[opensourcedex]

Deepnote is a free, open source business intelligence & reporting project written in TypeScript and released under Apache-2.0. It has 3,008 GitHub stars, 197 forks and 33 open issues, and was last pushed 9 hours ago. On this registry it ranks #17 of 34 tracked projects in Business Intelligence & Reporting, with 5 head-to-head comparisons available. It gained 1 stars over the last 6 tracked days.

What is Deepnote?

What it is

Deepnote is an open-source data notebook for Python, SQL, and AI workflows, positioned as a drop-in replacement for Jupyter. The project lives in the Jupyter ecosystem, using a Deepnote kernel that is described as backwards compatible while adding an AI agent, new block types, and native data integrations. It is written in TypeScript and licensed under Apache-2.0, with the repository presenting itself as the local foundation for working before scaling to Deepnote Cloud.

The problem it addresses is notebook friction: messy JSON, limited collaboration, and the gap between local experimentation and team deployment. Deepnote replaces .ipynb with human-readable .deepnote YAML, then connects local editing in VS Code, Cursor, and Windsurf to cloud collaboration, compute, and deployment. It targets data analysis, machine learning, exploratory data analysis, and visualization, with conversion tools moving existing notebooks into the new format.

Key capabilities

  • Converts existing .ipynb notebooks into .deepnote projects and back using the @deepnote/convert CLI and API.
  • Stores each notebook in its own .deepnote YAML file alongside integrations and settings, producing cleaner diffs for version control.
  • Extends code cells with block-based architecture for SQL, inputs, charts, and other block types defined through @deepnote/blocks.
  • Runs notebooks locally through VS Code, Cursor, and Windsurf integrations using the open-source Deepnote Toolkit.
  • Re-runs dependent blocks automatically when inputs or data change, supporting reactive execution and reproducibility.
  • Maintains compatibility with the Jupyter kernel, allowing movement between Jupyter and Deepnote workflows.
  • Scales from local files to Deepnote Cloud for real-time collaboration, team work, and deployable data apps.

Who uses it and how

  • Data professionals use it for Python, R, SQL, and AI-assisted notebook work, including exploratory data analysis, machine learning, and visualization.
  • Teams convert legacy Jupyter notebooks, edit .deepnote files locally, then drag and drop projects into Deepnote Cloud for shared work or stronger compute.
  • Developers use the VS Code, Cursor, and Windsurf extensions to edit and run Deepnote notebooks inside existing editor workflows.
  • The README names Estée Lauder, SoundCloud, Statsig, and Gusto as users and states that more than 500,000 data professionals use Deepnote.

Getting started

Install the conversion tool with npx @deepnote/convert notebook.ipynb, which creates a .deepnote file, then open that file in VS Code, Cursor, or Windsurf. The README also points to the Deepnote website, docs, changelog, examples, and community discussions.

When to use it — and when not to

Deepnote fits when you want Jupyter compatibility with a structured notebook format, local editor support, and a path to hosted collaboration. Choose it for teams willing to combine the open-source local toolkit with Deepnote Cloud for real-time collaboration and deployment. Hold back if you need a fully local AI agent, a local Deepnote UI, bring-your-own AI keys, or self-managed compute today, because the README lists those as roadmap items rather than current open-source features.

project readme (upstream, from github) — read inline

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Website • Docs • Changelog • X • Examples • Community

Deepnote is a data notebook for the AI era

Deepnote is used by 500,000+ data professionals at companies like Estée Lauder, SoundCloud, Statsig, Gusto, and beyond.

Get started with Deepnote in seconds:

npx @deepnote/convert notebook.ipynb # This will convert the notebook and create notebook.deepnote

Then open your .deepnote file in VS Code, Cursor, or Windsurf!

What is Deepnote?

Deepnote is a drop-in replacement for Jupyter. It uses the Deepnote kernel, which is more powerful but still backwards compatible, so you can seamlessly move between both, but it adds an AI agent, sleek UI, new block types, and native data integrations. Deepnote Open Source offers you everything you'd need to work locally before you scale your workflows in a team with Deepnote Cloud.

Why Deepnote Open Source?

