LLM Agent Memory

LLM agent memory studies how systems preserve useful facts, user preferences, task state, and reusable procedures beyond the current context window, so they can act consistently across sessions and long workflows. Common approaches include summarizing or retaining raw episodes, structured temporal or graph stores, retrieval-augmented memory, hierarchical episodic and procedural tiers, and learned policies for writing, consolidation, selection, and forgetting. Work is tested on long-horizon dialogue and personalization benchmarks such as LoCoMo and LongMemEval, as well as coding, web, tool-use, and embodied tasks; newer evaluations probe multi-turn learning, changing facts, and whether agents actually use retrieved evidence. The central tension is that compression and selective retrieval save tokens but can discard evidence or introduce stale, conflicting, biased, or poisoned memories, while larger stores and elaborate retrieval add cost and raise privacy and governance concerns.