This project automates location-based matching flows inside the Bumble app, enabling controlled geolocation shifts and repeatable interaction patterns across multiple Android devices. Bumble Location-based Matching becomes easier and more reliable thanks to automated triggers, consistent environment configuration, and structured task execution.
This automation system streamlines the repetitive process of adjusting device location, refreshing match queues, and performing app interactions at scale. It removes the need for manual tapping, waiting, and repositioning the device to simulate different geographic regions. The result is reduced operational time and a more predictable workflow for users or businesses managing multiple Android automation tasks.
- Dynamically simulates location changes with adjustable intervals and regions.
- Executes repeatable match-refresh patterns without manual intervention.
- Integrates with a resilient scheduler for batching multiple devices.
- Reduces human error by controlling timing, interaction logic, and optional delays.
- Supports modular workflows for plug-and-play customization.
| Feature | Description |
|---|---|
| Geolocation Spoofing Engine | Dynamically updates device coordinates to simulate movement across regions. |
| Automated Swipe Logic | Performs left/right actions based on configurable patterns. |
| Session Refresh Control | Periodically resets the app state to retrieve new profiles. |
| Region Rotation Scheduler | Cycles through predefined cities or coordinates automatically. |
| Multi-Device Orchestration | Manages task execution across many Android devices in parallel. |
| Rate-Limit Guardrails | Prevents overuse by adding randomized delays and adaptive pacing. |
| UI State Detection | Reads on-screen states to ensure actions occur only when safe. |
| Profile Interaction Logging | Captures metadata and timestamps for every action performed. |
| Failure Recovery Module | Recovers the app in case of crashes, freezes, or stale UI states. |
| Proxy & Network Routing | Supports optional routing logic to diversify network signatures. |
- Input or Trigger — A schedule, command, or external request initiates a new location-matching session.
- Core Logic — The automation adjusts geolocation, refreshes Bumble views, and executes interactions.
- Output or Action — Generates structured logs, match attempt outcomes, and device activity reports.
- Other Functionalities — Supports proxy rotation, session resets, and multi-device batching.
- Safety Controls — Uses cooldown timers, randomized delays, and state validation to avoid detection and errors.
Language: Python Frameworks: Appilot, UI Automator, minimal Appium components Tools: Scheduler, retry queues, structured logging utilities Infrastructure: Local or cloud device farm using horizontal worker scaling
automation-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── tasks.py
│ │ ├── scheduler.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── proxy_manager.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── requirements.txt
└── README.md
- Automation engineers use it to emulate location shifts, so they can efficiently test geolocation-driven UI flows.
- Growth teams use it to automate interaction patterns, so they can analyze engagement across regions.
- QA testers use it to replicate repeatable location-based behaviors, so they can validate app stability.
- Research teams use it to gather anonymized behavioral patterns, so they can study geolocation effects.
Q: Does this require rooting the device? A: No, it uses standard Android automation patterns and compatible geolocation APIs.
Q: Can it manage multiple devices at once? A: Yes, it includes a multi-worker architecture suitable for large fleets.
Q: How customizable are the interaction patterns? A: Fully configurable through YAML settings and modular task definitions.
Q: Does it support remote device farms? A: Yes, workers can run on distributed hosts as long as devices are accessible.
Execution Speed: Typically 40–60 actions per minute per device under standard device farm conditions. Success Rate: Averaging ~94% reliability across long-running sessions with retries applied. Scalability: Supports 300–1,000 devices through sharded queues and horizontally scaled workers. Resource Efficiency: ~1 vCPU and 350–450 MB RAM per active device worker. Error Handling: Automatic retries, exponential backoff, structured logs, alert hooks, and graceful recovery workflows.
