LangMem

Description

LangMem is a Python library from the LangChain team that adds long-term memory primitives to agent applications. Its conceptual guide names three memory types — semantic (facts that ground responses), episodic (successful past interactions used as learning examples), and procedural (how the agent should behave and respond) — and exposes typed extraction, storage, and prompt-optimization APIs over each. LangMem integrates with LangGraph's BaseStore so writes can be managed automatically and surfaced as tools the agent calls directly, rather than only as a sidecar service.

Solution

Library invoked from inside an agent's tool-calling loop, optionally backed by LangGraph's BaseStore. The agent (or a background manager) calls LangMem extractors that classify writes into semantic, episodic, and procedural buckets; semantic and episodic memories are persisted as searchable items, while procedural memory is materialised as updated system-prompt instructions via a prompt-optimizer (metaprompt / gradient / prompt_memory). Retrieved memories are injected back into the next prompt.

Primary use cases

• persistent agent memory across LangGraph runs
• episodic recall of prior successful interactions
• semantic fact stores that agents update over time
• procedural memory for evolving agent system prompts