c/ua (“koo-ah”) is Docker for Computer-Use Agents - it enables AI agents to control full operating systems in virtual containers and deploy them locally or to the cloud.

🚀 Quick Start with a Computer-Use Agent UI

Need to automate desktop tasks? Launch the Computer-Use Agent UI with a single command.

Option 1: Fully-managed install (recommended)

I want to be totally guided in the process

macOS/Linux/Windows (via WSL):

# Requires Python 3.11+
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/scripts/playground.sh)"

This script will:

• Ask if you want to use local VMs or C/ua Cloud Containers
• Install necessary dependencies (Lume CLI for local VMs)
• Download VM images if needed
• Install Python packages
• Launch the Computer-Use Agent UI

Option 2: Key manual steps

# Requires Python 3.11+ and C/ua API key
pip install -U "cua-computer[all]" "cua-agent[all]"
python -m agent.ui.gradio.app

# 1. Install Lume CLI
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"

# 2. Pull macOS image
lume pull macos-sequoia-cua:latest

# 3. Start VM
lume run macos-sequoia-cua:latest

# 4. Install packages and launch UI
pip install -U "cua-computer[all]" "cua-agent[all]"
python -m agent.ui.gradio.app

How it works: Computer module provides secure desktops (Lume CLI locally, C/ua Cloud Containers remotely), Agent module provides local/API agents with OpenAI AgentResponse format and trajectory tracing.

Supported Agent Loops

• UITARS-1.5 - Run locally on Apple Silicon with MLX, or use cloud providers
• OpenAI CUA - Use OpenAI’s Computer-Use Preview model
• Anthropic CUA - Use Anthropic’s Computer-Use capabilities
• OmniParser-v2.0 - Control UI with Set-of-Marks prompting using any vision model

💻 Developer Guide

Follow these steps to use C/ua in your own code. See Developer Guide for building from source.

Step 1: Install Lume CLI

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"

Lume CLI manages high-performance macOS/Linux VMs with near-native speed on Apple Silicon.

Step 2: Pull the macOS CUA Image

lume pull macos-sequoia-cua:latest

The macOS CUA image contains the default Mac apps and the Computer Server for easy automation.

Step 3: Install Python SDK

pip install "cua-computer[all]" "cua-agent[all]"

Step 4: Use in Your Code

from computer import Computer
from agent import ComputerAgent, LLM

async def main():
    # Start a local macOS VM
    computer = Computer(os_type="macos")
    await computer.run()

    # Or with C/ua Cloud Container
    computer = Computer(
      os_type="linux",
      api_key="your_cua_api_key_here",
      name="your_container_name_here"
    )

    # Example: Direct control of a macOS VM with Computer
    await computer.interface.left_click(100, 200)
    await computer.interface.type_text("Hello, world!")
    screenshot_bytes = await computer.interface.screenshot()
    
    # Example: Create and run an agent locally using mlx-community/UI-TARS-1.5-7B-6bit
    agent = ComputerAgent(
      computer=computer,
      loop="uitars",
      model=LLM(provider="mlxvlm", name="mlx-community/UI-TARS-1.5-7B-6bit")
    )
    async for result in agent.run("Find the trycua/cua repository on GitHub and follow the quick start guide"):
        print(result)

if __name__ == "__main__":
    asyncio.run(main())

For ready-to-use examples, check out our Notebooks collection.

Lume CLI Reference

# Install Lume CLI and background service
curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh | bash

# List all VMs
lume ls

# Pull a VM image
lume pull macos-sequoia-cua:latest

# Create a new VM
lume create my-vm --os macos --cpu 4 --memory 8GB --disk-size 50GB

# Run a VM (creates and starts if it doesn't exist)
lume run macos-sequoia-cua:latest

# Stop a VM
lume stop macos-sequoia-cua_latest

# Delete a VM
lume delete macos-sequoia-cua_latest

Lumier CLI Reference

For advanced container-like virtualization, check out Lumier - a Docker interface for macOS and Linux VMs.

# Install Lume CLI and background service
curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh | bash

# Run macOS in a Docker container
docker run -it --rm \
    --name lumier-vm \
    -p 8006:8006 \
    -v $(pwd)/storage:/storage \
    -v $(pwd)/shared:/shared \
    -e VM_NAME=lumier-vm \
    -e VERSION=ghcr.io/trycua/macos-sequoia-cua:latest \
    -e CPU_CORES=4 \
    -e RAM_SIZE=8192 \
    -e HOST_STORAGE_PATH=$(pwd)/storage \
    -e HOST_SHARED_PATH=$(pwd)/shared \
    trycua/lumier:latest

Resources

• How to use the MCP Server with Claude Desktop or other MCP clients - One of the easiest ways to get started with C/ua
• How to use OpenAI Computer-Use, Anthropic, OmniParser, or UI-TARS for your Computer-Use Agent
• How to use Lume CLI for managing desktops
• Training Computer-Use Models: Collecting Human Trajectories with C/ua (Part 1)
• Build Your Own Operator on macOS (Part 1)

