LangGraph Course: Build Production AI Agents, Free

Building Production AI Agents with LangGraph is a free, hands-on course that teaches you to build AI agents with LangGraph that are ready for real use, not just demos. It is for developers who have built a simple LLM app and now want agents that are stateful, durable, observable, evaluable, secure and ready for production.

Across 19 lessons and about 5.6 hours, you start with graphs, state, nodes, edges and control flow, then add tools, persistence, human approval, memory and multi-agent patterns, and finish with streaming, observability, evaluation, deployment, security and cost. See the LangGraph course page for the full outline.

Who this course is for

The course is labelled Intermediate. It suits:

• Python developers who have called an LLM API and want to build proper agents.
• AI and ML engineers moving from notebooks and prototypes to production systems.
• Back-end engineers asked to add agent features to an existing product.
• Technical leads who need to understand what makes an agent reliable before approving it.

You should be comfortable reading and running Python code. The first lesson covers environment setup, so you do not need LangGraph experience before you start.

What you will learn

The 19 lessons build on each other in six themes.

1. Graph fundamentals. Environment Setup, Why Graphs Not Chains, State Nodes Edges and Control Flow explain why a graph is a useful way to model an agent workflow, and how state, nodes, edges, reducers and conditional routing fit together. Everything later in the course is built on these ideas.

2. Tools and the ReAct loop. Tools And React adds tools and the ReAct agent loop, where the model reasons, calls a tool, reads the result and decides what to do next. This is the step from a chatbot to an agent.

3. Persistence and reliability. Checkpointers And Threads, Durable Execution, Human In The Loop and Time Travel cover saving state across conversation threads, building fault-tolerant workflows, pausing for a person to approve a step, and using checkpoints to debug. These features are what separate a production agent from a demo.

4. Memory and modular design. Memory covers long-term agent memory, and Subgraphs shows how to split a large agent into modular, reusable pieces.

5. Multi-agent patterns. Supervisor Teams, Swarm And Handoff and Plan Execute Reflection cover supervisor-based multi-agent systems, agent handoff, and planning, execution and reflection. Knowing several patterns helps you choose the simplest one that solves the problem.

6. Running agents in production. Streaming, Observability, Evaluation, Deployment and Security And Cost cover streaming agent events, tracing, testing and evaluating agents, deploying them, and keeping them secure and affordable.

How the course is organised

The course has 20 videos: a 7-minute overview and 19 numbered lessons, about 5.6 hours in total.

• Lessons 01 to 05: setup and the core graph model, ending with tools and ReAct.
• Lessons 06 to 09: checkpointers, durable execution, human in the loop and time travel.
• Lessons 10 to 14: memory, subgraphs and multi-agent patterns.
• Lessons 15 to 19: streaming, observability, evaluation, deployment, and security and cost.

Each lesson is a hands-on video of about 16 to 18 minutes, so the workload is steady from start to finish.

How to study it step by step

1. Watch the overview, then set up your environment with lesson 01. Make sure your setup works before moving on.
2. Code every graph lesson yourself. Lessons 02 to 04 are the foundation. Rebuild each example from scratch rather than only watching.
3. Build one small agent of your own after lesson 05. Give it one or two simple tools and run the ReAct loop end to end.
4. Add reliability to that agent. As you work through lessons 06 to 09, add a checkpointer, an approval step and checkpoint-based debugging to your own agent.
5. Try one multi-agent pattern. After lessons 11 to 14, choose the pattern that best fits your idea and rebuild your agent with it.
6. Treat lessons 15 to 19 as a production checklist. For each one, add streaming, tracing, an evaluation, a deployment plan and a cost and security review to your agent.
7. Pace yourself. Two lessons a day, with coding time, completes the course in about two weeks.

Follow the lessons in order on the YouTube playlist.

Related courses

If you want a wider view, AI Systems Engineering Beginner to Advanced covers building AI systems more broadly, and Agentic AI – Project Based Learning builds an agent in Python lab by lab, including its own ReAct loop. Start or resume this course any time from the LangGraph course page.

Frequently asked questions

Is the LangGraph course free?

Yes. All 20 videos are free on YouTube.

How long does the course take?

The videos total about 5.6 hours. If you code along and build your own agent, plan for about two weeks at two lessons a day.

Do I need to know LangGraph before starting?

No. Lesson 01 covers environment setup, and lessons 02 to 04 teach graphs, state, nodes and edges from the beginning. You should already be comfortable with Python.

Does it cover multi-agent systems and deployment?

Yes. Lessons 12 to 14 cover supervisor teams, swarm and handoff, and plan, execute and reflection patterns. Lessons 15 to 19 cover streaming, observability, evaluation, deployment, and security and cost.