Agent S: Open Computer Use

An open-source computer use agent that can operate a real GUI like a human or work in the way beyond human capacity.

🏆 Sai: Tops OSWorld 2.0, Beating GPT and Opus with Lower Costs (73%)

🏆 Agent S3: First to Surpass Human Performance on OSWorld (72.60%)

Agent S is an open-source computer use agent framework from Simular. Give it a task in plain English. It looks at the screen and completes the task by clicking, typing, and scrolling in ordinary desktop and web apps. It runs on macOS, Windows, and Linux.

🥳 Updates

• [x] 2026/10/20: Released Agent S4 and its paper "Evolving Agent Interfaces": an autonomous computer-use scientist that evolves the interfaces its agents work through from task experience. With Qwen3.8-27B, Agent S4 sets a new open-source state of the art on OSWorld V2 at 21.3%, 3.8 times the same model with fixed interfaces and above GPT-5.5 (13.0%) and Claude Opus 4.8 (20.6%) at a fraction of their cost.
• [x] 2026/10/01: The Simular API is live. Build your always-on computer agents in a minute. Simular Platform is an autonomous computer API for building always-on agents that fit your needs. World-leading agent infrastructure, 90% token saving. Get an API key →

🚀 Quick Start

• No setup: run Sai, our production agent, on a cloud computer through the Simular API. See the API docs and the workflow template gallery.
• On your own computer: follow the Agent S3 README.

📚 Agent S Versions

| Version | What it is | Docs | Paper | |---|---|---|---| | Agent S4 | Evolves the tool interfaces a computer use agent works through, from its own task experience. Open-source state of the art on OSWorld V2 (21.3% with Qwen3.8-27B). | README | arXiv 2026 | | Agent S3 | The agent in gui-agents today. First to surpass human performance on OSWorld (72.60%). | README | TMLR 2026 | | Agent S2.5 | A simpler, faster Agent S2. | README | — | | Agent S2 | A compositional generalist-specialist framework. | README | COLM 2025 | | Agent S | The first version: uses computers like a human, through an agent-computer interface. | README | ICLR 2025 |

💬 Citations

If you find this codebase useful, please cite:

@misc{Agent-S4,
      title={Evolving Agent Interfaces}, 
      author={Saaket Agashe and Yuzhe Yang and Gonzalo Gonzalez-Pumariega and Bhuvan Chandra Koduru and Chih-Lun Lee and Jiachen Yang and Ang Li and Xin Eric Wang},
      year={2026},
      eprint={2610.28601},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2610.28601}, 
}
@misc{Agent-S3,
      title={The Unreasonable Effectiveness of Scaling Agents for Computer Use}, 
      author={Gonzalo Gonzalez-Pumariega and Vincent Tu and Chih-Lun Lee and Jiachen Yang and Ang Li and Xin Eric Wang},
      year={2025},
      eprint={2510.02250},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2510.02250}, 
}
@misc{Agent-S2,
      title={Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents}, 
      author={Saaket Agashe and Kyle Wong and Vincent Tu and Jiachen Yang and Ang Li and Xin Eric Wang},
      year={2025},
      eprint={2504.00906},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2504.00906}, 
}
@inproceedings{Agent-S,
    title={{Agent S: An Open Agentic Framework that Uses Computers Like a Human}},
    author={Saaket Agashe and Jiuzhou Han and Shuyu Gan and Jiachen Yang and Ang Li and Xin Eric Wang},
    booktitle={International Conference on Learning Representations (ICLR)},
    year={2025},
    url={https://arxiv.org/abs/2410.08164}
}

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