LLM 智能体越来越多地部署在需要对长期交互历史进行持续推理的长期任务上。大规模实现这一点需要代理持久存储,、检索, 并跨会话更新自己的内存。一个丰富的代理内存系统生态系统已经出现，涵盖平面检索, LLM介导的提取,，整合事实存储,和代理控制流。然而, 他们的系统级行为仍然没有特征。我们提出了代理记忆的第一个系统特征。首先,我们引入了一种面向系统的分类法，沿着四个轴对代理内存系统进行分类。其次,，我们构建了一个阶段感知分析工具，将成本归因于构建, 检索, 和生成。第三, 我们描述了两个基准套件, 中的十个代表性系统的特征，揭示了设计选择如何跨写入和读取路径转移成本。最后,，我们得出 10 条系统建议，涵盖施工进度, 能力底线, 通过查询量进行摊销, 新鲜度-延迟权衡, 和车队规模管理。
LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale requires agents to persistently store, retrieve, and update their own memory across sessions. A rich ecosystem of agent memory systems has emerged spanning flat retrieval, LLM-mediated extraction, consolidating fact stores, and agentic control flows. Yet, their system-level behavior remains uncharacterized. We present the first systems characterization of agent memory. First, we introduce a system-oriented taxonomy classifying agent memory systems along four axes. Second, we build a phase-aware profiling harness attributing cost to construction, retrieval, and generation. Third, we characterize ten representative systems across two benchmark suites, uncovering how design choices shift cost across the write and read paths. Finally, we derive 10 system recommendations covering construction scheduling, capability floors, amortization via query volume, freshness-latency tradeoffs, and fleet-scale management.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)