OpenCUA

📢 Updates

• 2026-01-17: 🎉 vLLM now fully supports OpenCUA-7B, OpenCUA-32B, and OpenCUA-72B! Thanks to the Meituan EvoCUA Team for their contributions to vLLM integration. See vLLM Serve for usage instructions.

• 2025-12-17: You can now view AgentNet dataset trajectories online via AgentNet Data Viewer, or use the code in data/vis/ to visualize your own trajectory data. See vis/README.md for usage instructions. We also summarized the metadata of AgentNet here Metadata json.

• 2025-11-28: VLLM support of OpenCUA is available at [Model] Add OpenCUA-7B support #29068. Super grateful to lim4349 !

• 2025-10-12: OpenCUA-7B-exl2 is now live! ⚡️
  Thanks to Sujit Vasanth for producing a quantized exllamav2 version of OpenCUA-7B — enabling much faster inference with lower VRAM usage.

• 2025-10-03: New OpenCUA model!🔥 OpenCUA-72B now ranks #1 on the OSWorld-Verified leaderboard. It also has strong grounding ability, 37.3% (SOTA) on UI-Vision and 60.8% on ScreenSpot-Pro.

• 2025-08-13: We released our paper and project page. Check it out!

Introduction

OpenCUA is a comprehensive open-source framework for scaling CUA data and foundation models, consisting of:

• AgentNet: the first large-scale computer-use task dataset spanning 3 operating systems and 200+ applications and websites;
• AgentNetTool: an annotation infrastructure that seamlessly captures human computer-use demonstrations;
• AgentNetBench: an offline evaluator that benchmarks model-predicted low-level actions against ground-truth trajectories.
• OpenCUA Models: end-to-end computer-use foundation models than can produce executable actions in the computer environments with great planning and grounding capabilities.

With the help of OpenCUA framework, our end-to-end agent models demonstrate strong performance across CUA benchmarks. In particular, OpenCUA-72B achieves an average success rate of 45.0% on OSWorld-Verified, establishing a new state-of-the-art (SOTA) among open-source models.

🚀 Quick Start of OpenCUA Models

To align with our training infrastructure, we have modified the model in two places:

Installation & Download

First, install the required transformers dependencies:

conda create -n opencua python=3.10
conda activate opencua
pip install -r requirement.txt

Download the model weight from huggingface:

from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="xlangai/OpenCUA-7B",
    local_dir="OpenCUA-7B",                
    local_dir_use_symlinks=False  
)

🚀 vLLM Serve

We recommend using vLLM for production deployment. Requires vllm>=0.12.0 with --trust-remote-code.

# OpenCUA-7B (single GPU)
vllm serve xlangai/OpenCUA-7B \
  --trust-remote-code \
  --served-model-name opencua-7b \
  --host 0.0.0.0 \
  --port 8000

# OpenCUA-32B (4 GPUs, tensor parallel)
vllm serve xlangai/OpenCUA-32B \
  --trust-remote-code \
  --tensor-parallel-size 4 \
  --served-model-name opencua-32b \
  --host 0.0.0.0 \
  --port 8000

# OpenCUA-72B with data parallelism (tp=2, dp=4 for 4 instances on 8 GPUs)
vllm serve xlangai/OpenCUA-72B \
  --trust-remote-code \
  --tensor-parallel-size 2 \
  --data-parallel-size 4 \
  --gpu-memory-utilization 0.85 \
  --host 0.0.0.0 \
  --port 8000

Adjust --tensor-parallel-size, --data-parallel-size, and --gpu-memory-utilization based on your hardware configuration.

For more examples and inference code, see model/inference/vllm_inference.py.

🎯 GUI Grounding

First, start the vLLM server (using OpenCUA-7B as example):

vllm serve xlangai/OpenCUA-7B \
  --trust-remote-code \
  --served-model-name opencua-7b \
  --host 0.0.0.0 \
  --port 8000

Then run the grounding examples:

cd ./model/inference/
python vllm_inference.py

Or with HuggingFace Transformers (no server required):

python huggingface_inference.py

🖥️ Computer Use Agent

OpenCUAAgent is developed in the OSWorld environment based on OpenCUA models. It iteratively perceives the environment via screenshots, produces reflective long CoT as inner monologue, and predicts the next action to be executed. OpenCUAAgent uses 3 images in total and L2 CoT format in default.

