MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents

TL;DR

MemSkill introduces a self-evolving memory system for LLM agents that learns, refines, and adapts memory operations over time, leading to improved task performance and better handling of long interaction histories.

Contribution

It proposes a novel framework where memory operations are learnable and evolvable, enabling agents to adapt their memory management strategies dynamically.

Findings

Improves task performance across multiple benchmarks.

Demonstrates effective skill evolution and refinement.

Generalizes well across diverse settings.

Abstract

Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors about what to store and how to revise memory, making them rigid under diverse interaction patterns and inefficient on long histories. To this end, we present \textbf{MemSkill}, which reframes these operations as learnable and evolvable memory skills, structured and reusable routines for extracting, consolidating, and pruning information from interaction traces. Inspired by the design philosophy of agent skills, MemSkill employs a \emph{controller} that learns to select a small set of relevant skills, paired with an LLM-based \emph{executor} that produces skill-guided memories. Beyond learning skill selection, MemSkill introduces a \emph{designer} that periodically reviews hard cases where selected skills yield incorrect or incomplete memories, and evolves the skill set by proposing refinements and new skills. Together, MemSkill forms a closed-loop procedure that improves both the skill-selection policy and the skill set itself. Experiments on LoCoMo, LongMemEval, HotpotQA, and ALFWorld demonstrate that MemSkill improves task performance over strong baselines and generalizes well across settings. Further analyses shed light on how skills evolve, offering insights toward more adaptive, self-evolving memory management for LLM agents.