https://github.com/danmas0n/multi-agent-with-mcp
Multiple agents with LangGraph and MCP
https://github.com/danmas0n/multi-agent-with-mcp
Last synced: 3 months ago
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Multiple agents with LangGraph and MCP
- Host: GitHub
- URL: https://github.com/danmas0n/multi-agent-with-mcp
- Owner: danmas0n
- License: mit
- Created: 2025-02-07T00:40:08.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-14T03:35:32.000Z (8 months ago)
- Last Synced: 2025-02-14T04:28:32.004Z (8 months ago)
- Language: Python
- Size: 487 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LangGraph Coding Agent Team with MCP
[](https://langgraph-studio.vercel.app/templates/open?githubUrl=https://github.com/danmas0n/multi-agent-with-mcp)
This project implements a small team of coding agents using [LangGraph](https://github.com/langchain-ai/langgraph) and the [Model Context Protocol (MCP)](https://modelcontextprotocol.io). The agents use MCP servers to provide tools and capabilities through a unified gateway.
The overall objective of this agent team is to take requirements and code context and create multiple implementations of proposed features; human operators can then choose their preferred approach and proceed, discarding the others.
This project originated from the Anthropic MCP Hackathon in NYC on 12/11/2024 and has since evolved into its own standalone project.
## Architecture
The system consists of three main components:
1. **MCP Gateway Server**: A server that:
- Manages multiple MCP server processes
- Provides a unified API for accessing tools
- Handles communication with MCP servers
- Exposes tools through a simple HTTP interface
2. **MCP Servers**: Individual servers that provide specific capabilities:
- GitHub Server: Repo operations (read, write, list, search, create branch, create PR, etc.)
- Additional servers can be added for more capabilities
3. **Coding Agents**: There are three agents that collaborate to accomplish coding tasks:
- Orchestrator: Gathers context from human messages and uses MCP servers to access Linear and GitHub. Delegates to planner and coder as needed.
- Planner: Takes requirements and code context and creates a plan with multiple implementation suggestions. Does not use MCP.
- Coder: Takes code context and proposed implementations and implements all of them on separate GitHub branches.
## Getting Started
### 1. Install Dependencies
```bash
# Install the agent package
pip install -e .
# Install the gateway package
cd gateway
pip install -e .
cd ..
```
### 2. Configure Environment Variables
The agent supports multiple LLM providers through environment variables:
```bash
# LLM Configuration - supports multiple providers:
LLM_MODEL=provider/model-name
# Supported providers and example models:
# - Anthropic: anthropic/claude-3-5-sonnet-20240620
# - OpenAI: openai/gpt-4
# - OpenRouter: openrouter/openai/gpt-4o-mini
# - Google: google/gemini-1.5-pro
# API Keys for different providers
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
OPENROUTER_API_KEY=your_openrouter_api_key
GOOGLE_API_KEY=your_google_api_key
# OpenRouter Configuration (if using OpenRouter)
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
```
### 3. Configure MCP Servers
The gateway server is configured through `gateway/config.json`. By default, it starts two MCP servers:
```json
{
"mcp": {
"servers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/directory"
]
},
"memory": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
]
}
}
}
}
```
You can add more servers from the [official MCP servers repository](https://github.com/modelcontextprotocol/servers).
### 4. Start the Gateway Server
```bash
cd gateway
python -m mcp_gateway.server
```
The server will start on port 8808 by default.
### 5. Configure the Agent
The agent's connection to the gateway is configured in `langgraph.json`:
```json
{
"dependencies": ["."],
"graphs": {
"agent": "./src/react_agent/graph.py:graph"
},
"env": ".env",
"mcp": {
"gateway_url": "http://localhost:8808"
}
}
```
### 6. Use the Agent
Open the folder in LangGraph Studio! The agent will automatically:
1. Connect to the gateway server
2. Discover available tools
3. Make tools available for use in conversations
## Available Tools
The agent has access to tools from both MCP servers:
### Filesystem Tools
- `read_file`: Read file contents
- `write_file`: Create or update files
- `list_directory`: List directory contents
- `search_files`: Find files matching patterns
- And more...
### Memory Tools
- `create_entities`: Add entities to knowledge graph
- `create_relations`: Link entities together
- `search_nodes`: Query the knowledge graph
- And more...
## Development
### Adding New MCP Servers
1. Find a server in the [MCP servers repository](https://github.com/modelcontextprotocol/servers)
2. Add its configuration to `gateway/config.json`
3. The agent will automatically discover its tools
### Customizing the Agent
- Modify the system prompt in `src/react_agent/prompts.py`
- Update the agent's reasoning in `src/react_agent/graph.py`
- Add new capabilities by including more MCP servers
## Documentation
- [LangGraph Documentation](https://github.com/langchain-ai/langgraph)
- [Model Context Protocol](https://modelcontextprotocol.io)
- [MCP Servers Repository](https://github.com/modelcontextprotocol/servers)
## License
This project is licensed under the MIT License - see the LICENSE file for details.