Multi-Server Agent2Agent LangGraph
This project demonstrates a decoupled real-time agent architecture that connects LangGraph agents to remote tools served by custom MCP (Modular Command Protocol) servers. The architecture enables a flexible and scalable multi-agent system where each
What is Multi-Server Agent2Agent LangGraph?
Multi-Server Agent2Agent LangGraph is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to this project demonstrates a decoupled real-time agent architecture that connects langgraph agents to remote tools served by custom mcp (modular command protocol) servers. the architecture enables a fl...
This project demonstrates a decoupled real-time agent architecture that connects LangGraph agents to remote tools served by custom MCP (Modular Command Protocol) servers. The architecture enables a flexible and scalable multi-agent system where each
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- This project demonstrates a decoupled real-time agent archit
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-multiserver-interoperable-agent2agent-langgraph-ai-systemConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Multi-Server Agent2Agent LangGraph
This project demonstrates a decoupled, real-time multi-agent architecture that wires LangGraph agents to remote tools served by custom MCP servers. It shows how to build a flexible, scalable agent system where each specialized agent connects to one or more MCP tool servers — such as a math server or weather server — via stdio or SSE transport, enabling concurrent tool invocation across multiple servers in a single agent turn. Developers building complex agentic pipelines with LangChain and LangGraph will find this a practical reference implementation for multi-server MCP integration.
Prerequisites
- Python 3.10+ installed along with UV or pip for dependency management
- An OpenAI API key (OPENAI_API_KEY) for the LLM backend
- Familiarity with LangGraph and LangChain concepts
- An MCP-compatible runtime environment (the project runs its own MCP servers)
- Git to clone the repository
Clone the repository
Clone the project from GitHub and navigate into the project directory.
git clone https://github.com/junfanz1/MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-System.git
cd MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-SystemCreate a virtual environment and install dependencies
Create an isolated Python environment and install all required packages including langchain, langgraph, and the MCP SDK.
python -m venv .venv
source .venv/bin/activate
pip install langchain langchain-openai langgraph mcp python-dotenvConfigure environment variables
Create a .env file in the project root with your OpenAI API key.
echo 'OPENAI_API_KEY=your_openai_api_key_here' > .envStart the MCP tool servers
Launch the individual MCP tool servers (math and weather) before starting the agent clients. Each server runs as a separate process.
python math_server.py &
python weather_server.py &Run the multi-server agent client
Execute the multi-server client which connects to both tool servers and routes queries to the appropriate agent via LangGraph.
python multiserver_client.pyExplore the stdio client variant
Alternatively run the stdio-based client for environments that prefer stdin/stdout communication over network sockets.
python stdio_client.pyMulti-Server Agent2Agent LangGraph Examples
Client configuration
Example environment setup for the multi-agent system showing the required .env configuration.
{
"note": "This project runs its own MCP servers directly rather than registering with a desktop client.",
"setup": {
"OPENAI_API_KEY": "your_openai_api_key_here"
},
"servers": {
"math_server": "python math_server.py",
"weather_server": "python weather_server.py"
},
"client": "python multiserver_client.py"
}Prompts to try
Example queries that route through the LangGraph agent to the appropriate MCP tool server.
- "What is 1+1?" (routes to math_server via MCP)
- "What is 15% of 340?" (math tool invocation)
- "What's the weather in Chicago?" (routes to weather_server via MCP)
- "Get the weather forecast for New York for the next 3 days"
- "Are there any weather alerts for California?"
- "Calculate the compound interest on $10,000 at 5% for 10 years"Troubleshooting Multi-Server Agent2Agent LangGraph
Agent returns errors about missing OpenAI API key
Ensure OPENAI_API_KEY is set in your .env file and that python-dotenv is installed. Verify with `python -c "from dotenv import load_dotenv; load_dotenv(); import os; print(os.getenv('OPENAI_API_KEY')[:5])"`.
Client fails to connect to tool servers with connection refused errors
Start math_server.py and weather_server.py before running the client. Each server must be running in a separate terminal or as a background process. Check that the port numbers in the server and client configurations match.
ImportError for langgraph or langchain_openai
Install all dependencies explicitly: `pip install langchain langchain-openai langgraph mcp python-dotenv`. If using UV, run `uv pip install` within the activated virtual environment.
Frequently Asked Questions about Multi-Server Agent2Agent LangGraph
What is Multi-Server Agent2Agent LangGraph?
Multi-Server Agent2Agent LangGraph is a Model Context Protocol (MCP) server that this project demonstrates a decoupled real-time agent architecture that connects langgraph agents to remote tools served by custom mcp (modular command protocol) servers. the architecture enables a flexible and scalable multi-agent system where each It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Multi-Server Agent2Agent LangGraph?
Follow the installation instructions on the Multi-Server Agent2Agent LangGraph GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Multi-Server Agent2Agent LangGraph?
Multi-Server Agent2Agent LangGraph works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Multi-Server Agent2Agent LangGraph free to use?
Yes, Multi-Server Agent2Agent LangGraph is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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