LangGraph Agent with MCP Server

@galaxyxyz5

代码仓库

基本信息

分类

推理

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stdio

作者

galaxyxyz5

关于 LangGraph Agent with MCP Server

LangGraph Agent that integrates with MCP servers

#mcp #agents #llm #langgraph

LangGraph Agent with MCP Server 配置

暂无标准配置

该服务器的 README 中没有可解析的 MCP 配置块，请前往代码仓库查看安装说明。

代码仓库

LangGraph Agent with MCP Server 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is LangGraph Agent with MCP?

This project integrates Model Context Protocol (MCP) with a LangGraph Agent, enabling the agent to dynamically access external tools, data sources, and APIs. It is designed for developers who want to build more modular and powerful AI systems with automatic tool discovery and multi-server support.

How to use LangGraph Agent with MCP?

Clone the repository, install dependencies from requirements.txt, and create a .env file with TAVILY_API_KEY and OPENAI_API_KEY. Run servers/server.py to start the MCP server, then run agent.py in a new terminal to connect the agent and execute queries.

Key features of LangGraph Agent with MCP

Automatic tool discovery via MCP
Multi‑server support (Tavily, YouTube transcript, math, weather)
Predefined tools for web search and YouTube summarization
Modular architecture – easily add additional MCP servers
Connects LangGraph agents to external data and APIs

Use cases of LangGraph Agent with MCP

Performing web searches through a LangGraph agent
Summarizing YouTube video transcripts automatically
Extending an AI agent with custom tools by adding new MCP servers

常见问题

What is the Model Context Protocol (MCP)?

MCP is an open standard developed by Anthropic that provides a structured way for AI applications to interact with external data, tools, and APIs without needing custom integrations for each tool.

What does this project integrate?

It integrates a LangGraph agent with MCP servers, allowing the agent to automatically discover and use external tools.

What dependencies are required?

A Tavily API key and an OpenAI API key must be set in a `.env` file. Python dependencies are listed in `requirements.txt`.

How do I start the system?

First run `servers/server.py` to start the MCP server, then in another terminal run `agent.py`. The agent will connect via the MCP client and execute your query.

What known limitations exist?

The README does not mention any specific limitations or security considerations for this project.

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