Hi everyone, new member here. I’ve been exploring Relevance AI for a few weeks now, mainly for automating some documentation workflows, and I want to thank you all in advance for the helpful discussions I’ve been reading. This seems like a great community.
I’m coming from a background in DevOps and CI/CD, so the idea of managing complex workflows without a clear version control strategy makes me a bit nervous. Right now, my only method for backing up or tracking changes to my Relevance AI workflows is manually exporting the JSON from the Studio. This feels… fragile.
Is this the standard practice? I’m curious how others are handling this, especially those who might be integrating these workflows into larger systems. Do you just keep a folder of JSON exports with timestamped filenates? Have you found a way to use Git effectively with these exports, perhaps with a script to automate the process? Or is there a better pattern I’m missing?
I love the platform, but I’d feel much more confident if I could incorporate workflow changes into a proper Git workflow, with commits and rollback capabilities. Any insights or personal experiences you could share would be greatly appreciated.
still learning
Your DevOps instincts are spot on - manual JSON exports are indeed fragile and a common pain point. I've seen teams treat the JSON as the source of truth and integrate it into Git, but the friction is high.
A pattern that's worked for some is to wrap the export/import in a script. They'll use the Relevance API to fetch the workflow definition (if available) or automate the UI export via a headless browser, then commit the resulting JSON. The key is adding a pre-commit hook that validates the JSON schema to catch broken exports before they hit the repo. It's not elegant, but it gives you the diff and rollback you want.
The real gap is that these platforms often treat the UI as the primary interface, leaving the API as an afterthought. Until they offer a first-class CLI or a native Git integration, you're stuck building your own pipeline around their export artifacts. Have you looked into whether their API exposes a proper version endpoint?
SQL is not dead.
You're absolutely right about the API being the key. I've had mixed success with the pre-commit hook idea - the real headache is that the exported JSON often includes internal IDs or metadata that changes on every export, creating noisy, meaningless diffs that drown out actual logic changes. I ended up writing a small filter script to strip those out before committing.
What I'd add is that even with a clean diff, the JSON is an artifact, not a source. You can't *create* a valid workflow from scratch in it; you can only *restore* one. That means you can't do true CI/CD like linting or generating staging variants from a single source. You're just versioning snapshots of the vendor's UI state.
So yeah, the pattern works, but it's version control for backups, not for development.