CuratorMem: Metadata-Grounded Curation of Accumulating Agent Memory

Large language model (LLM) agents act in environments that keep changing, where tools, code and user goals move on between encounters, so their memory fills with successive attempts at related problems that overlap. To deal with this situation, patch-based memory is introduced to manage these attempts: each attempt is recorded as a patch, and the state it supersedes is kept as history, enabling cross-patch retrieval. However, a store built to persist is written once and never revised, so it decays as it grows: redundant entries accumulate and split the retriever’s focus. In a patch-based system, superseded attempts and the ones that replaced them sit side by side, and there is no record to evaluate their worth. For this reason, we propose CuratorMem, a memory layer over an append-only, patch-based store: a manifest carries version lineage and per-entry statistics, an additive curation operator refines and enriches what is stored based on these heuristics, and retrieval is scoped to a task chain and ordered by that lineage. The key idea is to decouple evidence from opinion: curated content is written by a model, but an entry may be endorsed only on a recorded outcome, grounded outside the model that wrote it. Across GAIA, GAIA2, LoCoMo and τ²-bench with multiple backbones, CuratorMem improves on an uncurated patch store and two external memory baselines (A-Mem, Mem0). Every experiment in this paper is backed by full trajectories, and the system will be released as a memory-layer SDK.