# Data Formulator

> Build and refine data visualizations with AI on an interactive canvas

- Category: App UIs & Builders / Data App Builders
- Language: Python
- Licence: MIT
- Repository: https://github.com/microsoft/data-formulator
- Canonical page: https://ai-tldr.dev/tools/data-formulator/

## In short

Data Formulator is a Microsoft Research project for exploring data with visualizations powered by AI agents. It combines a visual UI with natural-language input, so you connect a data source, ask questions, and get charts you can edit, branch, and share on one interactive canvas. It sits in the Data App Builders category of the App UIs & Builders tools and is open-source, written in Python and licensed MIT.

## Overview

Data Formulator is a Microsoft Research project for exploring data with visualizations powered by AI agents. It combines a visual UI with natural-language input, so you connect a data source, ask questions, and get charts you can edit, branch, and share on one interactive canvas.

It is aimed at two audiences: data and platform teams who wire up databases, warehouses, and BI sources once to give an org an AI-powered exploration layer, and analysts who want to ask, edit, and share insights without writing query and plotting code by hand.

As a data-app builder, it runs locally as a Python package and connects to your own model provider. It supports OpenAI, Azure, Ollama, and Anthropic through LiteLLM, and can read from sources like PostgreSQL, MySQL, MSSQL, BigQuery, S3, and Azure Blob, plus files, images, and text.

## What it does

- Natural-language plus direct-manipulation UI for creating and editing charts on a visual canvas
- A unified Data Agent with thread memory that inspects data, runs sandboxed code, and recommends next steps
- Data Thread keeps questions, intermediate results, and charts navigable so you can revisit steps, branch alternatives, and compare side by side
- Connectors for databases, warehouses, BI systems, object stores, and files (PostgreSQL, MySQL, MSSQL, BigQuery, S3, Azure Blob, and more)
- 30+ chart types via a semantic chart engine, plus a style-refinement agent for presentation-ready visuals
- Bring-your-own model: OpenAI, Azure, Ollama, and Anthropic supported through LiteLLM

## Getting started

Data Formulator runs locally as a Python package; install it with pip or run it instantly with uvx, then open the local web UI.

1. Install with pip

   Install the package from PyPI into your Python environment.

```bash
pip install data_formulator
```

2. Or run instantly with uvx

   Use uvx to download and run it without a manual install step.

```bash
uvx data_formulator
```

3. Start the app

   Launch Data Formulator, then open the local URL it prints in your browser and configure your model provider (OpenAI, Azure, Ollama, or Anthropic).

```bash
data_formulator
```

## When to use it

- Letting analysts ask questions of a connected database and get editable charts without writing SQL or plotting code
- Giving a data or platform team a reusable AI exploration layer over warehouses and BI sources
- Extracting structured data from Excel files, images, websites, or text and turning it into visualizations
- Branching into alternative views of the same dataset and comparing them side by side, then exporting a report as image or PDF