Overview

Cua empowers AI agents with the ability to interact with computers, bridging the gap between AI decision-making and real-world application execution. This project provides a comprehensive suite of open-source tools for developing, deploying, and evaluating computer-use agents. Scale your AI agent's capabilities from simple command-line tasks to complex graphical interface interactions across diverse operating systems.

Key Features

• Cross-OS Desktop Automation: Utilize Cua Driver for seamless inspection and operation of applications on macOS, Windows, and Linux, enabling agents to interact with native apps and browsers.
• Isolated Cloud Desktops (Cua Fleets): Provision and manage ephemeral cloud desktops for your agents. These sandboxed environments allow for safe and controlled execution of tasks, with SDKs for command execution, screenshot capture, and resource management.
• Local macOS & Linux VMs (Lume): Leverage Apple's Virtualization.Framework to create and manage local virtual machines for macOS and Linux on Apple Silicon, providing a flexible environment for development and testing.
• Specialized Decision Models (CUA-S1): Explore CUA-S1, a family of small, specialized System 1 models designed for fast, bounded decisions in computer-use tasks, such as form filling and element selection.
• Robust Benchmarking (Cua Bench): Build, evaluate, and export agent trajectories for training using a powerful benchmarking framework. Create simulated tasks that require no external dependencies for rapid iteration.
• Open-Source Drivers & SDKs: Benefit from a collection of open-source drivers and SDKs that abstract away the complexities of interacting with different operating systems and applications.

Typical Use Cases

• AI-Powered Application Interaction: Enable AI agents to use complex desktop applications like spreadsheets, design software, or customer relationship management tools.
• Automated Data Generation: Create sophisticated pipelines for generating synthetic datasets by having agents interact with applications and capture structured output.
• Agent Performance Evaluation: Benchmark and compare the effectiveness of different AI agents and models on a standardized set of computer-use tasks.
• Remote Workflows and Testing: Deploy AI agents in isolated cloud environments to perform remote tasks or conduct automated testing of software across different platforms.
• Research and Development in AI Agents: Facilitate cutting-edge research into agents that can understand and operate within graphical user interfaces and beyond.