     if tool_calls and execute_tool_fn:
                         # Convert tool_calls to a serializable format for all providers
                         serializable_tool_calls = []
@@ -535,6 +542,7 @@ class LLM:
                             "tool_calls": serializable_tool_calls
                         })
                         
+                        should_continue = False
                         for tool_call in tool_calls:
                             # Handle both object and dict access patterns
                             if isinstance(tool_call, dict):
@@ -574,6 +582,18 @@ class LLM:
                                 "content": json.dumps(tool_result) if tool_result is not None else "Function returned an empty output"
                             })
 
+                            # Check if we should continue (for tools like sequential thinking)
+                            # This mimics the logic from agent.py lines 1004-1007
+                            if function_name == "sequentialthinking" and arguments.get("nextThoughtNeeded", False):
+                                should_continue = True
+                        
+                        # If we should continue, increment iteration and continue loop
+                        if should_continue:
+                            iteration_count += 1
+                            continue
+
+                        # If we reach here, no more tool calls needed - get final response
+                        # Make one more call to get the final summary response
                         # Special handling for Ollama models that don't automatically process tool results
                         if self.model and self.model.startswith("ollama/") and tool_result:
                             # For Ollama models, we need to explicitly ask the model to process the tool results
@@ -666,115 +686,141 @@ class LLM:
                         else:
                             # Get response after tool calls with streaming
                             if verbose:
-                                with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
-                                    response_text = ""
+                                with Live(display_generating("", current_time), console=console, refresh_per_second=4) as live:
+                                    final_response_text = ""
                                     for chunk in litellm.completion(
                                         **self._build_completion_params(
                                             messages=messages,
+                                            tools=formatted_tools,
                                             temperature=temperature,
-                                            stream=True
+                                            stream=True,
+                                            **kwargs
                                         )
                                     ):
                                         if chunk and chunk.choices and chunk.choices[0].delta.content:
                                             content = chunk.choices[0].delta.content
-                                            response_text += content
-                                            live.update(display_generating(response_text, start_time))
+                                            final_response_text += content
+                                            live.update(display_generating(final_response_text, current_time))
                             else:
-                                response_text = ""
+                                final_response_text = ""
                                 for chunk in litellm.completion(
                                     **self._build_completion_params(
                                         messages=messages,
+                                        tools=formatted_tools,
                                         temperature=temperature,
-                                        stream=True
+                                        stream=True,
+                                        **kwargs
                                     )
                                 ):
                                     if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                        response_text += chunk.choices[0].delta.content
-
-                            response_text = response_text.strip()
-
-                    # Handle output formatting
-                    if output_json or output_pydantic:
-                        self.chat_history.append({"role": "user", "content": original_prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
+                                        final_response_text += chunk.choices[0].delta.content
+                            
+                            final_response_text = final_response_text.strip()
+                        
+                        # Display final response
                         if verbose:
-                            display_interaction(original_prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
-                        return response_text
+                            display_interaction(
+                                original_prompt,
+                                final_response_text,
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                        
+                        return final_response_text
+                    else:
+                        # No tool calls, we're done with this iteration
+                        break
+                        
+                except Exception as e:
+                    logging.error(f"Error in LLM iteration {iteration_count}: {e}")
+                    break
+                    
+            # End of while loop - return final response
+            if final_response_text:
+                return final_response_text
+            
+            # No tool calls were made in this iteration, return the response
+            if verbose:
+                display_interaction(
+                    original_prompt,
+                    response_text,
+                    markdown=markdown,
+                    generation_time=time.time() - start_time,
+                    console=console
+                )
+            
+            response_text = response_text.strip()
+            
+            # Handle output formatting
+            if output_json or output_pydantic:
+                self.chat_history.append({"role": "user", "content": original_prompt})
+                self.chat_history.append({"role": "assistant", "content": response_text})
+                if verbose:
+                    display_interaction(original_prompt, response_text, markdown=markdown,
+                                     generation_time=time.time() - start_time, console=console)
+                return response_text
 
-                    if not self_reflect:
-                        if verbose:
-                            display_interaction(original_prompt, response_text, markdown=markdown,
-                                             generation_time=time.time() - start_time, console=console)
-                        # Return reasoning content if reasoning_steps is True
-                        if reasoning_steps and reasoning_content:
-                            return reasoning_content
-                        return response_text
+            if not self_reflect:
+                if verbose:
+                    display_interaction(original_prompt, response_text, markdown=markdown,
+                                     generation_time=time.time() - start_time, console=console)
+                # Return reasoning content if reasoning_steps is True
+                if reasoning_steps and reasoning_content:
+                    return reasoning_content
+                return response_text
 
-                    # Handle self-reflection
-                    reflection_prompt = f"""
+            # Handle self-reflection loop
+            while reflection_count < max_reflect:
+                # Handle self-reflection
+                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'.
-                    """
-                    
-                    reflection_messages = messages + [
-                        {"role": "assistant", "content": response_text},
-                        {"role": "user", "content": reflection_prompt}
-                    ]
+                """
+                
+                reflection_messages = messages + [
+                    {"role": "assistant", "content": response_text},
+                    {"role": "user", "content": reflection_prompt}
+                ]
 
-                    # If reasoning_steps is True, do a single non-streaming call to capture reasoning
-                    if reasoning_steps:
-                        reflection_resp = litellm.completion(
-                            **self._build_completion_params(
-                                messages=reflection_messages,
-                                temperature=temperature,
-                                stream=False,  # Force non-streaming
-                                response_format={"type": "json_object"},
-                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
-                            )
+                # If reasoning_steps is True, do a single non-streaming call to capture reasoning
+                if reasoning_steps:
+                    reflection_resp = litellm.completion(
+                        **self._build_completion_params(
+                            messages=reflection_messages,
+                            temperature=temperature,
+                            stream=False,  # Force non-streaming
+                            response_format={"type": "json_object"},
+                            **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                         )
-                        # Grab reflection text and optional reasoning
-                        reasoning_content = reflection_resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
-                        reflection_text = reflection_resp["choices"][0]["message"]["content"]
+                    )
+                    # Grab reflection text and optional reasoning
+                    reasoning_content = reflection_resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                    reflection_text = reflection_resp["choices"][0]["message"]["content"]
 
-                        # Optionally display reasoning if present
-                        if verbose and reasoning_content:
-                            display_interaction(
-                                "Reflection reasoning:",
-                                f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
-                                markdown=markdown,
-                                generation_time=time.time() - start_time,
-                                console=console
-                            )
-                        elif verbose:
-                            display_interaction(
-                                "Self-reflection (non-streaming):",
-                                reflection_text,
-                                markdown=markdown,
-                                generation_time=time.time() - start_time,
-                                console=console
-                            )
-                    else:
-                        # Existing streaming approach
-                        if verbose:
-                            with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
-                                reflection_text = ""
-                                for chunk in litellm.completion(
-                                    **self._build_completion_params(
-                                        messages=reflection_messages,
-                                        temperature=temperature,
-                                        stream=True,
-                                        response_format={"type": "json_object"},
-                                        **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
-                                    )
-                                ):
-                                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                        content = chunk.choices[0].delta.content
-                                        reflection_text += content
-                                        live.update(display_generating(reflection_text, start_time))
-                        else:
+                    # Optionally display reasoning if present
+                    if verbose and reasoning_content:
+                        display_interaction(
+                            "Reflection reasoning:",
+                            f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
+                            markdown=markdown,
+                            generation_time=time.time() - start_time,
+                            console=console
+                        )
+                    elif verbose:
+                        display_interaction(
+                            "Self-reflection (non-streaming):",
+                            reflection_text,
+                            markdown=markdown,
+                            generation_time=time.time() - start_time,
+                            console=console
+                        )
+                else:
+                    # Existing streaming approach
+                    if verbose:
+                        with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
                             reflection_text = ""
                             for chunk in litellm.completion(
                                 **self._build_completion_params(
@@ -786,48 +832,102 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 )
                             ):
                                 if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                    reflection_text += chunk.choices[0].delta.content
-
-                    try:
-                        reflection_data = json.loads(reflection_text)
-                        satisfactory = reflection_data.get("satisfactory", "no").lower() == "yes"
-
-                        if verbose:
-                            display_self_reflection(
-                                f"Agent {agent_name} self reflection: reflection='{reflection_data['reflection']}' satisfactory='{reflection_data['satisfactory']}'",
-                                console=console
+                                    content = chunk.choices[0].delta.content
+                                    reflection_text += content
+                                    live.update(display_generating(reflection_text, start_time))
+                    else:
+                        reflection_text = ""
+                        for chunk in litellm.completion(
+                            **self._build_completion_params(
+                                messages=reflection_messages,
+                                temperature=temperature,
+                                stream=True,
+                                response_format={"type": "json_object"},
+                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                             )
+                        ):
+                            if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                reflection_text += chunk.choices[0].delta.content
 
