Persistent AI Agents: Long-Term Memory with LangGraph and LangMem

Static AI chatbots quickly forget context, limiting their usefulness in real-world applications. To build truly intelligent digital assistants, you need to equip them with persistent, long-term memory that spans multiple sessions. This text-based course guides you through the architecture of agentic memory, transitioning from basic state management to implementing advanced, self-improving memory systems that allow your AI agents to learn, adapt, and remember user preferences over time. What you'll learn: - Understand the core concepts of agentic memory, state management, and the difference between short-term and long-term memory. - Configure LangGraph to maintain state and persist agent memory across distinct conversational sessions. - Apply LangMem to automate memory extraction, summarization, and continuous learning from user interactions. - Integrate vector databases and semantic search patterns to help agents retrieve relevant historical context. - Design custom memory schemas that capture specific user preferences and behavioral patterns. - Practice debugging and refining memory updates to prevent information overload and hallucinations. You will start with the fundamental theory of memory in large language models before diving into practical, step-by-step code walkthroughs. Through clear written explanations and structured code snippets, you will build a fully functional, memory-enabled AI agent from scratch. This course is designed for software developers, AI enthusiasts, and tech professionals who want to build smarter applications. No prior experience with LangGraph or advanced AI agent frameworks is required, though a basic understanding of Python is helpful. Start reading today to build AI assistants that truly remember and adapt to their users.