What if your chatbot could remember—not just reply, but hold natural, flowing conversations, recall essential moments, and adapt intelligently to what's been said before?

That's precisely what you'll learn to implement in Build Conversational AI Memory with LangGraph—a hands-on, practical course designed for developers who want to take their AI agents beyond simple call-and-response systems.

We begin with a short introduction to the course and what you'll need to get started—ideal for learners who are familiar with LLMs and LangChain but new to LangGraph. You'll learn how to set up your development environment and prepare for building memory-powered conversational agents from scratch.

Next, we explore the core of LangGraph. You'll learn how conversational graphs are constructed using states, nodes, and edges and how to build your first functioning graph step by step. From there, we'll move into conditional routing—where your agent starts to make smart decisions based on user input.

A significant focus of the course is on message management and memory. You'll explore reducer functions, trimming logic, summarization techniques, and how to implement these using LangGraph's Annotated and MessagesState constructs.

Finally, we'll tackle thread-level persistence—a key component for enabling agents to remember across sessions. You'll experiment with in-memory and long-term storage using SQLite. By the end of this course, you'll have a complete understanding of how to structure, store, and manage memory in a conversational chatbot—and you'll have built one yourself.

Whether you're creating a customer support bot, a task assistant, or a personalized tutor, memory is what makes these tools worthwhile in the real world. This course shows you how to implement it—step by step.

Let your AI talk—and remember.

Start building with LangGraph today.