2026 年哪款移动 AI Agent 最强？
英文标题：🔶 Which mobile AI agent is the most powerful in 2026
MobiAgent、Mobile-Agent、Mobilerun 和 mobile-use 都致力于让 AI 操作手机或移动应用，但分别侧重研究系统、跨平台 GUI Agent、工程化设备控制与真实 App 任务执行。MobiAgent 的优势在于模型、记忆、执行器和评测组成的完整研究闭环；Mobile-Agent 覆盖手机、桌面、浏览器和工具调用等更广泛的 GUI Agent 技术路径。Mobilerun 提供模型无关的 CLI、Python API、Docker 与云设备工作流，更适合接入产品、QA 和自动化系统；mobile-use 则强调真实应用操作、任务拆解、结构化数据提取及 AndroidWorld 评测。对开发者而言，选择重点不再只是模型能否点击界面，而是设备控制稳定性、权限边界、可观测性、任务评测与结构化结果交付能力。
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中文
我最近连续整理了四个移动 GUI Agent 项目：MobiAgent、Mobile-Agent、Mobilerun 和 mobile-use。它们都围绕「让 AI 操作手机或移动 App」展开，但产品定位并不相同。
简而言之：MobiAgent 更接近一套可定制的手机 Agent 研究系统；Mobile-Agent 是通义实验室围绕 GUI Agent 形成的技术成果体系；Mobilerun 更像一套实用的本地/云端移动设备控制框架；mobile-use 则强调真实 App 操作、任务拆解、数据提取和 AndroidWorld 评测。
基本信息对比
| 项目 | 站内文章 | GitHub | 主要定位 | 设备/平台 | 许可证 | 最适合场景 |
|---|---|---|---|---|---|---|
| MobiAgent | 站内介绍 | IPADS-SAI/MobiAgent | 包含模型、执行器、记忆、加速和评测能力的可定制手机 GUI Agent 系统 | 主要为 Android/Harmony 手机 | Apache-2.0 | 研究人员和移动 Agent 实验团队 |
| Mobile-Agent | 站内介绍 | X-PLUG/MobileAgent | 覆盖移动端、桌面端、浏览器和工具调用的通义实验室 GUI Agent 项目族 | 手机、PC、网页、云手机/云桌面 | MIT | 跟踪 GUI Agent 技术路线的人群 |
| Mobilerun | 站内介绍 | droidrun/mobilerun | 提供 CLI、Python API 和云设备工作流的模型无关移动设备 Agent 框架 | Android、iOS、本地设备、云设备 | MIT | 开发者、QA 及自动化工作流团队 |
| mobile-use | 站内介绍 | minitap-ai/mobile-use | 通过自然语言操作真实移动 App，支持任务拆解、结构化提取并聚焦 AndroidWorld | Android 真机/模拟器、iOS 模拟器 | Apache-2.0 | 构建移动 App Agent、数据提取和评测方案的人群 |
MobiAgent
MobiAgent 来自 IPADS-SAI，定位是一套可定制的手机 Agent 系统。它不只是一个执行脚本，而是将 MobiMind 模型家族、AgentRR 动作记录与回放、MobiFlow 基准、手机执行器、数据采集以及 Android App 整合为一个系统。
它的主要优势在于研究系统的完整性。MobiAgent 关注真实手机任务中的准确率、效率、记忆能力以及可复用的操作序列。README 中提到的用户画像记忆、经验记忆、动作记忆和多任务执行，都表明它试图处理长时程任务和重复性任务。
它的使用门槛也相对较高。完整部署需要设备、ADB、模型部署、依赖项，以及可选的向量数据库和图数据库配置。相比普通用户可「安装后立即使用」的手机助手，它更适合研究或工程实验场景。
