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arXiv · 2606.22557

MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop

Yikun Fu·Bowen Fu·Zhenyu Wu·Shuang Cheng·Xiaowei Sun·Bowen Yang·Zehao Li·Yibo Zhao·Zichen Ding·Zhoumianze Liu·Shijie Wang·Biqing Qi·Bowen Zhou
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Abstract

Computer use agents (CUAs) have advanced rapidly in desktop automation, and a growing number of users deploy CUAs such as OpenClaw on Mac Mini for always-on automation. However, existing benchmarks, including those for macOS, evaluate agents without framework augmentation and rely on binary evaluation. As a result, they fail to capture both the framework capabilities leveraged by modern CUAs and the partial progress on long-horizon, multi-application tasks. We present MacAgentBench, a comprehensive macOS agent benchmark comprising 676 tasks across 25 applications, with nearly 60% involving both GUI and CLI interaction. The benchmark adopts deterministic rule-based evaluation and introduces fine-grained multi-checkpoint scoring with capability annotations for multi-application tasks. Experiments across three frameworks and 16 models show that the best configuration, Claude Opus 4.6 on OpenClaw, attains 73.7% Pass@1, while this advantage is primarily driven by the skill library rather than by framework design. Fine-grained metrics further reveal that models with similar Pass@1 can differ substantially in sub-goal completion. Our code and data are publicly available at https://github.com/JetAstra/MacAgentBench.

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Yikun Fu, Bowen Fu, Zhenyu Wu, Shuang Cheng, Xiaowei Sun, Bowen Yang, Zehao Li, Yibo Zhao, Zichen Ding, Zhoumianze Liu, Shijie Wang, Biqing Qi, Bowen Zhou. 2026-06-21. MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop. https://arxiv.org/abs/2606.22557

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