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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.

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

• VS Code extension - Full Deepnote support in Visual Studio Code
• Cursor extension - AI-powered notebook editing in Cursor
• Windsurf extension - Collaborative development in Windsurf

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

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 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:

Learn more: deepnote.com/education

• Join our Community!
• Open an issue for bug reports or feature requests
• Check out source code of related repositories: Deepnote VS Code extension and Deepnote Toolkit.
• Check out our documentation
• Want a low-code experience? Visit Deepnote Cloud together with Deepnote AI agent

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

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.