# Agent Memory

> Agent memory is how an AI agent retains information beyond its context window — working state during a task and persistent knowledge across sessions.

**Agent memory is the machinery that lets an agent know things its [context window](/glossary/context-window) no longer holds — working state within a long task, and persistent knowledge across sessions.**

The split mirrors the constraint. **Short-term memory** *is* the context window: ephemeral, complete, and finite — managed by compaction and careful loading. **Long-term memory** is storage outside the model: notes the agent writes, facts it accumulates, preferences it learns — persisted to files or databases and *retrieved* when relevant, which makes long-term memory largely a [retrieval](/glossary/rag) problem wearing a different hat.

Production patterns range from file-based (Claude Code's CLAUDE.md and [auto-memory](/guides/configuration/claude-code-memory-context) — transparent, versionable, user-editable) to dedicated memory layers ([Mem0](/tools/mem0), Zep) that extract, store, and retrieve facts automatically. The design questions that matter — what's worth remembering, when to recall it, how to forget what's stale — are the substance of [Agent Memory Architecture](/guides/concepts/agent-memory-architecture). The failure modes are instructive too: remember too little and the agent re-learns your codebase every session; remember too much and stale facts poison fresh work.

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_Source: https://agentscamp.com/glossary/agent-memory — Term on AgentsCamp._