Introduction to the Course

LangGraph is a framework for building AI workflows as graphs, nodes, edges, and shared state on top of the LangChain components. This course takes you from understanding why graphs exist to building a complete, production-style research assistant agent from scratch.

Who this course is for

You belong here if you have used LangChain for prompts, models, retrievers, or tool calling, and you have started running into the walls that every LangChain user eventually hits. Your workflow needs to branch based on a classification. It needs to retry when the output is not good enough. It needs to remember what happened two turns ago. It needs a human to review something before it proceeds. You have tried to handle these requirements inside a chain and found that the logic quickly becomes tangled and hard to debug. That is exactly the gap this course addresses.

You do not need prior LangGraph experience. You need basic Python, a passing familiarity with LangChain, and a problem you actually want to solve.

What you will build

The course has two parts that run in parallel.

• The first part builds individual patterns in isolation, routing, retries, tool calling, structured outputs, memory, persistence, human approval, and debugging. Each pattern is introduced in a focused lesson with a standalone, runnable example.

• The second part is a guided build chapter where all those patterns come together in one system: a research assistant that accepts a question, checks whether it is specific enough to search, decomposes it into targeted sub-queries, searches three knowledge domains, synthesises the findings into a structured response with source attribution and a confidence rating, and routes weak outputs to a recovery path before delivery.

Every lesson in the guided build produces a working intermediate version. You do not wait until the end to have something that runs.

The core shift

LangChain gives you components. LangGraph gives you a way to connect them with explicit control flow.

• When you use LangChain alone, the order of operations is determined by the structure of your code. When something goes wrong, the error points to a line. When you need to add branching, you add if statements. When you need memory, you manage it manually in the calling code.

• When you use LangGraph, the order of operations is represented as a graph, with nodes connected by edges and shared state flowing between them. When something goes wrong, the checkpoint history shows which node wrote the wrong value. When you need to add branching, you add a routing function and a conditional edge. When you need memory, you carry a list in state or compile with a checkpointer.

The shift is from “write code that calls steps in order” to “design a system where every step has one job, a clear input, and a clear output.” That is the mental model this course builds.

How the course is structured

The course moves through four chapters.

• The second chapter establishes the foundations: what graphs are, how state works, how to build a first workflow, and how LangGraph relates to the LangChain skills you already have.

• The third chapter covers the core workflow patterns: conditional routing, loops and retries, tool augmentation, multi-step agent workflows, and structured output handling.

• The fourth chapter covers reliability: memory, persistence, and checkpointing, human-in-the-loop approval patterns, debugging and observability, and graph design principles.

• The fifth chapter is the guided build: five lessons that take the research assistant from a blank state schema to a fully tested, extensible agent.

How to get the most out of this course

Reading the code is useful. Running it and reading the actual output is where the understanding happens. Attempt every exercise before reading the solution. The exercises are designed to take between ten and thirty minutes. They are not trick questions. The patterns needed to solve them are always covered in the lesson that precedes them.

When a graph produces unexpected output, start with the returned state. Most debugging sessions end there. The lessons on debugging and checkpointing will give you more tools, but reading the state dict first resolves the majority of issues in under a minute.

Treat the capstone as a template. The research assistant is built to be modified. Swapping in different knowledge domains, adding new routing paths, or replacing the simulated tool calls with real API calls are all small, bounded changes. The lessons in the guided build chapter show exactly where to make each kind of change.