A lightweight automation workflow that tracks Facebook Live events and instantly notifies users when a target page or profile goes live. The Facebook Live Notifier Bot removes the need for constant manual checking and delivers fast, consistent alerts across multiple monitored accounts.
This automation monitors specific Facebook pages, creators, or profiles for new Live sessions. It removes the repetitive workflow of refreshing feeds, switching accounts, or manually scanning for live indicators. Users and businesses benefit from instant alerts that help them react faster, engage earlier, or monitor brand-critical broadcasts.
- Fast reaction to breaking broadcasts improves engagement and response times.
- Automation outperforms manual monitoring during off-hours or high-volume tracking.
- Supports teams that rely on timely awareness of creator or competitor activity.
- Reduces human error and ensures consistent coverage across multiple monitored profiles.
- Scales to dozens or hundreds of tracked pages without additional labor.
| Feature | Description |
|---|---|
| Live Detection Engine | Continuously checks target Facebook pages for the “Live” state, optimizing intervals for reliability. |
| Multi-Profile Monitoring | Tracks several pages or creators in parallel using queued workers. |
| Smart Notification Routing | Sends push, email, or webhook alerts based on user configuration. |
| Appilot Android Automation | Utilizes Appilot-driven mobile interactions to detect UI-level Live indicators reliably. |
| Low-Noise Polling | Dynamic polling adjusts frequency based on recent activity patterns. |
| ADB-less Execution | Runs automation without requiring root access or direct ADB connections. |
| Retry & Backoff Logic | Handles temporary failures, reconnects cleanly, and resumes monitoring. |
| Secure Credential Handling | Stores login and session details in encrypted configuration files. |
| Logging & Audit Trail | Captures detailed logs for debugging, traceability, and performance analysis. |
| Scheduled Scanning | Built-in scheduler runs checks at optimal intervals without user interaction. |
- Input or Trigger — Users configure a list of Facebook pages or profiles to monitor.
- Core Logic — Appilot-powered Android automation inspects UI elements to detect whether a Live broadcast has started.
- Output or Action — When detected, the bot sends an alert via the chosen notification channel.
- Other Functionalities — Includes credential loading, proxy routing, and error-safe session recovery.
- Safety Controls — Rate limits, retries, cooldowns, and structured logging protect stability.
Language: Python Frameworks: Appilot, lightweight task scheduler Tools: UI Automator, session manager, logging utilities Infrastructure: Local device farm or cloud-hosted Android 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
- Creators use it to detect competitor or collaborator live sessions, so they can engage immediately.
- Businesses use it to monitor brand-related pages, so they can coordinate responses or join broadcasts quickly.
- Agencies use it to track multiple clients’ live sessions, so they can streamline reporting and coverage.
- Community managers use it to catch live streams instantly, so they never miss high-engagement opportunities.
Q: Does this require a logged-in Facebook session? A: Yes, but the bot securely loads it from encrypted configuration files.
Q: Can it track multiple pages at once? A: Yes, it supports multi-profile monitoring via queued workers.
Q: Is root or ADB access required? A: No. It uses an ADB-less Appilot Android automation flow.
Q: How often does it check for Live events? A: The scheduler adjusts intervals automatically based on activity.
Execution Speed: Typically 25–40 UI checks per minute per device under standard device farm loads. Success Rate: ~94% long-run detection accuracy with retries enabled. Scalability: Designed for 300–1,000 Android devices using sharded queues and horizontally scaled workers. Resource Efficiency: ~1 vCPU and 350–450 MB RAM per active worker-device pair. Error Handling: Automatic retries, exponential backoff, structured logs, alerting hooks, and full recovery workflows.
