LangGraph Memory with SQLite | Building a Persistent AI Chatbot

In this video, I walk you through how to build a LangGraph-powered AI chatbot with persistent memory using SQLite.

Unlike stateless chatbots, this implementation allows conversations to persist across sessions by storing chat history in a SQLite database. This is essential for building production-ready AI agents that can remember users, track conversation context, and maintain continuity over time.

What you’ll learn in this video:
What LangGraph is and how it differs from LangChain
Designing a stateful chatbot architecture
Implementing persistent memory using SQLite
Managing conversation threads and message history
Best practices for building scalable, memory-aware AI agents

Technologies used:
Python
LangGraph
SQLite
LLM-based conversational agents

This tutorial is ideal for AI engineers, backend developers, and anyone building production-grade chatbots who wants to move beyond simple, stateless LLM interactions.

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