手机上跑智能体不用再模拟点击了，PalmClaw直接调用摄像头、传感器等设备功能，速度快94%，值得试试。
PalmClaw是一个开源框架，直接在手机上运行智能体循环，管理会话、记忆、技能和工具。它将设备能力封装为带有明确参数和结构化结果的工具，使智能体直接调用手机功能。实验表明，相比最强基线，PalmClaw任务成功率相对提升11.5%，完成时间减少94.9%。代码已在GitHub开源。
PalmClaw: A Native On-Device Agent Framework for Mobile Phones
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 \textbf{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.