Enhanced AutoGen MCP Server

A comprehensive MCP server that provides deep integration with Microsoft's AutoGen framework v0.9+, featuring the latest capabilities including prompts, resources, advanced workflows, and enhanced agent types. This server enables sophisticated multi-agent conversations through a standardized Model Context Protocol interface.

# 🚀 Latest Features (v0.2.0)

# # ✨ Enhanced MCP Support

• Prompts: Pre-built templates for common workflows (code review, research, creative writing)
• Resources: Real-time access to agent status, chat history, and configurations
• Dynamic Content: Template-based prompts with arguments and embedded resources
• Latest MCP SDK: Version 1.12.3 with full feature support

# # 🤖 Advanced Agent Types

• Assistant Agents: Enhanced with latest LLM capabilities
• Conversable Agents: Flexible conversation patterns
• Teachable Agents: Learning and memory persistence
• Retrievable Agents: Knowledge base integration
• Multimodal Agents: Image and document processing (when available)

# # 🔄 Sophisticated Workflows

• Code Generation: Architect → Developer → Reviewer → Executor pipeline
• Research Analysis: Researcher → Analyst → Critic → Synthesizer workflow
• Creative Writing: Multi-stage creative collaboration
• Problem Solving: Structured approach to complex problems
• Code Review: Security → Performance → Style review teams
• Custom Workflows: Build your own agent collaboration patterns

# # 🎯 Enhanced Chat Capabilities

• Smart Speaker Selection: Auto, manual, random, round-robin modes
• Nested Conversations: Hierarchical agent interactions
• Swarm Intelligence: Coordinated multi-agent problem solving
• Memory Management: Persistent agent knowledge and preferences
• Quality Checks: Built-in validation and improvement loops

# 🛠️ Available Tools

# # Core Agent Management

• create_agent - Create agents with advanced configurations
• create_workflow - Build complete multi-agent workflows
• get_agent_status - Detailed agent metrics and health monitoring

# # Conversation Execution

• execute_chat - Enhanced two-agent conversations
• execute_group_chat - Multi-agent group discussions
• execute_nested_chat - Hierarchical conversation structures
• execute_swarm - Swarm-based collaborative problem solving

# # Workflow Orchestration

• execute_workflow - Run predefined workflow templates
• manage_agent_memory - Handle agent learning and persistence
• configure_teachability - Enable/configure agent learning capabilities

# 📝 Available Prompts

# # autogen-workflow

Create sophisticated multi-agent workflows with customizable parameters:

• Arguments: task_description, agent_count, workflow_type
• Use case: Rapid workflow prototyping and deployment

# # code-review

Set up collaborative code review with specialized agents:

• Arguments: code, language, focus_areas
• Use case: Comprehensive code quality assessment

# # research-analysis

Deploy research teams for in-depth topic analysis:

• Arguments: topic, depth
• Use case: Academic research, market analysis, technical investigation

# 📊 Available Resources

# # autogen://agents/list

Live list of active agents with status and capabilities

# # autogen://workflows/templates

Available workflow templates and configurations

# # autogen://chat/history

Recent conversation history and interaction logs

# # autogen://config/current

Current server configuration and settings

# Installation

# # Installing via Smithery

To install AutoGen Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @DynamicEndpoints/autogen_mcp - -client claude

# # Manual Installation

1. Clone the repository:

git clone https://github.com/yourusername/autogen-mcp.git
cd autogen-mcp

1. Install Node.js dependencies:

npm install

1. Install Python dependencies:

pip install -r requirements.txt - -user

1. Build the TypeScript project:

npm run build

1. Set up configuration:

cp .env.example .env
cp config.json.example config.json
#  Edit .env and config.json with your settings

# Configuration

# # Environment Variables

Create a .env file from the template:

#  Required
OPENAI_API_KEY=your-openai-api-key-here

#  Optional - Path to configuration file
AUTOGEN_MCP_CONFIG=config.json

#  Enhanced Features
ENABLE_PROMPTS=true
ENABLE_RESOURCES=true
ENABLE_WORKFLOWS=true
ENABLE_TEACHABILITY=true

#  Performance Settings
MAX_CHAT_TURNS=10
DEFAULT_OUTPUT_FORMAT=json

# # Configuration File

Update config.json with your preferences:

{
  "llm_config": {
    "config_list": [
      {
        "model": "gpt-4o",
        "api_key": "your-openai-api-key"
      }
    ],
    "temperature": 0.7
  },
  "enhanced_features": {
    "prompts": { "enabled": true },
    "resources": { "enabled": true },
    "workflows": { "enabled": true }
  }
}

# Usage Examples

# # Using with Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "autogen": {
      "command": "node",
      "args": ["path/to/autogen-mcp/build/index.js"],
      "env": {
        "OPENAI_API_KEY": "your-key-here"
      }
    }
  }
}

# # Command Line Testing

Test the server functionality:

#  Run comprehensive tests
python test_server.py

#  Test CLI interface
python cli_example.py create_agent "researcher" "assistant" "You are a research specialist"
python cli_example.py execute_workflow "code_generation" '{"task":"Hello world","language":"python"}'