Mobile-Agent
Mobile-Agent 来自 X-PLUG/通义实验室。该仓库已从早期的手机操作 Agent，发展为一个 GUI Agent 项目族：Mobile-Agent-v1/v2/v3/v3.5、Mobile-Agent-E、PC-Agent、GUI-Critic-R1、UI-S1、GUI-Owl、ToolCUA 等均位于同一技术路线中。
它最鲜明的特点是覆盖范围广。Mobile-Agent 不只面向手机，也覆盖桌面、浏览器、云手机、云桌面、GUI 感知、定位、错误诊断、强化学习，以及 GUI/工具路径编排。GUI-Owl 模型系列使其更像一条跨平台 GUI Agent 基础模型路线，而不仅是单一的移动自动化项目。
其弱点同样来自这种广度：该仓库更像是研究成果的集合，因此用户首先需要判断自己实际要运行的是哪个子项目、模型和场景。它适合跟踪技术演进和复现实验，但未必是快速接入某项业务工作流的最佳选择。
Mobilerun
Mobilerun 来自 droidrun，工程化取向更强：它让 LLM Agent 能够通过自然语言控制 Android 和 iOS 设备。它提供 CLI、TUI、Docker、Python API、基于 Portal 的控制方式、视觉模式、推理模式、结构化输出、自定义工具、App 卡片、执行轨迹以及云设备服务。
其最突出的特征是模型无关性和清晰的部署形态。开发者可以接入 OpenAI、Anthropic、Gemini、Ollama、DeepSeek、OpenRouter 或 OpenAI 兼容服务商；也可以选择本地框架或 Mobilerun Cloud。对于真实团队而言，设备控制层与模型层之间的这种分离非常重要。
它仍然面临移动自动化的典型门槛。Android 需要开启开发者选项、USB 调试并安装 Portal App；iOS 则有单独流程；复杂任务还需要处理权限弹窗、页面变化、失败重试和日志排查。它更适合愿意将移动 Agent 作为工程组件使用的人群。
mobile-use
mobile-use 来自 minitap-ai，目标是让 AI Agent 使用真实的 Android 和 iOS App。它支持自然语言控制、具备 UI 感知能力的自动化、数据提取和不同 LLM 配置，并强调其 AndroidWorld 基准表现。其 README 还称，该项目是首个在 AndroidWorld 基准上达到 100% 的 Agentic 框架。
它的亮点在于任务拆解和结构化提取。例如，在 Gmail 中查找未读邮件，并以指定 JSON 格式返回发件人和主题，这比仅仅「打开设置并查看电池电量」更接近真实生产需求。它推动移动 GUI Agent 从「能够操作」向「能够从 App 中组织信息」演进。
它的限制主要集中在设备支持和运行环境上。Android 可以使用真机或模拟器；iOS 目前主要支持 macOS 上的模拟器，尚不支持实体 iOS 设备。Docker 快速启动也主要面向 Android。在评估该项目时，应先确认目标设备和 App 场景是否已被当前执行路径覆盖。
功能对比
| 功能维度 | MobiAgent | Mobile-Agent | Mobilerun | mobile-use |
|---|---|---|---|---|
| 自然语言任务 | 支持 | 支持 | 支持 | 支持 |
| 真实手机操作 | 强，面向 Android/Harmony | 强，覆盖移动端和云手机 | 强，Android/iOS | 强，Android；iOS 更偏向模拟器 |
| 桌面/浏览器扩展 | 非重点 | 强，包含 PC-Agent、GUI-Owl、ToolCUA | 非主要定位 | 非主要定位 |
| 模型层 | 包含 MobiMind 系列 | GUI-Owl 和 Mobile-Agent 系列 | 模型无关，可连接多种模型 | 可配置多种 LLM |
| 执行器/Runner | 强，包含 ADB Runner 和多任务 Runner | 由不同子项目分别提供 | 强，CLI/TUI/Python API/Docker | 源代码、Docker 和平台入口 |
