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DynamicEndpoints

AutoGen MCP Server

by DynamicEndpoints
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Python
Hybrid

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.

šŸš€ Latest Features (v0.2.0)

āœØ 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

šŸ¤– 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)

šŸ”„ Sophisticated Workflows

  • Code Generation: Architect ā†’ Developer ā†’ Reviewer ā†’ Executor pipeline

  • Research Analysis: Researcher ā†’ Analyst ā†’ Critic ā†’ Synthesizer workflow

  • Creative Writing: Multi-stage creative collaboration

  • Problem Solving: Structured approach to complex problems

  • Code Review: Security ā†’ Performance ā†’ Style review teams

  • Custom Workflows: Build your own agent collaboration patterns

šŸŽÆ 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

Related MCP server: Stellastra MCP Server

šŸ› ļø 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

šŸ“ 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

šŸ“Š 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)

  • āœØ Enhanced MCP support with prompts and resources

  • šŸ¤– Advanced agent types (teachable, retrievable)

  • šŸ”„ Sophisticated workflows with quality checks

  • šŸŽÆ Smart speaker selection and nested conversations

  • šŸ“Š Real-time resource monitoring

  • šŸ§  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/ ā”œā”€ā”€ autogen_mcp/ ā”‚ ā”œā”€ā”€ __init__.py ā”‚ ā”œā”€ā”€ agents.py # Agent management and configuration ā”‚ ā”œā”€ā”€ config.py # Configuration handling and validation ā”‚ ā”œā”€ā”€ server.py # MCP server implementation ā”‚ ā””ā”€ā”€ workflows.py # Conversation workflow management

License

MIT License - See LICENSE file for details

Install Server
A
security ā€“ no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

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