Memory for Autonomous LLM Agents:Mechanisms, Evaluation, and Emerging Frontiers
Large language model (LLM) agents increasingly operate in settings where a single context window is far too small to capture what has happened, what was learned, and what should not be repeated. Memory -- the ability to persist, organize, and selectively recall information across interactions -- is what turns a stateless text generator into a genuinely adaptive agent. This survey offers a structured account of how memory is designed, implemented, and evaluated in modern LLM-based agents, covering work from 2022 through early 2026. We formalize agent memory as a \emph{write--manage--read} loop tightly coupled with perception and action, then introduce a three-dimensional taxonomy spanning ...
| Authors | Pengfei Du |
|---|---|
| Primary category | cs.AI |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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