-                        if satisfactory and reflection_count >= min_reflect - 1:
-                            if verbose:
-                                display_interaction(prompt, response_text, markdown=markdown,
-                                                 generation_time=time.time() - start_time, console=console)
-                            return response_text
+                try:
+                    reflection_data = json.loads(reflection_text)
+                    satisfactory = reflection_data.get("satisfactory", "no").lower() == "yes"
 
-                        if reflection_count >= max_reflect - 1:
-                            if verbose:
-                                display_interaction(prompt, response_text, markdown=markdown,
-                                                 generation_time=time.time() - start_time, console=console)
-                            return response_text
+                    if verbose:
+                        display_self_reflection(
+                            f"Agent {agent_name} self reflection: reflection='{reflection_data['reflection']}' satisfactory='{reflection_data['satisfactory']}'",
+                            console=console
+                        )
 
-                        reflection_count += 1
-                        messages.extend([
-                            {"role": "assistant", "content": response_text},
-                            {"role": "user", "content": reflection_prompt},
-                            {"role": "assistant", "content": reflection_text},
-                            {"role": "user", "content": "Now regenerate your response using the reflection you made"}
-                        ])
-                        continue
+                    if satisfactory and reflection_count >= min_reflect - 1:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
 
-                    except json.JSONDecodeError:
-                        reflection_count += 1
-                        if reflection_count >= max_reflect:
-                            return response_text
-                        continue
+                    if reflection_count >= max_reflect - 1:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
 
+                    reflection_count += 1
+                    messages.extend([
+                        {"role": "assistant", "content": response_text},
+                        {"role": "user", "content": reflection_prompt},
+                        {"role": "assistant", "content": reflection_text},
+                        {"role": "user", "content": "Now regenerate your response using the reflection you made"}
+                    ])
+                    
+                    # Get new response after reflection
+                    if verbose:
+                        with Live(display_generating("", time.time()), console=console, refresh_per_second=4) as live:
+                            response_text = ""
+                            for chunk in litellm.completion(
+                                **self._build_completion_params(
+                                    messages=messages,
+                                    temperature=temperature,
+                                    stream=True,
+                                    **kwargs
+                                )
+                            ):
+                                if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                    content = chunk.choices[0].delta.content
+                                    response_text += content
+                                    live.update(display_generating(response_text, time.time()))
+                    else:
+                        response_text = ""
+                        for chunk in litellm.completion(
+                            **self._build_completion_params(
+                                messages=messages,
+                                temperature=temperature,
+                                stream=True,
+                                **kwargs
+                            )
+                        ):
+                            if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                response_text += chunk.choices[0].delta.content
+                    
+                    response_text = response_text.strip()
+                    continue
+
+                except json.JSONDecodeError:
+                    reflection_count += 1
+                    if reflection_count >= max_reflect:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
+                    continue
                 except Exception as e:
                     display_error(f"Error in LLM response: {str(e)}")
                     return None
+            
+            # If we've exhausted reflection attempts
+            if verbose:
+                display_interaction(prompt, response_text, markdown=markdown,
+                                 generation_time=time.time() - start_time, console=console)
+            return response_text
 
         except Exception as error:
             display_error(f"Error in get_response: {str(error)}")

src/praisonai-agents/praisonaiagents/memory/memory.py

@@ -29,6 +29,12 @@ try:
 except ImportError:
     OPENAI_AVAILABLE = False
 
+try:
+    import litellm
+    LITELLM_AVAILABLE = True
+except ImportError:
+    LITELLM_AVAILABLE = False
+
 
 
 
@@ -340,14 +346,28 @@ class Memory:
             
         elif self.use_rag and hasattr(self, "chroma_col"):
             try:
-                from openai import OpenAI
-                client = OpenAI()
-                
-                response = client.embeddings.create(
-                    input=query,
-                    model="text-embedding-3-small"
-                )
-                query_embedding = response.data[0].embedding
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=query
+                    )
+                    query_embedding = response.data[0]["embedding"]
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI()
+                    
+                    response = client.embeddings.create(
+                        input=query,
+                        model="text-embedding-3-small"
+                    )
+                    query_embedding = response.data[0].embedding
+                else:
+                    self._log_verbose("Neither litellm nor openai available for embeddings", logging.WARNING)
+                    return []
                 
                 resp = self.chroma_col.query(
                     query_embeddings=[query_embedding],
@@ -464,19 +484,39 @@ class Memory:
         # Store in vector database if enabled
         if self.use_rag and hasattr(self, "chroma_col"):
             try:
-                from openai import OpenAI
-                client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Ensure API key is correctly set
-                
-                logger.info("Getting embeddings from OpenAI...")
-                logger.debug(f"Embedding input text: {text}")  # Log the input text
-                
-                response = client.embeddings.create(
-                    input=text,
-                    model="text-embedding-3-small"
-                )
-                embedding = response.data[0].embedding
-                logger.info("Successfully got embeddings")
-                logger.debug(f"Received embedding of length: {len(embedding)}")  # Log embedding details
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    logger.info("Getting embeddings from LiteLLM...")
+                    logger.debug(f"Embedding input text: {text}")
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=text
+                    )
+                    embedding = response.data[0]["embedding"]
+                    logger.info("Successfully got embeddings from LiteLLM")
+                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                    
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+                    
+                    logger.info("Getting embeddings from OpenAI...")
+                    logger.debug(f"Embedding input text: {text}")
+                    
+                    response = client.embeddings.create(
+                        input=text,
+                        model="text-embedding-3-small"
+                    )
+                    embedding = response.data[0].embedding
+                    logger.info("Successfully got embeddings from OpenAI")
+                    logger.debug(f"Received embedding of length: {len(embedding)}")
+                else:
+                    logger.warning("Neither litellm nor openai available for embeddings")
+                    return
                 
                 # Sanitize metadata for ChromaDB
                 sanitized_metadata = self._sanitize_metadata(metadata)
@@ -527,15 +567,28 @@ class Memory:
 
         elif self.use_rag and hasattr(self, "chroma_col"):
             try:
-                from openai import OpenAI
-                client = OpenAI()
-                
-                # Get query embedding
-                response = client.embeddings.create(
-                    input=query,
-                    model="text-embedding-3-small"  # Using consistent model
-                )
-                query_embedding = response.data[0].embedding
+                if LITELLM_AVAILABLE:
+                    # Use LiteLLM for consistency with the rest of the codebase
+                    import litellm
+                    
+                    response = litellm.embedding(
+                        model="text-embedding-3-small",
+                        input=query
+                    )
+                    query_embedding = response.data[0]["embedding"]
+                elif OPENAI_AVAILABLE:
+                    # Fallback to OpenAI client
+                    from openai import OpenAI
+                    client = OpenAI()
+                    
+                    response = client.embeddings.create(
+                        input=query,
+                        model="text-embedding-3-small"
+                    )
+                    query_embedding = response.data[0].embedding
+                else:
+                    self._log_verbose("Neither litellm nor openai available for embeddings", logging.WARNING)
+                    return []
                 
