Analysis updated 2026-07-03
Build a polished multi-page sales dashboard with filters and KPI cards in under 50 lines of Python without writing any CSS.
Let a non-engineer analyst describe a chart in plain English and have Vizro-AI generate the Plotly figure automatically.
Wire multiple charts together so that selecting a filter on one chart automatically updates all related visualizations on the page.
| mckinsey/vizro | sandai-org/magi-1 | canonical/cloud-init | |
|---|---|---|---|
| Stars | 3,688 | 3,688 | 3,687 |
| Language | Python | Python | Python |
| Setup difficulty | easy | hard | moderate |
| Complexity | 2/5 | 5/5 | 3/5 |
| Audience | data | researcher | ops devops |
Figures from each repo's GitHub metadata at analysis time.
Install with a single pip command from PyPI, Vizro-AI requires an OpenAI API key for the natural-language chart generation feature.
Vizro is an open-source Python toolkit, created by McKinsey, for building interactive data dashboards. Instead of writing thousands of lines of code, you describe what you want in a short configuration (a few dozen lines at most) and Vizro assembles a working multi-page dashboard for you. It is aimed at analysts, data scientists, and non-engineers who need a polished result without deep programming or design experience. A Vizro app is built from a small set of building blocks. Components are the visual pieces: charts, tables, summary cards, and KPI indicators. Controls let users interact with the data, for example drop-down menus, sliders, and filters. Actions wire things together so that selecting a filter on one chart updates others, or lets users drill into detail and export results. Layouts define how pieces are arranged on screen, and Navigation handles multi-page structure including nested menus. You describe all of this in Python dictionaries, JSON, YAML, or Pydantic models, whichever you prefer. The charts and interactive components are powered by Plotly and Dash, two well-established Python libraries for web-based data visualization. Pydantic handles the configuration layer, which means Vizro catches typos and invalid settings before the app even runs. The visual design defaults are built in, so the output looks professional without any custom CSS or design work. For users who want more control, Vizro supports optional code extensions. You can add custom chart types, override visual formatting, or write raw JavaScript, HTML, and CSS components that slot into the same configuration structure. This makes it possible to go from a quick prototype to a production-ready internal tool without switching frameworks. The repository also includes Vizro-AI, a companion package that lets you describe a chart in plain English and have it generated automatically. The main package is available on PyPI and installs with a single pip command. Live demo dashboards are hosted on Hugging Face Spaces and linked from the README.
Vizro is McKinsey's open-source Python toolkit for building interactive multi-page data dashboards in a few dozen lines of configuration, with built-in professional design and a companion AI feature that generates charts from plain-English descriptions.
Mainly Python. The stack also includes Python, Plotly, Dash.
Apache License 2.0, use freely for any purpose, including commercial projects, as long as you keep the copyright notice.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly data.
This repo across BitVibe Labs
Verify against the repo before relying on details.