What is Cua-Bench?

Understand how Cua-Bench defines, runs, and scores verifiable computer-use tasks.

Cua-Bench is an MIT-licensed framework for computer-use benchmarks and reinforcement-learning environments. It packages the task, starting state, agent interface, and evaluator needed to run a repeatable experiment across Linux, Windows, Android, browser, or simulated environments.

Why tasks are environments#

A computer-use score is meaningful only when the starting state and success condition are repeatable. A Cua-Bench task therefore defines more than a prompt. It prepares an environment, exposes observations and actions to an agent, and evaluates the final state with task-specific checks.

Task variations test whether an agent can solve the same objective from different inputs or starting conditions. Datasets group related tasks so a run can use one agent configuration and produce comparable traces and scores.

How the pieces fit#

• Tasks define the prompt, setup, variations, and evaluator.
• Providers supply the browser, simulated surface, container, VM, or hosted computer where the task runs.
• Agent adapters connect a model or agent implementation to the environment interface.
• The runner executes one task or a dataset, records traces, and supports parallel workers.

This separation lets the same task definition run against different agents and infrastructure. It also keeps evaluation logic independent from the model being tested.

Relationship to the rest of Cua#

Cua-Bench supplies the task and evaluation layer. Cua computer and sandbox components supply machine environments, while agent adapters translate model output into computer actions. The resulting trajectory can be inspected, scored, or used as training data.