// A HANDS-ON TUTORIAL · 1986 EDITION

Build robot agents
with LangChain & LangGraph

From one LLM call to a squad of robots taking orders in plain English. Every concept comes with a simulation you can play and the real Python code you can copy and run.

LangChain v1 LangGraph Gemini · DeepSeek · local
// HOW THIS WORKS

Understand it. Watch it. Run it.

Everyone codes with AI assistants now, so this course is about concepts and capabilities: what agents can do and which piece solves which problem. The simulations run in your browser with no API key, and the code is real, so your robot moves for real when you run it.

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Play the simulation

Pick a request, press Run and watch the robot think, call tools and read its manuals, message by message.

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Copy the real code

Every demo maps to a Python script in code/. Same tools, same world, same results, with a real LLM behind it.

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One robot, growing

Each chapter builds on the last: R-80 gets a brain, then hands, a manual, a memory and finally a squad.

// LIVE DEMO (EMULATED)

Meet R-80

A lab robot controlled by an LLM agent. Give it an order.

★ FIELD-TESTED · HOW WE BUILD IT

ROS 2 × LangChain on real robots

How we use LLM agents as high-level controllers of real robots at the BioRobotics Group: capability manifests, a safety gate the LLM can't bypass, and a slow reasoning loop on top of fast classic control.

Open the field chapter ▶
  • The LLM proposes, the gate decidesEvery command validated against a manifest.
  • Slow loop over fast loopReasoning in seconds, control at 100 Hz.
  • Swap the manifest, keep the architectureSame stack, different robot.
// COURSE MAP

From one robot to real hardware

Each stage adds one piece to the same robots. By the end, they coordinate a mission on their own.