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base on Agent S: an open agentic framework that uses computers like a human <h1 align="center"> <img src="images/agent_s.png" alt="Logo" style="vertical-align:middle" width="60"> Agent S: <small>Use Computer Like a Human</small> </h1> <p align="center">  🌐 <a href="https://www.simular.ai/articles/agent-s2-technical-review">[S2 blog]</a>  📄 <a href="https://arxiv.org/abs/2504.00906">[S2 Paper (COLM 2025)]</a>  🎥 <a href="https://www.youtube.com/watch?v=wUGVQl7c0eg">[S2 Video]</a> </p> <p align="center">  🌐 <a href="https://www.simular.ai/agent-s">[S1 blog]</a>  📄 <a href="https://arxiv.org/abs/2410.08164">[S1 Paper (ICLR 2025)]</a>  🎥 <a href="https://www.youtube.com/watch?v=OBDE3Knte0g">[S1 Video]</a> </p> <p align="center">  <a href="https://trendshift.io/repositories/13151" target="_blank"><img src="https://trendshift.io/api/badge/repositories/13151" alt="simular-ai%2FAgent-S | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p> <p align="center"> <img src="https://img.shields.io/badge/OS-Windows-blue?logo=windows&logoColor=white" alt="Windows"> <img src="https://img.shields.io/badge/OS-macOS-black?logo=apple&logoColor=white" alt="macOS"> <img src="https://img.shields.io/badge/OS-Linux-yellow?logo=linux&logoColor=black" alt="Linux"> <a href="https://discord.gg/E2XfsK9fPV"> <img src="https://dcbadge.limes.pink/api/server/https://discord.gg/E2XfsK9fPV?style=flat" alt="Discord"> </a>    <a href="https://pepy.tech/projects/gui-agents"> <img src="https://static.pepy.tech/badge/gui-agents" alt="PyPI Downloads"> </a> </p> <div align="center">  <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=de">Deutsch</a> | <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=es">Español</a> | <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=fr">français</a> | <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=ja">日本語</a> | <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=ko">한국어</a> | <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=pt">Português</a> | <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=ru">Русский</a> | <a href="https://www.readme-i18n.com/simular-ai/Agent-S?lang=zh">中文</a> </div> <div align="center">    <p>Skip the setup? Try Agent S in <a href="https://cloud.simular.ai/">Simular Cloud</a> </div> ## 🥳 Updates - [x] **2025/08/01**: Agent S2.5 is released (gui-agents v0.2.5): simpler, better, and faster! New SOTA on [OSWorld-Verified](https://os-world.github.io)! - [x] **2025/07/07**: The [Agent S2 paper](https://arxiv.org/abs/2504.00906) is accepted to COLM 2025! See you in Montreal! - [x] **2025/04/27**: The Agent S paper won the Best Paper Award 🏆 at ICLR 2025 Agentic AI for Science Workshop! - [x] **2025/04/01**: Released the [Agent S2 paper](https://arxiv.org/abs/2504.00906) with new SOTA results on OSWorld, WindowsAgentArena, and AndroidWorld! - [x] **2025/03/12**: Released Agent S2 along with v0.2.0 of [gui-agents](https://github.com/simular-ai/Agent-S), 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! - [x] **2025/01/22**: The [Agent S paper](https://arxiv.org/abs/2410.08164) is accepted to ICLR 2025! - [x] **2025/01/21**: Released v0.1.2 of [gui-agents](https://github.com/simular-ai/Agent-S) library, with support for Linux and Windows! - [x] **2024/12/05**: Released v0.1.0 of [gui-agents](https://github.com/simular-ai/Agent-S) library, allowing you to use Agent-S for Mac, OSWorld, and WindowsAgentArena with ease! - [x] **2024/10/10**: Released the [Agent S paper](https://arxiv.org/abs/2410.08164) and codebase! ## Table of Contents 1. [💡 Introduction](#-introduction) 2. [🎯 Current Results](#-current-results) 3. [🛠️ Installation & Setup](#%EF%B8%8F-installation--setup) 4. [🚀 Usage](#-usage) 5. [🤝 Acknowledgements](#-acknowledgements) 6. [💬 Citation](#-citation) ## 💡 Introduction Welcome to **Agent S**, an open-source framework designed to enable autonomous interaction with computers through Agent-Computer Interface. Our mission is to build intelligent GUI agents that can learn from past experiences and perform complex tasks autonomously on your computer. Whether you're interested in AI, automation, or contributing to cutting-edge agent-based systems, we're excited to have you here! ## 🎯 Current Results <div align="center"> <table border="0" cellspacing="0" cellpadding="5"> <tr> <th>Benchmark</th> <th>Agent S2.5</th> <th>Previous SOTA</th> </tr> <tr> <td>OSWorld Verified (100 step)</td> <td><b>56.0%</b></td> <td>53.1%</td> </tr> <tr> <td>OSWorld Verified (50 step)</td> <td><b>54.2%</b></td> <td>50.6%</td> </tr>  </table> </div> ## 🛠️ Installation & Setup ### Prerequisites - **Single Monitor**: Our agent is designed for single monitor screens - **Security**: The agent runs Python code to control your computer - use with care - **Supported Platforms**: Linux, Mac, and Windows ### Installation To install Agent S2.5 without cloning the repository, run ```bash pip install gui-agents ``` If you would like to test Agent S2.5 while making changes, clone the repository and install using ``` pip install -e . ``` ### API Configuration #### Option 1: Environment Variables Add to your `.bashrc` (Linux) or `.zshrc` (MacOS): ```bash export OPENAI_API_KEY=<YOUR_API_KEY> export ANTHROPIC_API_KEY=<YOUR_ANTHROPIC_API_KEY> export HF_TOKEN=<YOUR_HF_TOKEN> ``` #### Option 2: Python Script ```python import os os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>" ``` ### Supported Models We support Azure OpenAI, Anthropic, Gemini, Open Router, and vLLM inference. See [models.md](models.md) for