Command for running OpenCUA-7B and OpenCUA-32B in OSWorld:

    python run_multienv_opencua.py \
        --headless \
        --observation_type screenshot \
        --model OpenCUA-32B \
        --result_dir ./results --test_all_meta_path evaluation_examples/test_all_no_gdrive.json \
        --max_steps 100 \
        --num_envs 30  \
        --coordinate_type qwen25

---

Performance

Online Agent Evaluation

OpenCUA models achieves strong performance on OSWorld-Verified. OPENCUA-32B achieves the best performance among all open-source models with an average success rate of 34.8%, outperforming prior baselines by large margins. It also closes the gap to proprietary Claude models.

Model	15 Steps	50 Steps	100 Steps
Proprietary
OpenAI CUA	26.0	31.3	31.4
Seed 1.5-VL	27.9	—	34.1
Claude 3.7 Sonnet	27.1	35.8	35.9
Claude 4 Sonnet	31.2	43.9	41.5
Open-Source
Qwen 2.5-VL-32B-Instruct	3.0	—	3.9
Qwen 2.5-VL-72B-Instruct	4.4	—	5.0
Kimi-VL-A3B	9.7	—	10.3
UI-TARS-72B-DPO	24.0	25.8	27.1
UI-TARS-1.5-7B	24.5	27.3	27.4
OpenCUA-7B (Ours)	24.3	27.9	26.6
OpenCUA-32B (Ours)	29.7	34.1	34.8
OpenCUA-72B(Ours)**	39.0	44.9	45.0

OpenCUA scores are the mean of 3 independent runs.

GUI Grounding Performance

Model	OSWorld-G	ScreenSpot-V2	ScreenSpot-Pro	UI-Vision
Qwen2.5-VL-7B	31.4	88.8	27.6	0.85
Qwen2.5-VL-32B	46.5	87.0	39.4	-
UI-TARS-72B	57.1	90.3	38.1	25.5
OpenCUA-7B	55.3	92.3	50.0	29.7
OpenCUA-32B	59.6	93.4	55.3	33.3
OpenCUA-72B	59.2	92.9	60.8	37.3

AgentNetBench (Offline Evaluation)

Model	Coordinate Actions	Content Actions	Function Actions	Average
Qwen2.5-VL-7B	50.7	40.8	3.1	48.0
Qwen2.5-VL-32B	66.6	47.2	41.5	64.8
Qwen2.5-VL-72B	67.2	52.6	50.5	67.0
OpenAI CUA	71.7	57.3	80.0	73.1
OpenCUA-7B	79.0	62.0	44.3	75.2
OpenCUA-32B	81.9	66.1	55.7	79.1

---

AgentNet Dataset - Large-Scale Computer-Use Dataset

AgentNet is the first large-scale desktop computer-use agent trajectory dataset, containing 22.6K human-annotated computer-use tasks across Windows, macOS, and Ubuntu systems.

👉 AgentNet Huggingface Dataset

Download the dataset here：

pip install -U huggingface_hub
huggingface-cli download xlangai/AgentNet --repo-type dataset --local-dir ./AgentNet

Use the following command to unzip the file (For exmaple, Ubuntu data):

cd path_to_your_zip_files

# Merge all the zips
zip -s 0 images.zip --out images-full.zip

# Unzip
unzip images-full.zip -d path_to_your_target_dir

Collecting computer-use agent training data requires 3 steps:

• Demonstrate human computer-use task via AgentNetTool;
• Preprocess the demonstration using Action Reduction & State-Action Matching;
• For each step, synthesize reflective long CoT

1 AgentNetTool – Annotation & Verification Tool

Our AgentNetTool is a cross-platform GUI recorder that runs unobtrusively on annotators’ machines. It captures synchronized screen video, mouse/keyboard events, and accessibility trees, then provides an in-browser UI for reviewing, trimming, and submitting demonstrations. AgentNet Tool is available on Windows, macOS and Ubuntu.

