Databox is an agentic analytics platform that brings performance data from across your business together with consistent metrics and business context. Ask its AI Analyst questions about your business, use Routines to surface important changes automatically, and turn insights into reports that explain what happened and why it matters. Databox does more of the recurring reporting and analysis, so you can understand performance faster and take better-informed action.
This is the 11th launch from Databox. View more
Databox CLI
Launched this week
Create data sources, load data, build metrics, and automate workflows directly from your terminal. Use 90+ commands yourself, or let AI coding agents like Claude Code, Cursor, and Codex do the work with 10 ready-to-use agent skills.
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Launch Team / Built With
Framer AI AgentsDesign and publish professional sites with AI
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From a customer side, what matters is that it's safe to hand to an agent. Destructive commands like delete and purge need an explicit confirmation or a --force flag, and errors come back with clear messages. Teams can let an agent manage their setup without worrying it will wipe a dataset by accident.
@emil_korpar Exactly, Emil. One more layer on top of that: the API key acts as the user who created it, so an agent can never do more than that user’s role allows. Want an agent that only reads and loads data? Run it with a key from a user who has a more limited role, and deletes or purges are blocked by the API before anything happens.
congrats on the launch tadej. to your question, i would automate a weekly pull of signup and trial data into one table, so the landing page review doesnt start with opening a bunch of dashboards
@agshin_rajabov Thanks Agshin! That’s a solid one. Push the weekly signup and trial numbers into a single dataset with dataset ingest, add a metric on top, and the review starts with one view instead of five tabs. Easy to hand to an agent on a weekly schedule too.
dataset ingest on a weekly schedule sounds right -> one view instead of five tabs is exactly the point
I tend to look at CLI tools as something that is going to be used by agents even more than humans. A few small things here matter a lot once an agent is the one calling it:
--json on every command, so agents never have to scrape text
documented exit codes, so a failed call reads as failed instead of ambiguous
--all on list commands so you (or your agent) don't have to write your own paging loop
idempotency keys on ingest so a retry can't ever double your data (this one is a real battle scar)
You can point Claude Code at it, have it push a CSV into a dataset, and if it hits a rate limit halfway it can just retry with the same idempotency key without doubling anything.
The question I get most on calls with technical buyers is 'can we automate this?' Until now the answer was 'yes, through the API, if you write the code.' Now it's one npm install. For teams that live in the terminal or already use AI coding agents, that's a much shorter path from demo to a working setup.
@kenneth_won Well put, Kenneth. That gap between “yes, through the API” and a working setup is exactly what we wanted to close. Now it’s npm install -g databox-cli, add the skills, and an agent can build the first dataset during the call.
Worth flagging for anyone who tried the earlier version that 1.0 is a big jump. It now runs on the Databox V2 API, covers 91 commands instead of 18, and ships with 10 agent skills instead of 5. If you have scripts on the old version, the changelog lists every renamed command and flag.
I genuinely found this useful! I like how it makes tracking data and getting meaningful insights feel a lot less complicated. Definitely something I can see myself using to make better, more informed decisions. Great find! All the best @grega_cej