Systematic Study of Agent Memory Systems for LLMs Reveals No One-Size-Fits-All Architecture

Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution.

Our extensive end-to-end evaluation shows that no single architecture dominates across all scenarios; instead, effectiveness depends heavily on how well the memory structure aligns with the workload bottleneck.

We reveal cost-performance trade-offs under realistic workloads, showing localized maintenance is more cost-efficient than global reorganization.

Based on these findings, we identify promising directions towards building truly agent-native memory systems.

Critical system-level concerns, including operational costs, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates, remain insufficiently explored.