| 记忆能力 | 用户画像、经验和动作记忆 | v3/v3.5 强调记忆与反思 | 更侧重轨迹、日志和工程调试 | 更侧重任务拆解和有状态执行 |
| 评测 | MobiFlow | 多个论文/基准方向 | 提供基准结果入口 | AndroidWorld 表现突出 |
| 云设备 | 非主要卖点 | 支持云手机/云桌面体验 | Mobilerun Cloud 是重点 | 提供平台入口 |
| 结构化输出 | 可通过工程流程实现 | 取决于具体子项目 | 明确支持 | 明确支持 |
优势与弱点
MobiAgent 的优势是系统完整性。它适合研究手机 GUI Agent 中模型、记忆、加速和评测构成的闭环。其弱点是部署链路长、工程配置较重，普通开发者的上手成本相对较高。
Mobile-Agent 的优势是技术路线最广。它展示了 GUI Agent 如何从手机扩展至桌面、浏览器、工具调用和基础模型。其弱点是项目族的复杂性：如果希望直接落地某一个具体场景，首先需要进行更多筛选。
Mobilerun 的优势是工程接口清晰、模型无关，并明确区分本地框架和云服务。它适合将移动设备自动化接入产品或内部工具。其弱点是仍需处理移动设备权限、运行环境、App 状态和云服务成本。
mobile-use 的优势是专注于真实 App 使用、任务拆解和结构化数据提取。AndroidWorld 这一评测视角也让它更容易被评估。其弱点是对实体 iOS 设备的支持有限，并且完整部署依然需要配置模型、设备和运行环境。
建议使用场景
如果你希望研究移动 Agent，应优先关注 MobiAgent 和 Mobile-Agent。前者更聚焦手机端系统的闭环，后者则更适合观察 GUI Agent 的跨平台演进。
如果你希望实现移动 App 自动化、QA、数据提取或内部工作流，应优先关注 Mobilerun 和 mobile-use。Mobilerun 更像一个可接入工程系统的运行时框架，而 mobile-use 更适合验证基于自然语言的 App 操作和结构化提取。
如果你关注未来个人助手的形态，这四个项目都值得追踪。MobiAgent 代表手机 Agent 的系统化研究，Mobile-Agent 代表跨平台 GUI Agent 路径，Mobilerun 代表设备控制基础设施，mobile-use 则代表真实 App 任务拆解和评测驱动的开发方向。
我的看法
这四个项目之间的差异表明，移动 GUI Agent 已不再只是「让模型看截图并点击按钮」。真正的问题已经变成：模型如何理解界面、执行器如何可靠控制设备、任务如何拆解和评估、云设备如何管理、结果如何以结构化形式返回，以及风险如何受到约束。
短期来看，最现实的落地场景是 QA、数据提取、内部工作流自动化和受控设备池。长期来看，谁能够稳定解决设备控制、模型能力、权限边界、日志追踪和用户确认机制，谁就更接近真正可用的移动 AI 助手。
English / 原文
I recently organized four mobile GUI agent projects in a row: MobiAgent, Mobile-Agent, Mobilerun, and mobile-use. They are all about “letting AI operate phones or mobile apps”, but their positioning is not the same.
In short: MobiAgent is closer to a customizable research system for phone agents; Mobile-Agent is Tongyi Lab’s body of work around GUI agents; Mobilerun is more of a practical local/cloud mobile device control framework; and mobile-use emphasizes real app operation, task decomposition, data extraction, and AndroidWorld evaluation.
