Are We Ready For An Agent-Native Memory System?

Hugging Face Daily Papers Papers
agent-memory memory-systems llm-agents data-management performance-analysis

Summary

This paper presents a systematic experimental study of agent memory systems from a data management perspective, decomposing memory into four core modules and evaluating 12 representative systems across 11 datasets, finding no single architecture dominates and highlighting cost-performance trade-offs.

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. 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. Furthermore, through fine-grained ablation studies, we quantify their individual effects on representation fidelity, retrieval precision, update correctness, and long-horizon stability. Finally, 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. The code is publicly available at https://github.com/OpenDataBox/MemoryData.
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Paper page - Are We Ready For An Agent-Native Memory System?

Source: https://huggingface.co/papers/2606.24775

Abstract

Large language model agents’ memory systems have evolved into complex data management frameworks requiring systematic evaluation across multiple modules and workloads to understand their performance characteristics and trade-offs.

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 acrossmemory modules, and robustness under dynamic knowledge updates, remain insufficiently explored. In this paper, we present a systematic experimental study of agent memory from adata management perspective. We propose an analytical framework that decomposes agent memory into four core modules:memory representation and storage,extraction,retrieval and routing, andmaintenance. Under this framework, we evaluate 12 representative memory systems and two reference baselines across five benchmark workloads spanning 11 datasets. 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 theworkload bottleneck. Furthermore, through fine-grained ablation studies, we quantify their individual effects onrepresentation fidelity,retrieval precision,update correctness, andlong-horizon stability. Finally, we revealcost-performance trade-offsunder realistic workloads, showing localizedmaintenanceis more cost-efficient than global reorganization. Based on these findings, we identify promising directions towards building trulyagent-native memory systems. The code is publicly available at https://github.com/OpenDataBox/MemoryData.

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