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The course starts by wiring an agent together by hand, so the split between what the language model decides and what the surrounding code does stays visible. Only after that does it move the same agent into LangGraph, where each component becomes a node and the flow between nodes is declared explicitly.
A longer exercise builds an essay-writing agent that follows the steps a human researcher would take, which makes the value of structured control flow easy to see.
Intermediate Python programmers who want agents they can steer and debug, rather than open-ended chat loops, will find the material pitched at the right level. The build-it-yourself opening supplies the groundwork before the framework enters.
LangChain, a popular open source framework for building LLM applications, recently introduced LangGraph. This extension allows developers to create highly controllable agents.
In this course you will learn to build an agent from scratch using Python and an LLM, and then you will rebuild it using LangGraph, learning about its components and how to combine them to build flow-based applications.
Additionally, you will learn about agentic search, which returns multiple answers in an agent-friendly format, enhancing the agent’s built-in knowledge. This course will show you how to use agentic search in your applications to provide better data for agents to enhance their output.
In detail:
Start building more controllable agents using LangGraph!
Who should join?
If you have intermediate Python knowledge and want to learn how to create more controllable agents using the LangGraph open source framework, this course is for you.
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