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Video course · 15 chapters · 190 min · certificate

LangGraph for Beginners: Build AI Agents & Workflows with Python

Learn LangGraph from scratch in Python: type annotations, core elements, five graph-building agents with exercises (single node, multiple inputs, sequential, conditional, looping), then AI agents: a simple bot, a chatbot with memory, a ReAct agent, a drafter agent with human-in-the-loop and a RAG agent.

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What you'll learn

  • Use type annotations for graph state
  • Explain LangGraph's core elements
  • Build sequential, conditional and looping graphs
  • Build chatbots with memory
  • Build a ReAct agent with tools
  • Build a RAG agent

Chapters

15 chapters · 189:51
  1. 8:35 01Foundations Members Introduction and type annotations TypedDict, Union, Optional, Annotated.
  2. 12:33 02Concepts Members Elements of LangGraph State, nodes, graph, edges.
  3. 11:14 03Graphs Members Agent 1: hello world graph A single-node graph.
  4. 11:05 04Graphs Members Agent 2: multiple inputs Exercise and multi-input state.
  5. 13:10 05Graphs Members Agent 3: sequential graph Chaining nodes.
  6. 16:32 06Routing Members Agent 4: conditional graph Branching.
  7. 16:10 07Loops Members Agent 5: looping graph Repeating until done.
  8. 14:39 08AI agents Members AI agent 1: simple bot Adding an LLM.
  9. 19:22 09Memory Members AI agent 2: chatbot with memory Keeping history.
  10. 9:08 10ReAct Members AI agent 3: ReAct intro Reasoning and acting.
  11. 16:18 11ReAct Members AI agent 3: ReAct code Tools and ToolNode.
  12. 2:13 12Drafter Members AI agent 4: drafter intro Human-in-the-loop editing.
  13. 21:10 13Drafter Members AI agent 4: drafter code Building the loop.
  14. 14:06 14RAG Members RAG agent Retrieval over a PDF.
  15. 3:36 15Wrap-up Members Testing and outro Running the RAG agent.