PalmClaw：一种面向手机的原生设备端代理框架

摘要

大语言模型（LLM）代理已从生成回复演进到通过调用工具、观察结果并迭代决定下一步行动来执行多步骤任务。大多数代理系统运行在支持工具使用和任务自动化的桌面端或服务器上。移动设备因其广泛可用性且包含用户数据、传感器及日常应用程序，同样是重要的代理环境。现有移动代理主要通过点击、滑动和输入等图形用户界面（GUI）操作控制智能手机，这些操作通常形成冗长且依赖界面的序列，无法直接访问设备功能，且执行边界难以定义。我们提出PalmClaw，一款原生运行于手机的开源代理框架，可在设备端直接管理会话、记忆、技能、工具和代理循环。PalmClaw将设备功能暴露为具有显式参数、结构化结果和明确定义执行边界的设备工具。该设计使代理能直接调用移动设备功能，同时保持每个动作的显式性和可控性。实验结果显示，与最强基线方法相比，任务成功率相对提升11.5%，完成时间减少94.9%，且设置负担更低，追踪记录清晰展示了执行边界的应用方式。代码已开源：https://github.com/ModalityDance/PalmClaw

English

Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action. Most agent systems run on desktops or servers, which support tool use and task automation. Mobile devices are also important agent environments because they are widely accessible and contain users' data, sensors, and daily-use applications. Existing mobile agents mainly operate smartphones through graphical user interface (GUI) actions such as tapping, swiping, and typing, which often form long, interface-dependent sequences, cannot directly access device capabilities, and make execution boundaries difficult to define. We present PalmClaw, an open-source agent framework that runs natively on mobile phones and manages the sessions, memory, skills, tools, and agent loop directly on the device. PalmClaw exposes device capabilities as device tools with explicit arguments, structured results, and clearly defined execution boundaries. This design enables agents to use mobile capabilities directly while keeping each action explicit and controlled. Experiments show an 11.5\% relative improvement in task success and a 94.9\% reduction in completion time over the strongest baseline, with lower setup burden and traces illustrating how execution boundaries are applied. Code is available at https://github.com/ModalityDance/PalmClaw.