Modules

Computer Interface Reference

For complete examples, see computer_examples.py or computer_nb.ipynb

# Mouse Actions
await computer.interface.left_click(x, y)       # Left click at coordinates
await computer.interface.right_click(x, y)      # Right click at coordinates
await computer.interface.double_click(x, y)     # Double click at coordinates
await computer.interface.move_cursor(x, y)      # Move cursor to coordinates
await computer.interface.drag_to(x, y, duration)  # Drag to coordinates
await computer.interface.get_cursor_position()  # Get current cursor position

# Keyboard Actions
await computer.interface.type_text("Hello")     # Type text
await computer.interface.press_key("enter")     # Press a single key
await computer.interface.hotkey("command", "c") # Press key combination

# Screen Actions
await computer.interface.screenshot()           # Take a screenshot
await computer.interface.get_screen_size()      # Get screen dimensions

# Clipboard Actions
await computer.interface.set_clipboard(text)    # Set clipboard content
await computer.interface.copy_to_clipboard()    # Get clipboard content

# File System Operations
await computer.interface.file_exists(path)      # Check if file exists
await computer.interface.directory_exists(path) # Check if directory exists
await computer.interface.run_command(cmd)       # Run shell command

# Accessibility
await computer.interface.get_accessibility_tree() # Get accessibility tree

# Python Virtual Environment Operations
await computer.venv_install("demo_venv", ["requests", "macos-pyxa"]) # Install packages in a virtual environment
await computer.venv_cmd("demo_venv", "python -c 'import requests; print(requests.get(`https://httpbin.org/ip`).json())'") # Run a shell command in a virtual environment
await computer.venv_exec("demo_venv", python_function_or_code, *args, **kwargs) # Run a Python function in a virtual environment and return the result / raise an exception

# Example: Use sandboxed functions to execute code in a C/ua Container
from computer.helpers import sandboxed

@sandboxed("demo_venv")
def greet_and_print(name):
    """Get the HTML of the current Safari tab"""
    import PyXA
    safari = PyXA.Application("Safari")
    html = safari.current_document.source()
    print(f"Hello from inside the container, {name}!")
    return {"greeted": name, "safari_html": html}

# When a @sandboxed function is called, it will execute in the container
result = await greet_and_print("C/ua")
# Result: {"greeted": "C/ua", "safari_html": "<html>...</html>"}
# stdout and stderr are also captured and printed / raised
print("Result from sandboxed function:", result)

ComputerAgent Reference

For complete examples, see agent_examples.py or agent_nb.ipynb

# Import necessary components
from agent import ComputerAgent, LLM, AgentLoop, LLMProvider

# UI-TARS-1.5 agent for local execution with MLX
ComputerAgent(loop=AgentLoop.UITARS, model=LLM(provider=LLMProvider.MLXVLM, name="mlx-community/UI-TARS-1.5-7B-6bit"))   
# OpenAI Computer-Use agent using OPENAI_API_KEY  
ComputerAgent(loop=AgentLoop.OPENAI, model=LLM(provider=LLMProvider.OPENAI, name="computer-use-preview"))
# Anthropic Claude agent using ANTHROPIC_API_KEY
ComputerAgent(loop=AgentLoop.ANTHROPIC, model=LLM(provider=LLMProvider.ANTHROPIC))

# OmniParser loop for UI control using Set-of-Marks (SOM) prompting and any vision LLM
ComputerAgent(loop=AgentLoop.OMNI, model=LLM(provider=LLMProvider.OLLAMA, name="gemma3:12b-it-q4_K_M"))      
# OpenRouter example using OAICOMPAT provider
ComputerAgent(
    loop=AgentLoop.OMNI,
    model=LLM(
        provider=LLMProvider.OAICOMPAT, 
        name="openai/gpt-4o-mini",
        provider_base_url="https://openrouter.ai/api/v1"
    ),
    api_key="your-openrouter-api-key"
)

Community

Join our Discord community to discuss ideas, get assistance, or share your demos!

License

Cua is open-sourced under the MIT License - see the LICENSE file for details.

Microsoft’s OmniParser, which is used in this project, is licensed under the Creative Commons Attribution 4.0 International License (CC-BY-4.0) - see the OmniParser LICENSE file for details.

Contributing

We welcome contributions to CUA! Please refer to our Contributing Guidelines for details.

Trademarks

Apple, macOS, and Apple Silicon are trademarks of Apple Inc. Ubuntu and Canonical are registered trademarks of Canonical Ltd. Microsoft is a registered trademark of Microsoft Corporation. This project is not affiliated with, endorsed by, or sponsored by Apple Inc., Canonical Ltd., or Microsoft Corporation.

Stargazers

Thank you to all our supporters!

Contributors

f-trycua 💻	Pedro Piñera Buendía 💻	Amit Kumar 💻	Dung Duc Huynh (Kaka) 💻	Zayd Krunz 💻	Prashant Raj 💻	Leland Takamine 💻
ddupont 💻	Ethan Gutierrez 💻	Ricter Zheng 💻	Rahul Karajgikar 💻	trospix 💻	Ikko Eltociear Ashimine 💻	한석호(MilKyo) 💻
Rahim Nathwani 💻	Matt Speck 💻	FinnBorge 💻	Jakub Klapacz 💻	Evan smith 💻