deepnote
Deepnote is an open-source notebook platform designed as a Jupyter replacement, adding AI features, real-time collaboration, and a human-readable YAML format. It supports Python, R, and SQL locally via VS Code extensions, with optional cloud scaling through Deepnote Cloud.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | deepnote/deepnote |
| Owner | deepnote |
| Primary language | TypeScript |
| License | Apache-2.0 — OSI-approved |
| Stars | 2.9k |
| Forks | 201 |
| Open issues | 18 |
| Latest release | @deepnote/[email protected] (2026-06-29) |
| Last updated | 2026-07-07 |
| Source | https://github.com/deepnote/deepnote |
What deepnote is
TypeScript-based notebook runtime built on Jupyter kernel with backwards compatibility, featuring reactive cell execution, block-based architecture (@deepnote/blocks), bidirectional notebook conversion (@deepnote/convert supporting .ipynb, .qmd, .py, marimo), and separation of code/output via snapshot files for cleaner version control.
Get the deepnote source
Clone the repository and explore it locally.
git clone https://github.com/deepnote/deepnote.gitcd deepnote# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Jupyter kernel requirement: local Python/R/SQL runtime must be installed and configured; no bundled runtime provided in open-source repo.
- VS Code extension dependency: local notebook editing requires installing the Deepnote extension; no standalone editor or web UI in open source.
- Notebook conversion: existing .ipynb files can be converted via @deepnote/convert CLI, but custom cell metadata or third-party Jupyter extensions may not roundtrip cleanly.
- Reactive execution model differs from Jupyter: blocks auto-re-run on dependency changes; existing notebooks may exhibit unexpected re-execution behavior.
- Local compute limitations: open-source cannot scale to cloud; teams requiring shared compute must adopt Deepnote Cloud separately.
When to avoid it — and what to weigh
- Require production ML model serving/inference — Deepnote is a notebook/development environment, not a model serving platform. Productionization requires separate deployment infrastructure.
- Need browser-based UI in local environment — Current open-source implementation is VS Code extension-only. The notebook UI from Deepnote Cloud is not yet available locally (roadmap item).
- Depend on closed-source or proprietary extensions — The open-source repository includes only Jupyter-compatible core and VS Code integration. Deepnote Cloud-specific features (managed compute, agent, native integrations) require the commercial platform.
- Require long-term stability guarantees on format — Repository created September 2025 with active development. .deepnote format specification exists but real-world stability under production load is unproven; breaking changes possible during maturation.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and liability/warranty disclaimers.
Apache-2.0 permits commercial use, modification, and redistribution provided license and copyright notices are included and changes are documented. However, verify your use case: if you plan to monetize a notebook platform or integrate substantially into a commercial product, conduct IP review. Deepnote Cloud (proprietary) is the company's commercial offering; forking/extending the open-source runtime may create support/trademark considerations—contact Deepnote for clarification on commercial deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
No security audit history or disclosures found. Key considerations: notebook execution runs arbitrary Python/R/SQL locally—trust notebook source. Local VS Code setup inherits VS Code security model. Deepnote Cloud (not covered here) is proprietary; its security posture is unknown from this data. If handling sensitive data, review notebook contents before execution and apply OS/network-level isolation as needed.
Alternatives to consider
Jupyter / JupyterLab
Mature, de facto standard for notebooks. No AI agent or reactive execution; heavier setup; JSON format less Git-friendly. Better for teams already invested in Jupyter ecosystem.
Marimo
Reactive Python notebook framework focused on .py files with @app.cell syntax. Lightweight, strong version control story. Lacks SQL, R, and Deepnote's Jupyter compatibility; different learning curve.
Quarto
Document-first notebooks with publication output (HTML, PDF, dashboards). Stronger for reporting and publishing; less interactive for exploration. Not a replacement for dynamic notebooks.
Build on deepnote with DEV.co software developers
Deepnote offers a cleaner, Git-friendly alternative to Jupyter with AI-first design. Start locally with VS Code, scale to cloud collaboration when needed. Let Devco help you evaluate and integrate Deepnote into your data science stack.
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deepnote FAQ
Can I use this without Deepnote Cloud?
Will my Jupyter notebooks work as-is?
Is this ready for production?
How do I set up team collaboration locally?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If deepnote is part of your open-source databases roadmap, our team can implement, customize, migrate, and maintain it.
Ready to modernize your notebook workflow?
Deepnote offers a cleaner, Git-friendly alternative to Jupyter with AI-first design. Start locally with VS Code, scale to cloud collaboration when needed. Let Devco help you evaluate and integrate Deepnote into your data science stack.