# # Using Prompts

The server provides several built-in prompts:

1. autogen-workflow - Create multi-agent workflows
2. code-review - Set up collaborative code review
3. research-analysis - Deploy research teams

# # Accessing Resources

Available resources provide real-time data:

• autogen://agents/list - Current active agents
• autogen://workflows/templates - Available workflow templates
• autogen://chat/history - Recent conversation history
• autogen://config/current - Server configuration

# Workflow Examples

# # Code Generation Workflow

{
  "workflow_name": "code_generation",
  "input_data": {
    "task": "Create a REST API endpoint",
    "language": "python",
    "requirements": ["FastAPI", "Pydantic", "Error handling"]
  },
  "quality_checks": true
}

# # Research Workflow

{
  "workflow_name": "research", 
  "input_data": {
    "topic": "AI Ethics in 2025",
    "depth": "comprehensive"
  },
  "output_format": "markdown"
}

# Advanced Features

# # Agent Types

• Assistant Agents: LLM-powered conversational agents
• User Proxy Agents: Code execution and human interaction
• Conversable Agents: Flexible conversation patterns
• Teachable Agents: Learning and memory persistence (when available)
• Retrievable Agents: Knowledge base integration (when available)

# # Chat Modes

• Two-Agent Chat: Direct conversation between agents
• Group Chat: Multi-agent discussions with smart speaker selection
• Nested Chat: Hierarchical conversation structures
• Swarm Intelligence: Coordinated problem solving (experimental)

# # Memory Management

• Persistent agent memory across sessions
• Conversation history tracking
• Learning from interactions (teachable agents)
• Memory cleanup and optimization

# Troubleshooting

# # Common Issues

1. API Key Errors: Ensure your OpenAI API key is valid and has sufficient credits
2. Import Errors: Install all dependencies with pip install -r requirements.txt - -user
3. Build Failures: Check Node.js version (>= 18) and run npm install
4. Chat Failures: Verify agent creation succeeded before attempting conversations

# # Debug Mode

Enable detailed logging:

export LOG_LEVEL=DEBUG
python test_server.py

# # Performance Tips

• Use gpt-4o-mini for faster, cost-effective operations
• Enable caching for repeated operations
• Set appropriate timeout values for long-running workflows
• Use quality checks only when needed (increases execution time)

# Development

# # Running Tests

#  Full test suite
python test_server.py

#  Individual workflow tests  
python -c "
import asyncio
from src.autogen_mcp.workflows import WorkflowManager
wm = WorkflowManager()
print(asyncio.run(wm.execute_workflow('code_generation', {'task': 'test'})))
"

# # Building

npm run build
npm run lint

# # Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

# Version History

# # v0.2.0 (Latest)

• ✨ Enhanced MCP support with prompts and resources
• 🤖 Advanced agent types (teachable, retrievable)
• 🔄 Sophisticated workflows with quality checks
• 🎯 Smart speaker selection and nested conversations
• 📊 Real-time resource monitoring
• 🧠 Memory management and persistence

# # v0.1.0

• Basic AutoGen integration
• Simple agent creation and chat execution
• MCP tool interface

# Support

For issues and questions:

• Check the troubleshooting section above
• Review the test examples in test_server.py
• Open an issue on GitHub with detailed reproduction steps

# License

MIT License - see LICENSE file for details.

OpenAI API Key (optional, can also be set in config.json)

OPENAI_API_KEY=your-openai-api-key

# # #  Server Configuration

1. Copy `config.json.example` to `config.json`:
```bash
cp config.json.example config.json

1. Configure the server settings:

{
  "llm_config": {
    "config_list": [
      {
        "model": "gpt-4",
        "api_key": "your-openai-api-key"
      }
    ],
    "temperature": 0
  },
  "code_execution_config": {
    "work_dir": "workspace",
    "use_docker": false
  }
}

# Available Operations

The server supports three main operations:

# # 1. Creating Agents

{
  "name": "create_agent",
  "arguments": {
    "name": "tech_lead",
    "type": "assistant",
    "system_message": "You are a technical lead with expertise in software architecture and design patterns."
  }
}

# # 2. One-on-One Chat

{
  "name": "execute_chat",
  "arguments": {
    "initiator": "agent1",
    "responder": "agent2",
    "message": "Let's discuss the system architecture."
  }
}

# # 3. Group Chat

{
  "name": "execute_group_chat",
  "arguments": {
    "agents": ["agent1", "agent2", "agent3"],
    "message": "Let's review the proposed solution."
  }
}

# Error Handling

Common error scenarios include:

1. Agent Creation Errors

{
  "error": "Agent already exists"
}

1. Execution Errors

{
  "error": "Agent not found"
}

1. Configuration Errors

{
  "error": "AUTOGEN_MCP_CONFIG environment variable not set"
}

# Architecture

The server follows a modular architecture:

src/
├── autogen_mcp/
│   ├── __init__.py
│   ├── agents.py      #  Agent management and configuration
│   ├── config.py      #  Configuration handling and validation
│   ├── server.py      #  MCP server implementation
│   └── workflows.py   #  Conversation workflow management

# License

MIT License - See LICENSE file for details