AI 中文总结
研究以Oracle数据库为基础的Oracle代理内存作为长期人工智能代理的企业内存基础，围绕内存生命周期、分层架构及评估方法展开探讨，总结LongMemEval结果，与基线比较，展示其优势，并提供相关实现附录。
AI 中文摘要
代理内存是长期代理的系统问题。实际部署需要在长时间对话中保留任务状态，跨会话恢复用户特定事实和偏好，以及从先前结果中积累程序知识。这些要求超出文档检索范畴。本报告研究基于Oracle数据库构建的Oracle代理内存作为数据库原生内存基础。讨论围绕内存的生命周期，包括摄取、提取、整合、检索、总结、修订或删除；分层架构，将活动内存核心与被动内存存储接口分离并进行显式范围控制；评估方法，通过以内存为中心的指标（如证据检索、召回率、延迟和估计令牌使用）补充下游任务准确性。报告总结了LongMemEval结果，准确率达93.8%，并与扁平历史基线比较，使用的令牌少约10.7倍，还与可用的已发布或报告的外部基线比较，最后附有涵盖设置、线程生命周期和搜索语义的面向实现的附录材料。
英文摘要
Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery of user-specific facts and preferences across sessions, and accumulation of procedural knowledge from prior outcomes. These requirements extend beyond document retrieval: a memory layer must determine which interactions become durable state, how that state is scoped, how it is retrieved under latency constraints, and how it is revised or removed over time. This report studies Oracle Agent Memory as a database-native memory substrate built on Oracle Database. Three themes organize the discussion: memory as a lifecycle spanning ingestion, extraction, consolidation, retrieval, summarization, and revision or removal; a layered architecture that separates an active memory core from a passive memory-store interface with explicit scope control across users, agents, and threads; and evaluation methodology in which downstream task accuracy is complemented by memory-centric measures such as evidence retrieval, recall, latency, and estimated token use. The report summarizes LongMemEval results, reaching 93.8% accuracy, compares Oracle Agent Memory against flat-history baselines, using about 10.7x fewer tokens, and published or reported external baselines where available, and closes with implementation-oriented appendix material covering setup, thread lifecycle, and search semantics.
Comments23 pages, 7 figures. Technical report on Oracle Agent Memory
