LangMem SDK for agent long-term memory
AI Summary
LangChain's February 2025 release of LangMem, a storage-agnostic SDK for building the three memory types — semantic (facts), episodic (past interactions as few-shot examples), procedural (saved as updated prompts) — into any agent. The post is less a product launch and more a normative claim: these are the three types that matter, this is how you implement each, and your agent framework should be able to compose them rather than pick one. Pair with the LangGraph store documentation, which operationalizes the same taxonomy with namespaces and checkpointers. Useful because it commits to a vocabulary when most frameworks are still hand-waving about what memory even means.
Original excerpt
Today we're releasing the LangMem SDK, a library that helps your agents learn and improve through long-term memory.
It provides tooling to extract information from conversations, optimize agent behavior through prompt updates, and maintain long-term memory about behaviors, facts, and events.
You can use its core API with any storage system and within any Agent framework, and it integrates natively with LangGraph's long-term memory layer. We are also launching a managed service that provides additional long-term memory results for free - sign up here if you are interested in using it in production.
Our goal is to make it easier for anyone to build AI experiences that become smarter and more…
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