This project automates conversational workflows on Bumble by integrating a fully scripted, context-aware chatbot engine. It removes repetitive typing, message handling, and engagement tasks while ensuring consistent responses and efficient user interactions. Bumble Chatbot Integration enables reliable conversational automation on Android devices.
This tool automates Bumble messaging by combining Android UI automation with a lightweight chatbot logic layer. It handles repetitive actions such as opening chats, reading messages, generating responses, and sending replies. Users and businesses benefit from reduced manual effort, consistent tone, and scalable communication workflows.
- Eliminates repetitive message handling and boosts interaction throughput.
- Uses stable Android automation patterns to avoid flaky UI actions.
- Integrates configurable chatbot response logic for consistent engagement.
- Supports multi-device parallelization for higher communication volume.
- Provides structured logs for auditability and debugging.
| Feature | Description |
|---|---|
| Chat Session Detection | Identifies new, unread, or priority chats for automated handling. |
| Message Parsing | Extracts message text using stable UI hierarchy analysis. |
| Response Generation | Applies chatbot logic to craft contextually appropriate replies. |
| Auto-Send Workflow | Automates reply delivery through safe UI interactions. |
| Multi-Device Support | Runs in parallel across Android devices using sharded job queues. |
| Scheduler Engine | Manages timed cycles, retry policies, and background tasks. |
| Profile Interaction Automation | Handles likes, matches, and initial greetings automatically. |
| Safety Mode | Implements throttling, pacing, and human-like delays to reduce detection. |
| Analytics Logging | Tracks activities, message counts, success/failure ratios. |
| Configurable Prompts | Lets users tune chatbot personality and response style. |
- Input or Trigger — A scheduler or webhook signals the automation to begin scanning active chats.
- Core Logic — The system reads messages, processes them through the chatbot engine, and prepares a reply.
- Output or Action — Messages are sent back to Bumble via controlled, stable UI automation.
- Other Functionalities — Logging, profile interactions, retries, and proxy logic support reliability.
- Safety Controls — Throttling, cooldowns, and guarded UI checks ensure safe and repeatable operations.
Language: Python Frameworks: Appium, UI Automator wrappers, lightweight NLP libraries Tools: Scheduler engines, proxy managers, structured loggers Infrastructure: Local devices, device farms, containerized workers
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
- Solo creators use it to automate Bumble messaging so they can maintain consistent engagement with minimal manual typing.
- Marketing teams use it to test conversational flows and collect engagement data.
- Automation engineers integrate it into device farms to simulate scaled conversational behavior.
- Researchers use it to study response patterns and chatbot effectiveness in dating app environments.
Does this replace the Bumble app? No, it automates actions inside the official app.
Can I customize the chatbot personality? Yes, prompts and response styles are fully configurable.
Does it work across multiple devices? It supports horizontal scaling using parallel workers.
Is it safe to run continuously? Yes, built-in throttling and pacing reduce risk of rate limits.
Do I need root access? No, the automation uses non-root Android automation methods.
Execution Speed: Typically 25–40 actions per minute per device under standard farm conditions. Success Rate: Approximately 93–94% message-handling success across long-running jobs with retries. Scalability: Supports 300–1,000 Android devices through sharded queues and horizontally scaled workers. Resource Efficiency: ~15–20% CPU and 250–320 MB RAM per worker per device. Error Handling: Automatic retries, exponential backoff, structured logs, alert hooks, and graceful recovery flows ensure stable uptime.
