Explore the LangMem SDK for agent long-term memory features, architecture, and how it enables persistent, context-aware AI agents.

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LangMem SDK, released by the LangChain team, adds persistent long-term memory to LLM-powered AI agents that are otherwise stateless and context-window-bound. The SDK provides a memory manager (LLM-driven), storage-agnostic backend support, and two core tools—manage_memory and search_memory—that agents can call to store and retrieve facts, preferences, and task history across sessions. The article covers LangMem's layered architecture, a step-by-step Python integration using LangGraph's InMemoryStore and optionally PostgresStore for production persistence, namespace-based user isolation to prevent memory leakage, and performance considerations including memory pruning, retrieval efficiency, context window usage, and cost management. A comparison table contrasts LangMem against DIY RAG-style memory and other memory SDKs, highlighting LangMem's faster integration and cleaner separation of memory logic.

LangMem SDK for Agent Long-Term Memory

Why Long-Term Memory Matters for AI Agents