English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 45 lectures (3h 23m) | 1.82 GB
Master the Fundamentals of AI Agents with LangGraph
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!
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
Table of Contents
Introduction
1 Why this course and why should be listen to me
2 What you will learn and what will you not learn
3 Prerequisites
4 Evolution the of LangChain Ecosystem from OOP to Graphs
5 LLM Based Workflows as State Machines Graphs
6 Clone Repository & Set Up Environment
Core Data Structure LangGraph uses
7 TypedDict vs. Pydantic BaseModel
LangGraph Basics
8 State, Nodes, Edges
9 Why not just use LCEL
10 Cycles & Conditional Edges
11 Reducer Functions
12 State with Pydantic BaseModel
Tool Calling – Connect your Agent to the real world
13 Tool Calling Theory
14 Tool Calling in Practice
Agent Basics
15 First LLM based Agent
16 Memory with Checkpointers
RAG (Retrievel Augmented Generation) Agent
17 RAG in Theory (short recap)
18 RAG in Practice
19 RAG Agent with Classifier
20 RAG as Tool Calling Agent
21 Complex RAG Agent Graph Walkthrough
22 Complex RAG Agent in practice
Concepts for Lean and Dynamic Workflows
23 Input- & Output State
24 Dynamic Runtime Configuration
Human-in-the-Loop Workflow
25 Why we need Human-in-the-Loop Workflows
26 Interrupt and resume a Workflow
27 Timetravel Replays and Forks
28 Human Expert as ToolNode
29 The new Command class
30 Human-in-the-Loop with interrupt and Command
Production-Ready Workflows Parallel & Async Nodes
31 Parallel Node Execution
32 Async Agents & Streaming When to use it
33 Async & Streaming in Practice
Subgraphs
34 Execute Agents as Subgraphs in another Agent
Agent Patterns – Theory & Practice
35 Agent Patterns Theory
36 Hierarchical Agent – Create independent agents
37 Supervisor Agent
38 Human-in-the-Loop in Multiagent Workflow
Fullstack Application
39 Real-World Application Beyond Jupyter Notebooks
40 Fullstack Application – Demo
41 Code Walkthrough
Long-Term Memory Integration
42 Long-Term Memory vs. Short-Term Memory in Theory
43 Long-Term Memory with the store class
Optional Unit Testing
44 Test node functions with Pytest
Congratulations!
45 You did it!
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