  • Human-readable format: The .deepnote YAML format replaces .ipynb's messy JSON with clean, version-control and human-friendly structure for projects and notebooks. Each notebook lives in its own .deepnote file alongside its integrations and settings, which keeps diffs focused and collaboration clean.
  • Block-based architecture: Extend notebooks beyond code cells with blocks for SQL, inputs, charts, and much more — all defined and validated through the open @deepnote/blocks package.
  • Work wherever: Run notebooks locally in VS Code or anywhere else with the open-source Deepnote Toolkit. Once you want to work in a team on the same notebook with beefier compute - drag and drop your project into Deepnote Cloud.
  • Reactive notebook execution: Automatically re-runs dependent blocks when inputs or data change, ensuring notebooks stay consistent and reproducible without manual execution.
  • Open and extendable: Built on the Jupyter kernel for full compatibility with your existing notebooks.
  • Effortless conversion: Convert .ipynb notebooks into .deepnote projects and back again using the open @deepnote/convert CLI and API.

What can you do right now?

This open-source repository lets you, edit and run Deepnote notebooks directly in your favorite AI-native code editors:

Roadmap

You'll soon be able to:

  • Take the UI you're used to from Deepnote Cloud and run it locally
  • Edit notebooks with a local AI agent
  • Bring your own keys for AI services
  • Run your own compute

Deepnote vs. Jupyter

Deepnote extends Jupyter in a number of ways. Here are the key differences:

Feature Deepnote Jupyter
Setup Zero setup via cloud or local installation Local installation required
AI features Native AI agent and AI code completion Third-party extensions
Version control Built-in Git integration Manual Git workflow
Sharing Share with a link Export files manually
Compute Managed cloud compute Local resources only
Integrations Native database & API connections Manual configuration

What's inside this repository

Reusable packages and libraries powering Deepnote's notebook, runtime, and collaboration features.

@deepnote/blocks TypeScript types and utilities for working with Deepnote notebook blocks.

  • Block type definitions: Code, SQL, Text, Markdown, Input, Visualization, Button, Big Number, Image, Separator
  • Python code generation: Convert blocks to executable Python code
  • Markdown conversion: Convert text blocks to/from markdown format
  • Input block support: Text, textarea, checkbox, select, slider, file, date, and date-range inputs

@deepnote/convert CLI tool and library for bidirectional conversion between Deepnote (.deepnote) and multiple notebook formats.

  • Supported formats: Jupyter (.ipynb), Quarto (.qmd), Percent (.py with # %%), and Marimo (.py with @app.cell)
  • Platform compatible: Import Jupyter notebooks (.ipynb) from Google Colab, Amazon SageMaker, Kaggle, or Azure ML, edit locally, and export back — all platform-specific metadata is preserved during roundtrip
  • CLI tool: deepnote-convert command for batch conversions
  • Programmatic API: Use in Node.js/TypeScript applications
  • Bidirectional: Any format → Deepnote, and Deepnote → any format (via --outputFormat)

File Formats Complete specification of the .deepnote and .snapshot.deepnote file formats, including schema details, block types, and snapshot management. Snapshots separate outputs from source code for cleaner Git history, while contentHash provides code provenance — verifying which code produced each output.

Deepnote for academia

Deepnote Cloud is free for students and educators! Get unlimited access to all core features, cloud compute, and real-time collaboration for your research and teaching. If you use Deepnote in your research, please cite Deepnote using:

@misc{deepnote,
    title = {Deepnote: the data notebook for the AI era},
    author = {Deepnote Team},
    year = {2025},
    url = {https://github.com/deepnote/deepnote},
    note = {Open-source collaborative data science notebook platform}
}

Learn more: deepnote.com/education

Need help?

Contributing

We love external contributors! Whether you're fixing bugs, adding features, or improving documentation, your contributions are welcome, please see CONTRIBUTING.md, or join our team

Acknowledgements

We owe a huge thank-you to the Jupyter community and everyone who has contributed to open notebooks over the past decade. Jupyter set the standard for how the world explores data and shares ideas through code when it launched in 2013. Deepnote builds directly on that legacy — and extends it into the AI-native, collaborative future. None of this would exist without the foundations Jupyter created, and we’re proud to keep contributing back to the same open ecosystem.

Built with 💙

Frequently asked questions

Is Deepnote free to use?

Deepnote is open source under the Apache-2.0 licence. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does Deepnote do?

Python, SQL, and AI in a collaborative data notebook

What is Deepnote written in?

Deepnote is primarily written in TypeScript. Its source is publicly available at https://github.com/deepnote/deepnote, and it has 3,008 GitHub stars.