Building an AI Agent With LangGraph and Python

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.

What’s Included:

• 17 Lessons
• Video Subtitles and Full Transcripts
• Translated Subtitles in 8 Languages
• 2 Downloadable Resources
• Accompanying Text-Based Tutorial
• Interactive Quiz to Check Your Progress
• 3 Hands-On Coding Exercises
• Q&A With Python Experts: Ask a Question
• Mentor AI: Ask Questions as You Learn
• Certificate of Completion

Downloadable Resources:

• Course Slides (.pdf)
• Sample Code (.zip)

Related Learning Paths:

• LLM Application Development With Python