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Discord Community: https://discord.gg/gHQ6yfx52q Github Repo: https://github.com/homayounsrp/MCP In this video, I show you how to build an AI agent using LangGraph and the Model Context Protocol (MCP). If you’re into intelligent agents, agent workflows, or autonomous systems, this is your starting point. LangGraph lets you design complex agent flows, and I’ll walk you through integrating it with MCP, a powerful tool to persist memory, manage state, and share context between agents. We’ll also explore how to connect your LangGraph agent with Neo4j, turning your AI into a knowledge-driven system. Using Neo4j and LangGraph together allows your agents to reason over connected data like never before. Whether you're a developer, researcher, or just curious about building smarter AI, this guide will give you a practical look into how LangGraph, MCP, and Neo4j all come together to build robust, context-aware agents. By the end, you'll understand how the Model Context Protocol (MCP) works under the hood, how LangGraph agents manage memory, and how Neo4j helps them think. Don’t miss this deep dive into the future of agent architecture! Watch, like, and subscribe for more content on LangGraph, Neo4j, AI agents, and real-world applications of the Model Context Protocol. Timestamps: 0:00 - Introduction 0:44 - What is MCP? 3:12 - MCP Implementation using LangGraph and Neo4j Vectorstore 10:33 - Outro LangGraph,MCP,Neo4j,Model Context Protocol,AI Agent,LangGraph tutorial,build AI agent,LangChain agent,Neo4j knowledge graph,LangGraph MCP integration,AI workflows,agent architecture,graph-based reasoning,AI agents,knowledge graphs,LangChain,Retrieval-Augmented Generation,GraphRAG,AI agent development,LangGraph agents,Neo4j integration,MCP integration,LangGraph workflows,AI agent frameworks,Neo4j knowledge graphs,LangChain tools,Neo4j graph database
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