Building ViraClip: My Journey Creating an AI Video Processing Platform
Published:
For the past several months, I’ve been building ViraClip — an AI-powered platform that automates video creation and processing for social media content.
The Problem
Content creators and marketers spend hours manually editing videos, adding subtitles, resizing for different platforms (TikTok, Instagram Reels, YouTube Shorts), and generating engaging clips. I wanted to automate all of this.
The Stack
ViraClip is built with a multi-service architecture:
- Backend: FastAPI (Python) for the core API
- Video Processing: FFmpeg with a custom pool manager for concurrent processing
- AI Integration: Groq API, OpenAI, and custom ComfyUI workflows
- Queue: Redis for job management and real-time progress tracking
- Database: PostgreSQL with Alembic migrations
- Orchestration: Docker Compose with dedicated services for each component
- Rust Agent: A self-healing watchdog agent that monitors and restores services
Key Challenges
FFmpeg Concurrency
Running multiple FFmpeg processes simultaneously is tricky. Too many concurrent jobs crash the server; too few and the queue backs up. I built a custom ffmpeg_pool.py that manages worker slots dynamically based on CPU and memory availability.
ComfyUI Integration
Integrating ComfyUI for AI video generation required building a custom workflow executor that translates API requests into ComfyUI node graphs, monitors progress via WebSocket, and handles GPU memory gracefully.
Self-Healing Architecture
The Rust agent (rust-agent/) monitors all services and automatically restarts failed containers, notifies via webhook, and runs diagnostics — all without human intervention.
What’s Next
- LTX video model integration for high-quality AI video generation
- Multi-tenant support for agency clients
- Automated content pipelines with n8n integration
If you’re building something similar or want to collaborate, feel free to reach out via GitHub.