Memory OS of AI Agent

• URL: http://arxiv.org/abs/2506.06326v1
• Date: Fri, 30 May 2025 15:36:51 GMT
• Title: Memory OS of AI Agent
• Authors: Jiazheng Kang, Mingming Ji, Zhe Zhao, Ting Bai,
• Abstract summary: Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management.<n>We propose a Memory Operating System, i.e., MemoryOS, to achieve comprehensive and efficient memory management for AI agents.
• Score: 3.8665965906369375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management, leading to a severe shortage of long-term memory capabilities and limited personalization in the interactive experience with AI agents. To overcome this challenge, we innovatively propose a Memory Operating System, i.e., MemoryOS, to achieve comprehensive and efficient memory management for AI agents. Inspired by the memory management principles in operating systems, MemoryOS designs a hierarchical storage architecture and consists of four key modules: Memory Storage, Updating, Retrieval, and Generation. Specifically, the architecture comprises three levels of storage units: short-term memory, mid-term memory, and long-term personal memory. Key operations within MemoryOS include dynamic updates between storage units: short-term to mid-term updates follow a dialogue-chain-based FIFO principle, while mid-term to long-term updates use a segmented page organization strategy. Our pioneering MemoryOS enables hierarchical memory integration and dynamic updating. Extensive experiments on the LoCoMo benchmark show an average improvement of 49.11% on F1 and 46.18% on BLEU-1 over the baselines on GPT-4o-mini, showing contextual coherence and personalized memory retention in long conversations. The implementation code is open-sourced at https://github.com/BAI-LAB/MemoryOS.

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