The android-device-farm-automation-bot automates large-scale Android device operations, eliminating manual, repetitive ADB workflows and providing fast, reliable execution across hundreds of devices. This system streamlines orchestration, improves consistency, and enables teams to automate complex tasks at scale.
This automation tool manages and executes actions across Android devices in bulk. It handles repetitive workflows such as installing apps, gathering logs, running tests, and device resets. The result is faster execution, fewer errors, and a predictable workflow for engineering teams and automation pipelines.
- Executes parallel tasks across hundreds of real or virtual Android devices.
- Reduces manual ADB operations, saving engineering hours.
- Centralized scheduler ensures consistent and repeatable execution.
- Built-in resource and session isolation prevents device conflicts.
- Designed for continuous, long-running automation pipelines.
| Feature | Description |
|---|---|
| Parallel Device Execution | Run tasks on many Android devices simultaneously. |
| ADB Command Automation | Automate installs, uninstalls, log pulls, and shell actions. |
| Device Health Monitoring | Check connectivity, temperature, battery, and resource status. |
| Job Scheduler | Queue, schedule, and distribute workloads across the farm. |
| Retry & Backoff Logic | Automatically recover from transient failures. |
| Proxy & Network Controls | Manage per-device networking rules for isolation. |
| Structured Logging | Centralized logs for each device and task. |
| Result Aggregation | Export outcomes as JSON, CSV, and structured artifacts. |
| Plugin Task System | Extend with custom automation modules. |
| Robust Error Handling | Gracefully handle crashes, timeouts, and device offline events. |
Input or Trigger — User submits tasks or schedules via config or CLI. Core Logic — Scheduler assigns tasks to available devices based on capacity and health. Output or Action — Devices execute ADB-driven workflows and return structured results. Other Functionalities — Logging, monitoring, proxy control, and plugin task execution. Safety Controls — Session isolation, cooldown periods, bounded retries, and resource checks.
Language: Python
Frameworks: AsyncIO, FastAPI (optional service mode)
Tools: ADB, schedulers, task queues, logging utilities
Infrastructure: Local device racks, USB hubs, cloud device instances, 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
Engineers use it to automate repetitive ADB workflows, so they can focus on high-level debugging. QA teams use it to run parallel device tests, so they can shorten release cycles. Ops teams use it to manage large device racks, so they can maintain fleet health automatically. Developers use it to validate builds on multiple devices, so they can detect compatibility issues early.
How do I configure this automation for multiple accounts?
Use per-device or per-profile configuration files stored in the config/ directory. Each device session loads its own environment, credentials, and task parameters for full isolation.
Does it support proxy rotation or anti-detection? Yes — proxy pools and per-device bindings allow each device to use distinct network paths, with optional randomization and pacing for realistic behavior.
Can I schedule it to run periodically? The scheduler supports cron-like expressions, recurring jobs, queues, and retry policies for continuous operations.
What about emulator vs real device parity? Both are supported. Real devices are recommended for hardware parity, while emulators are ideal for fast, scalable testing.
Execution Speed: Handles 20–40 actions/min per device under standard farm conditions.
Success Rate: Maintains a stable 93–94% success rate across long-running jobs with retries.
Scalability: Supports 300–1,000 Android devices using sharded queues and horizontally scaled workers.
Resource Efficiency: Targets ~2–5% CPU and 150–300MB RAM per worker and per device session.
Error Handling: Uses structured logs, auto-retries, exponential backoff, alerts, and controlled recovery workflows.
