AIエージェント
OpenGUI
Android GUI agent framework: AI that sees, plans, and taps real mobile apps.
★ 1.8k スターCustom
ガイドを見る →mobile-useは、minitap-aiが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: minitap-ai/mobile-use）。GitHubスターは3,234、フォークは295で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年9月14日です。
AI agents that use real Android and iOS apps like a human — from minitap.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
Physical Android phones over USB, Android emulators, and iOS simulators on macOS.
Describe the task in plain language; the agent plans and acts on the live UI.
A containerised launch path, with troubleshooting docs for device-IP and image-pull issues.
uv venv + uv sync gives a locked, reproducible local setup.
The README reports benchmark results for the agent's mobile task performance.
An agentic system overview in the README explains the perception/action loop.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/minitap-ai/mobile-use.git
cd mobile-useClone, create the uv environment from the project's Python version, activate it and sync locked dependencies.
git clone https://github.com/minitap-ai/mobile-use.git && cd mobile-use
uv venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
uv syncConnect a device (ADB for Android, fb-idb for iOS) and give the agent a goal.
python ./minitap/mobile_use/main.py "Go to settings and tell me my current battery level"It is maintained by minitap-ai on GitHub (minitap-ai/mobile-use), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
Yes — the agent reasons with an LLM, so you configure a provider key as the README describes; devices themselves are your own hardware/emulators.
Compared with Appium scripts or desktop browser-use agents, mobile-use's distinguishing focus is: aI agents that use real Android and iOS apps like a human — from minitap. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/minitap-ai/mobile-use (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
Android GUI agent framework: AI that sees, plans, and taps real mobile apps.
Reliable computer use for any agent: drive real apps through accessibility trees, not pixel guessing.
Control Android and iOS devices with natural language — an LLM-agnostic mobile agent framework and CLI.
Tutti — a real-time shared workspace where people and multiple AI agents build in tune, sharing context, files, apps and tasks
The open-source library that lets AI agents control a real browser.
Benchmark computer-use agents on long, real-world desktop tasks.
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — mobile-useが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でmobile-useとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
Control Gmail, Calendar, Docs, Sheets, Drive and more from AI — a full Google Workspace MCP server.
An open protocol for secure AI-driven payments.
EvoAgentX — a framework for building self-evolving ecosystems of AI agents.
A local MCP server for serious arXiv work — LaTeX section reads, BibTeX and topic watches.
Build production-ready AI agents in both Python and Typescript.
OSWorld — benchmark multimodal agents on open-ended tasks in real computer environments.