Long-Term Memory with LangGraph

Ever had an AI assistant forget what you told it yesterday? Frustrating, right? In this tutorial, Ricardo Ferreira, Developer Advocate at Redis, demonstrates how to build AI agents that remember conversations over time—creating truly human-like interactions. Ricardo shows you what long-term memory means for AI applications and guides you through implementing persistent memory using LangGraph and Redis. You'll learn to build a vector store that preserves user data across multiple conversations and master practical techniques for natural data retrieval. This tutorial is perfect for intermediate developers familiar with Python and basic AI concepts who want to take their AI agents to the next level. By the end of this video, you'll have the skills to build AI applications that actually remember who your users are and what they care about - creating more personalized, effective experiences that feel genuinely human. 00:00 Intro 01:13 Managing dependencies 04:30 OpenAI API Key 04:53 Vector store 07:05 Graph implementation 11:36 Memory testing 16:04 Inspecting memories 17:15 Closing 🧑🏻‍💻 GitHub repository: ▪️ LangGraph apps with Redis: https://github.com/redis-developer/la... 💡 Creating an OpenAI API key: ▪️ https://platform.openai.com/docs/quic... 🧑🏻‍💻 Other examples using LangGraph: ▪️ https://github.com/redis-developer/la...