README.md
[03-02-2026] Data Formulator 0.7 (alpha) — More charts, new experience, enterprise-ready
DataAgent replacing four separate agents, plus new recommendation and insight agents.uv.lock; uv sync + uv run data_formulator.Here are milestones that lead to the current design:
uvx data_formulator or uv pip install data_formulator[07-10-2025] Data Formulator 0.2.2: Start with an analysis goal
[05-13-2025] Data Formulator 0.2.1.3/4: External Data Loader
[04-23-2025] Data Formulator 0.2: working with large data 📦📦📦
[03-20-2025] Data Formulator 0.1.7: Anchoring ⚓︎
[02-20-2025] Data Formulator 0.1.6 released!
[02-12-2025] More models supported now!
.env to avoid typing them every time (copy .env.template to .env and fill in your keys).[11-07-2024] Minor fun update: data visualization challenges!
[10-11-2024] Data Formulator python package released!
[10-01-2024] Initial release of Data Formulator, check out our [blog] and [video]!
Data Formulator is a Microsoft Research prototype for data exploration with visualizations powered by AI agents.
Data Formulator enables analysts to iteratively explore and visualize data. Started with data in any format (screenshot, text, csv, or database), users can work with AI agents with a novel blended interface that combines user interface interactions (UI) and natural language (NL) inputs to communicate their intents, control branching exploration directions, and create reports to share their insights.
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:
# Run data formulator directly (no install needed)
uvx data_formulator
Or install it in a project/virtual environment:
# Install data_formulator
uv pip install data_formulator
# Run data formulator
python -m data_formulator
Data Formulator will be automatically opened in the browser at http://localhost:5567.
Option 2: Install via pip
Use pip for installation (recommend: install it in a virtual environment).
# install data_formulator
pip install data_formulator
# Run data formulator with this command
python -m data_formulator
Data Formulator will be automatically opened in the browser at http://localhost:5567.
you can specify the port number (e.g., 8080) by python -m data_formulator --port 8080 if the default port is occupied.
Option 3: Codespaces (5 minutes)
You can also run Data Formulator in Codespaces; we have everything pre-configured. For more details, see CODESPACES.md.
Option 4: Working in the developer mode
You can build Data Formulator locally if you prefer full control over your development environment and develop your own version on top. For detailed instructions, refer to DEVELOPMENT.md.
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.
There are four levels to explore data based depending on whether you want more vibe or more control:
https://github.com/user-attachments/assets/164aff58-9f93-4792-b8ed-9944578fbb72
Use the report builder to compose a report of the style you like, based on selected charts. Then share the reports to others!
<!-- ### The basics of data visualization * Set up model provider, for agentic experience, model with reasoning and strong code generation ablity is recommended. * Describe the exploration https://github.com/user-attachments/assets/0fbea012-1d2d-46c3-a923-b1fc5eb5e5b8 ### Create visualization beyond the initial dataset (powered by 🤖) * You can type names of **fields that do not exist in current data** in the encoding shelf: - this tells Data Formulator that you want to create visualizations that require computation or transformation from existing data, - you can optionally provide a natural language prompt to explain and clarify your intent (not necessary when field names are self-explanatory). * Click the **Formulate** button. - Data Formulator will transform data and instantiate the visualization based on the encoding and prompt. * Inspect the data, chart and code. * To create a new chart based on existing ones, follow up in natural language: - provide a follow up prompt (e.g., *``show only top 5!''*), - you may also update visual encodings for the new chart. https://github.com/user-attachments/assets/160c69d2-f42d-435c-9ff3-b1229b5bddba https://github.com/user-attachments/assets/c93b3e84-8ca8-49ae-80ea-f91ceef34acb Repeat this process as needed to explore and understand your data. Your explorations are trackable in the **Data Threads** panel. -->Follow the developers' instructions to build your new data analysis tools on top of Data Formulator.
Help wanted:
@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},
}
@article{wang2023data,
title={Data Formulator: AI-powered Concept-driven Visualization Authoring},
author={Wang, Chenglong and Thompson, John and Lee, Bongshin},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2023},
publisher={IEEE}
}
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
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