We present MAI-UI, a family of GUI agent foundation models spanning the full spectrum of sizes, including 2B, 8B, 32B, and 235B-A22B variants. Our core contribution includes:

  • 🔧 Agent-user interaction and MCP augmentation: enabling agent to interact with user and use MCP tools to complete the task.
  • ☁️ Device–cloud collaboration system: dynamically selecting on-device or cloud execution based on task execution state and data sensitivity.
  • 📈 Dynamic RL Scaling: large-scale reinforcement learning with scaling parallel environments (up to 512) and context length (up to 50).
  • 🏆 State-of-the-Art Performance: MAI-UI establishes new benchmark SOTA results across GUI grounding and navigation tasks.

📰 News

  • [2026-03-20] 📄 Blog Posts: Our Grounding and Navigation Blog Posts are available now!
  • [2026-01-15] 🥇 New Record on AndroidWorld: MAI-UI-235B takes #1 on the AndroidWorld Leaderboard for pure-vision, end-to-end models with a 76.7% success rate.
  • [2026-01-13] 🥇 MAI-UI Sweeps ScreenSpot-Pro: MAI-UI (32B, 8B, 2B) now ranks #1 in all size categories on the ScreenSpot-Pro leaderboard. We achieved record scores of 67.9%, 65.7%, and 57.4% respectively—notably reaching these benchmarks without any zoom-in tricks.
  • [2026-01-04] 🤝 We're Hiring! We're actively looking for Research Scientists, Engineers, and Interns to work on foundational GUI agents and their applications. Interested candidates please send your resume to: [email protected]
  • [2025-12-29] 🏆 New Leaderboard Record: MAI-UI achieves a 41.7% success rate on the MobileWorld benchmark, setting a new record for end-to-end model performance!
  • [2025-12-29] 📄 Technical Report & Website: Our technical report is now available on arXiv, and the official project website is live.
  • [2025-12-29] 🤗 Model Release: We are excited to release the weights for MAI-UI-8B and MAI-UI-2B on Hugging Face.

📑 Table of Contents

🎥 Demo

Demo 1 - Daily Life Scenario

Trigger ask_user for more information to complete the task.

Demo 2 - Navigation

Use mcp_call to invoke AMap tools for navigation.

Demo 3 - Shopping

Cross-apps collaboration to complete the task.

Demo 4 - Work

Cross-apps collaboration to complete the task.

Demo 5 - Device-only

Device-cloud collaboration for simple tasks, no need cloud model invocation.

Demo 6 - Device-cloud Collaboration

Device-cloud collaboration for complex tasks, requiring cloud model invocation when the task is beyond the device models capabilities.

🚀 Installation & Quick Start

Step 1: Clone the Repository

git clone https://github.com/Tongyi-MAI/MAI-UI.git
cd MAI-UI

Step 2: Start Model API Service with vLLM

Download the model from HuggingFace and deploy the API service using vLLM:

HuggingFace model path:

Deploy the model using vLLM:

# Install vLLM
pip install vllm==0.11.0  # vllm==0.11.0 and transformers>=4.57.0

# Start vLLM API server (replace MODEL_PATH with your local model path or HuggingFace model ID)
python -m vllm.entrypoints.openai.api_server \
    --model <huggingface_model_path> \
    --served-model-name MAI-UI-8B \
    --host 0.0.0.0 \
    --port 8000 \
    --tensor-parallel-size 1 \
    --trust-remote-code

💡 Tips:

  • IMPORTANT: Must use VLLM=0.11.0
  • Adjust --tensor-parallel-size based on your GPU count for multi-GPU inference
  • The model will be served at http://localhost:8000/v1

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Run cookbook notebooks

We provide two notebooks in the cookbook/ directory:

4.1 Grounding Demo

The grounding.ipynb demonstrates how to use the MAI Grounding Agent to locate UI elements:

cd cookbook
jupyter notebook grounding.ipynb

Before running, update the API endpoint in the notebook:

agent = MAIGroundingAgent(
    llm_base_url="http://localhost:8000/v1",  # Update to your vLLM server address
    model_name="MAI-UI-8B",                   # Use the served model name
    runtime_conf={
        "history_n": 3,
        "temperature": 0.0,
        "top_k": -1,
        "top_p": 1.0,
        "max_tokens": 2048,
    },
)

4.2 Navigation Agent Demo

The run_agent.ipynb demonstrates the full UI navigation agent:

cd cookbook
jupyter notebook run_agent.ipynb

Similarly, update the API endpoint configuration:

agent = MAIUINaivigationAgent(
    llm_base_url="http://localhost:8000/v1",  # Update to your vLLM server address
    model_name="MAI-UI-8B",                   # Use the served model name
    runtime_conf={
        "history_n": 3,
        "temperature": 0.0,
        "top_k": -1,
        "top_p": 1.0,
        "max_tokens": 2048,
    },
)

📝 Citation

If you find this project useful for your research, please consider citing our works:

@article{zhou2025mai,
  title={MAI-UI Technical Report: Real-World Centric Foundation GUI Agents},
  author={Zhou, Hanzhang and Zhang, Xu and Tong, Panrong and Zhang, Jianan and Chen, Liangyu and Kong, Quyu and Cai, Chenglin and Liu, Chen and Wang, Yue and Zhou, Jingren and others},
  journal={arXiv preprint arXiv:2512.22047},
  year={2025}
}
@article{kong2025mobileworld,
  title={MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments},
  author={Kong, Quyu and Zhang, Xu and Yang, Zhenyu and Gao, Nolan and Liu, Chen and Tong, Panrong and Cai, Chenglin and Zhou, Hanzhang and Zhang, Jianan and Chen, Liangyu and others},
  journal={arXiv preprint arXiv:2512.19432},
  year={2025}
}
@article{chen2025ui,
  title={UI-Ins: Enhancing GUI Grounding with Multi-Perspective Instruction-as-Reasoning},
  author={Chen, Liangyu and Zhou, Hanzhang and Cai, Chenglin and Zhang, Jianan and Tong, Panrong and Kong, Quyu and Zhang, Xu and Liu, Chen and Liu, Yuqi and Wang, Wenxuan and others},
  journal={arXiv preprint arXiv:2510.20286},
  year={2025}
}

📧 Contact

For questions and support, please contact:

📄 License

MAI-UI Mobile is a foundation GUI agent developed by Alibaba Cloud and licensed under the Apache License (Version 2.0).

This product contains various third-party components under other open source licenses. See the NOTICE file for more information.