                 # Search ChromaDB with embedding
                 resp = self.chroma_col.query(
@@ -910,18 +963,44 @@ class Memory:
         """
 
         try:
-            # Use OpenAI client from main.py
-            from ..main import client
-            
-            response = client.chat.completions.create(
-                model=llm or "gpt-4o",
-                messages=[{
-                    "role": "user", 
-                    "content": custom_prompt or default_prompt
-                }],
-                response_format={"type": "json_object"},
-                temperature=0.3
-            )
+            if LITELLM_AVAILABLE:
+                # Use LiteLLM for consistency with the rest of the codebase
+                import litellm
+                
+                # Convert model name if it's in litellm format
+                model_name = llm or "gpt-4o-mini"
+                
+                response = litellm.completion(
+                    model=model_name,
+                    messages=[{
+                        "role": "user", 
+                        "content": custom_prompt or default_prompt
+                    }],
+                    response_format={"type": "json_object"},
+                    temperature=0.3
+                )
+            elif OPENAI_AVAILABLE:
+                # Fallback to OpenAI client
+                from openai import OpenAI
+                client = OpenAI()
+                
+                response = client.chat.completions.create(
+                    model=llm or "gpt-4o-mini",
+                    messages=[{
+                        "role": "user", 
+                        "content": custom_prompt or default_prompt
+                    }],
+                    response_format={"type": "json_object"},
+                    temperature=0.3
+                )
+            else:
+                logger.error("Neither litellm nor openai available for quality calculation")
+                return {
+                    "completeness": 0.0,
+                    "relevance": 0.0,
+                    "clarity": 0.0,
+                    "accuracy": 0.0
+                }
             
             metrics = json.loads(response.choices[0].message.content)
             

src/praisonai-agents/praisonaiagents/task/task.py

@@ -1,6 +1,7 @@
 import logging
 import asyncio
-from typing import List, Optional, Dict, Any, Type, Callable, Union, Coroutine, Literal
+import inspect
+from typing import List, Optional, Dict, Any, Type, Callable, Union, Coroutine, Literal, Tuple, get_args, get_origin
 from pydantic import BaseModel
 from ..main import TaskOutput
 from ..agent.agent import Agent
@@ -40,7 +41,10 @@ class Task:
         quality_check=True,
         input_file: Optional[str] = None,
         rerun: bool = False, # Renamed from can_rerun and logic inverted, default True for backward compatibility
-        retain_full_context: bool = False # By default, only use previous task output, not all previous tasks
+        retain_full_context: bool = False, # By default, only use previous task output, not all previous tasks
+        guardrail: Optional[Union[Callable[[TaskOutput], Tuple[bool, Any]], str]] = None,
+        max_retries: int = 3,
+        retry_count: int = 0
     ):
         # Add check if memory config is provided
         if memory is not None or (config and config.get('memory_config')):
@@ -80,6 +84,10 @@ class Task:
         self.quality_check = quality_check
         self.rerun = rerun # Assigning the rerun parameter
         self.retain_full_context = retain_full_context
+        self.guardrail = guardrail
+        self.max_retries = max_retries
+        self.retry_count = retry_count
+        self._guardrail_fn = None
 
         # Set logger level based on config verbose level
         verbose = self.config.get("verbose", 0)
@@ -141,6 +149,55 @@ class Task:
 
             self.output_pydantic = LoopModel
 
+        # Initialize guardrail
+        self._setup_guardrail()
+
+    def _setup_guardrail(self):
+        """Setup the guardrail function based on the provided guardrail parameter."""
+        if self.guardrail is None:
+            self._guardrail_fn = None
+            return
+            
+        if callable(self.guardrail):
+            # Validate function signature
+            sig = inspect.signature(self.guardrail)
+            positional_args = [
+                param for param in sig.parameters.values()
+                if param.default is inspect.Parameter.empty
+            ]
+            if len(positional_args) != 1:
+                raise ValueError("Guardrail function must accept exactly one parameter (TaskOutput)")
+            
+            # Check return annotation if present
+            return_annotation = sig.return_annotation
+            if return_annotation != inspect.Signature.empty:
+                return_annotation_args = get_args(return_annotation)
+                if not (
+                    get_origin(return_annotation) is tuple
+                    and len(return_annotation_args) == 2
+                    and return_annotation_args[0] is bool
+                    and (
+                        return_annotation_args[1] is Any
+                        or return_annotation_args[1] is str
+                        or return_annotation_args[1] is TaskOutput
+                        or return_annotation_args[1] == Union[str, TaskOutput]
+                    )
+                ):
+                    raise ValueError(
+                        "If return type is annotated, it must be Tuple[bool, Any]"
+                    )
+            
+            self._guardrail_fn = self.guardrail
+        elif isinstance(self.guardrail, str):
+            # Create LLM-based guardrail
+            from ..guardrails import LLMGuardrail
+            if not self.agent:
+                raise ValueError("Agent is required for string-based guardrails")
+            llm = getattr(self.agent, 'llm', None) or getattr(self.agent, 'llm_instance', None)
+            self._guardrail_fn = LLMGuardrail(description=self.guardrail, llm=llm)
+        else:
+            raise ValueError("Guardrail must be either a callable or a string description")
+
     def __str__(self):
         return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')"
 
@@ -187,6 +244,37 @@ class Task:
         logger.info(f"Task {self.id}: execute_callback called")
         logger.info(f"Quality check enabled: {self.quality_check}")
 
+        # Process guardrail if configured
+        if self._guardrail_fn:
+            try:
+                guardrail_result = self._process_guardrail(task_output)
+                if not guardrail_result.success:
+                    if self.retry_count >= self.max_retries:
+                        raise Exception(
+                            f"Task failed guardrail validation after {self.max_retries} retries. "
+                            f"Last error: {guardrail_result.error}"
+                        )
+                    
+                    self.retry_count += 1
+                    logger.warning(f"Task {self.id}: Guardrail validation failed (retry {self.retry_count}/{self.max_retries}): {guardrail_result.error}")
+                    # Note: In a real execution, this would trigger a retry, but since this is a callback
+                    # the retry logic would need to be handled at the agent/execution level
+                    return
+                
+                # If guardrail passed and returned a modified result
+                if guardrail_result.result is not None:
+                    if isinstance(guardrail_result.result, str):
+                        # Update the task output with the modified result
+                        task_output.raw = guardrail_result.result
+                    elif isinstance(guardrail_result.result, TaskOutput):
+                        # Replace with the new task output
+                        task_output = guardrail_result.result
+                
+                logger.info(f"Task {self.id}: Guardrail validation passed")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Error in guardrail processing: {e}")
+                # Continue execution even if guardrail fails to avoid breaking the task
+
         # Initialize memory if not already initialized
         if not self.memory:
             self.memory = self.initialize_memory()
@@ -220,7 +308,11 @@ class Task:
                 if self.agent:
                     if getattr(self.agent, '_using_custom_llm', False) and hasattr(self.agent, 'llm_instance'):
                         # For custom LLM instances (like Ollama)
-                        llm_model = self.agent.llm_instance
+                        # Extract the model name from the LLM instance
+                        if hasattr(self.agent.llm_instance, 'model'):
+                            llm_model = self.agent.llm_instance.model
+                        else:
+                            llm_model = "gpt-4o-mini"  # Default fallback
                     elif hasattr(self.agent, 'llm') and self.agent.llm:
                         # For standard model strings
                         llm_model = self.agent.llm
@@ -334,4 +426,34 @@ Context:
                 loop.run_until_complete(self.execute_callback(task_output))
         except RuntimeError:
             # If no loop is running in this context
-            asyncio.run(self.execute_callback(task_output))
+            asyncio.run(self.execute_callback(task_output))
+
+    def _process_guardrail(self, task_output: TaskOutput):
+        """Process the guardrail validation for a task output.
+        
+        Args:
+            task_output: The task output to validate
+            
+        Returns:
+            GuardrailResult: The result of the guardrail validation
+        """
+        from ..guardrails import GuardrailResult
+        
+        if not self._guardrail_fn:
+            return GuardrailResult(success=True, result=task_output)
+        
+        try:
+            # Call the guardrail function
+            result = self._guardrail_fn(task_output)
+            
+            # Convert the result to a GuardrailResult
+            return GuardrailResult.from_tuple(result)
+            
+        except Exception as e:
+            logger.error(f"Task {self.id}: Error in guardrail validation: {e}")
+            # On error, return failure
+            return GuardrailResult(
+                success=False,
+                result=None,
+                error=f"Guardrail validation error: {str(e)}"
+            )

