Mobilegym is live · free · last verified 31 Oct 2026 · viability monitor
Mobilegym
Open-source browser-hosted Android simulator for reproducible, parallel mobile GUI agent research.
If your lab runs online RL on mobile GUI agents, MobileGym solves the reproducibility problem real-device pipelines can't: structured JSON state, millisecond snapshot/fork/restore, and programmatic judges with zero false accept/reject across 416 templates (versus 10.2% VLM judge error). The +40.7 point real-device gain with 95.1% retention from Qwen3-VL-4B fine-tuning is the strongest sim-to-real evidence in this category. Choose it over AndroidEnv or MiniWob if you need real daily apps like WeChat and Alipay in the loop. Skip it for production QA — it simulates reimplemented apps rather than instrumenting your real builds.
Verdict generatedVerified 13m ago · liveness 60/100 · cite: rightaichoice.com/tools/mobilegym
- Mobile GUI agent research labs
- RL researchers needing clean reward signals for online training
- Developers building autonomous mobile agents for daily apps
- Academics studying sim-to-real transfer
- Production mobile app QA testing
- Non-technical users unfamiliar with RL or agent frameworks
- Projects needing broad physical device diversity
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Skip MobileGym if you need to test your real app builds on physical devices — it runs in-browser React/TypeScript reimplementations of 28 apps, so it validates agent training and evaluation, not production app QA.
Running GRPO fine-tuning of a model like Qwen3-VL-4B needs GPU capacity you supply yourself — the simulator is free but the training compute is not.
MobileGym is released as open source at no listed cost, which puts it in a different bracket from commercial GUI-agent evaluation and device-farm services you pay per device-hour or per seat. The real budget line is the GPU capacity for GRPO fine-tuning plus roughly 400 MB of RAM per parallel browser instance you keep running. For a research lab that already has the GPUs, the software side is effectively free; for a team without them, that is the cost that decides whether the platform is cheap
In short
Mobilegym — Open-source browser-hosted Android simulator for reproducible, parallel mobile GUI agent research. Best for Mobile GUI agent research labs, RL researchers needing clean reward signals for online training, Developers building autonomous mobile agents for daily apps. Free to use.
What is Mobilegym?
Mobilegym — Open-source browser-hosted Android simulator for reproducible, parallel mobile GUI agent research. Best for Mobile GUI agent research labs, RL researchers needing clean reward signals for online training, Developers building autonomous mobile agents for daily apps. Free to use.
MobileGym is an open-source simulation platform that runs a full Android-style simulator inside your browser, built for mobile GUI agent research. It reimplements 28 real-world apps — 12 daily apps (WeChat, Alipay, RedNote, Bilibili, Railway, Maps, Reddit, X, Spotify, eBay, WeRead, Meeting) plus 16 system apps — in React/TypeScript with Android-style task stacks, Intent routing, ContentProviders, and permission flows. Apps are registered via manifest auto-discovery, so adding one needs zero changes to the OS or benchmark layer (~3–4 person-days per daily app, under one day per system app), and each browser instance is lightweight at roughly 400 MB, so batch-parallel rollouts fit on a single machine. The core design choice is that the entire environment runs on structured JSON, addressing three structural walls of real-device pipelines: state you can't read, state you can't reset, and actions you can't take back. Programmatic state judges read that JSON directly — across the released validation suite there are zero false accept/reject cases over 416 parameterized task templates (256 test, 160 train), versus a 10.2% misjudgment rate when a VLM scores the same real-device trajectories by screenshot. Snapshot, fork, and restore finish in milliseconds, making hundreds of identical rollouts on one machine practical — a prerequisite for GRPO. A State Builder lets you patch runtime state live, injecting contacts, messages, tickets, bills, battery, location, and weather without restarting the device. Sim-to-real experiments show GRPO fine-tuning of Qwen3-VL-4B lifts overall simulation success rate by +12.8 points (9.4%→22.2%) and, on the 59-task real-device-runnable signal-bucket subset, the +42.8 point simulation gain is preserved as +40.7 points on a real device — 95.1% retention. It's a research environment for reproducible, accelerated mobile agent development, not a production QA tool.
Key features
- Browser-hosted Android simulator with no device or emulator install required
- 28 in-browser apps: 12 daily (WeChat, Alipay, RedNote, Bilibili, Railway, Maps, Reddit, X, Spotify, eBay, WeRead, Meeting) plus 16 system apps
- Apps reimplemented in React/TypeScript with Android-style task stacks and Intent routing
- ContentProviders and Android permission flows simulated
- Manifest auto-discovery app registration — new apps need zero OS or benchmark changes
- Structured JSON environment state that is readable, writable, and controllable
- Programmatic state judges reading JSON directly, with zero false accept/reject over released checks
- Snapshot, fork, and restore operations completing in milliseconds
- Batch-parallel rollouts: ~400 MB per browser instance on a single machine
- GRPO-compatible online reinforcement learning support
- State Builder for live runtime state injection without restarting the device
- Cross-app data injection: contacts, messages, SMS, tickets/bills, account balance, weather
- Simulated device controls: language/locale, battery status, real or simulated time, location presets and custom coordinates
- 416 parameterized task templates (256 test, 160 train)
- Agent integration with the built-in demo model or a custom OpenAI-compatible vision endpoint
Our long-form take written Aug 1, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-10-25
Limitations
- MobileGym is a research platform for parallel Android simulation of GUI agents; it is not a production QA tool and does not test your real app builds.
