LangGraph + MCP Changes Everything | The Key to Scalable AI Agents

Want to learn how to build scalable AI agents with LangGraph and the Model Context Protocol (MCP)? In this step-by-step tutorial, I’ll show you how to integrate any MCP server into your LangGraph agents, from building your own local MCP server to configuring pre-built ones like GitHub, Supabase, and file system tools. You’ll see how MCP transforms agent development by standardizing tool integration, unlocking dozens of powerful capabilities for AI workflows—from version control to project management and data analysis. Whether you’re just starting out in AI development or looking to scale your existing AI applications, this video will give you the practical foundation you need to start building real-world AI agents with LangGraph + MCP.

Want to learn more about building AI agents? Continue with my 'Master Langgraph' playlist!
   • Master Advanced AI Agents with Langgraph  

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🛠️ Resources
1. Github Rep: https://github.com/kenneth-liao/mcp-i...
2. MCP Official Docs: https://modelcontextprotocol.io/intro...
3. Official MCP Repo & Servers: https://github.com/modelcontextprotoc...
4. Glama MCP Server Directory: https://glama.ai/mcp/servers
5. Build MCP Client from Scratch: https://modelcontextprotocol.io/quick...

🕒 Sections
00:00 - Intro
00:43 - What is MCP (review)
04:02 - Project Demo
09:53 - Project Setup
14:01 - Building our own MCP Server
17:33 - MCP Server Config
24:45 - Langgraph Agent
28:54 - Langgraph’s multi-server MCP Client
31:23 - Building MCP Clients

#mcp #langgraph #langchain #aiagents #aiagent