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.97"
+version = "0.0.102"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [
@@ -25,7 +25,8 @@ mcp = [
 ]
 
 memory = [
-    "chromadb>=1.0.0"
+    "chromadb>=1.0.0",
+    "litellm>=1.50.0",
 ]
 
 knowledge = [
@@ -63,5 +64,6 @@ all = [
     "praisonaiagents[api]"
 ]
 
-[tool.setuptools]
-packages = ["praisonaiagents"]
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["praisonaiagents*"]

src/praisonai-agents/sequence.py

@@ -0,0 +1,11 @@
+from praisonaiagents import Agent, MCP
+import os
+
+sequential_agent = Agent(
+    instructions="""You are a helpful assistant that can break down complex problems.
+    Use the available tools when relevant to perform step-by-step analysis.""",
+    llm="gpt-4o-mini",
+    tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+)
+
+sequential_agent.start("Break down the process of making a cup of tea")

src/praisonai-agents/sequence_llm.py

@@ -0,0 +1,11 @@
+from praisonaiagents import Agent, MCP
+import os
+
+sequential_agent = Agent(
+    instructions="""You are a helpful assistant that can break down complex problems.
+    Use the available tools when relevant to perform step-by-step analysis.""",
+    llm="openai/gpt-4o-mini",
+    tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+)
+
+sequential_agent.start("Break down the process of making a cup of tea")

src/praisonai-agents/simple_guardrail_test.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""
+Simple test for guardrails functionality without full dependencies.
+"""
+
+import sys
+import os
+from typing import Tuple, Any
+
+# Import only what we need for testing
+from praisonaiagents.guardrails import GuardrailResult, LLMGuardrail
+from praisonaiagents.main import TaskOutput
+
+
+def test_guardrail_result():
+    """Test GuardrailResult helper methods."""
+    print("Testing GuardrailResult...")
+    
+    # Test success case
+    success_result = GuardrailResult.from_tuple((True, "Modified output"))
+    assert success_result.success
+    assert success_result.result == "Modified output"
+    assert success_result.error == ""
+    print("✓ Success result created correctly")
+    
+    # Test failure case
+    failure_result = GuardrailResult.from_tuple((False, "Validation failed"))
+    assert not failure_result.success
+    assert failure_result.result is None
+    assert failure_result.error == "Validation failed"
+    print("✓ Failure result created correctly")
+    
+    print("GuardrailResult test passed!\n")
+
+
+def test_function_guardrail():
+    """Test function-based guardrail logic."""
+    print("Testing function-based guardrail logic...")
+    
+    def validate_output(task_output: TaskOutput) -> Tuple[bool, Any]:
+        """Simple validation function."""
+        if "error" in task_output.raw.lower():
+            return False, "Output contains errors"
+        if len(task_output.raw) < 10:
+            return False, "Output is too short"
+        return True, task_output
+    
+    # Test with good output
+    good_output = TaskOutput(
+        description="Test task",
+        raw="Hello! This is a friendly greeting message from the agent.",
+        agent="Test Agent"
+    )
+    
+    result = validate_output(good_output)
+    guardrail_result = GuardrailResult.from_tuple(result)
+    assert guardrail_result.success, f"Good output should pass: {guardrail_result.error}"
+    print("✓ Good output passed function guardrail")
+    
+    # Test with bad output
+    bad_output = TaskOutput(
+        description="Test task", 
+        raw="Error occurred",
+        agent="Test Agent"
+    )
+    
+    result = validate_output(bad_output)
+    guardrail_result = GuardrailResult.from_tuple(result)
+    assert not guardrail_result.success, "Bad output should fail guardrail"
+    print("✓ Bad output failed function guardrail as expected")
+    
+    print("Function-based guardrail logic test passed!\n")
+
+
+def test_llm_guardrail():
+    """Test LLM guardrail logic."""
+    print("Testing LLM guardrail logic...")
+    
+    # Mock LLM for testing that correctly parses the validation prompt
+    class MockLLM:
+        def chat(self, prompt, **kwargs):
+            # Extract the actual output to validate from the prompt
+            # The LLMGuardrail sends a structured prompt with "Output to Validate:" section
+            if "Output to Validate:" in prompt:
+                # Split by "Output to Validate:" and get the content after it
+                parts = prompt.split("Output to Validate:")
+                if len(parts) > 1:
+                    output_content = parts[1].strip()
+                    # Check only the output content, not the validation criteria
+                    if "error" in output_content.lower():
+                        return "FAIL: The output contains error messages"
+                    return "PASS"
+            
+            # Fallback: if no "Output to Validate:" section, return pass
+            return "PASS"
+    
+    # Create LLM guardrail
+    mock_llm = MockLLM()
+    llm_guardrail = LLMGuardrail(
+        description="Check if the output is professional and does not contain errors",
+        llm=mock_llm
+    )
+    
+    # Test with good output
+    good_output = TaskOutput(
+        description="Test task",
+        raw="Hello! This is a professional greeting message.",
+        agent="Test Agent"
+    )
+    
+    result = llm_guardrail(good_output)
+    guardrail_result = GuardrailResult.from_tuple(result)
+    assert guardrail_result.success, f"Good output should pass: {guardrail_result.error}"
+    print("✓ Good output passed LLM guardrail")
+    
+    # Test with bad output  
+    bad_output = TaskOutput(
+        description="Test task",
+        raw="There was an error in the system",
+        agent="Test Agent"
+    )
+    
+    result = llm_guardrail(bad_output)
+    guardrail_result = GuardrailResult.from_tuple(result)
+    assert not guardrail_result.success, "Bad output should fail LLM guardrail"
+    print("✓ Bad output failed LLM guardrail as expected")
+    
+    print("LLM guardrail logic test passed!\n")
+
+
+def main():
+    """Run all tests."""
+    print("Running Simple Guardrails Tests...\n")
+    
+    try:
+        test_guardrail_result()
+        test_function_guardrail()
+        test_llm_guardrail()
+        
+        print("🎉 All simple guardrail tests passed!")
+        print("\nGuardrails core logic is working correctly!")
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+    
+    return True
+
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)

