Artificial intelligence is advancing rapidly in mobile automation, with several projects pushing the boundaries of how AI agents operate phones and apps. Four notable open-source initiatives—MobiAgent, Mobile-Agent, Mobilerun, and mobile-use—each approach mobile GUI automation with different priorities, strengths, and limitations.
MobiAgent: Research-Grade System for Phone Agents
MobiAgent, developed by IPADS-SAI, is a comprehensive research system combining AI models, action recording, replay, and benchmarking tools. It aims to handle complex, long-term phone tasks with memory and multitasking capabilities. However, its deployment requires technical expertise, including device setup, ADB configuration, and model management, making it better suited for research and engineering experiments rather than immediate end-user applications.
Mobile-Agent: Broad Cross-Platform GUI Agent Framework
Originating from Tongyi Lab’s X-PLUG, Mobile-Agent has evolved from a phone operation tool into a broad GUI agent ecosystem covering desktops, browsers, cloud environments, and reinforcement learning. Its main strength lies in its extensive technical scope, but this breadth can overwhelm users seeking a straightforward solution, as the repository contains many subprojects requiring careful selection.
Mobilerun: Engineering-Focused Device Control Framework
Mobilerun, from droidrun, offers a practical, model-agnostic framework for controlling Android and iOS devices via natural language. It supports multiple language models and deployment options, including local and cloud services. Its clear separation between device control and model layers makes it ideal for integrating mobile automation into products or internal workflows. However, it still faces typical mobile automation challenges such as permissions, debugging, and app state management.
mobile-use: Real App Operation and Structured Data Extraction
Developed by minitap-ai, mobile-use focuses on real app usage, task decomposition, and extracting structured data from apps. It boasts top performance on the AndroidWorld benchmark, making it attractive for QA, data extraction, and workflow automation. Limitations include partial iOS device support and the need for specific runtime environments.
Practical Impact for Businesses and Developers
- Research and Development: MobiAgent and Mobile-Agent offer valuable platforms for exploring advanced agent capabilities and cross-platform GUI automation evolution.
- Product Integration: Mobilerun’s engineering-friendly design suits teams building AI-powered mobile control into applications or internal tools.
- App Automation and Data Extraction: mobile-use excels in practical scenarios requiring natural language control and structured output from real apps.
All four projects reflect a shift from simple screen tapping towards sophisticated understanding, reliable device control, task decomposition, and evaluation-driven development. For businesses, the immediate opportunities lie in quality assurance, workflow automation, and controlled device management. Long term, stabilising device interaction, permission handling, and user confirmation will be key to truly usable mobile AI assistants.
While these projects vary in complexity and focus, they collectively highlight the growing maturity of AI mobile agents. Developers and enterprises should select tools aligned with their goals—whether research, integration, or production use—and stay informed as this space evolves.
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Disclaimer: This article summarises current open-source projects and their documented capabilities. Actual deployment may require technical expertise and adaptation to specific environments. No medical or clinical claims are made.