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Most agent systems reason well in the moment but fail to improve over time because they forget everything once execution ends. In this episode, we explore how to design long-term memory for LangGraph agents, moving beyond short-term context toward durable, structured memory that remains transparent and controllable. You’ll learn:

* Why long-term memory is an architectural problem, not a prompt-engineering trick, and how different memory types (working, semantic, episodic, procedural) interact in agent systems

* What LangGraph provides out of the box for memory management—and where it stops, especially when building agents that must persist, update, and reason over memory across sessions

* How to implement schema-driven long-term memory with Trustcall, enabling safe extraction, controlled updates, and debuggable memory writes inside LangGraph nodes

If you’d rather read than listen, the full article (with diagrams, code examples, and implementation details) is available on Substack:

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PODCAST — Long-Term Memory: Unlocking Smarter, Scalable AI Agents