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While maintaining the same level of rigorous, reproducible evaluation as AndroidWorld, MobileWorld offers a more challenging online mobile-use benchmark by introducing four additional features that better capture real-world agent behavior.

• 🎯 Broad Real-World Coverage: 201 carefully curated tasks across 20 mobile applications
• 🔄 Long-Horizon Tasks: Multi-step reasoning and cross-app workflows
• 👥 Agent-User Interaction: Novel tasks requiring dynamic human-agent collaboration
• 🔧 MCP-Augmented Tasks: Support Model Context Protocol (MCP) to evaluate hybrid tool usage

Difficulty comparison between MobileWorld and AndroidWorld

📢 Updates

• 2026-04-29: Head-to-Head Arena & Community Submissions🔥
  • 🆚 New Arena Comparison Page: Compare any two models side-by-side at tongyi-mai.github.io/MobileWorld/arena. Renders both trajectories step-by-step with screenshots and thinking traces, plus a confusion matrix to filter tasks by outcome (both pass / both fail / one wins / the other wins).
  • 📤 Submit Your Results: Community-contributed trajectories are now accepted via site/bundle_trajs.py. See docs/submit.md.
  • 📊 Trajectories now browsable for: Claude-Opus-4.7 (56.4% GUI / 59.1% User-Int), Claude-Opus-4.6 (44.5% / 34.1%), Kimi-K2.6 (55.6% / 56.8%), Kimi-K2.5 (49.6% / 51.2%), Seed-2.0-Pro (63.2% / 61.4%).
• 2026-04-22: Added Claude-Opus-4.7 (56.4% GUI-Only) and Kimi-K2.6 (55.6% GUI-Only) to the leaderboard. Trajectory viewer now available for inspecting per-task agent traces.
• 2026-04-15: Important Fix — Mattermost Session Expiry If you pulled the Docker image before this date, Mattermost task evaluations may produce false negatives due to expired authentication tokens in the emulator snapshot. Please git pull the latest codebase — the fix runs automatically during task initialization (no Docker image rebuild required).

See CHANGELOG.md for the full release history.

📋 Table of Contents

• Updates
• Overview
• Installation
• Quick Start
• Testing on Real Devices
• Available Commands
• Documentation
• Submit Your Results
• Benchmark Statistics
• Contact
• Acknowledgements
• Citation

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📖 Overview

MobileWorld is a comprehensive benchmark for evaluating autonomous mobile agents in realistic scenarios. Our benchmark features a robust infrastructure and deterministic evaluation methodology:

🏗️ System Architecture

Containerized Environment
The entire evaluation environment runs in Docker-in-Docker containers, including:

• Rooted Android Virtual Device (AVD)
• Self-hosted application backends
• API server for orchestration

This design eliminates external dependencies and enables consistent deployment across different host systems.

Open-Source Applications
We build stable, reproducible environments using popular open-source projects:

• Mattermost: Enterprise communication (Slack alternative)
• Mastodon: Social media platform (X/Twitter alternative)
• Mall4Uni: E-commerce platform

Self-hosting provides full backend access, enabling precise control over task initialization and deterministic verification.

Snapshot-Based State Management
AVD snapshots capture complete device states, ensuring each task execution begins from identical initial conditions for reproducible results.

✅ Task Evaluation

We implement multiple complementary verification methods for reliable assessment:

• Textual Answer Verification: Pattern matching and string comparison for information retrieval tasks
• Backend Database Verification: Direct database queries to validate state changes (messages, posts, etc.)
• Local Storage Inspection: ADB-based inspection of application data (calendar events, email drafts, etc.)
• Application Callbacks: Custom APIs capturing intermediate states for validation

💾 Installation

System Requirements

• Docker with privileged container support
• KVM (Kernel-based Virtual Machine) for Android emulator acceleration
• Python 3.12+
• Linux host system (or Windows with WSL2 + KVM enabled), MacOS support is in progress.

Quick Install

# Clone the repository
git clone https://github.com/Tongyi-MAI/MobileWorld.git
cd MobileWorld

# Install dependencies with uv
uv sync

Environment Configuration

Create a .env file from .env.example in the project root:

cp .env.example .env

Edit the .env file and configure the following parameters:

Required for Agent Evaluation:

• API_KEY: Your OpenAI-compatible API key for the agent model
• USER_AGENT_API_KEY: API key for user agent LLM (used in agent-user interactive tasks)
• USER_AGENT_BASE_URL: Base URL for user agent API endpoint
• USER_AGENT_MODEL: Model name for user agent (e.g., gpt-4.1)

Required for MCP-Augmented Tasks:

• DASHSCOPE_API_KEY: DashScope API key for MCP services
• MODELSCOPE_API_KEY: ModelScope API key for MCP services

Example .env file:

API_KEY=your_api_key_for_agent_model
DASHSCOPE_API_KEY=dashscope_api_key_for_mcp
MODELSCOPE_API_KEY=modelscope_api_key_for_mcp

USER_AGENT_API_KEY=your_user_agent_llm_api_key
USER_AGENT_BASE_URL=your_user_agent_base_url
USER_AGENT_MODEL=gpt-4.1

Note:

• MCP API keys are only required if you plan to run MCP-augmented tasks
• User agent settings are only required for agent-user interactive tasks
• See MCP Setup Guide for detailed MCP server configuration

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🚀 Quick Start

1. Check Environment & Pull Docker Image

sudo uv run mw env check

This command verifies Docker, KVM support, and prompts to pull the latest mobile_world Docker image if needed.

