This project automates interactions and routine workflows inside the Bumble Virtual Gift Shop environment, focusing on repetitive tasks that drain time when performed manually. It streamlines navigation, captures relevant data, and triggers automated actions, resulting in consistent, fast, and error-resistant outcomes.
This automation system simulates user flows within the Bumble Virtual Gift Shop, helping users or teams process large volumes of in-app tasks without manual tapping. It replaces repetitive gift-shop actions with programmatic routines and delivers predictable results at scale.
- Reduces manual navigation overhead through coordinated UI flows.
- Ensures timing-accurate interactions with on-screen elements.
- Introduces consistency in multi-step, error-prone tasks.
- Scales actions across device farms with configurable rates.
- Supports resilient retries when elements are slow or missing.
| Feature | Description |
|---|---|
| UI Element Detection | Uses automated selectors to detect and interact with in-app gift shop components. |
| Inventory Parsing | Reads visible items, metadata, and states to trigger correct actions. |
| Automated Purchasing Logic | Applies rules to simulate taps for virtual gifts. |
| Queue-Based Task Scheduling | Distributes workflows across multiple workers for throughput. |
| Session Persistence | Maintains stable logged-in sessions across long-running cycles. |
| Adaptive Timing Engine | Dynamically adjusts tap delays based on device performance. |
| Error Recovery | Auto-retries stalled or partial flows with context logging. |
| Parallel Device Execution | Runs tasks simultaneously across Android devices. |
| Proxy & Network Handling | Manages rotating proxies and connection integrity. |
| Report Generation | Outputs structured JSON/CSV summaries for downstream analytics. |
Input or Trigger — A scheduled job or manual command initializes the workflow. Core Logic — The automation navigates the gift shop, interprets UI elements, and triggers predefined actions. Output or Action — Results are captured as JSON/CSV files and logged for auditability. Other Functionalities — Includes proxy management, error-handling routines, and session tracking. Safety Controls — Applies rate limits, validation checks, and recovery paths to avoid misfires.
Language: Python Frameworks: Appilot, UI Automator, Appium Tools: Scheduler, logger, proxy rotation, configuration loader Infrastructure: Local runners, device farms, queue-based workload distribution
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 orchestrate gift shop tasks so they can scale processing across multiple devices.
- Market researchers use it to collect item visibility or state changes so they can analyze engagement patterns.
- Developers use it to prototype automated flows so they can validate complex UI behaviors.
- Ops teams use it to streamline repetitive app interactions so they can reduce labor and errors.
Q: Does it require root access? No, it operates with standard Android automation stacks.
Q: Can it run on multiple devices? Yes, tasks can be sharded across many devices via queues.
Q: How do I configure proxies?
Edit the proxy settings in settings.yaml and restart the worker.
Q: Does it support retries? Yes, built-in retry and backoff logic is included.
Execution Speed: Typically 45–60 automated actions per minute on mid-range device farms. Success Rate: About 94% across long-running jobs with retries and fallback selectors. Scalability: Supports 300–1,000 Android devices via sharded queues and horizontally scaled workers. Resource Efficiency: Each worker targets ~15–20% CPU and 250–350MB RAM per active device. Error Handling: Structured logs, automatic retries, exponential backoff, proxy failover, and session restoration flows.
