Automate timely, relevant replies to YouTube comments using Android devices and emulators. This project watches for new comments, matches them against configurable keyword rules, and posts human-like responses—boosting engagement while cutting repetitive work. Built for teams that need reliable, scalable YouTube comment automation without risking account trust.
Created by Appilot, built to showcase our approach to Automation!
If you are looking for custom YouTube Comment Reply Bot, you've just found your team — Let’s Chat.👆👆
What it does: Monitors YouTube video/comment feeds and auto-replies to comments that match defined keywords, intents, or text patterns.
What it automates: The repetitive workflow of scanning comments and drafting appropriate replies across multiple videos and channels.
Benefit: Faster engagement, consistent brand tone, and measurable uplift in community interaction—at any scale.
- Intent-aware rules: Keyword + regex + sentiment gates ensure relevant replies only.
- Human-like pacing: Random delays, jitter, cooldowns, and device gestures minimize detection risk.
- Multi-account rotation: Safely operate across channels with isolated sessions and proxy support.
- Zero-touch ops: Scheduler, retry logic, and alerting keep the system running with minimal supervision.
- Auditability: Structured logs, replayable runs, and exportable reports.
- Real Devices and Emulators: Run on physical Android phones or emulator stacks (Bluestacks/Nox). Device profiles, screen sizes, and locales are handled automatically for robust UI targeting.
- No-ADB Wireless Automation: Appilot-powered wireless control for devices without persistent USB; connect, orchestrate, and execute actions over Wi-Fi with session recovery.
- Mimicking Human Behavior: Random swipe/scroll/tap paths, type speed variance, reading dwell times, and reply composition pauses to simulate authentic user activity.
- Multiple Accounts Support: Profile isolation (per-app clone), cookie/state vaults, and per-account rate caps to protect account health across many channels.
- Multi-Device Integration: Horizontal scale via device farm; concurrent workers with per-device job queues and throttling.
- Exponential Growth for Your Account: Faster response times boost comment threads, surface engagement, and improve community retention—compounding discovery signals.
- Premium Support: SLA-backed assistance, onboarding, and tailor-made rulesets for your niche and languages.
- Keyword & Regex Match Engine: Layered matching (exact, fuzzy, regex) with per-video overrides and negative keywords to prevent off-topic replies.
- Template & Spintax Replies: Multi-language templates, variables ({{username}}, {{video_title}}), and spintax for reply variation.
- Scheduler & Windows: Define quiet hours, burst windows, and campaign calendars to align with audience activity.
Additional Feature Set
| Feature | Description |
|---|---|
| Sentiment Gate | Optional sentiment threshold to respond only to positive/neutral (or to rescue negative) comments. |
| Language Detection | Detects comment language and picks the right reply template set. |
| Moderator Escalation | Flags edge-cases into a human review queue with quick-approve reply actions. |
| Proxy & Fingerprint Rules | Per-account proxy mapping and device fingerprint diversity to reduce correlation. |
| Failure & Retry Logic | Smart backoff with screenshot-on-failure, DOM snapshotting, and auto-retry on transient issues. |
| Webhook & Integrations | Push events (replied, skipped, flagged) to Slack/Discord/Telegram or your webhook endpoint. |
- Input or Trigger — The automation is triggered through the Appilot dashboard, where the user sets keyword rules, reply templates, target videos/channels, and schedules on selected Android devices or emulators.
- Core Logic — Appilot controls the device through UI Automator or ADB, navigating YouTube Studio/YouTube app, scanning comments, matching rules (keywords/regex/sentiment), and composing replies with human-like delays.
- Output or Action — The bot posts the designated replies, logs outcomes (replied/skipped/flagged), and optionally notifies connected systems via webhooks.
- Other functionalities— Retry logic, error handling, structured logging, screenshots on failure, and parallel processing across devices can be configured in the Appilot dashboard for smooth execution and fast troubleshooting.
- Language: Kotlin, Java, JavaScript, Python
- Frameworks: Appium, UI Automator, Espresso, Robot Framework, Cucumber
- Tools: Appilot, Android Debug Bridge (ADB), Appium Inspector, Bluestacks, Nox Player, Scrcpy, Firebase Test Lab, MonkeyRunner, Accessibility
- Infrastructure: Dockerized device farms, Cloud-based emulators, Proxy networks, Parallel Device Execution, Task Queues, Real device farm
#Directory Structure
youtube-comment-reply-bot/
│
├── src/
│ ├── main.py
│ ├── bot/
│ │ ├── runner.py
│ │ ├── matchers/
│ │ │ ├── keywords.py
│ │ │ └── sentiment.py
│ │ ├── actions/
│ │ │ ├── reply.py
│ │ │ └── escalate.py
│ │ └── utils/
│ │ ├── device.py
│ │ ├── humanizer.py
│ │ └── logger.py
│ ├── android/
│ │ ├── uiactions.kt
│ │ └── appium_hooks.java
│ └── webhooks/
│ └── dispatcher.js
│
├── config/
│ ├── rules.yaml
│ ├── templates/
│ │ ├── en.yml
│ │ └── es.yml
│ ├── devices.yaml
│ └── proxies.yaml
│
├── dashboards/
│ └── appilot.json
│
├── logs/
│ ├── run.log
│ └── screenshots/
│ └── .keep
│
├── output/
│ ├── replies.jsonl
│ └── report.csv
│
├── tests/
│ ├── unit/
│ └── e2e/
│
├── requirements.txt
├── README.md
└── LICENSE
- Creators use it to auto-reply to common questions on new uploads, so they can maintain fast engagement without staying online 24/7.
- Agencies use it to run branded reply playbooks across multiple client channels, so they can scale community management efficiently.
- Support Teams use it to auto-acknowledge feedback and route complex cases, so they can shorten response times and capture issues early.
- Growth Marketers use it to run seasonal/campaign replies at peak hours, so they can boost thread depth and social proof.
How do I configure this automation for multiple accounts?
Define each account in config/devices.yaml with its proxy and device profile. The scheduler assigns jobs per account with isolated sessions and per-account rate limits.
Does it support proxy rotation or anti-detection?
Yes. Map static or rotating proxies per account; combine with app clones and device fingerprint variation for reduced correlation.
Can I schedule it to run periodically?
Absolutely. Use campaign windows (e.g., launch week) and quiet hours. The scheduler enforces time windows, daily caps, and cooldowns.
What if a comment doesn’t match any keyword?
You can skip it, send it to a moderator queue, or use a safe default reply with low frequency to avoid noise.
Will it work on emulators and real devices?
Yes. It supports both, with best results on diversified real devices plus selective emulator coverage.
- Execution Speed: Scans ~400–800 comments/hour per device (rule complexity dependent); replies are paced with human-like delays to preserve trust.
- Success Rate: 95% end-to-end reply placement on stable connections and supported app versions.
- Scalability: Proven patterns for 300–1,000 Android devices via sharded queues and per-device workers.
- Resource Efficiency: Lightweight runners (<250MB RAM per worker typical); screenshot-on-failure compressed storage.
- Error Handling: Exponential backoff, capped retries, circuit breakers on repeated UI failures, and alerting via webhooks/Discord/Telegram.
