Content
# Enhanced AutoGen MCP Server
[](https://smithery.ai/server/@DynamicEndpoints/autogen_mcp)
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](https://smithery.ai/server/@DynamicEndpoints/autogen_mcp):
```bash
npx -y @smithery/cli install @DynamicEndpoints/autogen_mcp --client claude
```
### Manual Installation
1. **Clone the repository:**
```bash
git clone https://github.com/yourusername/autogen-mcp.git
cd autogen-mcp
```
2. **Install Node.js dependencies:**
```bash
npm install
```
3. **Install Python dependencies:**
```bash
pip install -r requirements.txt --user
```
4. **Build the TypeScript project:**
```bash
npm run build
```
5. **Set up configuration:**
```bash
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:
```bash
# 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:
```json
{
"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`:
```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:
```bash
# 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
```json
{
"workflow_name": "code_generation",
"input_data": {
"task": "Create a REST API endpoint",
"language": "python",
"requirements": ["FastAPI", "Pydantic", "Error handling"]
},
"quality_checks": true
}
```
### Research Workflow
```json
{
"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:
```bash
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
```bash
# 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
```bash
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
```
2. Configure the server settings:
```json
{
"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
```json
{
"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
```json
{
"name": "execute_chat",
"arguments": {
"initiator": "agent1",
"responder": "agent2",
"message": "Let's discuss the system architecture."
}
}
```
### 3. Group Chat
```json
{
"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
```json
{
"error": "Agent already exists"
}
```
2. Execution Errors
```json
{
"error": "Agent not found"
}
```
3. Configuration Errors
```json
{
"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