- Because the 28 apps are React/TypeScript reimplementations rather than instrumented native binaries, the environment cannot capture every real-world dynamic — network conditions and sensor noise are simulated, not physical — and it does not offer physical device diversity.
- Some concrete figures published by the project are scoped to a subset: the +40.7 point real-device gain was measured on a 59-task signal-bucket subset, not the full suite, and the overall simulation success rate after GRPO fine-tuning of Qwen3-VL-4B was 22.2%.
- Getting value out of the platform assumes working knowledge of reinforcement learning, agent loops, and OpenAI-compatible vision endpoints.
as of 2026-10-10
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Is Mobilegym still active?
Mobilegym is live · free · last verified 31 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Mobilegym? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Mobilegym?
What people actually say about Mobilegym — is it worth it?
Our community sentiment research written Aug 1, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Mobilegym— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 31 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Mobilegym 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed setup time, switching in, our verdict, description, pricing fit, switching out, features, summary, pricing tiers, who should skip it, hidden costs
- — changed pricing tiers
- — changed SSL certificate
- — changed who it suits, description, our verdict, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed domain expiry
- — changed SSL certificate
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Mobilegym cost in 2026?
Mobilegym: Free to use. Pricing last verified 31 Oct 2026.
Plans compared
Every published Mobilegym tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open source | Free | — | Research labs, RL groups, and developers who can supply their own GPU capacity and want reproducible mobile GUI agent training at no software cost.Adds: Starting tier — the platform itself is free and open source; your cost is the GPU compute for GRPO fine-tuning and the RAM for parallel browser instances. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Mobilegym's pricing actually pencils out — and where peers do it cheaper.
MobileGym is released as open source at no listed cost, which puts it in a different bracket from commercial GUI-agent evaluation and device-farm services you pay per device-hour or per seat. The real budget line is the GPU capacity for GRPO fine-tuning plus roughly 400 MB of RAM per parallel browser instance you keep running. For a research lab that already has the GPUs, the software side is effectively free; for a team without them, that is the cost that decides whether the platform is cheap
Has Mobilegym's pricing changed?
No plan price for Mobilegym has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Mobilegym?
Pick Mobilegym if you are Mobile GUI agent research labs, RL researchers needing clean reward signals for online training, Developers building autonomous mobile agents for daily apps, Academics studying sim-to-real transfer.
Look elsewhere if you are Production mobile app QA testing, Non-technical users unfamiliar with RL or agent frameworks, Projects needing broad physical device diversity, Workflows requiring native app code instrumentation.
Skip it if Skip MobileGym if you need to test your real app builds on physical devices — it runs in-browser React/TypeScript reimplementations of 28 apps, so it validates agent training and evaluation, not production app QA.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Mobilegym actually fits — and what changes day-one when you adopt it.
You open the browser simulator, load a task template from the 416 parameterized set (256 test, 160 train), connect your OpenAI-compatible vision endpoint or the built-in demo model, and launch a batch of identical rollouts using snapshot and fork instead of rebuilding device state each time.
Outcome: Hundreds of parallel rollouts on one machine with a deterministic JSON-based reward signal, replacing the 10.2% VLM-judge misjudgment you get scoring real-device trajectories by screenshot.
You use the State Builder to patch a WeChat chat, an Alipay balance, an incoming SMS, and a 12306 train ticket directly into runtime state, then run your agent against a task like sending a message to a specific contact.
Outcome: Every state patch persists across reloads without restarting the device, so you can reproduce a failing run exactly rather than guessing why the agent picked the wrong contact.
You fine-tune Qwen3-VL-4B in the simulator with GRPO, measure the +12.8 point overall simulation success-rate lift (9.4%→22.2%), then re-run the model on the 59-task real-device-runnable subset.
Outcome: The +42.8 point simulation gain is preserved as +40.7 points on the real device — 95.1% retention — giving you a defensible sim-to-real result to report.