Basic Information Comparison
| Project | Site Article | GitHub | Main Positioning | Device/Platform | License | Best For |
|---|---|---|---|---|---|---|
| MobiAgent | Site intro | IPADS-SAI/MobiAgent | Customizable phone GUI agent system with models, runner, memory, acceleration, and evaluation | Mainly Android/Harmony phones | Apache-2.0 | Researchers and mobile agent experiment teams |
| Mobile-Agent | Site intro | X-PLUG/MobileAgent | Tongyi Lab GUI agent family covering mobile, desktop, browser, and tool use | Phones, PCs, web pages, cloud phones/cloud desktops | MIT | People tracking GUI agent technology paths |
| Mobilerun | Site intro | droidrun/mobilerun | LLM-agnostic mobile device agent framework with CLI, Python API, and cloud device workflows | Android, iOS, local devices, cloud devices | MIT | Developers, QA, and automation workflow teams |
| mobile-use | Site intro | minitap-ai/mobile-use | Operates real mobile apps through natural language, with task decomposition, structured extraction, and AndroidWorld focus | Android devices/emulators, iOS simulators | Apache-2.0 | People building mobile app agents, data extraction, and evaluations |
MobiAgent
MobiAgent comes from IPADS-SAI and is positioned as a customizable phone agent system. It is not just an execution script. It puts the MobiMind model family, AgentRR action recording and replay, the MobiFlow benchmark, phone runners, data collection, and an Android app into one system.
Its main strength is the completeness of the research system. MobiAgent cares about accuracy, efficiency, memory, and reusable action sequences in real phone tasks. The user profile memory, experience memory, action memory, and multi-task execution mentioned in the README all show that it is trying to handle long-horizon and repeated tasks.
Its entry barrier is also relatively high. A full setup requires devices, ADB, model deployment, dependencies, and optional vector database and graph database configuration. It is better suited to research or engineering experiments than to an “install and use immediately” phone assistant for ordinary users.
Mobile-Agent
Mobile-Agent comes from X-PLUG/Tongyi Lab. The repository has grown from an early phone operation agent into a GUI agent family: Mobile-Agent-v1/v2/v3/v3.5, Mobile-Agent-E, PC-Agent, GUI-Critic-R1, UI-S1, GUI-Owl, ToolCUA, and more all sit on the same technical line.
Its defining feature is breadth. Mobile-Agent is not only about phones; it also covers desktop, browser, cloud phones, cloud desktops, GUI perception, grounding, error diagnosis, reinforcement learning, and GUI/tool path orchestration. The GUI-Owl model series makes it feel more like a cross-platform GUI agent foundation-model track than a single mobile automation project.
The weakness also comes from that breadth: the repository is more like a collection of research results, so users first need to decide which subproject, model, and scenario they actually want to run. It is good for tracking technical evolution and reproducing experiments, but it may not be the fastest choice for plugging into a business workflow.
Mobilerun
Mobilerun comes from droidrun and is more engineering-oriented: it lets LLM agents control Android and iOS devices through natural language. It provides CLI, TUI, Docker, Python API, portal-based control, vision mode, reasoning mode, structured output, custom tools, app cards, execution traces, and cloud device services.
Its most prominent quality is model agnosticism and clear deployment shape. Developers can connect OpenAI, Anthropic, Gemini, Ollama, DeepSeek, OpenRouter, or OpenAI-compatible providers; they can also choose a local framework or Mobilerun Cloud. For real teams, this separation between the device control layer and the model layer matters a lot.
It still has the usual mobile automation barriers. Android requires developer options, USB debugging, and the Portal app; iOS has a separate flow; complex tasks also need to handle permission popups, page changes, retries after failure, and log investigation. It is better for people willing to use mobile agents as engineering components.
mobile-use
mobile-use comes from minitap-ai and aims to let AI agents use real Android and iOS apps. It supports natural-language control, UI-aware automation, data extraction, and different LLM configurations, and it emphasizes AndroidWorld benchmark performance. Its README also says the project is the first agentic framework to reach 100% on the AndroidWorld benchmark.
Its highlight is task decomposition and structured extraction. For example, finding unread email in Gmail and returning the sender and subject in a specified JSON format is much closer to real production needs than simply “opening Settings and checking the battery level”. It pushes mobile GUI agents from “can operate” toward “can organize information from apps”.
