LibreAI / KI-Agenten / mobile-use
// agents · minitap-ai/mobile-use

mobile-use

mobile-use ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von minitap-ai (minitap-ai/mobile-use auf GitHub). Es hat 3.234 Sterne auf GitHub und 295 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 14. September 2026 auf GitHub aktualisiert.

AI agents that use real Android and iOS apps like a human — from minitap.

★ 3,2 Tsd. Sterne⑂ 295 ForksApache-2.0aktualisiert Sep 2026
▦ Visual — das README dieses Projekts enthält keinen einbettbaren Screenshot,
darum hier ein Platzhalter. Die Anleitung unten ist trotzdem vollständig.
// projektdaten

Projektdaten

GitHub-Sterne
3.234
Forks
295
Lizenz
Apache-2.0
Zuletzt auf GitHub aktualisiert
14. September 2026
Kategorie
KI-Agenten
Eintragstyp
Vollständige Installationsanleitung
Offizielle Website
Official site
// so funktioniert es

So funktioniert es

  1. Clarify your goal

    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.

  2. Get the project

    Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.

  3. Install and configure

    Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.

  4. Run your first task

    Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.

// funktionen

Warum es so beliebt ist

Real devices and simulators

Physical Android phones over USB, Android emulators, and iOS simulators on macOS.

Natural-language control

Describe the task in plain language; the agent plans and acts on the live UI.

Docker quick launch

A containerised launch path, with troubleshooting docs for device-IP and image-pull issues.

Development mode

uv venv + uv sync gives a locked, reproducible local setup.

Benchmarked

The README reports benchmark results for the agent's mobile task performance.

LangGraph-based system

An agentic system overview in the README explains the perception/action loop.

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// selbst ausführen

Führe es selbst aus

  1. Clone the repository

    Get the source so you can inspect, configure and run it locally.

    git clone https://github.com/minitap-ai/mobile-use.git
    cd mobile-use
  2. Clone and set up locally

    Clone, 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 sync
  3. Run a task

    Connect 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"
// anwendungsfälle

Was du damit bauen kannst

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// häufige fragen

Häufige Fragen

Who maintains mobile-use?

It is maintained by minitap-ai on GitHub (minitap-ai/mobile-use), where the source, issues and releases live.

Is it free to use?

Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.

Does it need a paid API key?

Yes — the agent reasons with an LLM, so you configure a provider key as the README describes; devices themselves are your own hardware/emulators.

How does it compare to Appium scripts or desktop browser-use agents?

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.

How do I get started?

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.

// weiter entdecken

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// alternativen

Alternativen im Bereich KI-Agenten

Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls mobile-use nicht das Richtige ist.

// mehr in agents

Mehr in KI-Agenten

Verwandte Projekte der Kategorie KI-Agenten im Katalog, die mobile-use bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.

Hol dir mobile-use

Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.