Data Formulator

<h1 align="center"> <img src="https://raw.githubusercontent.com/microsoft/data-formulator/main/public/favicon.ico" alt="Data Formulator icon" width="28">&nbsp; Data Formulator: AI-powered Data Visualization </h1> <p align="center"> 🪄 Explore data with visualizations, powered by AI agents. </p> <p align="center"> &nbsp; </p> <p align="center"> <a href="https://github.com/microsoft/data-formulator/actions/workflows/python-build.yml"><img src="https://github.com/microsoft/data-formulator/actions/workflows/python-build.yml/badge.svg" alt="build"></a>&ensp; </p> <!-- [](https://codespaces.new/microsoft/data-formulator?quickstart=1) --> <!-- https://github.com/user-attachments/assets/8ca57b68-4d7a-42cb-bcce-43f8b1681ce2 -->

Why Data Formulator?

Your data lives everywhere — databases, warehouses, BI tools, files. Coding agents can help, but only after someone wires them up, and answers come back as walls of code or text that are hard to follow, refine, or share.

Data Formulator makes it simple: connect any data, ask anything, get charts you can edit, branch, and share — all on one interactive, visual canvas.

• Data & platform teams: wire up your databases, warehouses, and BI sources once, and give the whole org an AI-powered data exploration layer.
• Analysts & users: ask, edit, branch, share. It's so easy to get insights from good-looking charts.

https://github.com/user-attachments/assets/8e4f8a08-6423-4227-a1f7-559e0126ce31

News 🔥🔥🔥

[05-28-2026] Data Formulator 0.7 — turn ANY data into insights in five easy steps:

1. Connect. Governed, reusable connections to databases, warehouses, BI systems, object stores, and files (Superset, Kusto, Cosmos DB, MySQL, PostgreSQL, MSSQL, BigQuery, S3, Azure Blob, …). Need a custom source? Point your coding agent at the data loader plugin guide.
2. Load. Ask the data-loading agent to find tables from connected databases, or extract data from Excel files, images, websites, and text.
3. Explore. A unified Data Agent with thread memory inspects data, runs sandboxed code, and weaves explanation, exploration, and recommendation into one fluid conversation — grounded in your context. The Data Thread keeps questions, intermediate results, and charts navigable: revisit earlier steps, branch into alternatives, and compare side by side.
4. Refine. 30+ chart types (area, streamgraph, candlestick, radar, maps, KPI, …) via a new semantic chart engine, plus a style-refinement agent that turns rough charts into presentation-ready visuals through natural language.
5. Share. Build reports and export as image or PDF to tell the story.

➕ Persistent sessions & workspaces — identity-isolated, saved across restarts. Data Formulator is your de facto data analysis pane.

Multilingual UI — Data Formulator now speaks Chinese in addition to English (没错，DF现在会说中文了！). More languages on the way — contributions welcome.

Install with pip install data_formulator or run instantly with uvx data_formulator.

[!TIP] Are you a developer? Join us to shape the future of AI-powered data exploration! We're looking for help with new agents, data connectors, chart templates, and more. Check out the Developers' Guide and our open issues.

Previous Updates

Here are milestones that lead to the current design:

• v0.7 alpha 2 (05-11-2026): Early preview of data connectors, the unified DataAgent with thread memory, persistent workspaces, the semantic chart engine, and experimental knowledge distillation.
• v0.6 (Demo): Real-time insights from live data — connect to URLs and databases with automatic refresh
• uv support: Faster installation with uv — uvx data_formulator or uv pip install data_formulator
• v0.5.1 (Demo): Community data loaders, US Map & Pie Chart, editable reports, snappier UI
• v0.5: Vibe with your data, in control — agent mode, data extraction, reports
• v0.2.2 (Demo): Goal-driven exploration with agent recommendations and performance improvements
• v0.2.1.3/4 (Readme | Demo): External data loaders (MySQL, PostgreSQL, MSSQL, Azure Data Explorer, S3, Azure Blob)
• v0.2 (Demos): Large data support with DuckDB integration
• v0.1.7 (Demos): Dataset anchoring for cleaner workflows
• v0.1.6 (Demo): Multi-table support with automatic joins
• Model Support: OpenAI, Azure, Ollama, Anthropic via LiteLLM (feedback)
• Python Package: Easy local installation (try it)
• Visualization Challenges: Test your skills (challenges)
• Data Extraction: Parse data from images and text (demo)
• Initial Release: Blog | Video

Overview

Data Formulator is a Microsoft Research project for data exploration with visualizations powered by AI agents. It combines UI interactions with natural language so analysts can communicate intent, branch into alternative analyses, and share results — starting from any data format (screenshot, text, CSV, or database).

Get Started

Play with Data Formulator with one of the following options.

• Option 1: Install via uv (recommended)

  uv is an extremely fast Python package manager. If you have uv installed, you can run Data Formulator directly without any setup:

  uvx data_formulator
  

  Run uvx data_formulator --help to see all available options, such as custom port, sandboxing mode, and data storage location.

• Option 2: Install via pip

  Use pip for installation (recommend: install it in a virtual environment).

  pip install data_formulator # install
  python -m data_formulator # run
  

  Data Formulator will be automatically opened in the browser at http://localhost:5567.

• Option 3: Run with Docker

  docker compose up --build
  

  Open http://localhost:5567 in your browser. To stop, press Ctrl+C or run docker compose down.

• Option 4: Codespaces

  You can run Data Formulator in Codespaces; we have everything pre-configured. For more details, see CODESPACES.md.

  

• Option 5: Working as developer

  You can build Data Formulator locally and develop your own version. Check out details in DEVELOPMENT.md.

Using Data Formulator

Besides uploading csv, tsv or xlsx files that contain structured data, you can ask Data Formulator to extract data from screenshots, text blocks or websites, or load data from databases use connectors. Then you are ready to explore. Ask visualizaiton questions, edit charts, or delegate some exploration tasks to agents. Then, create reports to share your insights.

https://github.com/user-attachments/assets/164aff58-9f93-4792-b8ed-9944578fbb72

Research Papers

• Data Formulator 2: Iteratively Creating Rich Visualizations with AI

@article{wang2024dataformulator2iteratively,
      title={Data Formulator 2: Iteratively Creating Rich Visualizations with AI}, 
      author={Chenglong Wang and Bongshin Lee and Steven Drucker and Dan Marshall and Jianfeng Gao},
      year={2024},
      booktitle={ArXiv preprint arXiv:2408.16119},
}