LangGraph Agent with MCP Server

@galaxyxyz5

リポジトリ

基本情報

カテゴリ

推論

トランスポート

stdio

作者

galaxyxyz5

LangGraph Agent with MCP Server について

LangGraph Agent that integrates with MCP servers

#mcp #agents #llm #langgraph

LangGraph Agent with MCP Server の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

LangGraph Agent with MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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.

「推論」の他のコンテンツ

MCP Servers

junfanz1

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 tool can be hosted independent

Unified MCP Suite

Godzilla675

A suite of Model Context Protocol (MCP) servers designed to enhance AI agent capabilities. Provides tools for media search/understanding (images, video), web information retrieval, PDF generation, and PowerPoint presentation creation, enabling agents to interact with diverse data

MCP Sandbox

danstarns

Turn any JavaScript module into a sandboxed MCP (Model Context Protocol) server with automatic reflection and type inference.

End-to-End Agentic AI Automation Lab

MDalamin5

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

NeoCoder: Neo4j-Guided AI Coding Workflow

angrysky56

An MCP server allowing AI assistants to use a Neo4j knowledge graph as their primary, dynamic instruction manual and long term project memory with adaptive templating and autonomous tool development tools.