Databox
Agentic analytics platform for you and your agents.
4.9•15 reviews•2.9K followers
Agentic analytics platform for you and your agents.
4.9•15 reviews•2.9K followers
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.
Free Options
Launch Team / Built With
Render Sandboxes: a computer for every agentGive every agent its own machine. Starts in under a second
Promoted
HubSpot
Another step toward making analytics easier for AI agents to set up, manage, and automate!
@gregor_sulcer Thanks Gregor! That’s the direction - less clicking through setup screens, more letting agents handle the routine work.
gotta share this with my builder friends asap
@shushantlakhyani Thank you! Happy to hear the feedback
A must-have in the AI/Agentic era.
cracked team
@mcarmonas Thanks Marti!
alright, i'm all about making my terminal life easier. honestly, the thought of not having to switch tabs to check analytics feels like a dream. but 91 commands? yeah, i might just cherry-pick the ones i actually learn.
@therayess Cherry-picking is the right way to use it. Most people only need a handful of commands day to day, usually dataset ingest, metric list and metric drilldown. The skills are split by area too, so you can install just the ones you use, e.g. npx skills add databox/databox-cli --skill databox-metrics.
Getting a clear added/updated/rejected count back after every push is the part I like. It makes cron jobs much easier to trust. Does it handle schema drift when a script starts sending a new field?
@famitha Thanks Famitha! The schema is set when the dataset is created, or by the first ingest if you skip --schema, and it doesn’t change on its own after that. When records don’t match, the ingestion status shows which ones were rejected and why, so drift shows up in your cron job’s check instead of quietly ending up in your data. To add a new field on purpose, the clean path is updating the dataset setup first, then sending the new shape.
If Claude Code pushes a dataset schema change through the CLI while ingestion already has rows in the old shape, does it reject the change or migrate the existing data?
@igorblink Good question. Neither - the CLI doesn’t change a dataset’s stored schema after the first ingest. dataset update only renames the dataset. Changes like renaming columns, converting types, adding formula columns or hiding columns go through modifications, which sit on top of the stored rows. Claude Code can run dataset preview-modification first to see the result before saving anything. If new records don’t match the schema, the ingestion fails and the error names each rejected record, so nothing gets silently mixed. For a real breaking change, the clean path is a new dataset and a re-ingest.