PalmClaw让大模型直接在手机上执行任务，突破界面操作限制。
PalmClaw: A Native On-Device Agent Framework for Mobile Phones
- 在手机本地运行，直接调用设备工具，动作清晰可控。
- 任务成功率提升11.5%，完成时间减少94.9%。
- 适合想在手机上实现自动化的人，尤其是开发者和极客。
大型语言模型代理已从生成回复演变为通过调用工具、观察结果并迭代决策来执行多步任务。现有代理系统大多部署在桌面或服务器端，支持工具使用与任务自动化。移动设备作为重要代理环境，因其广泛可及性及用户数据、传感器与日常应用的集成而具备独特优势。然而，现有移动端代理主要依赖图形界面（GUI）操作，如点击、滑动、输入，常形成冗长且依赖界面的序列，无法直接访问设备能力，执行边界模糊。本文提出开源框架PalmClaw，可在手机本地原生运行，直接管理会话、记忆、技能、工具与代理循环。PalmClaw将设备功能抽象为具明确参数、结构化输出和清晰执行边界的工具，使代理能直接调用移动能力，同时确保每一步操作显式可控。实验表明，相比最强基线，任务成功率提升11.5%，完成时间减少94.9%，且设置负担更低。代码已公开于https://github.com/ModalityDance/PalmClaw。
原文摘要 · Abstract (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 \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.
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