details. ### Grounding Models (Required) For optimal performance, we recommend [UI-TARS-1.5-7B](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) hosted on Hugging Face Inference Endpoints or another provider. See [Hugging Face Inference Endpoints](https://huggingface.co/learn/cookbook/en/enterprise_dedicated_endpoints) for setup instructions. ## 🚀 Usage > ⚡️ **Recommended Setup:** > For the best configuration, we recommend using **OpenAI gpt-5-2025-08-07** as the main model, paired with **UI-TARS-1.5-7B** for grounding. ### CLI Run Agent S2.5 with the required parameters: ```bash agent_s \ --provider openai \ --model gpt-5-2025-08-07 \ --ground_provider huggingface \ --ground_url http://localhost:8080 \ --ground_model ui-tars-1.5-7b \ --grounding_width 1920 \ --grounding_height 1080 ``` #### Required Parameters - **`--provider`**: Main generation model provider (e.g., openai, anthropic, etc.) - Default: "openai" - **`--model`**: Main generation model name (e.g., gpt-5-2025-08-07) - Default: "gpt-5-2025-08-07" - **`--ground_provider`**: The provider for the grounding model - **Required** - **`--ground_url`**: The URL of the grounding model - **Required** - **`--ground_model`**: The model name for the grounding model - **Required** - **`--grounding_width`**: Width of the output coordinate resolution from the grounding model - **Required** - **`--grounding_height`**: Height of the output coordinate resolution from the grounding model - **Required** #### Optional Parameters - **`--model_temperature`**: The temperature to fix all model calls to (necessary to set to 1.0 for models like o3 but can be left blank for other models) #### Grounding Model Dimensions The grounding width and height should match the output coordinate resolution of your grounding model: - **UI-TARS-1.5-7B**: Use `--grounding_width 1920 --grounding_height 1080` - **UI-TARS-72B**: Use `--grounding_width 1000 --grounding_height 1000` #### Optional Parameters - **`--model_url`**: Custom API URL for main generation model - Default: "" - **`--model_api_key`**: API key for main generation model - Default: "" - **`--ground_api_key`**: API key for grounding model endpoint - Default: "" - **`--max_trajectory_length`**: Maximum number of image turns to keep in trajectory - Default: 8 - **`--enable_reflection`**: Enable reflection agent to assist the worker agent - Default: True ### `gui_agents` SDK First, we import the necessary modules. `AgentS2_5` is the main agent class for Agent S2.5. `OSWorldACI` is our grounding agent that translates agent actions into executable python code. ```python import pyautogui import io from gui_agents.s2_5.agents.agent_s import AgentS2_5 from gui_agents.s2_5.agents.grounding import OSWorldACI # Load in your API keys. from dotenv import load_dotenv load_dotenv() current_platform = "linux" # "darwin", "windows" ``` Next, we define our engine parameters. `engine_params` is used for the main agent, and `engine_params_for_grounding` is for grounding. For `engine_params_for_grounding`, we support custom endpoints like HuggingFace TGI, vLLM, and Open Router. ```python engine_params = { "engine_type": provider, "model": model, "base_url": model_url, # Optional "api_key": model_api_key, # Optional "temperature": model_temperature # Optional } # Load the grounding engine from a custom endpoint ground_provider = "<your_ground_provider>" ground_url = "<your_ground_url>" ground_model = "<your_ground_model>" ground_api_key = "<your_ground_api_key>" # Set grounding dimensions based on your model's output coordinate resolution # UI-TARS-1.5-7B: grounding_width=1920, grounding_height=1080 # UI-TARS-72B: grounding_width=1000, grounding_height=1000 grounding_width = 1920 # Width of output coordinate resolution grounding_height = 1080 # Height of output coordinate resolution engine_params_for_grounding = { "engine_type": ground_provider, "model": ground_model, "base_url": ground_url, "api_key": ground_api_key, # Optional "grounding_width": grounding_width, "grounding_height": grounding_height, } ``` Then, we define our grounding agent and Agent S2.5. ```python grounding_agent = OSWorldACI( platform=current_platform, engine_params_for_generation=engine_params, engine_params_for_grounding=engine_params_for_grounding, width=1920, # Optional: screen width height=1080 # Optional: screen height ) agent = AgentS2_5( engine_params, grounding_agent, platform=current_platform, max_trajectory_length=8, # Optional: maximum image turns to keep enable_reflection=True # Optional: enable reflection agent ) ``` Finally, let's query the agent! ```python # Get screenshot. screenshot = pyautogui.screenshot() buffered = io.BytesIO() screenshot.save(buffered, format="PNG") screenshot_bytes = buffered.getvalue() obs = { "screenshot": screenshot_bytes, } instruction = "Close VS Code" info, action = agent.predict(instruction=instruction, observation=obs) exec(action[0]) ``` Refer to `gui_agents/s2_5/cli_app.py` for more details on how the inference loop works. ### OSWorld To deploy Agent S2.5 in OSWorld, follow the [OSWorld Deployment instructions](osworld_setup/s2_5/OSWorld.md). ## 💬 Citations If you find this codebase useful, please cite: ``` @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} } ``` ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=simular-ai/Agent-S&type=Date)](https://star-history.com/#simular-ai/Agent-S&Date) ", Assign "at most 3 tags" to the expected json: {"id":"13151","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

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