A practical guide to agent memory: three layers, storage choices, and pitfalls

大模型之路 · wechat · 2026-08-18

This tutorial-style article walks through engineering agent memory. Core claim: agent memory — persisting information across sessions and tasks — is distinct from context windows and RAG: context is per-session token capacity, RAG temporarily retrieves external knowledge, and memory lets the model keep its own notes. Memory is typically split into three layers: working memory for current-task state, episodic memory as a diary of events, and semantic memory for distilled user preferences and business rules; putting data in the wrong layer causes cross-task pollution or noisy retrieval.

Practical guidance: use Redis-like ephemeral storage for working memory, timestamped vector stores with TTL expiry for episodic memory, and databases with dedup/versioning for semantic memory; all reads/writes must carry user ownership tags or multi-user memory bleed becomes a top production incident source; never store plaintext secrets in semantic memory — sanitize and enforce tenant isolation. The litmus test for whether to add memory at all: does doing the task a second time benefit from the first? If not, skip the complexity.

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A practical guide to agent memory: three layers, storage choices, and pitfalls

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