OpenCUA: Open Foundations for Computer-Use Agents
Authors: name: Xinyuan Wang, name: Bowen Wang, name: Dunjie Lu, name: Junlin Yang, name: Tianbao Xie, name: Junli Wang, name: Jiaqi Deng, name: Xiaole Guo, name: Yiheng Xu, name: Chen Henry Wu, name: Zhennan Shen, name: Zhuokai Li, name: Ryan Li, name: Xiaochuan Li, name: Junda Chen, name: Boyuan Zheng, name: Peihang Li, name: Fangyu Lei, name: Ruisheng Cao, name: Yeqiao Fu, name: Dongchan Shin, name: Martin Shin, name: Jiarui Hu, name: Yuyan Wang, name: Jixuan Chen, name: Yuxiao Ye, name: Danyang Zhang, name: Dikang Du, name: Hao Hu, name: Huarong Chen, name: Zaida Zhou, name: Yipu Wang, name: Heng Wang, name: Diyi Yang, name: Victor Zhong, name: Flood Sung, name: Y. Charles, name: Zhilin Yang, name: Tao Yu

Vision-language models have demonstrated impressive capabilities as
computer-use agents (CUAs) capable of automating diverse computer tasks. As
their commercial potential grows, critical details of the most capable CUA
systems remain closed. As these agents will increasingly mediate digital
interactions and execute consequential decisions on our behalf, the research
community needs access to open CUA frameworks to study their capabilities,
limitations, and risks. To bridge this gap, we propose OpenCUA, a comprehensive
open-source framework for scaling CUA data and foundation models. Our framework
consists of: (1) an annotation infrastructure that seamlessly captures human
computer-use demonstrations; (2) AgentNet, the first large-scale computer-use
task dataset spanning 3 operating systems and 200+ applications and websites;
(3) a scalable pipeline that transforms demonstrations into state-action pairs
with reflective long Chain-of-Thought reasoning that sustain robust performance
gains as data scales. Our end-to-end agent models demonstrate strong
performance across CUA benchmarks. In particular, OpenCUA-32B achieves an
average success rate of 34.8% on OSWorld-Verified, establishing a new
state-of-the-art (SOTA) among open-source models and surpassing OpenAI CUA
(GPT-4o). Further analysis confirms that our approach generalizes well across
domains and benefits significantly from increased test-time computation. We
release our annotation tool, datasets, code, and models to build open
foundations for further CUA research.