src/praisonai-agents/test.py

@@ -0,0 +1,33 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+llm_config = {
+    "model": "openai/gpt-4o-mini",
+    "api_key": os.getenv('OPENAI_API_KEY'),
+    "temperature": 0.7,
+    "max_tokens": 2000
+}
+
+blog_agent = Agent(
+    role="Blog Writer",
+    goal="Write a blog post about AI",
+    backstory="Expert at writing blog posts",
+    llm="gpt-4o-mini",
+)
+
+blog_task = Task(
+    description="Write a blog post about AI trends in 1 paragraph",
+    expected_output="Well-written blog post about AI trends",
+    agent=blog_agent
+)
+
+agents = PraisonAIAgents(
+    agents=[blog_agent],
+    tasks=[blog_task],
+    memory=True
+)
+
+result = agents.start()

src/praisonai-agents/test_agent_guardrails.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python3
+"""
+Simple test for Agent guardrail functionality.
+"""
+
+def test_agent_guardrails():
+    """Test basic Agent guardrail functionality."""
+    print("Testing Agent guardrail functionality...")
+    
+    try:
+        from praisonaiagents import Agent, TaskOutput
+        print("✓ Basic imports successful")
+        
+        # Test function-based guardrail
+        def test_guardrail(task_output: TaskOutput):
+            if len(task_output.raw) < 10:
+                return False, "Too short"
+            return True, task_output
+        
+        # Test Agent creation with function guardrail
+        agent1 = Agent(
+            name="TestAgent1",
+            instructions="You are a test agent",
+            guardrail=test_guardrail,
+            max_guardrail_retries=2
+        )
+        print("✓ Agent with function guardrail created successfully")
+        print(f"  - Agent name: {agent1.name}")
+        print(f"  - Has guardrail function: {agent1._guardrail_fn is not None}")
+        print(f"  - Max retries: {agent1.max_guardrail_retries}")
+        
+        # Test Agent creation with string guardrail
+        agent2 = Agent(
+            name="TestAgent2",
+            instructions="You are a test agent",
+            guardrail="Ensure the response is polite and professional",
+            max_guardrail_retries=3
+        )
+        print("✓ Agent with LLM guardrail created successfully")
+        print(f"  - Agent name: {agent2.name}")
+        print(f"  - Has guardrail function: {agent2._guardrail_fn is not None}")
+        print(f"  - Max retries: {agent2.max_guardrail_retries}")
+        
+        # Test Agent without guardrail
+        agent3 = Agent(
+            name="TestAgent3",
+            instructions="You are a test agent"
+        )
+        print("✓ Agent without guardrail created successfully")
+        print(f"  - Agent name: {agent3.name}")
+        print(f"  - Has guardrail function: {agent3._guardrail_fn is None}")
+        
+        print("\n🎉 All Agent guardrail tests passed!")
+        return True
+        
+    except Exception as e:
+        print(f"✗ Error: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+if __name__ == "__main__":
+    test_agent_guardrails()

src/praisonai-agents/test_both_formats.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+
+from praisonaiagents import Agent, MCP
+import os
+
+def test_agent_direct():
+    """Test using gpt-4o-mini directly (agent.py path)"""
+    print("="*50)
+    print("Testing gpt-4o-mini (agent.py path)")
+    print("="*50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 5 steps to make coffee?")
+    print("✅ Agent direct path completed successfully")
+    return result
+
+def test_llm_class():
+    """Test using openai/gpt-4o-mini (llm.py path)"""
+    print("\n" + "="*50)
+    print("Testing openai/gpt-4o-mini (llm.py path)")
+    print("="*50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="openai/gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 5 steps to make coffee?")
+    print("✅ LLM class path completed successfully")
+    return result
+
+if __name__ == "__main__":
+    try:
+        # Test both approaches
+        result1 = test_agent_direct()
+        result2 = test_llm_class()
+        
+        print("\n" + "="*50)
+        print("SUMMARY")
+        print("="*50)
+        print("✅ Both formats work correctly!")
+        print("✅ gpt-4o-mini uses agent.py direct OpenAI calls")
+        print("✅ openai/gpt-4o-mini uses llm.py LiteLLM wrapper")
+        print("✅ Both support tool calling and MCP integration")
+        
+    except Exception as e:
+        print(f"❌ Error occurred: {e}")
+        import traceback
+        traceback.print_exc()

src/praisonai-agents/test_guardrails.py

@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+"""
+Test script for guardrails functionality in PraisonAI Agents.
+"""
+
+import os
+import sys
+import logging
+
+from praisonaiagents import Agent, Task, TaskOutput
+from praisonaiagents.guardrails import GuardrailResult, LLMGuardrail
+from typing import Tuple, Any
+
+
+def test_function_guardrail():
+    """Test function-based guardrail."""
+    print("Testing function-based guardrail...")
+    
+    def validate_output(task_output: TaskOutput) -> Tuple[bool, Any]:
+        """Simple validation function."""
+        if "error" in task_output.raw.lower():
+            return False, "Output contains errors"
+        if len(task_output.raw) < 10:
+            return False, "Output is too short"
+        return True, task_output
+    
+    # Create agent and task with guardrail
+    agent = Agent(
+        name="Test Agent",
+        role="Tester", 
+        goal="Test guardrails",
+        backstory="I am testing the guardrail functionality"
+    )
+    
+    task = Task(
+        description="Write a simple hello message",
+        expected_output="A friendly greeting message",
+        agent=agent,
+        guardrail=validate_output,
+        max_retries=2
+    )
+    
+    # Test with good output
+    good_output = TaskOutput(
+        description="Test task",
+        raw="Hello! This is a friendly greeting message from the agent.",
+        agent="Test Agent"
+    )
+    
+    result = task._process_guardrail(good_output)
+    assert result.success, f"Good output should pass: {result.error}"
+    print("✓ Good output passed guardrail")
+    
+    # Test with bad output
+    bad_output = TaskOutput(
+        description="Test task", 
+        raw="Error occurred",
+        agent="Test Agent"
+    )
+    
+    result = task._process_guardrail(bad_output)
+    assert not result.success, "Bad output should fail guardrail"
+    print("✓ Bad output failed guardrail as expected")
+    
+    print("Function-based guardrail test passed!\n")
+
+
+def test_string_guardrail():
+    """Test string-based LLM guardrail."""
+    print("Testing string-based LLM guardrail...")
+    
+    # Mock LLM for testing
+    class MockLLM:
+        def chat(self, prompt, **kwargs):
+            # Extract the actual output to validate from the prompt
+            # The LLMGuardrail sends a structured prompt with "Output to Validate:" section
+            if "Output to Validate:" in prompt:
+                # Split by "Output to Validate:" and get the content after it
+                parts = prompt.split("Output to Validate:")
+                if len(parts) > 1:
+                    output_content = parts[1].strip()
+                    # Check only the output content, not the validation criteria
+                    if "error" in output_content.lower():
+                        return "FAIL: The output contains error messages"
+                    return "PASS"
+            
+            # Fallback: if no "Output to Validate:" section, check the prompt directly
+            # This should rarely happen with proper LLMGuardrail usage
+            if "error" in prompt.lower() and "check if" not in prompt.lower():
+                return "FAIL: The output contains error messages"
+            return "PASS"
+    
+    # Create agent with mock LLM
+    agent = Agent(
+        name="Test Agent",
+        role="Tester",
+        goal="Test guardrails", 
+        backstory="I am testing the guardrail functionality"
+    )
+    agent.llm = MockLLM()
+    
+    task = Task(
+        description="Write a simple hello message",
+        expected_output="A friendly greeting message",
+        agent=agent,
+        guardrail="Check if the output is professional and does not contain errors",
+        max_retries=2
+    )
+    
+    # Test with good output
+    good_output = TaskOutput(
+        description="Test task",
+        raw="Hello! This is a professional greeting message.",
+        agent="Test Agent"
+    )
+    
+    result = task._process_guardrail(good_output)
+    assert result.success, f"Good output should pass: {result.error}"
+    print("✓ Good output passed LLM guardrail")
+    
+    # Test with bad output  
+    bad_output = TaskOutput(
+        description="Test task",
+        raw="There was an error in the system",
+        agent="Test Agent"
+    )
+    
+    result = task._process_guardrail(bad_output)
+    assert not result.success, "Bad output should fail LLM guardrail"
+    print("✓ Bad output failed LLM guardrail as expected")
+    
+    print("String-based LLM guardrail test passed!\n")
+
+
+def test_guardrail_result():
+    """Test GuardrailResult helper methods."""
+    print("Testing GuardrailResult...")
+    
+    # Test success case
+    success_result = GuardrailResult.from_tuple((True, "Modified output"))
+    assert success_result.success
+    assert success_result.result == "Modified output"
+    assert success_result.error == ""
+    print("✓ Success result created correctly")
+    
+    # Test failure case
+    failure_result = GuardrailResult.from_tuple((False, "Validation failed"))
+    assert not failure_result.success
+    assert failure_result.result is None
+    assert failure_result.error == "Validation failed"
+    print("✓ Failure result created correctly")
+    
+    print("GuardrailResult test passed!\n")
+
+
+def main():
+    """Run all tests."""
+    print("Running PraisonAI Agents Guardrails Tests...\n")
+    
+    try:
+        test_guardrail_result()
+        test_function_guardrail()
+        test_string_guardrail()
+        
+        print("🎉 All guardrail tests passed!")
+        print("\nGuardrails implementation is working correctly!")
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+    
+    return True
+
+
+if __name__ == "__main__":
+    # Set up basic logging
+    logging.basicConfig(level=logging.WARNING)
+    
+    success = main()
+    sys.exit(0 if success else 1)

