Stop writing invisible "Glue Code": Why I use N8N to orchestrate Python Microservices

When building YouClip (an AI video clipper), I had heavy Python scripts for FFmpeg and AI analysis running in Docker containers.

The traditional way to connect them to my Next.js frontend would be writing a complex message queue system (using Redis/Celery/BullMQ).

The Problem

Writing queue logic is boring. Debugging stuck jobs involves digging through obscure terminal logs. You can't "see" where the process failed easily.

The Solution: N8N as a "Control Plane"

I didn't replace the heavy code—the video processing still happens in optimized Python scripts. But I replaced the "Glue Code" (retries, state management, error handling) with self-hosted N8N workflows.

Why this architecture wins:

1. Visual Debugging: I can see exactly which step a video generation failed at (e.g., did transcription fail or rendering?).
2. Modularity: I can swap out Gemini for OpenAI just by changing one node, without touching the Python worker code.
3. Built-in Retries: N8N handles exponential backoffs automatically.

It feels like cheating, but it makes the architecture incredibly robust and keeps my Next.js backend clean.

The Source Code

I recently landed a full-time AI Engineering role, so I packaged this entire architecture (Next.js + N8N + Python) into a starter kit for others to use.

You can grab the architecture breakdown and code here:
👉 https://github.com/suryaelidanto/Opus-Pro-Clone-AI-Video-Clipper-SaaS