GUI agents drive applications through their visual interfaces instead of programmatic APIs, interacting with arbitrary software via taps, swipes, and keystrokes, reaching a long tail of applications that CLI-based agents cannot. Yet progress in this area is bottlenecked less by modeling capacity than by the absence of a coherent full-stack infrastructure: online RL training suffers from environment instability and closed pipelines, evaluation protocols drift silently across works, and trained agents rarely reach real users on real devices. We present \textbf{ClawGUI}, an open-source framework addressing these three gaps within a single harness. \textbf{ClawGUI-RL} provides the first open-source GUI agent RL infrastructure with validated support for both parallel virtual environments and real physical devices, integrating GiGPO with a Process Reward Model for dense step-level supervision. \textbf{ClawGUI-Eval} enforces a fully standardized evaluation pipeline across 6 benchmarks and 11+ models, achieving 95.8\% reproduction against official baselines. \textbf{ClawGUI-Agent} brings trained agents to Android, HarmonyOS, and iOS through 12+ chat platforms with hybrid CLI-GUI control and persistent personalized memory. Trained end to end within this pipeline, \textbf{ClawGUI-2B} achieves 17.1\% Success Rate on MobileWorld GUI-Only, outperforming the same-scale MAI-UI-2B baseline by 6.0\%.

翻译：GUI代理通过视觉界面而非编程接口驱动应用程序，通过点击、滑动和按键操作与任意软件交互，从而覆盖了基于CLI的代理无法触及的大量长尾应用。然而，该领域的进展瓶颈并非建模能力不足，而是缺乏一个连贯的全栈基础设施：在线RL训练面临环境不稳定性和封闭流程，评估协议在不同研究中间接漂移，而训练后的代理极少能到达真实用户手中的真实设备。我们提出**ClawGUI**，一个开源框架，在单一框架内解决上述三个空白。**ClawGUI-RL**提供了首个开源的GUI代理RL基础设施，经验证支持并行虚拟环境和真实物理设备，将GiGPO与过程奖励模型结合以实现密集的步级监督。**ClawGUI-Eval**在6个基准测试和11+模型上强制执行完全标准化的评估流程，与官方基线相比达到了95.8%的可复现率。**ClawGUI-Agent**通过12+个聊天平台将训练后的代理部署到Android、HarmonyOS和iOS，支持混合CLI-GUI控制和持久化个性化记忆。在此流程中端到端训练的**ClawGUI-2B**在MobileWorld GUI-Only上实现了17.1%的成功率，比同尺度的MAI-UI-2B基线高出6.0%。