src/praisonai-agents/test_self_reflection.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+"""Test self-reflection functionality in both Agent and LLM classes"""
+
+from praisonaiagents import Agent
+from praisonaiagents.llm import LLM
+
+def test_agent_self_reflection():
+    """Test self-reflection in Agent class"""
+    print("=== Testing Agent Self-Reflection ===")
+    agent = Agent(
+        name="ReflectiveAgent",
+        instructions="You are a helpful assistant.",
+        llm="gpt-4o-mini",
+        self_reflect=True,
+        min_reflect=1,
+        max_reflect=3
+    )
+    
+    response = agent.start("What is 2+2? Be brief.")
+    print(f"Agent Response: {response}")
+    print()
+
+def test_llm_self_reflection():
+    """Test self-reflection in LLM class directly"""
+    print("=== Testing LLM Self-Reflection ===")
+    llm = LLM(model="gpt-4o-mini")
+    
+    response = llm.get_response(
+        prompt="What is 2+2? Be brief.",
+        system_prompt="You are a helpful assistant.",
+        self_reflect=True,
+        min_reflect=1,
+        max_reflect=3,
+        verbose=True
+    )
+    print(f"LLM Response: {response}")
+    print()
+
+def test_llm_no_reflection():
+    """Test LLM without self-reflection for comparison"""
+    print("=== Testing LLM Without Self-Reflection ===")
+    llm = LLM(model="gpt-4o-mini")
+    
+    response = llm.get_response(
+        prompt="What is 2+2? Be brief.",
+        system_prompt="You are a helpful assistant.",
+        self_reflect=False,
+        verbose=True
+    )
+    print(f"LLM Response: {response}")
+    print()
+
+if __name__ == "__main__":
+    # Test without reflection first
+    test_llm_no_reflection()
+    
+    # Test with reflection
+    test_llm_self_reflection()
+    
+    # Test agent reflection
+    test_agent_self_reflection()

src/praisonai-agents/test_sequential_fix.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+
+from praisonaiagents import Agent, MCP
+import os
+
+def test_agent_direct():
+    """Test using gpt-4o-mini directly (agent.py path)"""
+    print("=" * 50)
+    print("Testing gpt-4o-mini (agent.py path)")
+    print("=" * 50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 3 steps to make coffee?")
+    print("✅ Agent direct path completed successfully")
+    return result
+
+def test_llm_class():
+    """Test using openai/gpt-4o-mini (llm.py path)"""
+    print("\n" + "=" * 50)
+    print("Testing openai/gpt-4o-mini (llm.py path)")
+    print("=" * 50)
+    
+    agent = Agent(
+        instructions="You are a helpful assistant that can break down complex problems.",
+        llm="openai/gpt-4o-mini",
+        tools=MCP("npx -y @modelcontextprotocol/server-sequential-thinking")
+    )
+    
+    result = agent.start("What are 3 steps to make coffee?")
+    print("✅ LLM class path completed successfully")
+    return result
+
+if __name__ == "__main__":
+    try:
+        # Test both approaches
+        result1 = test_agent_direct()
+        result2 = test_llm_class()
+        
+        print("\n" + "=" * 50)
+        print("SUMMARY")
+        print("=" * 50)
+        print("✅ Both formats work correctly!")
+        print("✅ gpt-4o-mini uses agent.py direct OpenAI calls")
+        print("✅ openai/gpt-4o-mini uses llm.py LiteLLM wrapper")
+        print("✅ Both support sequential tool calling and MCP integration")
+        print("✅ Sequential thinking tool works properly in both modes")
+        
+    except Exception as e:
+        print(f"❌ Error occurred: {e}")
+        import traceback
+        traceback.print_exc()

src/praisonai-agents/tests/guardrails_example.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+"""
+Example demonstrating guardrails functionality in PraisonAI Agents.
+
+This example shows both function-based and LLM-based guardrails
+for validating task outputs.
+"""
+
+import sys
+import os
+from typing import Tuple, Any
+
+# Add project to path
+sys.path.insert(0, os.path.abspath('..'))
+
+from praisonaiagents import Agent, Task, TaskOutput
+
+
+def email_validator(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """
+    Function-based guardrail to validate email content.
+    
+    Args:
+        task_output: The task output to validate
+        
+    Returns:
+        Tuple of (success, result_or_error)
+    """
+    content = task_output.raw.lower()
+    
+    # Check for required email components
+    if "subject:" not in content:
+        return False, "Email must include a subject line"
+    
+    if "dear" not in content and "hello" not in content:
+        return False, "Email must include a proper greeting"
+    
+    if len(content) < 50:
+        return False, "Email content is too short"
+        
+    if "error" in content or "problem" in content:
+        return False, "Email should not mention errors or problems"
+        
+    return True, task_output
+
+
+def main():
+    """Run the guardrails example."""
+    print("PraisonAI Agents - Guardrails Example")
+    print("=====================================\n")
+    
+    # Create an agent
+    agent = Agent(
+        name="Email Assistant",
+        role="Professional Email Writer",
+        goal="Write clear, professional emails",
+        backstory="I am an AI assistant specialized in writing professional emails"
+    )
+    
+    print("1. Testing Function-based Guardrail")
+    print("------------------------------------")
+    
+    # Create task with function-based guardrail
+    task_with_function_guardrail = Task(
+        description="Write a professional email to a client about project completion",
+        expected_output="A well-formatted professional email",
+        agent=agent,
+        guardrail=email_validator,  # Function-based guardrail
+        max_retries=2
+    )
+    
+    print(f"Task created with function guardrail: {email_validator.__name__}")
+    print(f"Max retries: {task_with_function_guardrail.max_retries}")
+    
+    # Simulate a task output that should pass
+    good_output = TaskOutput(
+        description="Email task",
+        raw="""Subject: Project Completion Update
+
+Dear Client,
+
+I am pleased to inform you that your project has been completed successfully. 
+All deliverables have been reviewed and are ready for your review. 
+Please let me know if you have any questions.
+
+Best regards,
+Project Team""",
+        agent="Email Assistant"
+    )
+    
+    result = task_with_function_guardrail._process_guardrail(good_output)
+    print(f"Good email result: {'PASSED' if result.success else 'FAILED'}")
+    if not result.success:
+        print(f"Error: {result.error}")
+    
+    # Simulate a task output that should fail
+    bad_output = TaskOutput(
+        description="Email task",
+        raw="Hi there, there was an error with your project.",
+        agent="Email Assistant"
+    )
+    
+    result = task_with_function_guardrail._process_guardrail(bad_output)
+    print(f"Bad email result: {'PASSED' if result.success else 'FAILED'}")
+    if not result.success:
+        print(f"Error: {result.error}")
+    
+    print("\n2. Testing String-based LLM Guardrail")
+    print("-------------------------------------")
+    
+    # Create task with string-based guardrail
+    task_with_llm_guardrail = Task(
+        description="Write a marketing email for a new product launch",
+        expected_output="Engaging marketing content",
+        agent=agent,
+        guardrail="Ensure the content is professional, engaging, includes a clear call-to-action, and is free of errors",
+        max_retries=3
+    )
+    
+    print("Task created with LLM-based guardrail")
+    print("Guardrail description: 'Ensure the content is professional, engaging, includes a clear call-to-action, and is free of errors'")
+    print(f"Max retries: {task_with_llm_guardrail.max_retries}")
+    
+    print("\n3. Backward Compatibility")
+    print("-------------------------")
+    
+    # Create task without guardrail (backward compatible)
+    task_without_guardrail = Task(
+        description="Write a simple thank you note",
+        expected_output="A brief thank you message",
+        agent=agent
+    )
+    
+    print("Task created without guardrail (backward compatible)")
+    print(f"Guardrail function: {task_without_guardrail._guardrail_fn}")
+    
+    # Test that it doesn't break existing functionality
+    simple_output = TaskOutput(
+        description="Thank you task",
+        raw="Thank you for your business!",
+        agent="Email Assistant"
+    )
+    
+    result = task_without_guardrail._process_guardrail(simple_output)
+    print(f"No guardrail result: {'PASSED' if result.success else 'FAILED'}")
+    
+    print("\n✅ Guardrails example completed successfully!")
+    print("\nKey Features Demonstrated:")
+    print("- Function-based guardrails with custom validation logic")
+    print("- String-based LLM guardrails using natural language")
+    print("- Configurable retry mechanism")
+    print("- Backward compatibility with existing tasks")
+    print("- Integration with TaskOutput validation")
+
+
+if __name__ == "__main__":
+    main()