👉 AgentNetTool Document

2 DataProcessor – Action Reduction & State–Action Matching

Raw demonstrations can contain thousands of low-level events that are too dense for model training.
The DataProcessor module (./data/data-process/) performs two key steps:

1. Action Reduction — merges granular signals into concise, semantically meaningful PyAutoGUI actions (e.g., collapsing mouse moves → click, coalescing scrolls, grouping key-press sequences into text or hotkeys).
2. State–Action Matching — aligns every reduced action with the last visually distinct frame before the action begins, avoiding future-information leakage and yielding compact state–action pairs.

These processed trajectories underlie all downstream training and evaluation.

---

3 CoTGenerator – Synthesizing Reflective Long Chain-of-Thought Inner Monologue

To boost robustness and interpretability, we augment each trajectory with reflective long Chain-of-Thought (CoT) reasoning.
The CoTGenerator pipeline (./data/cot-generator/) synthesizes step-level reflections that:

• reflect on the previous action,
• explain why an action is chosen given the current observation and history,
• note potential alternative actions, and
• forecast the expected next state.

Empirically, models trained with these rich CoTs scale better with data and generalize across unseen applications.

AgentNetBench

AgentNetBench (./AgentNetBench/) provides a realistic offline evaluator for OS agent trajectories. It compares model-predicted low-level actions (click, moveTo, write, press, scroll, terminate, etc.) against ground-truth human actions and reports detailed metrics.

👉 See AgentNetBench/README.md for usage instructions.

TODO

• vLLM Support ✅

  • vLLM now fully supports OpenCUA-7B, OpenCUA-32B, and OpenCUA-72B.
  • See vLLM Serve section for usage instructions.
  • Thanks to the Meituan EvoCUA Team for their contributions!

• Training Code

  • OpenCUA models are developed based on the training infrastructure of Kimi Team.
  • Currently developing the training pipeline based on open-source infrastructure.

Star History

Acknowledge

Research and Commercial Use

OpenCUA (including the model, dataset, tools, and code) may be used for research, educational, and commercial purposes under the MIT License (see LICENSE).

Citation and Acknowledgement

If you use OpenCUA models and/or the AgentNet dataset in any report, technical report, publication, thesis, presentation, blog post, documentation, or other publicly shared material, we kindly ask that you include an explicit acknowledgement in the main text and cite the OpenCUA paper.

Prohibited Uses

• The model, dataset, tool, and code may not be used for any purpose or activity that violates applicable laws or regulations in any jurisdiction
• Use for illegal, unethical, or harmful activities is strictly prohibited

Disclaimer

• The authors, contributors, and copyright holders are not responsible for any illegal, unethical, or harmful use of the Software, nor for any direct or indirect damages resulting from such use
• Use of the "OpenCUA" name, logo, or trademarks does not imply any endorsement or affiliation unless separate written permission is obtained
• Users are solely responsible for ensuring their use complies with applicable laws and regulations

Citation

If you use OpenCUA in your research, please cite our work:

@misc{wang2025opencuaopenfoundationscomputeruse,
      title={OpenCUA: Open Foundations for Computer-Use Agents}, 
      author={Xinyuan Wang and Bowen Wang and Dunjie Lu and Junlin Yang and Tianbao Xie and Junli Wang and Jiaqi Deng and Xiaole Guo and Yiheng Xu and Chen Henry Wu and Zhennan Shen and Zhuokai Li and Ryan Li and Xiaochuan Li and Junda Chen and Boyuan Zheng and Peihang Li and Fangyu Lei and Ruisheng Cao and Yeqiao Fu and Dongchan Shin and Martin Shin and Jiarui Hu and Yuyan Wang and Jixuan Chen and Yuxiao Ye and Danyang Zhang and Dikang Du and Hao Hu and Huarong Chen and Zaida Zhou and Haotian Yao and Ziwei Chen and Qizheng Gu and Yipu Wang and Heng Wang and Diyi Yang and Victor Zhong and Flood Sung and Y. Charles and Zhilin Yang and Tao Yu},
      year={2025},
      eprint={2508.09123},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2508.09123}, 
}