Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (1): 1-11.doi: 10.11896/jsjkx.250500002

• Research and Application of Large Language Model Technology • Previous Articles     Next Articles

Comprehensive Survey of LLM-based Agent Operating Systems

GUO Luxiang, WANG Yueyu, LI Qianyue, LI Shasha, LIU Xiaodong, JI Bin, YU Jie   

  1. College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
  • Received:2025-05-06 Revised:2025-07-22 Published:2026-01-08
  • About author:GUO Luxiang,born in 1995,Ph.D candidata,is a member of CCF(No.Y4591G).His main research interests include artificial intelligence,large language models,agent,and agent operation systems.
    WANG Yueyu,born in 2000,graduate.His main research interests include artificial intelligence,large language mo-dels,agent,and agent operating systems.
    LI Qianyue,born in 2002,Ph.D candidata.Her main research interests include artificial intelligence,large language models,agent,and agent operating system.
    LI Shasha,born in 1982,Ph.D,associate professor,Ph.D supervisor.Her main research interests include artificial intelligence,large language models,agent,and agent operation systems.
  • Supported by:
    National Key Research and Development Program of China(2024YFB4506200).

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Abstract: Large language model-based agent operating systems(Agent OS),as core platforms for integrating large models,tool resources,and multi-agent collaboration,are gradually becoming a key research direction for advancing general artificial intelligence.This paper systematically reviews the research progress in the field of Agent OS.It begins by discussing foundational theories,reviewing the evolution of various large language models,and progress in agent technology and traditional operating systems.This paper then elaborates on how their hierarchical architectures and modular designs achieve resource management and intelligent scheduling,focusing on typical architectures such as AIOS.Furthermore,it clarifies existing technical bottlenecks in scalability,context integration,and security within current systems.It also proposes future directions,including the use of lightweight designs,self-supervised learning mechanisms,and dynamic scheduling algorithms to optimize multi-agent cooperation efficiency.The main contributions of this paper are integrating fragmented research to provide a clearer technical framework,and highlighting the current limitations of Agent OS in covering emerging applications and industry-specific customizations.Future work should focus on enhancing the capability of cross-domain Agent OS for self-evolution and accelerating their implementation across diverse fields.

Key words: Large language model, Agent OS, General artificial intelligence, Agent, Traditional operating system

CLC Number: 

  • TP316
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