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Machine Learning

Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability

Sizhe Zhou, Sheldon Yu, Hui Wei, Junda Wu, Siru Ouyang, Yizhu Jiao, Shijia Pan, Julian McAuley, Yu Zhang, Tong Yu, Jiawei Han

Featured August 2, 2026

Original Paper

AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

The paper studies how LLM agents use a simple file system to remember things, showing that organizing memory helps find info faster but doesn't always make answers better, depending on the agent's smarts and tools.

In depth
The paper systematically explores filesystem-based memory for LLM agents, formalizing it with distinct roles for managing, searching, and executing. It investigates how agents organize and maintain memory as it grows, finding that while organization significantly reduces retrieval cost for large stores, it does not consistently improve answer quality, and its effectiveness depends on the agent's capability and toolset.

Key Takeaways

  • 1
    Filesystem memory is formalized with three roles: a management agent, a search agent, and an execution agent, unifying declarative and procedural memory.
  • 2
    Organization of memory primarily reduces search cost for large datasets, but does not consistently translate to improved answer quality.
  • 3
    The management agent's capability and the available toolset significantly influence memory organization and its utility, especially for distilling procedural knowledge.

Conceptual Flow

HIGH LEVEL
1
Methodology: How LLM Agents Use Filesystem Memory

The paper studies how LLM agents use a simple file system to remember things, breaking down the process into three main jobs: one agent organizes, another finds answers, and a third does tasks.

Agent Actions
User Questions
Manage & Find
Organized Files
Task Answers
2
Results: Impact of Memory Organization

They found that organizing memory helps agents find information faster, but it doesn't always make their answers more accurate, and how well it works depends on the agent's smarts and tools.

Organized Memory
Flat Memory
Compare Performance
Faster Search
Similar Answers

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