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LangGraph courses and tutorials

Compare LangGraph learning for stateful AI agents, graph workflows, and debugging, with practical guidance for experienced Python developers.

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About LangGraph

LangGraph expresses an agent workflow as state moving between nodes. It is useful when branching, repeated steps, and recovery need a visible structure. Understanding the state and termination conditions matters more than producing an elaborate graph.

Choose a useful starting point

The focused Eden Marco course assumes programming experience and familiarity with the surrounding LangChain ecosystem. A broader agent course can provide context before that specialization. Compare how examples handle state updates, tool results, and failures; match dependencies to the lesson repository.

Build on the lessons

Create a three-step workflow with one conditional branch. Inspect state after each node, simulate a failed tool, and explain how the run ends. Add persistence only after the basic transitions are clear.

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