OpenCUA: Open Foundations for Computer-Use Agents
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.
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Xinyuan Wang
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Junlin Yang
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Nick Yang
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Tianbao Xie
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Junli Wang
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Junli Wang
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Jiaqi Deng
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Yuxiao Ye
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Zaida Zhou
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Yipu Wang
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Heng Wang
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Diyi Yang
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Victor Zhong
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Flood Sung
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Tao Yu
OpenCUA: Open Foundations for Computer-Use Agents
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.
OpenCUA is an open-source framework for vision-language models as computer-use agents, featuring an annotation infrastructure, a large-scale dataset, and a scalable pipeline that achieves state-of-the-art performance.
vision-language models computer-use agents annotation infrastructure AgentNet state-action pairs Chain-of-Thought reasoning OSWorld-Verified OpenAI CUA GPT-4o