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

Clone the repository:

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

Install Node.js dependencies:

npm install

Install Python dependencies:

pip install -r requirements.txt --user

Build the TypeScript project:

npm run build

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:

autogen-workflow - Create multi-agent workflows
code-review - Set up collaborative code review
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

API Key Errors: Ensure your OpenAI API key is valid and has sufficient credits
Import Errors: Install all dependencies with pip install -r requirements.txt --user
Build Failures: Check Node.js version (>= 18) and run npm install
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

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
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

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:

Agent Creation Errors

{
  "error": "Agent already exists"
}

Execution Errors

{
  "error": "Agent not found"
}

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

Enhanced AutoGen MCP Server

🚀 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

Clone the repository:

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

Install Node.js dependencies:

npm install

Install Python dependencies:

pip install -r requirements.txt --user

Build the TypeScript project:

npm run build

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:

autogen-workflow - Create multi-agent workflows
code-review - Set up collaborative code review
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

API Key Errors: Ensure your OpenAI API key is valid and has sufficient credits
Import Errors: Install all dependencies with pip install -r requirements.txt --user
Build Failures: Check Node.js version (>= 18) and run npm install
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

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
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

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:

Agent Creation Errors

{
  "error": "Agent already exists"
}

Execution Errors

{
  "error": "Agent not found"
}

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