Agent-S

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

• 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. (OSWorld V2 is scored as a strict binary pass/fail with no partial credit, so 21.3% reflects benchmark difficulty, not low reliability — see the Agent S4 README for details.)
• 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 →

• 2026/08/28: Sai tops OSWorld 2.0, beating GPT and Opus with lower costs (73%) — our production computer use agent is now state of the art on the hardest desktop benchmark!
• 2026/07/30: The Agent S3 paper "Scaling Agents for Computer Use" is accepted to TMLR 2026!
• 2025/12/15: Agent S3 is the first to surpass human-level performance on OSWorld with an impressive score of 72.60%!
• 2025/10/02: Released Agent S3 and its technical paper, setting a new SOTA of 69.9% on OSWorld (approaching 72% human performance), with strong generalizability on WindowsAgentArena and AndroidWorld! It is also simpler, faster, and more flexible.
• 2025/08/01: Agent S2.5 is released (gui-agents v0.2.5): simpler, better, and faster! New SOTA on OSWorld-Verified!
• 2025/07/07: The Agent S2 paper is accepted to COLM 2025! See you in Montreal!
• 2025/04/27: The Agent S paper won the Best Paper Award 🏆 at ICLR 2025 Agentic AI for Science Workshop!
• 2025/04/01: Released the Agent S2 paper with new SOTA results on OSWorld, WindowsAgentArena, and AndroidWorld!
• 2025/03/12: Released Agent S2 along with v0.2.0 of gui-agents, the new state-of-the-art for computer use agents (CUA), outperforming OpenAI's CUA/Operator and Anthropic's Claude 3.7 Sonnet Computer-Use!
• 2025/01/22: The Agent S paper is accepted to ICLR 2025!
• 2025/01/21: Released v0.1.2 of gui-agents library, with support for Linux and Windows!
• 2024/12/05: Released v0.1.0 of gui-agents library, allowing you to use Agent-S for Mac, OSWorld, and WindowsAgentArena with ease!
• 2024/10/10: Released the Agent S paper and codebase!

🚀 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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