Charts built for Chat
AI agents make an unauditable mess of dashboards. Give them dbt Charts, a simple declarative YAML language, and they build better dashboards you can govern, using fewer tokens.
Every dashboard should show its work.
Queries, charts, layout, and the explanation live in one text file. SQL defines the data, a few declarations describe how to show it, and people and agents read, edit, and review the same file.
Build your next dashboard on an open foundation.
Start a conversation in the platform, or hand your coding agent the open-source language.
uv tool install dbt-chartsDeclarative design. Interactive charts.
Confidence should ship with every change.
Validate board structure in CI, then check queries against your dbt models or the warehouse. A missing column fails on the branch, not in front of your readers.
Try the platform
Good design should be the default.
A good chart makes a comparison easy; a good report says what it means. That craft is built into the defaults, and the renderer flags problems like crowded labels.
Explore charts and themes
One workflow for models and charts.
Boards live beside your dbt models, on the same branch, in the same pull request. Run the same board on your laptop, in CI, and on the platform. Apache 2.0.
Explore the open-source projectLanguage first BI
An open language for the charts. A platform for collaboration, with agents and with each other.
The language
Create boards as YAML that live next to your dbt models and render them on your laptop.
How the language works
The platform
Build conversational analytics on your warehouse using the visual editor and share with permissions.
Explore the platformPoint it at your warehouse. Ask anything.
Start with the free workspace. Connect your warehouse and your dbt repo, then ask your first question.