LangGraph is a versatile Python framework designed for stateful, cyclic, and multi-actor Large Language Model (LLM) applications. LangGraph builds on its parent library, LangChain, and allows you to build sophisticated workflows that can handle the complexities of real-world LLM applications. Throughout this LangGraph video course, you’ll build a complete email-processing agent from the ground up.
By the end of this video course, you’ll understand that:
- You can use LangGraph to build LLM workflows by defining state graphs with nodes and edges.
- LangGraph extends LangChain’s capabilities by enabling complex LLM workflows with state, conditional edges, and cycles.
- LLM agents in LangGraph autonomously process tasks by using state graphs to make decisions and interact with tools or APIs.
- You can use LangGraph independently of LangChain, though they’re often used together to complement each other.
Follow along with the full video course to gain hands-on experience with LangGraph, including setting up workflows and building a LangGraph agent that can autonomously parse and send emails and interact with API services.
While you’ll get a brief primer on LangChain in this course, you’ll benefit from prior knowledge of LangChain fundamentals. You’ll also want to ensure you have intermediate Python knowledge, specifically in object-oriented programming concepts like classes and methods.
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