Memory OS of AI Agent
Jiazheng Kang, Mingming Ji, Zhe Zhao, Ting Bai

TL;DR
This paper introduces MemoryOS, a hierarchical memory management system inspired by operating systems, designed to enhance long-term memory and personalization in AI agents, demonstrated by significant improvements on the LoCoMo benchmark.
Contribution
MemoryOS is a novel hierarchical memory management framework for AI agents, enabling dynamic memory updates and integration across multiple storage levels.
Findings
49.11% improvement in F1 score on LoCoMo benchmark
46.18% improvement in BLEU-1 score on LoCoMo benchmark
Enhanced contextual coherence and personalized memory retention
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Ferroelectric and Negative Capacitance Devices