Its limitations are mainly device support and runtime environment. Android can use physical phones or emulators; iOS currently mainly supports simulators on macOS, while physical iOS devices are not yet supported. Docker quick start is also mainly aimed at Android. When evaluating it, first confirm whether the target device and app scenario are covered by the current execution path.
Feature Comparison
| Feature Dimension | MobiAgent | Mobile-Agent | Mobilerun | mobile-use |
|---|---|---|---|---|
| Natural-language tasks | Supported | Supported | Supported | Supported |
| Real phone operation | Strong, Android/Harmony oriented | Strong, includes mobile and cloud phones | Strong, Android/iOS | Strong, Android; iOS leans simulator |
| Desktop/browser expansion | Not the focus | Strong, includes PC-Agent, GUI-Owl, ToolCUA | Not the main positioning | Not the main positioning |
| Model layer | Includes MobiMind series | GUI-Owl and Mobile-Agent series | LLM-agnostic, connects many models | Configurable with multiple LLMs |
| Executor/runner | Strong, includes ADB runner and multi-task runner | Provided separately by subprojects | Strong, CLI/TUI/Python API/Docker | Source code, Docker, and platform entry points |
| Memory ability | User profile, experience, and action memory | v3/v3.5 emphasize memory and reflection | More about traces, logs, and engineering debugging | More about task decomposition and stateful execution |
| Evaluation | MobiFlow | Multiple paper/benchmark directions | Has benchmark result entry points | Strong AndroidWorld performance |
| Cloud devices | Not the main selling point | Supports cloud phone/cloud desktop experiences | Mobilerun Cloud is a focus | Has platform entry points |
| Structured output | Can be implemented through engineering flows | Depends on the subproject | Explicitly supported | Explicitly supported |
Strengths and Weaknesses
MobiAgent’s strength is system completeness. It is suitable for studying the closed loop of models, memory, acceleration, and evaluation for phone GUI agents. Its weakness is the long deployment chain, heavy engineering configuration, and relatively high onboarding cost for ordinary developers.
Mobile-Agent’s strength is the broadest technical path. It shows GUI agents evolving from phones to desktops, browsers, tool use, and foundation models. Its weakness is the complexity of the project family: if you want to land one specific scenario directly, you need to do more filtering first.
Mobilerun’s strength is a clear engineering interface, model agnosticism, and explicit separation between local framework and cloud service. It is suitable for integrating mobile device automation into products or internal tools. Its weakness is that it still has to deal with mobile device permissions, environments, app state, and cloud cost.
mobile-use’s strength is its focus on real app usage, task decomposition, and structured data extraction. The AndroidWorld angle also makes it easier to evaluate. Its weakness is limited support for physical iOS devices, and a complete setup still requires model, device, and runtime configuration.
Suggested Use Cases
If you want to research mobile agents, look first at MobiAgent and Mobile-Agent. The former focuses more on a closed loop for phone-side systems, while the latter is better for observing the cross-platform evolution of GUI agents.
If you want mobile app automation, QA, data extraction, or internal workflows, look first at Mobilerun and mobile-use. Mobilerun is more like a runtime framework that can plug into engineering systems, while mobile-use is better for validating natural-language app operation and structured extraction.
If you care about future personal-assistant forms, all four are worth tracking. MobiAgent represents systematic research on phone agents, Mobile-Agent represents the cross-platform GUI agent path, Mobilerun represents device-control infrastructure, and mobile-use represents real-app task decomposition and evaluation-driven development.
My Take
The differences between these four projects show that mobile GUI agents are no longer just about “letting a model look at screenshots and tap buttons”. The real questions have become: how models understand interfaces, how executors control devices reliably, how tasks are decomposed and evaluated, how cloud devices are managed, how results are returned in structured form, and how risks are constrained.
In the short term, the most realistic landing scenarios are QA, data extraction, internal workflow automation, and controlled device pools. In the long run, whoever can stabilize device control, model capability, permission boundaries, log tracing, and user confirmation mechanisms will be closer to a truly usable mobile AI assistant.