MobileRun
Summary
Headless browsers and Playwright scripts break the moment an app enforces real-device fingerprinting, carrier verification, or Face ID — which is most production mobile workflows worth automating. Mobilerun puts a real Android or iOS phone in your agent's hands instead.
The platform pairs an LLM agent — Claude, Hermes, or their own Mobilerun VA — with a persistent real device that has a real SIM, a real residential IP, and a real number receiving SMS. The agent reads screen state via accessibility tree plus vision, then taps, swipes, and types end-to-end. That identity stack is why apps like Instagram, WhatsApp, and Stripe behave the way they do in production rather than flagging the session. Fleet tooling handles parallel runs across up to 10,000 devices. The ceiling appears when workflows require branch logic the natural-language task description cannot express — at that point you are writing SDK code.
Bottom line: Pick Mobilerun when your workflow fails on emulators because the app checks carrier or device identity; plan for custom SDK work when your agent needs conditional branching that a task string cannot capture.
Pricing Plans
Subscription- Price
- $5/month
Personal Phone
Quick testing, BYO persistence, 250 agent credits/mo
- Remote physical hardware
- Multi profiles
- App support
Physical Phone
Account workflows at scale, dedicated premium device, 5,000 credits/mo
- Dedicated persistence
- Premium real device
- EU / US location
Mobile RPA Cloud Phone
Dedicated virtual device, 2,500 credits/mo
- Persistent hardware
- Virtual device
- EU location
View full pricing on mobilerun.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Real SIM and residential IP on every device, so apps that fingerprint carrier or network identity do not flag or block the session — the failure mode that kills emulator-based automation at login.
- Persistent device sessions that survive across runs and regions, which means creator accounts stay authenticated and WhatsApp threads remain open without re-auth loops eating agent credits.
- LLM-agnostic design supporting Claude, Hermes, OpenClaw, and the vendor's own Mobilerun VA, so swapping models when costs shift or a new release outperforms the current one is a config change, not a rewrite.
- MIT-licensed local framework with a one-config-change path to managed cloud, so a team can validate the workflow on their own hardware before committing to fleet spend.
- Structured JSON output from native app screen state, so mobile data pipelines can feed typed records into downstream systems without building a separate parsing layer.
Cons
Sign in to edit- Natural-language task strings handle linear flows cleanly; branching logic based on what the previous screen returned requires SDK code. Teams running conditional mobile RPA at scale end up maintaining both a task-description layer and a code layer — two systems that can drift.
- Agent credit allocations on entry-level plans exhaust quickly under production-volume runs. Teams that hit this ceiling and need dedicated hardware at scale move to the enterprise tier or evaluate competitors that offer unlimited execution models tied to device slots rather than per-action credits.
- iOS device availability depends on plan tier and the vendor's fleet provisioning — teams that need guaranteed iOS hardware across specific regions cannot self-provision and must coordinate with the vendor, which introduces lead time that Android-first workflows on the virtual tier do not face.
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About
- Platforms
- Android, iOS
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-07-09T12:36:36.921Z
Best For
Who it's for
- Teams running autonomous mobile agents
- Scalable creator or account automation
- Production mobile RPA requiring real device identity
What it does well
- AI agent workflows controlling real phones end-to-end
- Creator account management across multiple profiles and regions
- Mobile data pipelines extracting structured JSON from native apps
- Customer messaging via WhatsApp, Telegram, or in-app from real numbers
Integrations
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Frequently Asked Questions
- Is MobileRun free?
- MobileRun is a paid tool ($5/month). No permanent free tier is offered.
- Is MobileRun open source?
- Yes. MobileRun is open source.
- Does MobileRun have an API?
- Yes. MobileRun exposes a developer API. See the official documentation at https://mobilerun.ai for details.
- Can I self-host MobileRun?
- Yes. MobileRun supports self-hosting on your own infrastructure.
- What platforms does MobileRun support?
- MobileRun is available on: Android, iOS.
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Curated lists that include this category
Most automation scripts die the moment a mobile app checks whether the device is real, the SIM is live, or the number has received a verification SMS. Mobilerun routes around that wall by giving LLM agents persistent access to physical Android and iOS hardware, each with a dedicated SIM, a residential or dedicated IP, and a consistent device fingerprint that apps see as a normal handset. The core workflow is a single API call or natural-language task string — the agent reads the screen, taps through the app, and returns structured JSON when the task completes.
The differentiating layer is identity persistence. Sessions are not shared and do not reset between runs, so creator accounts stay logged in across posts, WhatsApp threads stay open across regions, and Stripe exports run on a schedule without re-authentication loops. The vendor describes this as the property that lets automations ‘just keep going’ where scraper-style approaches get blocked or logged out. eSIM support on select plans enables multi-region, multi-profile operation from a single device slot.
The open-source MIT-licensed framework installs via pip and runs locally on any device — the same codebase that powers the cloud. Moving from local to cloud is described by the vendor as a single config change, which means a team can prototype on their own hardware and deploy to a managed fleet without rewriting agent logic. Fleet management scales from one device to 10,000 with parallel execution, webhooks, and fleet sync exposed through the API.
Where the platform strains: natural-language task descriptions work cleanly for linear flows, but multi-step branching based on what the previous screen returned requires dropping into the SDK. Teams running complex conditional mobile RPA at scale will write code alongside the task strings — at which point they are maintaining two layers of logic. There is no free tier; the entry point is a paid personal plan with a credit allocation that suits testing but will exhaust quickly on production-volume runs.