Orchestrate agents with a state graph: LangGraph, multi-agent, memory, patterns and MCP

Orchestrate agents with LangGraph: shared state, conditional routing, human validation, a multi-agent supervisor, short and long term memory.

Start lesson 1 The Applied AI domain

5 lessons
~4 h 10 of reading
5 quizzes
0 proof lab

The path

Where it comes from, where it leads

What you need to know first, where you stand on this skill, and what it opens next. Your status is computed in your browser, from what you have read and passed.

Before · 1 prerequisite

Build a tool-using AI agent: ReAct loop, function calling and typed output

Recommended before · 2

This skill, for you

Orchestrate agents with a state graph: LangGraph, multi-agent, memory, patterns and MCP

Computing

It unlocks · 1 skill

Assemble a sovereign AI stack and expose it behind a secured web interface

Lessons

How to learn it

5 lessons, about ~4 h 10, in program order. Each lesson ends with its quiz.

The program

5 lessons · ~4 h 10

1Agents stateful avec LangGraph (in French)· 50 min
2LangGraph multi-agents (in French)· 50 min
3La mémoire des agents (in French)· 50 min
4Patterns agentiques avancés (in French)· 50 min
5Connecter un agent au MCP (in French)· 50 min

Beyond the lessons

Assess, practise, pass the proof

Reading a lesson confirms nothing. A skill is confirmed by a passed quiz, practised in a guided lab, and put to the test by a challenge or a mission; an imported lab result is not verified by the site.

Assess yourself

5 quizzes

At the end of the lessons: passed at 70%, they confirm the skill.

Practice

No guided lab

No guided lab practices it yet.

Proof

No proof

Neither a challenge nor a mission with a proof yet.

Around it

Who requires it, who teaches it

Required by

No published job requires it yet.

Taught by

Python IA (in French)

Same block · Build AI agents