MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing

2026-05-16

AI Summary

Existing memory systems for large language model agents suffer from high maintenance overhead, poor scalability, and increasing latency as memory grows, primarily due to coarse-grained state management and sequential update mechanisms. This work reframes memory management as a write-efficient time-series data problem and introduces MemTree—a hierarchical temporal indexing structure that replaces global summarization with time-ordered trees, enabling path-localized updates. Additionally, it incorporates parallel chunk extraction and a decoupled memory construction pipeline. The proposed approach substantially reduces maintenance costs while preserving the temporal evolution of states, achieving a 79.8% pass@1 accuracy on LongMemEval-S and a memory construction throughput six times higher than the current best method, such as EverMemOS.

Abstract

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.

Problem

• agent memory
• long-context LLM
• memory maintenance overhead
• sequential update
• state management

Innovation

• hierarchical temporal indexing
• parallel chunk extraction
• write-efficient memory
• agent memory system
• localized updates