OS Agents: A Survey on MLLM-based Agents for General Computing Devices
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Authors: name: Xueyu Hu, name: Tao Xiong, name: Biao Yi, name: Zishu Wei, name: Ruixuan Xiao, name: Yurun Chen, name: Jiasheng Ye, name: Meiling Tao, name: Xiangxin Zhou, name: Ziyu Zhao, name: Yuhuai Li, name: Shengze Xu, name: Shenzhi Wang, name: Xinchen Xu, name: Shuofei Qiao, name: Zhaokai Wang, name: Kun Kuang, name: Tieyong Zeng, name: Liang Wang, name: Jiwei Li, name: Yuchen Eleanor Jiang, name: Wangchunshu Zhou, name: Guoyin Wang, name: Keting Yin, name: Zhou Zhao, name: Hongxia Yang, name: Fan Wu, name: Shengyu Zhang, name: Fei Wu

The dream to create AI assistants as capable and versatile as the fictional
J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution
of (multi-modal) large language models ((M)LLMs), this dream is closer to
reality, as (M)LLM-based Agents using computing devices (e.g., computers and
mobile phones) by operating within the environments and interfaces (e.g.,
Graphical User Interface (GUI)) provided by operating systems (OS) to automate
tasks have significantly advanced. This paper presents a comprehensive survey
of these advanced agents, designated as OS Agents. We begin by elucidating the
fundamentals of OS Agents, exploring their key components including the
environment, observation space, and action space, and outlining essential
capabilities such as understanding, planning, and grounding. We then examine
methodologies for constructing OS Agents, focusing on domain-specific
foundation models and agent frameworks. A detailed review of evaluation
protocols and benchmarks highlights how OS Agents are assessed across diverse
tasks. Finally, we discuss current challenges and identify promising directions
for future research, including safety and privacy, personalization and
self-evolution. This survey aims to consolidate the state of OS Agents
research, providing insights to guide both academic inquiry and industrial
development. An open-source GitHub repository is maintained as a dynamic
resource to foster further innovation in this field. We present a 9-page
version of our work, accepted by ACL 2025, to provide a concise overview to the
domain.