src/praisonai-agents/uv.lock

@@ -2398,7 +2398,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.97"
+version = "0.0.102"
 source = { editable = "." }
 dependencies = [
     { name = "mcp" },
@@ -2444,6 +2444,7 @@ mcp = [
 ]
 memory = [
     { name = "chromadb" },
+    { name = "litellm" },
 ]
 
 [package.metadata]
@@ -2455,6 +2456,7 @@ requires-dist = [
     { name = "fastapi", marker = "extra == 'api'", specifier = ">=0.115.0" },
     { name = "fastapi", marker = "extra == 'mcp'", specifier = ">=0.115.0" },
     { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.50.0" },
+    { name = "litellm", marker = "extra == 'memory'", specifier = ">=1.50.0" },
     { name = "markitdown", extras = ["all"], marker = "extra == 'knowledge'", specifier = ">=0.1.0" },
     { name = "mcp", specifier = ">=1.6.0" },
     { name = "mcp", marker = "extra == 'mcp'", specifier = ">=1.6.0" },

src/praisonai-ts/examples/README-tool-examples.md

@@ -191,3 +191,17 @@ const agent = new Agent({
 // Start the agent with a prompt that will trigger tool usage
 agent.start("What's the weather in Paris, the time in Tokyo, and what is 25 * 4?");
 ```
+
+## MCP SSE Tool Integration
+
+PraisonAI can use remote tools exposed via the Model Context Protocol over Server-Sent Events.
+
+1. Start the Python SSE server in another terminal:
+```bash
+python ../../praisonai-agents/tests/mcp-sse-direct-server.py --host 127.0.0.1 --port 8080
+```
+
+2. Run the TypeScript example which connects to this server:
+```bash
+npx ts-node examples/tools/mcp-sse.ts
+```

src/praisonai-ts/examples/tools/mcp-sse.ts

@@ -0,0 +1,70 @@
+import { Agent } from '../../src/agent';
+import { MCP } from '../../src/tools/mcpSse';
+
+async function main() {
+  // Connect to the running SSE server
+  const mcp = new MCP('http://127.0.0.1:8080/sse');
+  try {
+    await mcp.initialize();
+  } catch (error) {
+    console.error('Failed to connect to MCP SSE server:', error);
+    console.error('Please ensure the server is running at http://127.0.0.1:8080/sse');
+    process.exit(1);
+  }
+
+  // Create tool functions that call the remote MCP tools
+  const toolFunctions: Record<string, (...args: any[]) => Promise<any>> = {};
+  
+  if (mcp.tools.length === 0) {
+    console.warn('Warning: No MCP tools available. Make sure the MCP server is running.');
+  }
+  
+  for (const tool of mcp) {
+    if (!tool || typeof tool.name !== 'string') {
+      console.warn('Skipping invalid tool:', tool);
+      continue;
+    }
+    
+    const paramNames = Object.keys(tool.schemaProperties || {});
+    toolFunctions[tool.name] = async (...args: any[]) => {
+      const params: Record<string, any> = {};
+      
+      if (args.length === 1 && typeof args[0] === 'object' && args[0] !== null) {
+        // If single object argument, use it directly as params
+        Object.assign(params, args[0]);
+      } else {
+        // Map positional arguments with validation
+        if (args.length > paramNames.length) {
+          console.warn(
+            `Tool ${tool.name}: Too many arguments provided. Expected ${paramNames.length}, got ${args.length}`
+          );
+        }
+        for (let i = 0; i < Math.min(args.length, paramNames.length); i++) {
+          params[paramNames[i]] = args[i];
+        }
+      }
+      return tool.execute(params);
+    };
+  }
+
+  const agent = new Agent({
+    instructions: 'Use the tools to greet people and report the weather.',
+    name: 'MCPAgent',
+    tools: mcp.toOpenAITools(),
+    toolFunctions
+  });
+
+  try {
+    const result = await agent.start('Say hello to John and tell the weather in London.');
+    console.log('\nFinal Result:', result);
+  } catch (error) {
+    console.error('Agent execution failed:', error);
+  } finally {
+    // Clean up MCP connection
+    await mcp.close();
+  }
+}
+
+if (require.main === module) {
+  main();
+}

src/praisonai-ts/package.json

@@ -62,7 +62,8 @@
     "fast-xml-parser": "^4.5.1",
     "node-fetch": "^2.6.9",
     "openai": "^4.81.0",
-    "praisonai": "^1.0.19"
+    "praisonai": "^1.0.19",
+    "@modelcontextprotocol/sdk": "^1.12.1"
   },
   "optionalDependencies": {
     "boxen": "^7.1.1",

src/praisonai-ts/src/tools/index.ts

@@ -20,4 +20,5 @@ export class BaseTool implements Tool {
 }
 
 // Export all tool modules
-export * from './arxivTools';
+export * from './arxivTools';
+export * from './mcpSse';

src/praisonai-ts/src/tools/mcpSse.ts

@@ -0,0 +1,116 @@
+import { Client } from '@modelcontextprotocol/sdk/client/index.js';
+import { SSEClientTransport } from '@modelcontextprotocol/sdk/client/sse.js';
+import { BaseTool } from './index';
+
+export interface MCPToolInfo {
+  name: string;
+  description?: string;
+  inputSchema?: any;
+}
+
+export class MCPTool extends BaseTool {
+  private client: Client;
+  private inputSchema: any;
+
+  constructor(info: MCPToolInfo, client: Client) {
+    super(info.name, info.description || `Call the ${info.name} tool`);
+    this.client = client;
+    this.inputSchema = info.inputSchema || { type: 'object', properties: {}, required: [] };
+  }
+
+  get schemaProperties(): Record<string, any> | undefined {
+    return this.inputSchema?.properties;
+  }
+
+  async execute(args: any = {}): Promise<any> {
+    try {
+      const result: any = await this.client.callTool({ name: this.name, arguments: args });
+      if (result.structuredContent) {
+        return result.structuredContent;
+      }
+      if (Array.isArray(result.content) && result.content.length > 0) {
+        const item = result.content[0];
+        if (typeof item.text === 'string') return item.text;
+      }
+      return result;
+    } catch (error) {
+      throw new Error(`Failed to execute tool ${this.name}: ${error instanceof Error ? error.message : String(error)}`);
+    }
+  }
+
+  toOpenAITool() {
+    return {
+      type: 'function',
+      function: {
+        name: this.name,
+        description: this.description,
+        parameters: this.inputSchema
+      }
+    };
+  }
+}
+
+export class MCP implements Iterable<MCPTool> {
+  tools: MCPTool[] = [];
+  private client: Client | null = null;
+
+  constructor(private url: string, private debug = false) {
+    if (debug) {
+      console.log(`MCP client initialized for URL: ${url}`);
+    }
+  }
+
+  async initialize(): Promise<void> {
+    if (this.client) {
+      if (this.debug) console.log('MCP client already initialized');
+      return;
+    }
+    
+    try {
+      this.client = new Client({ name: 'praisonai-ts-mcp', version: '1.0.0' });
+      const transport = new SSEClientTransport(new URL(this.url));
+      await this.client.connect(transport);
+      const { tools } = await this.client.listTools();
+      this.tools = tools.map((t: any) => new MCPTool({
+        name: t.name,
+        description: t.description,
+        inputSchema: t.inputSchema
+      }, this.client as Client));
+      
+      if (this.debug) console.log(`Initialized MCP with ${this.tools.length} tools`);
