Are We Ready For An Agent-Native Memory System?

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. Despite this evolution, existing evaluations still benchmark agent memory mainly through end-to-end task success metrics (e.g., F1, BLEU), while treating the underlying system as a monolithic black box. As a result, critical system-level concerns, including operational costs, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates, remain insufficiently explored. In this paper, we present a systematic experimental study of agent memory from a data management perspective. We propose an analytical framework that decomposes agent memory into four core modules: memory representation and storage, extraction, retrieval and routing, and maintenance. Under this framework, we evaluate 12 representative memory systems and two reference baselines across five benchmark workloads spanning 11 datasets.

Introduction. The rapid evolution of Large Language Model (LLM) agents has sparked a large body of exciting research and industrial efforts in building agent memory, i.e., the data management system of the LLM agent that supports long-horizon stateful execution and personalized interaction [9, 13, 17, 19, 23, 24, 28]. As shown in Figure 1, existing agent memory systems span a diverse set of architectural designs. (1) Stream-and-Reflection Memory System (e.g., MemoryBank [39]) maintains experiences as timestamped memory streams and periodically summarizes them into higher-level reflections that are written back into the stream; beyond a single inference step (e.g., historical interactions, environmental observations, and intermediate tool executions) decoupled from the LLMs’ parametric weights and volatile context windows. Agent frameworks rely on these external memory systems (e.g., Mem0 [5], Letta [25], Zep [26], and A-MEM [33]) to actively write, update, index, and route relevant context back into the reasoning loop.