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Logo Agent S: Using Computers Like a Human

🌐 [Website] 📄 [Paper] 🎥 [Video] 🗨️ [Discord]

🥳 Updates

  • 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 Agent S paper and codebase!

Table of Contents

  1. 💡 Introduction
  2. 🎯 Current Results
  3. 🛠️ Installation
  4. 🚀 Usage
  5. 🙌 Contributors
  6. 💬 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


Results of Successful Rate (%) on the OSWorld full test set of all 369 test examples using Image + Accessibility Tree input.

🛠️ Installation & Setup

❗Warning❗: If you are on a Linux machine, creating a conda environment will interfere with pyatspi. As of now, there's no clean solution for this issue. Proceed through the installation without using conda or any virtual environment.

Clone the repository:

git clone https://github.com/simular-ai/Agent-S.git

Install the gui-agents package:

pip install gui-agents

Set your LLM API Keys and other environment variables. You can do this by adding the following line to your .bashrc (Linux), or .zshrc (MacOS) file.

export OPENAI_API_KEY=<YOUR_API_KEY>

Alternatively, you can set the environment variable in your Python script:

import os
os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"

We also support Azure OpenAI, Anthropic, and vLLM inference. For more information refer to models.md.

Setup Retrieval from Web using Perplexica

Agent S works best with web-knowledge retrieval. To enable this feature, you need to setup Perplexica:

  1. Ensure Docker Desktop is installed and running on your system.

  2. Navigate to the directory containing the project files.

     cd Perplexica
 git submodule update --init

  3. Rename the sample.config.toml file to config.toml. For Docker setups, you need only fill in the following fields:

    • OPENAI: Your OpenAI API key. You only need to fill this if you wish to use OpenAI's models.

    • OLLAMA: Your Ollama API URL. You should enter it as http://host.docker.internal:PORT_NUMBER. If you installed Ollama on port 11434, use http://host.docker.internal:11434. For other ports, adjust accordingly. You need to fill this if you wish to use Ollama's models instead of OpenAI's.

    • GROQ: Your Groq API key. You only need to fill this if you wish to use Groq's hosted models.

    • ANTHROPIC: Your Anthropic API key. You only need to fill this if you wish to use Anthropic models.

      Note: You can change these after starting Perplexica from the settings dialog.

    • SIMILARITY_MEASURE: The similarity measure to use (This is filled by default; you can leave it as is if you are unsure about it.)

  4. Ensure you are in the directory containing the docker-compose.yaml file and execute:

    docker compose up -d

  5. Our implementation of Agent S incorporates the Perplexica API to integrate a search engine capability, which allows for a more convenient and responsive user experience. If you want to tailor the API to your settings and specific requirements, you may modify the URL and the message of request parameters in agent_s/query_perplexica.py. For a comprehensive guide on configuring the Perplexica API, please refer to Perplexica Search API Documentation

For a more detailed setup and usage guide, please refer to the Perplexica Repository.

Setup Paddle-OCR Server

Switch to a new terminal where you will run Agent S. Set the OCR_SERVER_ADDRESS environment variable as shown below. For a better experience, add the following line directly to your .bashrc (Linux), or .zshrc (MacOS) file.

export OCR_SERVER_ADDRESS=http://localhost:8000/ocr/

Run the ocr_server.py file code to use OCR-based bounding boxes.

cd Agent-S
python gui_agents/utils/ocr_server.py

You can change the server address by editing the address in agent_s/utils/ocr_server.py file.

❗Warning❗: The agent will directly run python code to control your computer. Please use with care.

🚀 Usage

CLI

Run agent_s on your computer using:

agent_s --model gpt-4o

This will show a user query prompt where you can enter your query and interact with Agent S. You can use any model from the list of supported models in models.md.

gui_agents SDK

To deploy Agent S on MacOS or Windows:

import pyautogui
import io
from gui_agents.core.AgentS import GraphSearchAgent
import platform

if platform.system() == "Darwin":
  from gui_agents.aci.MacOSACI import MacOSACI, UIElement
  grounding_agent = MacOSACI()
elif platform.system() == "Windows":
  from gui_agents.aci.WindowsOSACI import WindowsACI, UIElement
  grounding_agent = WindowsACI()
elif platform.system() == "Linux":
  from gui_agents.aci.LinuxOSACI import LinuxACI, UIElement
  grounding_agent = LinuxACI()
else:
  raise ValueError("Unsupported platform")

engine_params = {
    "engine_type": "openai",
    "model": "gpt-4o",
}

agent = GraphSearchAgent(
  engine_params,
  grounding_agent,
  platform="ubuntu",  # "macos", "windows"
  action_space="pyautogui",
  observation_type="mixed",
  search_engine="Perplexica"
)

# Get screenshot.
screenshot = pyautogui.screenshot()
buffered = io.BytesIO() 
screenshot.save(buffered, format="PNG")
screenshot_bytes = buffered.getvalue()

# Get accessibility tree.
acc_tree = UIElement.systemWideElement()

obs = {
  "screenshot": screenshot_bytes,
  "accessibility_tree": acc_tree,
}

instruction = "Close VS Code"
info, action = agent.predict(instruction=instruction, observation=obs)

exec(action[0])

Refer to cli_app.py for more details on how the inference loop works.

OSWorld

To deploy Agent S in OSWorld, follow the OSWorld Deployment instructions.

WindowsAgentArena

To deploy Agent S in WindowsAgentArena, follow the WindowsAgentArena Deployment instructions.

🙌 Contributors

We’re grateful to all the amazing people who have contributed to this project. Thank you! 🙏

💬 Citation

@misc{agashe2024agentsopenagentic,
      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},
      year={2024},
      eprint={2410.08164},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2410.08164}, 
}