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. If you find this helpful, don’t forget to like, subscribe, and share. *Check the code on GitHub:* https://github.com/CodeByFelix/AI-Agent-Tutorial *Connect with me* *LinkedIn:* https://www.linkedin.com/in/felix-ibeamaka/ *X:* https://x.com/Electronics__ *Facebook:* https://www.facebook.com/felixibeamaka/ *Instagram:* https://www.instagram.com/electroni6_247/ *TikTok:* https://www.tiktok.com/@UClF6nEUxzBRYcd8vjQOenyA *Join My WhatsApp Group:* https://chat.whatsapp.com/C9mZeTCLiNQAe4yxCTmVy4 #langgraph #aichatbot #PersistentMemory #sqlite #sqlite3 #pythonai #llm #langchain #backendengineering