Bubble CLI — mirror your app's data into a local SQLite database

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Hey everyone,

I built a small command-line tool that mirrors a Bubble app’s data into a local SQLite database via the Data API. Sharing it here in case it’s useful — and to get feedback from anyone who tries it.

What it does

You point it at a Bubble app, run bubble scan, and it pulls your types and fields from the /meta endpoint. Then bubble pull downloads the records into a SQLite file sitting next to your project. From there you can query it with anything that speaks SQL — DB Browser, DBeaver, a Python notebook, Metabase, whatever.

A few things it handles

  • Schema discovery with a fallback: if /meta doesn’t return types (older apps, certain configs), it samples a record per type and infers the fields.

  • Two sync modes — full re-fetch or incremental (only records with Modified Date newer than the last sync).

  • Schema diff on every scan, so you see which types/fields were added or removed since last run.

  • Per-folder projects — config lives in bubble.json, so you can keep multiple apps side by side without them stepping on each other.

  • Interactive menu when you run bubble with no args, plus scriptable subcommands for cron/CI.

  • Bilingual UI (English / Português), auto-detected from your locale.

Use cases I had in mind

  • Local backups of production data without paying for a separate backup service.

  • Ad-hoc analytics — JOINs, GROUP BYs and window functions that aren’t really practical inside Bubble’s search.

  • Feeding data into BI tools, notebooks, or downstream pipelines.

  • Snapshotting before risky migrations.

Install

pipx install git+https://github.com/moabe-br-2019/bubble_cli.git
bubble init
bubble scan
bubble pull --all

Requires Python 3.10+ and a Data API key from your app’s Settings → API. Each type you want to sync needs “Expose for Data API” turned on.

Repo: https://github.com/moabe-br-2019/bubble_cli

Looking for feedback on

  • Edge cases in the schema inference — apps with unusual field types, option sets, nested data, etc.

  • Whether the incremental cursor (Modified Date > last_sync_at) holds up well on larger datasets.

  • Anything you’d expect a tool like this to do that it currently doesn’t.

If you give it a try, issues and PRs are very welcome on the repo. Happy to answer questions here too.

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niice, will try out for sure when we’re going live.