A lightweight automation system designed to streamline managing and updating dating app profiles across Android devices. Bumble Profile Manager reduces repetitive editing tasks, ensures consistent profile metadata, and helps users maintain optimized profiles with minimal manual input. The goal is improved efficiency, quality, and reliability in profile management workflows.
This project automates frequent profile-related actions within the Bumble app—such as updating photos, editing bio text, adjusting preferences, or refreshing visibility. By removing repetitive manual steps, it saves time and provides consistent, predictable output for individuals or teams managing multiple accounts.
- Reduces repetitive UI interactions through controlled Android automation flows.
- Ensures consistent formatting and metadata across profiles.
- Increases reliability through scheduled refresh tasks and validation layers.
- Improves operational speed when working at scale on device farms.
- Modular components allow easy customization for different workflow needs.
| Feature | Description |
|---|---|
| Profile Photo Rotation | Cycles photos automatically based on defined scheduling rules and priorities. |
| Bio Auto-Update | Refreshes bio content using templates, variables, or dynamic rules. |
| Preference Sync | Updates filters such as age range or distance through UI path automation. |
| Health Check Validator | Verifies that all profile elements are properly applied and visible. |
| Screenshot Logger | Captures UI states for debugging, audits, and result tracking. |
| Scheduled Refresh Tasks | Runs timed jobs to keep profiles active and updated. |
| Multi-Device Worker Support | Allows distributed execution across large device clusters. |
| Proxy & Network Control | Manages network routes or proxies for cleaner session control. |
| Retry & Backoff Engine | Recovers from UI failures with structured retry policies. |
| Metrics & Telemetry | Emits performance and reliability metrics for monitoring. |
- Input or Trigger — A scheduled job, command invocation, or queue message initiates a profile update workflow.
- Core Logic — The engine interacts with device UI elements using Appilot or UI Automator to perform edits.
- Output or Action — Updated profile states, screenshots, and logs are stored in the output directory.
- Other Functionalities — Optional enrichment layers apply templates, rotate images, or update metadata.
- Safety Controls — Includes rate limiting, action cool-downs, retry logic, and validation checkpoints.
Language: Python Frameworks: Appilot, UI Automator, lightweight scheduling libraries Tools: Device controller, queue workers, structured logger Infrastructure: Local devices, remote Android farms, sharded job runners
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
- Freelancers use it to automate updating multiple profiles so they can reduce repetitive manual work.
- Agencies use it to ensure consistent profile formatting across accounts so they improve operational throughput.
- Individual users automate profile refreshes so they can maintain visibility with minimal effort.
- QA testers simulate profile-editing flows so they can validate app behavior across devices.
- Developers integrate the engine into larger automation suites so they can orchestrate multi-step pipelines.
Does it require root access? No, it runs on standard Android setups using UI automation layers.
Can it run on multiple devices simultaneously? Yes, workers can be horizontally scaled across 300–1,000 devices.
Is it safe for personal accounts? Operations follow conservative pacing and validation rules to reduce risk.
How customizable are the templates? Profiles, bios, and metadata rules are fully configurable via YAML.
Does it store credentials? Credentials are kept in environment files that the user manages locally.
Execution Speed: Typically 45–60 actions/min on mid-range device farms. Success Rate: ~93–94% across long-running job batches with retry policies enabled. Scalability: Supports 300–1,000 Android devices via sharded queues and horizontally distributed workers. Resource Efficiency: ~15–25% CPU and 300–450MB RAM per worker controlling 1–3 devices. Error Handling: Automated retries with exponential backoff, structured logging, alert triggers, and self-recovery routines.