2. Launch Docker Containers

sudo uv run mw env run --count 5 --launch-interval 20

This launches 5 containerized Android environments with:

• --count 5: Number of parallel containers
• --launch-interval 20: Wait 20 seconds between container launches

3. Run Evaluation

sudo uv run mw eval \
    --agent_type qwen3vl \
    --task ALL \
    --max_round 50 \
    --model_name Qwen3-VL-235B-A22B \
    --llm_base_url [openai_compatible_url] \
    --step_wait_time 3 \
    --log_file_root traj_logs/qwen3_vl_logs \
    --enable_mcp \
    --enable_user_interaction

Flags:

• --enable_mcp: Include MCP-augmented tasks in evaluation
• --enable_user_interaction: Include agent-user interaction tasks. Without this flag, only GUI-only tasks are evaluated.

4. View Results

uv run mw logs view --log_dir traj_logs/qwen3_vl_logs

Opens an interactive web-based visualization at http://localhost:8760 to explore task trajectories and results.

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📱 Testing on Real Devices

Beyond the containerized emulator, MobileWorld can drive real Android phones via ADB — evaluating frontier models (Claude, Gemini, Qwen, Kimi, Seed-2.0-Pro, …) as true end-to-end mobile agents. See docs/real-devices.md for the setup walkthrough and the per-model coordinate-system reference.

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🔧 Available Commands

MobileWorld provides a comprehensive CLI (mw or mobile-world) with the following commands:

Command	Description
mw env check	Check prerequisites (Docker, KVM) and pull latest image
mw env run	Launch Docker container(s) with Android emulators
mw env list	List running MobileWorld containers
mw env rm	Remove/destroy containers
mw env info	Get detailed info about a container
mw env restart	Restart the server in a container
mw env exec	Open a shell in a container
mw eval	Run benchmark evaluation suite
mw test	Run a single ad-hoc task for testing
mw info task	Display available tasks
mw info agent	Display available agents
mw info app	Display available apps
mw info mcp	Display available MCP tools
mw logs view	Launch interactive log viewer
mw logs results	Print results summary table
mw logs export	Export logs as static HTML site
mw device	View live Android device screen
mw server	Start the backend API server

Use mw <command> --help for detailed options.

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📚 Documentation

For detailed documentation, see the docs/ directory:

Document	Description
Development Guide	Dev mode, debugging, container management workflows
Real Device Setup	Run frontier models on a physical Android phone
Submit Your Results	Bundle trajectories and contribute to the leaderboard
MCP Setup	Configure MCP servers for external tool integration
Windows Setup	WSL2 and KVM setup instructions for Windows
AVD Configuration	Customize and save Android Virtual Device snapshots

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📤 Submit Your Results

Have a trajectory run from a new model or agent configuration? We accept community contributions to the leaderboard and arena.

1. Bundle your traj_logs/<run> directory with site/bundle_trajs.py (use --with-screenshots to include arena-viewable frames).
2. Open a GitHub issue attaching the resulting .json.gz (+ optional .mp4) and a draft site/leaderboard.json entry.

See docs/submit.md for the bundling commands, the full leaderboard-entry schema, and the asset-repo upload flow.

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🎯 Benchmark Statistics

📬 Contact

For questions, issues, or collaboration inquiries:

• GitHub Issues: Open an issue
• Email: Contact the maintainers
• Discord: Join our Discord server
• WeChat Group: Scan to join our discussion group

Acknowledgements

We thank Android World and Android-Lab for their open source contributions. We also thank all the open-source contributors!

Citation

If you find MobileWorld useful in your research, please cite our paper:

@inproceedings{kong2025mobileworld,
      title={MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive, and MCP-Augmented Environments},
      author={Quyu Kong and Xu Zhang and Zhenyu Yang and Nolan Gao and Chen Liu and Panrong Tong and Chenglin Cai and Hanzhang Zhou and Jianan Zhang and Liangyu Chen and Zhidan Liu and Steven Hoi and Yue Wang},
      booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL)},
      year={2026},
      url={https://arxiv.org/abs/2512.19432},
}

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⭐ Star History

If you find MobileWorld helpful, please consider giving us a star ⭐!