youtubepilot-ai
Autonomous YouTube Shorts channel automation platform that generates content, designs slides, renders videos, publishes on schedule, seeds engagement, and replies to comments — all unattended with white-label branding.
Ficha actualizada 22 sept 2026, 18:36 · upstream 22 sept 2026, 17:42 · release v1.0.0
Qué es youtubepilot-ai
Descripción editorial construida a partir del repositorio, su documentación y sus señales de mantenimiento.
YouTubePilot AI is a production-grade SaaS that fully automates a YouTube Shorts channel end-to-end. It invents on-topic ideas, writes scripts using AI models (Grok or Gemini), renders branded multi-slide video cards, stitches them into vertical shorts with music, generates metadata including search tags and custom thumbnails, publishes via the YouTube Data API v3 at configured times, seeds engagement comments, and replies to viewer comments in real-time. The platform is white-label: each brand can customize its app name, niche, persona, voice, handle, and content-type labels entirely from the UI without touching code. It supports any niche — cooking, fitness, finance, travel, education, tech, beauty, history, etc. — by simply updating topic seeds in Settings → Brand. The system uses a rotating topic list with anti-repetition logic that avoids reusing recent titles, and includes an image quiz content type where users answer questions about images rather than watching videos.
- A fitness brand creates daily workout Shorts using the 'Pro Tip' and 'Myth vs Fact' content types, each designed to drive sign-ups for their membership
- An educational channel uses 'Educational' and 'Case Study' formats to teach a specific subject while building authority through real-world examples
- A finance company runs automated Shorts about market trends using the 'Preventive' format with actionable tips that get saved by viewers
- A travel agency creates carousel content showcasing destinations, each slide telling a mini-story and including a call-to-action to book
- An IT support business uses 'Knowledge Quiz' type content to engage customers who want to learn about common problems before they become tickets
- Fully white-label architecture where each brand can customize app name, niche, persona, voice, and handle independently without code changes
- Generates complete vertical video cards (9:16) with multiple content slides that are rasterized to SVG and stitched into MP4 using ffmpeg
- Uses the YouTube Data API v3 for all publishing operations including scheduling, comment seeding, and analytics sync
- Includes a robust topic generator with anti-repetition logic that avoids reusing recent titles within a theme category
- Supports real-time comment replies via Grok AI matching viewer language and script style to maintain engagement quality
- Provides comprehensive health reporting including publish failures, 24-hour analytics summaries, and system status emails
- Relies on external API keys for Groq/Gemini content generation and YouTube Data API v3 — no built-in key management or rotation
- Content quality depends entirely on the AI provider's output; hallucinated facts in scripts can lead to channel penalties if not reviewed
- Carousel content type requires manual slide creation per Short as it cannot auto-generate multi-slide decks from a single topic
- Comment reply system only replies to comments posted within 24 hours of posting, limiting engagement on older threads
- No native support for YouTube Shorts' new 'vertical video' format — all content is rendered in the standard horizontal 16:9 format
Perfil de infraestructura
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Deploy using Docker Compose with PostgreSQL and Redis. Start with `docker compose up -d --build` to build the Next.js frontend, then run `npm run db:migrate` for schema updates. Set environment variables from `.env.example`, ensuring all required API keys (Groq/Gemini, YouTube Data API v3) are set before starting. For production, use Railway or Render with Docker Compose as a managed service. Enable auto-scaling on the Next.js app and configure Redis connection pooling for high throughput of comment replies. Monitor database connections via pgAdmin or psql to prevent connection leaks under load.
Stack y dependencias
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