Agent S: An Open Agentic Framework that Uses Computers Like a Human
Authors: name: Saaket Agashe, name: Jiuzhou Han, name: Shuyu Gan, name: Jiachen Yang, name: Ang Li, name: Xin Eric Wang

We present Agent S, an open agentic framework that enables autonomous
interaction with computers through a Graphical User Interface (GUI), aimed at
transforming human-computer interaction by automating complex, multi-step
tasks. Agent S aims to address three key challenges in automating computer
tasks: acquiring domain-specific knowledge, planning over long task horizons,
and handling dynamic, non-uniform interfaces. To this end, Agent S introduces
experience-augmented hierarchical planning, which learns from external
knowledge search and internal experience retrieval at multiple levels,
facilitating efficient task planning and subtask execution. In addition, it
employs an Agent-Computer Interface (ACI) to better elicit the reasoning and
control capabilities of GUI agents based on Multimodal Large Language Models
(MLLMs). Evaluation on the OSWorld benchmark shows that Agent S outperforms the
baseline by 9.37% on success rate (an 83.6% relative improvement) and achieves
a new state-of-the-art. Comprehensive analysis highlights the effectiveness of
individual components and provides insights for future improvements.
Furthermore, Agent S demonstrates broad generalizability to different operating
systems on a newly-released WindowsAgentArena benchmark. Code available at
https://github.com/simular-ai/Agent-S.