LangGraph is a versatile Python framework designed for stateful, cyclic, and multi-actor Large Language Model (LLM) applications. LangGraph builds upon 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 expands LangChain’s capabilities by providing tools to build complex LLM workflows with state, conditional edges, and cycles.
- LLM agents in LangGraph autonomously process tasks using state graphs to make decisions and interact with tools or APIs.
- You can use LangGraph independently of LangChain, although 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 emails, send emails, and interact with API services.
While you’ll get a brief primer on LangChain in this video course, you’ll benefit from having 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.
