Which AI Mobile Automation Project Is Stronger? MobiAgent, Mobile-Agent, Mobilerun, and mobile-use Compared

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Which AI Mobile Automation Project Is Stronger? MobiAgent, Mobile-Agent, Mobilerun, and mobile-use Compared

A comparison of four mobile GUI agent projects: MobiAgent, Mobile-Agent, Mobilerun, and mobile-use, covering basic information, functional focus, strengths, weaknesses, and suitable use cases.

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<br>Site Article<br>GitHub<br>Main Positioning<br>Device/Platform<br>License<br>Best For

MobiAgent<br>Site intro<br>IPADS-SAI/MobiAgent<br>Customizable phone GUI agent system with models, runner, memory, acceleration, and evaluation<br>Mainly Android/Harmony phones<br>Apache-2.0<br>Researchers and mobile agent experiment teams

Mobile-Agent<br>Site intro<br>X-PLUG/MobileAgent<br>Tongyi Lab GUI agent family covering mobile, desktop, browser, and tool use<br>Phones, PCs, web pages, cloud phones/cloud desktops<br>MIT<br>People tracking GUI agent technology paths

Mobilerun<br>Site intro<br>droidrun/mobilerun<br>LLM-agnostic mobile device agent framework with CLI, Python API, and cloud device workflows<br>Android, iOS, local devices, cloud devices<br>MIT<br>Developers, QA, and automation workflow teams

mobile-use<br>Site intro<br>minitap-ai/mobile-use<br>Operates real mobile apps through natural language, with task decomposition, structured extraction, and AndroidWorld focus<br>Android devices/emulators, iOS simulators<br>Apache-2.0<br>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 &ldquo;install and use immediately&rdquo; 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...