Part 1. Real-Time LangGraph Agent with MCP Tool Execution

What is MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-System?

This project showcases a decoupled real-time agent architecture that connects LangGraph agents to remote tools via custom Modular Command Protocol (MCP) servers, enabling a flexible and scalable multi-agent system.

How to use MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-System?

To use this system, set up the MCP servers (e.g., math_server.py, weather_server.py) and connect them with LangGraph agents using the provided client scripts. The agents can then invoke tools hosted on these servers asynchronously.

Key features of MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-System?

• Decoupled architecture for agent orchestration and tool execution.
• Asynchronous programming for non-blocking I/O.
• Integration of MCP with LangGraph for seamless tool invocation.
• Support for multiple server connections and dynamic tool discovery.
• Real-time communication between agents and tools.

Use cases of MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-System?

1. Real-time weather forecasting using the weather server.
2. Mathematical problem-solving through the math server.
3. Building modular AI applications that can integrate various tools dynamically.

FAQ from MCP-MultiServer-Interoperable-Agent2Agent-LangGraph-AI-System?

• Can this system support multiple programming languages?

Yes! The MCP protocol is designed to be language-agnostic, supporting various environments.

• Is it necessary to run all servers locally?

No, the servers can be hosted in the cloud or in containerized environments as well.

• How does the system handle tool failures?

The architecture includes error handling mechanisms to manage tool server failures gracefully.