Memory is a fundamental component for enabling long-context LLM agents, supporting persistent state across interactions through a continuous serve-and-update lifecycle. Despite substantial prior work, existing systems suffer from significant maintenance overhead due to two key limitations: coarse-grained state management and inherently sequential update pipelines. In particular, updates are often tightly coupled with LLM inference and require full-state rewrites, leading to poor scalability and growing latency as memory accumulates. To address these challenges, we present MemForest, a memory framework that reformulates agent memory as a write-efficient temporal data management problem. MemForest breaks the sequential bottleneck via parallel chunk extraction, decoupling memory construction into concurrent, independent operations. To further eliminate coarse-grained maintenance, we introduce MemTree, a hierarchical temporal index that organizes memory as time-ordered trees rather than flat global summaries. This design replaces full-state rewrites with localized per-node updates, reducing maintenance cost to the affected tree paths while naturally preserving temporally evolving states. We evaluate MemForest on two long-context memory benchmarks, LongMemEval-S and LoCoMo. On LongMemEval-S, MemForest achieves the best overall performance among stateful baselines, reaching 79.8% pass@1 accuracy while sustaining a memory construction throughput approximately 6x higher than state-of-the-art approaches including EverMemOS.

翻译：记忆是实现长上下文大语言模型智能体的基础组件，通过持续的存储与更新生命周期维持交互过程中的持久状态。尽管已有大量研究工作，现有系统仍存在两大关键局限导致的显著维护开销：粗粒度的状态管理机制和内在的串行更新管线。具体而言，更新操作常与LLM推理过程紧密耦合且需执行全状态重写，导致系统可扩展性差，且随着记忆积累出现延迟增长。针对上述挑战，我们提出MemForest记忆框架，将智能体记忆重构为写优化的时序数据管理问题。该框架通过并行分块抽取机制打破串行瓶颈，将记忆构建解耦为可独立执行的并发操作。为消除粗粒度维护问题，我们进一步引入MemTree层次化时序索引，采用时序有序树结构而非扁平化全局摘要来组织记忆。该设计将全状态重写替换为局部逐节点更新，将维护开销限制在受影响的树路径上，同时自然保留时序演化的状态信息。我们在两个长上下文记忆基准测试LongMemEval-S和LoCoMo上对MemForest进行评估。在LongMemEval-S上，MemForest在持久化基线方法中达到最佳综合性能，实现79.8%的pass@1准确率，同时记忆构建吞吐量较包括EverMemOS在内的最新方法提升约6倍。