# From Commands to Prompts: LLM-based Semantic File System for AIOS

arXiv: https://arxiv.org/abs/2410.11843 · HTML: https://mlsanity.com/paper/2410.11843/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 23 Sep 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Built on by (most cited first, 14 of 14)

- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [OmniRouter: Budget and Performance Controllable Multi-LLM Routing](https://mlsanity.com/paper/2502.20576/) (2025)
- [OS Agents: A Survey on MLLM-based Agents for General Computing Devices Use](https://mlsanity.com/paper/2508.04482/) (2025)
- [AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems](https://mlsanity.com/paper/2605.08715/) (2026)
- [Cerebrum (AIOS SDK): A Platform for Agent Development, Deployment, Distribution, and Discovery](https://mlsanity.com/paper/2503.11444/) (2025)
- [Everything is Context: Agentic File System Abstraction for Context Engineering](https://mlsanity.com/paper/2512.05470/) (2025)
- [HyMem: Hybrid Memory Architecture with Dynamic Retrieval Scheduling](https://mlsanity.com/paper/2602.13933/) (2026)
- [AgentLens: Interpretable Safety Steering via Mechanistic Subspaces for Multi-Turn Coding Agent](https://mlsanity.com/paper/2606.22673/) (2026)
- [Towards Machine-Generated Code for the Resolution of User Intentions](https://mlsanity.com/paper/2504.17531/) (2025)
- [Structured Information for Improving Spatial Relationships in Text-to-Image Generation](https://mlsanity.com/paper/2509.15962/) (2025)
- [ObjectGraph: From Document Injection to Knowledge Traversal -- A Native File Format for the Agentic Era](https://mlsanity.com/paper/2604.27820/) (2026)
- [StackPilot: Autonomous Function Agents for Scalable and Environment-Free Code Execution](https://mlsanity.com/paper/2508.11665/) (2025)
- [Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions](https://mlsanity.com/paper/2510.08576/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated significant potential in the development of intelligent applications and systems such as LLM-based agents and agent operating systems (AIOS). However, when these applications and systems interact with the underlying file system, the file system still remains the traditional paradigm: reliant on manual navigation through precise commands. This paradigm poses a bottleneck to the usability of these systems as users are required to navigate complex folder hierarchies and remember cryptic file names. To address this limitation, we propose an LLM-based semantic file system ( LSFS ) for prompt-driven file management. Unlike conventional approaches, LSFS incorporates LLMs to enable users or agents to interact with files through natural language prompts, facilitating semantic file management. At the macro-level, we develop a comprehensive API set to achieve semantic file management functionalities, such as semantic file retrieval, file update monitoring and summarization, and semantic file rollback). At the micro-level, we store files by constructing semantic indexes for them, design and implement syscalls of different semantic operations (e.g., CRUD, group by, join) powered by vector database. Our experiments show that LSFS offers significant improvements over traditional file systems in terms of user convenience, the diversity of supported functions, and the accuracy and efficiency of file operations. Additionally, with the integration of LLM, our system enables more intelligent file management tasks, such as content summarization and version comparison, further enhancing its capabilities.