MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing

Hugging Face Daily Papers Papers
memory-framework long-context hierarchical-indexing parallel-chunk-extraction llm-agents temporal-data-management

Summary

MemForest proposes a memory framework for long-context LLM agents that improves scalability and reduces latency through parallel chunk extraction and hierarchical temporal indexing, achieving 6x higher throughput on benchmarks.

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.
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Paper page - MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing

Source: https://huggingface.co/papers/2605.23986

Abstract

MemForest presents a memory framework for long-context LLM agents that improves scalability and reduces latency through parallel chunk extraction and hierarchical temporal indexing.

Memory is a fundamental component for enablinglong-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 requirefull-state rewrites, leading to poor scalability and growing latency as memory accumulates. To address these challenges, we present MemForest, amemory frameworkthat reformulates agent memory as a write-efficienttemporal data managementproblem. MemForest breaks the sequential bottleneck viaparallel chunk extraction, decoupling memory construction into concurrent, independent operations. To further eliminate coarse-grained maintenance, we introduceMemTree, ahierarchical temporal indexthat organizes memory as time-ordered trees rather than flat global summaries. This design replacesfull-state rewriteswithlocalized 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.

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