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In this video, LangGraph with FastAPI – Build and Run ReAct AI Agents with MCP Tools inside FastAPI Applications, viewers learn how to connect LangGraph’s agentic workflows with MCP servers and expose them through a FastAPI backend. The lesson walks step-by-step through creating a ReAct agent that loads MCP configuration, fetches tools from a GitHub MCP server, and wraps them with Human-in-the-Loop approval for safer execution. Evgeny shows how to build reusable helper functions for loading and caching MCP tools, keeping shared short-term memory across requests with a checkpoint, and running asynchronous agents efficiently inside FastAPI routes. The video then demonstrates full interaction testing — starting a new agent thread, approving or rejecting tool calls, and watching how the system handles each case gracefully. It’s a concise yet practical deep dive for developers learning how to run LangGraph agents as real-world web apps, combining FastAPI, MCP, and Human-in-the-Loop workflows into a single production-ready setup. I get it—tech content takes time to research, code, and polish. If my videos have helped you save time, avoid headaches, or just made debugging less painful, a coffee would be highly appreciated! https://buymeacoffee.com/grabduck 00:00 – Intro 01:12 – Preparing Components in Notebook (MCP Config, Tools, ReAct Agent & HITL Wrapper) 06:12 – Integrating MCP Agent into FastAPI Application 10:50 – Running and Testing Agent via API Requests 🔗 Related Videos ▶️ Testing Human in the Loop with LangGraph Example – Building a Fullstack Feedback Loop with FastAPI • Testing Human in the Loop with LangGraph E... ▶️ LangGraph HITL – Upgrading FastAPI Feedback Loops with Token Streaming and SSE • LangGraph HITL – Upgrading FastAPI Feedbac... 🌟 Related Courses & Tutorials • LangGraph Introduction Series – Learn how to build AI agents with LangGraph in this step‑by‑step course. From basic graphs to memory, human‑in‑the‑loop (HITL), streaming, FastAPI, and React integration — this playlist covers everything you need to create autonomous AI agents and real‑world applications. • LangGraph Intro • LangSmith Introduction Series – Master AI app monitoring and debugging with LangSmith. This tutorial series covers tracing, run types, and step‑by‑step methods for understanding, testing, and improving LLM and LangChain applications. Perfect for developers learning how to monitor AI apps and build more reliable AI agents. • LangSmith Intro 💡 Explore More: Check out the GitHub repo with all the code we used in this session: https://github.com/esurovtsev/langgra... #aidevelopment #aiintegration #aiprogramming ============================= Hi, I’m Evgeny, a freelance software engineer passionate about solving complex problems with technologies like Java, Kotlin, Spring Boot, TypeScript, Python, and Node.js. On this channel, you’ll find content centered around AI, search technologies, and security, with in-depth coding sessions and theoretical discussions to enhance your knowledge. Join me as we explore the latest innovations in backend development and beyond. ============================= Just Meditate It is our own mindfulness & design brand. Operated by ECONOR s.r.o., Czech Republic. ============================= Legal Notice: Operated by ECONOR s.r.o., Czech Republic This channel and website are intended for an international English-speaking audience.