Use cases
- Train mobile GUI agents with online RL using a deterministic reward signal instead of screenshot scoring
- Evaluate agent performance across 416 parameterized task templates with programmatic judges
- Run hundreds of identical GRPO rollouts in parallel on a single machine using snapshot and fork
- Inject cross-app state — contacts, messages, tickets, weather — without restarting the simulator
- Benchmark sim-to-real transfer by fine-tuning a model in simulation and validating on real devices
- Study agent behavior on apps like WeChat and Alipay that real-device pipelines can't safely or reproducibly expose
- Conduct reproducible research using versioned snapshots and millisecond time-travel
- Prototype and manually poke at an agent's decisions through the browser live demo
Setup time & first value
How long it actually takes to get something useful out of Mobilegym — broken out by persona, not the marketing-page minute.
For an RL researcher already comfortable with agent loops and OpenAI-compatible vision endpoints, expect minutes rather than days: open the browser simulator, apply a scenario in the State Builder, point the agent panel at your endpoint, and run a task. A developer new to the platform will spend a bit longer wiring the endpoint and learning the JSON state model. Adding a brand-new app to the
Switching to or from Mobilegym
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From real-device pipelines such as adb-driven or accessibility-tree harnesses: move to MobileGym when you need readable, resettable state on daily apps like WeChat and Alipay instead of screens you cannot inspect.
- →From screenshot-scored evaluation with a VLM judge: replace the 10.2%-error scoring pass with programmatic JSON state judges over the 416 parameterized templates.
- →From one-device-at-a-time rollouts: swap sequential device sessions for snapshot/fork rollouts at ~400 MB per browser instance on a single machine.
- ↗To production QA tooling: leave MobileGym when your goal shifts from training and evaluating agents to testing your own app builds on real devices.
- ↗To instrumented native-app environments: move on when you need your actual app binary in the loop rather than a React/TypeScript reimplementation.
- ↗To broad physical device farms: move on when hardware and sensor diversity matter more than deterministic, readable state.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Mobilegym”, and we withheld 6: 6 could not be judged, because “Mobilegym” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Mobilegym.
Official links
What are the best Mobilegym alternatives?
The closest alternatives to Mobilegym we list are Hermes Desktop, DeerFlow and Anthropic Cybersecurity Skills. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Hermes Desktop | Free | Free | — |
| DeerFlow | Free | Free | — |
| Anthropic Cybersecurity Skills | Free | Free | — |
Alternatives to Mobilegym
View all
Hermes Desktop
Hermes Desktop is a free, open-source desktop GUI for the Hermes Agent — a self-hosted AI agent with a built-in learning loop that turns
DeerFlow
DeerFlow is an open-source SuperAgent harness that runs deep research, coding, and creation tasks in a persistent Docker sandbox.
Anthropic Cybersecurity Skills
Open-source library of 817 structured cybersecurity skill files your AI coding agent loads on demand, mapped to MITRE ATT&CK and licensed Apache 2.0.
Tools that pair well with Mobilegym
Common stack mates teams adopt alongside Mobilegym, with the specific reason each pairing earns its keep.
Hermes Desktop
Hermes Desktop is a free, open-source desktop GUI for the Hermes Agent — a self-hosted AI agent with a built-in learning loop that turns
DeerFlow
DeerFlow is an open-source SuperAgent harness that runs deep research, coding, and creation tasks in a persistent Docker sandbox.
Anthropic Cybersecurity Skills
Open-source library of 817 structured cybersecurity skill files your AI coding agent loads on demand, mapped to MITRE ATT&CK and licensed Apache 2.0.
Mobilegym vs its closest rival
Mobilegym's closest head-to-head is with Spider Cloud — Spider Cloud and Mobilegym serve completely different purposes. If you need to scrape and crawl web pages for your AI agent or RAG pipeline, choose Spider Cloud for its Rust engine, low cost, and new Browser AI commands. If you are a researcher focused on training mobile GUI agents in a simulated Android environment with verifiable rewards, Mobilegym is a free, dedicated platform. There is no overlap in use cases. Verdict generated
Head-to-head comparisons
Mobilegym vs Spider Cloud
Spider Cloud and Mobilegym serve completely different purposes. If you need to scrape and crawl web pages for your AI agent or RAG pipeline, choose Spider Cloud for its Rust engine, low cost, and new Browser AI commands. If you are a researcher focused on training mobile GUI agents in a simulated Android environment with verifiable rewards, Mobilegym is a free, dedicated platform. There is no overlap in use cases.
GeneratedMobilegym vs Praktika
Choose Praktika if you want to improve your spoken language skills with AI tutor conversations – it’s a polished mobile learning app. Choose MobileGym if you’re a researcher building or testing mobile GUI agents – it’s a free, verifiable simulator with RL support. They solve entirely different problems and are not substitutes.
GeneratedMobilegym vs Temporal Ai
If you need reliable backend orchestration for AI agents or microservices, Temporal AI is the clear choice with its durable execution and extensive SDKs. For researchers focused on mobile GUI agent training and evaluation, Mobilegym offers a free, verifiable simulator perfect for RL and sim-to-real transfer. They serve completely different domains—choose based on your problem space.
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