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LangGraph in Action: Develop Advanced AI Agents with LLMs
Rating: 4.5 out of 5(888 ratings)
6,423 students

LangGraph in Action: Develop Advanced AI Agents with LLMs

Master the Fundamentals of AI Agents with LangGraph (Version 1.0.0)
Created byMarkus Lang
Last updated 1/2026
English

What you'll learn

  • Understand the core functions and concepts of LangGraph, including nodes, edges, and checkpointers
  • Develop an AI agent with LangGraph that effectively uses both short-term and long-term memory
  • Implement advanced multi-agent workflows and subgraphs for handling complex real-world scenarios
  • Build production-ready AI agents using FastAPI, Docker, and unit testing for maintainable workflows

Course content

16 sections • 48 lectures • 3h 31m total length

  • Why this course and why should be listen to me?3:09
  • What you will learn and what will you not learn1:02
  • Prerequisites1:40
  • Evolution the of LangChain Ecosystem from OOP to Graphs4:54
  • LLM Based Workflows as State Machines / Graphs3:37
  • Clone Repository & Set Up Environment2:54

    Clone the course repository, set up a Python virtual environment, install dependencies from requirements.txt, and configure your OpenAI API key to launch the notebooks in VS Code.

  • LangGraph 1.0.0 Update0:54

Requirements

  • Intermediate Python Skills (OOP, Datatypes, Functions, modules etc.)
  • LangChain Basics
  • Basic Terminal and Docker knowledge

Description

What to Expect from This Course
Welcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life.

Course Highlights

  • State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents.

  • Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn.

  • Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns.

  • Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker.

By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents.

With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together!

Who this course is for:

  • Software Engineers with Experience in LangChain who want to dive into the world of AI Agents

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