OpenMobile-2: Building Versatile Mobile Agents with Scalable Environments and App-Native Tools
Abstract
OpenMobile-2 is an open mobile agent trained with diverse simulated apps, large-scale supervised and reinforcement learning data, and hybrid GUI-tool use, achieving strong benchmark results and real-device transfer.
We introduce OpenMobile-2, a near-frontier mobile agent with fully open training environments and recipes. We make three key advances: (1) Diverse environments with simulated commercial apps: We build MobileGym++, featuring 35 realistic, functionally rich commercial-style apps with cross-app workflows, while preserving full controllability for reset and verification. Together with newly configured apps in Android emulators, this yields a diverse playground spanning over 110 apps. (2) Open training data at scale: Building on this foundation, we curate nearly 12K mobile interaction trajectories for supervised fine-tuning and the largest open collection of verifiable RL training data for mobile agents, comprising over 2K executable tasks with automatic rewards. (3) Hybrid GUI and app-native tool use: We explore an experimental mobile-use setting where apps expose selected functionalities as app-native tools alongside their GUIs, allowing agents to interleave GUI actions and tool calls within a task. We implement this setting in MobileGym++ with over 300 carefully scoped tools across 50+ apps. Additionally, we introduce MobileGym++ Bench for complex, long-horizon commercial mobile scenarios, supporting both GUI-only and hybrid GUI-tool evaluation on a shared task suite. OpenMobile-2 performs competitively across established benchmarks, including AndroidWorld (79.9) and MobileWorld (50.4), while showing promising transfer to real-device mobile use, nearly doubling SPA-Bench performance from 31.9 to 59.6. All resources are available at https://os-copilot.github.io/OpenMobile2-Home/.
Community
π Introducing OpenMobile-2: Building Versatile Mobile Agents with Scalable Environments and App-Native Tools
We're excited to share OpenMobile-2, our latest work on mobile agents, with environments, data, benchmarks, and models fully open-sourced!
We explore two key directions in mobile agent research:
1. Scalable Training Environments. Existing emulator-based environments are heavyweight and cover limited apps, especially commercial ones. We introduce MobileGym++, featuring 35 high-fidelity, fully controllable commercial-style apps with cross-app workflow, built through humanβcoding agent collaboration. Together with emulator-based apps, we provide an open playground of 110+ diverse apps for scalable data synthesis and agentic RL. You can try our apps directly on the project website!
2. Hybrid GUI & CLI. While hybrid computer use is gaining traction, mobile agents remain largely limited to GUI interactions. We explore a new setting where mobile apps expose app-native tools, enabling agents to interleave GUI actions and tool calls within a task.
We also introduce MobileGym++ Bench, supporting both GUI-only and hybrid evaluation on challenging commercial-app tasks. OpenMobile-2 achieves competitive results on established benchmarks and nearly 2Γ performance on real-device mobile tasks.
π₯ Everything is open-source! We'd appreciate your support with an HF Paper upvote and GitHub star!
π Project (try apps online): https://os-copilot.github.io/OpenMobile2-Home/
π€ HF Paper: https://huggingface.co/papers/2610.14157
π» GitHub: https://github.com/OpenMobile2/OpenMobile2-Code
π MobileGym++: https://github.com/OpenMobile2/MobileGym-plusplus
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