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AutoGen MCP Server

by DynamicEndpoints
GitHub
Python
MIT License
13
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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

Deploy Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

An MCP server that provides integration with Microsoft's AutoGen framework, enabling multi-agent conversations through a standardized interface.

  1. Features
    1. Installation
      1. Configuration
        1. Environment Variables
        2. Server Configuration
      2. Available Operations
        1. 1. Creating Agents
        2. 2. One-on-One Chat
        3. 3. Group Chat
      3. Error Handling
        1. Architecture
          1. License

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