Turn Your LangGraph Agent into a Production API (with Auth & Docker)

In this langgraph tutorial, I'm going to walk you through deploying AI agents built with Langgraph, to the cloud, with authentication for a production-ready application. We'll cover the backend setup, the *cloud deployment* process, and adding authentication with a clean proxy server. While we're going to focus on deploying a Langgraph agent, the design of this deployment can work for agents built with any framework in python! We will be deploying Rocket, a YouTube content researcher with access to multiple MCP servers and MCP tools. I've started using this AI Agent daily!

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🎥 Referenced Videos
1. Intro to Langgraph:    • Full Langgraph Tutorial - How to Build Adv...  
2. MCP + Langgraph:    • LangGraph + MCP Changes Everything | The K...  
3. Langgraph Deployments part 1:    • Deploy ANY Langgraph AI Agent in Minutes!  

🛠️ Resources
1. REPO: https://github.com/kenneth-liao/deplo...
2. UV Setup: https://docs.astral.sh/uv/guides/inst...
3. MCP Official Docs: https://modelcontextprotocol.io/intro...
4. Render (Platform as a service): https://render.com/

🕒 Sections
00:00 - Intro
00:57 - Deployed Agent Demo
010:40 - Deployment Architecture
13:04 - Project Setup
30:53 - Local Deployment & Testing with Docker
52:53 - Cloud Deployment

#langgraph #aiagent #mcp