+    } catch (error) {
+      if (this.client) {
+        await this.client.close().catch(() => {});
+        this.client = null;
+      }
+      throw new Error(`Failed to initialize MCP client: ${error instanceof Error ? error.message : 'Unknown error'}`);
+    }
+  }
+
+  [Symbol.iterator](): Iterator<MCPTool> {
+    return this.tools[Symbol.iterator]();
+  }
+
+  toOpenAITools() {
+    return this.tools.map(t => t.toOpenAITool());
+  }
+
+  async close(): Promise<void> {
+    if (this.client) {
+      try {
+        await this.client.close();
+      } catch (error) {
+        if (this.debug) {
+          console.warn('Error closing MCP client:', error);
+        }
+      } finally {
+        this.client = null;
+        this.tools = [];
+      }
+    }
+  }
+
+  get isConnected(): boolean {
+    return this.client !== null;
+  }
+}

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.26.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.26.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.30.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.30.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/deploy.py

@@ -56,7 +56,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.26 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.30 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.26"
+version = "2.2.30"
 description = "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."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.97",
+    "praisonaiagents>=0.0.102",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -95,7 +95,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.26"
+version = "2.2.30"
 description = "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."
 authors = ["Mervin Praison"]
 license = ""
@@ -113,7 +113,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.97"
+praisonaiagents = ">=0.0.102"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/tests/unit/test_comprehensive_import.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""
+Comprehensive test script to verify all import scenarios work correctly
+Tests the original failing import from the GitHub issue
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+def test_original_failing_import():
+    """Test the exact import that was failing in the GitHub issue"""
+    print("=== Testing Original Failing Import ===")
+    try:
+        from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+        print('✅ SUCCESS: from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_direct_import():
+    """Test direct Memory import"""
+    print("\n=== Testing Direct Memory Import ===")
+    try:
+        from praisonaiagents.memory import Memory
+        print('✅ SUCCESS: from praisonaiagents.memory import Memory')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_from_package_root():
+    """Test Memory import from package root"""
+    print("\n=== Testing Memory Import from Package Root ===")
+    try:
+        from praisonaiagents import Memory
+        print('✅ SUCCESS: from praisonaiagents import Memory')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_session_import():
+    """Test Session import which depends on Memory"""
+    print("\n=== Testing Session Import ===")
+    try:
+        from praisonaiagents.session import Session
+        print('✅ SUCCESS: from praisonaiagents.session import Session')
+        return True
+    except ImportError as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+    except Exception as e:
+        print(f'❌ UNEXPECTED ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_memory_instantiation():
+    """Test that Memory can be instantiated without errors"""
+    print("\n=== Testing Memory Instantiation ===")
+    try:
+        from praisonaiagents.memory import Memory
+        
+        # Test with minimal config (no external dependencies)
+        config = {"provider": "none"}
+        memory = Memory(config=config)
+        print('✅ SUCCESS: Memory instance created with provider="none"')
+        
+        # Test basic methods don't fail immediately
+        memory.store_short_term("test content", metadata={"test": True})
+        results = memory.search_short_term("test", limit=1)
+        print('✅ SUCCESS: Basic memory operations work')
+        
+        return True
+    except Exception as e:
+        print(f'❌ ERROR: {e}')
+        import traceback
+        traceback.print_exc()
+        return False
+
+def run_all_tests():
+    """Run all tests and report results"""
+    print("🔍 Running comprehensive import tests...")
+    
+    tests = [
+        test_original_failing_import,
+        test_memory_direct_import, 
+        test_memory_from_package_root,
+        test_session_import,
+        test_memory_instantiation
+    ]
+    
+    results = []
+    for test in tests:
+        results.append(test())
+    
+    print(f"\n📊 Test Results: {sum(results)}/{len(results)} tests passed")
+    
+    if all(results):
+        print("🎉 ALL TESTS PASSED! The Memory import issue has been resolved.")
+    else:
+        print("❌ Some tests failed. The issue may not be fully resolved.")
+    
+    return all(results)
+
+if __name__ == "__main__":
+    success = run_all_tests()
+    sys.exit(0 if success else 1)

src/praisonai/tests/unit/test_memory_import.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+"""
+Test script to verify Memory import works correctly
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+try:
+    from praisonaiagents.memory import Memory
+    print('SUCCESS: Memory import works correctly')
+    print('Memory class found:', Memory)
+    
+    # Try to create a minimal instance to ensure it doesn't fail immediately
+    config = {"provider": "none"}  # Use the simplest provider
+    memory = Memory(config=config)
+    print('SUCCESS: Memory instance created successfully')
+    
+except ImportError as e:
+    print('ERROR:', e)
+    import traceback
+    traceback.print_exc()
+except Exception as e:
+    print('UNEXPECTED ERROR:', e)
+    import traceback
+    traceback.print_exc()

src/praisonai/tests/unit/test_session_import.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+"""
+Test script to verify session.py import works correctly (which imports Memory)
+"""
+
+import sys
+import os
+
+# Add the praisonai-agents source to Python path
+sys.path.insert(0, '/home/runner/work/PraisonAI/PraisonAI/src/praisonai-agents')
+
+try:
+    from praisonaiagents.session import Session
+    print('SUCCESS: Session import works correctly')
+    print('Session class found:', Session)
+    
+    # This was the failing import chain
+    from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+    print('SUCCESS: Agent, Task, PraisonAIAgents import works correctly')
+    
+except ImportError as e:
+    print('ERROR:', e)
+    import traceback
+    traceback.print_exc()
+except Exception as e:
+    print('UNEXPECTED ERROR:', e)
+    import traceback
+    traceback.print_exc()