OpenComputer 解决了计算机使用智能体评估缺乏可靠验证的问题，做智能体开发和自动化研究的团队可以直接用它来测试和训练模型，比 LLM 裁判更靠谱。
OpenComputer 是一个基于验证器的框架，用于构建可验证的软件世界，以评估和训练计算机使用智能体。它集成了四个组件：应用状态验证器、自进化验证层、任务生成管道和评估工具。目前覆盖 33 个桌面应用和 1000 个任务，包括浏览器、办公工具、创意软件等。实验表明，硬编码验证器比 LLM 作为裁判更准确，尤其在细粒度状态依赖的任务中。前沿模型在端到端完成上仍显吃力，开源模型表现下降，暴露了计算机自动化中的持续差距。
OpenComputer: Verifiable Software Worlds for Computer-Use Agents
We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four components: (1) app-specific state verifiers that expose structured inspection endpoints over real applications, (2) a self-evolving verification layer that improves verifier reliability using execution-grounded feedback, (3) a task-generation pipeline that synthesizes realistic and machine-checkable desktop tasks, and (4) an evaluation harness that records full trajectories and computes auditable partial-credit rewards. In its current form, OpenComputer covers 33 desktop applications and 1,000 finalized tasks spanning browsers, office tools, creative software, development environments, file managers, and communication applications. Experiments show that OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state. Frontier agents struggle with end-to-end completion despite partial progress, and open-source models exhibit sharp drops from their OSWorld-Verified scores, exposing a persistent gap in robust computer automation.