LangGraph Agent Orchestration
Most LangGraph tutorials freeze at the API surface they were written against, and LangGraph's surface has moved: the prebuilt agent factory that half the internet's quickstarts still import was deprecated in October 2025. This course rebuilds a 42-exercise curriculum from the ground up against the current API, and goes further than API currency: it teaches state as a typed contract instead of a data bag, tool selection as a naming discipline instead of magic, graph-native parallel execution instead of asyncio bolted on sideways, and human-in-the-loop as a mechanism you design per node instead of a bullet point on a deploy checklist. Every claim traces to a confirmed source: a live LangGraph reference page, a named prior course's own measured receipt, or this stream's own direct read of the original exercises' exact lines. Eight lessons, no padding, no exercise re-taught twice under a different component scaffold.
ToolChecked against LangGraph v1.0 on 2026-08-20.
Rod Rivera
Professor
Desk
First lesson is free to preview.
What You'll Learn
- Tell which parts of a LangGraph tutorial are still current and which quietly moved, starting with the exact import that most search results still get wrong
- Design a State type where every field has a clear owner and a reducer where one is needed, instead of extending state as a free action
- Choose between a sliding window and a summary for growing message history based on what you can afford to lose, not on which one a tutorial showed first
- Build a classifier node that scores its own confidence and route on that score using the same conditional-edge mechanism every graph already has
- Bind multiple tools to one agent and write descriptions that keep tool selection reliable as the tool count grows
- Explain what ToolNode actually does when a model picks a tool, and confirm for yourself which parts of langgraph.prebuilt are current and which aren't
- Fan out to parallel work using LangGraph's own graph-native scheduling instead of asyncio.gather, and say specifically why that's the current recommendation
- Design a human-in-the-loop pause point using interrupt() and a checkpointer, and decide which nodes actually need one using a side-effect-tier framework instead of guessing
Prerequisites
- Comfort reading Python; every lesson works through real code, not pseudocode
- No prior LangGraph or LangChain experience required — lesson 01 builds the current API from the ground up
- Helpful but not required: having followed an older LangGraph tutorial and wondering why some of it doesn't quite match what you're seeing now
Syllabus
Ready to keep going?
The lessons in LangGraph Agent Orchestration are open. The Prof Rod weekly lesson is the next thing, not a door.
