Free online courseLangGraph Masterclass: Build LLM Agents, RAG, Memory, Multi-Agent Systems and Streaming

Course Description

Designing LLM applications that work once is easy; making them reliable, debuggable, and safe to run in production is the real challenge. In this free online course, you’ll learn how to build agentic systems with LangGraph, a graph-based approach that gives you precise control over how an AI system thinks, acts with tools, retries, and stops. Instead of hoping a chain behaves, you’ll structure behavior as explicit nodes and edges, creating workflows you can trace, test, and evolve.

You’ll progress from core ideas like autonomy levels and ReAct-style reasoning to more advanced patterns that improve answer quality through critique and revision loops. Along the way, you’ll see how structured outputs reduce ambiguity, how tool calls can be executed safely, and how state management changes everything when your app needs consistent behavior across turns. By introducing custom state, you gain a clearer contract for what your system knows, what it should remember, and what gets updated at each step.

The course also tackles the practical reality of building assistants people actually use: memory and persistence. You’ll learn why checkpointing matters, how to resume conversations after restarts, and how to create human-in-the-loop flows where a person can approve actions before tools run. Tracing and observability are treated as first-class concerns, helping you understand what happened inside the graph and why a run produced a particular outcome.

On the retrieval side, you’ll work with RAG patterns that go beyond basic similarity search, including routing and question rewriting for multi-step reasoning. You’ll also explore multi-agent architectures where specialized agents collaborate under a supervisor, enabling more scalable problem-solving for complex tasks.

Finally, you’ll connect these ideas to real product implementation: streaming responses for great UX, a fullstack capstone built around a production-style chat experience, and deployment considerations so your agent can move from notebook to live app. If you want a practical path to building robust, controllable LLM agents, this course will give you the architecture and mindset to do it.