PalmClaw：原生設備端手機代理框架

摘要

大語言模型（LLM）智能體已不再局限於生成回應，而是能夠透過調用工具、觀察結果並迭代決策下一步行動，來執行多步驟任務。多數智能體系統運行於桌面或伺服器環境，支援工具使用與任務自動化。行動裝置同樣是重要的智能體環境，因其普及率高，且包含使用者資料、感測器及日常應用。現有行動智能體主要透過圖形用戶介面（GUI）操作（如點擊、滑動與輸入）來操控智慧型手機，但此類操作常形成冗長且依賴介面的序列，無法直接存取裝置功能，亦難以界定執行邊界。我們提出 PalmClaw，這是一個原生運行於手機上的開源智能體框架，可在裝置上直接管理對話、記憶、技能、工具與智能體循環。PalmClaw 將裝置功能以裝置工具的形式呈現，具備明確參數、結構化結果與清晰定義的執行邊界。此設計使智能體能直接使用行動裝置功能，同時確保每個操作明確且可控。實驗結果顯示，相較於最強基線，任務成功率相對提升 11.5%，完成時間減少 94.9%，且設置負擔更低，並以記錄軌跡說明執行邊界的應用方式。程式碼開源於 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.