# Project export: Mobius

This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small.

## Project metadata

- Hackathon: TreeHacks 2025
- Tagline: Mobile Device Automation Agents
- Devpost: https://devpost.com/software/mobius-e7jidz
- GitHub: not linked
- Video: https://www.youtube.com/embed/tWHi_PiZEF8?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### Inspiration

Mobile automation is still stuck in the dark ages of complex scripting frameworks like Appium, making it inaccessible to non-developers and tedious even for experienced engineers. While tools like Operator and BrowserUse exist for browser automation, there is no equivalent for mobile devices. We set out to build Mobius, an autonomous smartphone agent framework that can execute any task on a mobile device, scale to a fleet of agents, and provide a well-documented API for developers to build upon.

### What it does

Mobius is an agent-driven, fleet-ready automation platform that enables developers to: Run AI-powered agents on mobile devices—"Buy me a pizza," "Set up my phone," or literally anything you’d rather not tap through yourself. Scale across a fleet of devices—Deploy hundreds of agents handling different workflows in parallel. Seamlessly integrate via APIs—Build powerful applications on top of our shockingly well-documented automation stack. With just a few lines of code, Mobius makes mobile automation effortless: Example Use Cases: AI-driven mobile assistants – Voice-controlled agents that interact with apps autonomously. Software Testing & CI/CD – Run automated UI/UX tests with natural language. User Experience Research – Simulate and analyze real mobile interactions. Enterprise Mobile Automation – Automate complex workflows across multiple devices. Fleet-based Automation – Scale operations across hundreds of mobile instances.

### How we built it

AI Agent Core: Built on VLM-powered reasoning using LangChain + LangGraph for autonomous task execution. Mobile Automation: Android Studio + ADB for real-device interaction and app control. Fleet Infrastructure: Scalable architecture for parallel execution across multiple agents. On-Device Execution: LLM inference runs locally, preserving privacy and reducing latency. FastAPI Backend: API-first design for seamless integration into developer workflows.

### Challenges we ran into

Autonomous mobile interaction – Designing a reliable agent that can interpret UI changes dynamically (because mobile UIs love to change things randomly). Scaling to a fleet – Ensuring hundreds of mobile agents run efficiently in parallel without everything catching fire. Simplifying the developer experience – Making the API intuitive yet powerful (seriously, check our docs—it’s easy).

### Accomplishments we're proud of

Built a VLM-powered agent that can autonomously complete tasks on mobile (yes, it actually works). Developed fleet automation infrastructure, supporting scalable, parallel execution (it’s as cool as it sounds). Designed an API-first framework, making mobile automation accessible to all developers (you’ll want to use this).

### What we learned

Multi-modal models (VLMs) enable deep UI understanding for mobile automation. Fleet-based automation is essential for large-scale mobile workflows (because running one bot is boring). APIs drive adoption—clear documentation and developer-friendly design are key to success (seriously, check our repo).

### What's next

Open-source release—Empower developers to extend and customize our framework (so you don’t have to reinvent the wheel). Expanding beyond Android—Supporting iOS and other mobile platforms. Exploring SaaS potential—Building an enterprise-ready cloud deployment model. Refining agent intelligence—Improving VLM-driven reasoning for even more accurate task execution.

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- FastAPI (technology) — claimed on Devpost, not found in the code
- LangChain (technology) — claimed on Devpost, not found in the code
- OpenAI (technology) — claimed on Devpost, not found in the code
- Python (language) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.