# deepnote/deepnote

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps. https://deepnote.com/

Repository: https://github.com/deepnote/deepnote
Canonical: https://ross.abutalabs.com/products/deepnote
Homepage: https://deepnote.com/?utm_source=github&utm_medium=github&utm_campaign=github&utm_content=readme_main
Language: TypeScript
License: Apache-2.0
License Family: permissive
Topics: artificial-intelligence, data, data-analysis, data-science, data-visualization, eda, jupyter, jupyterhub, jupyterlab, machine-learning, notebooks, python, r, sql, deepnote
Last push: 2026-08-26T21:27:02+00:00

## Health v2 (maintenance only)
Score: 79/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 85, longevity 24
- inputs: {"age_days": 338, "days_push": 7, "days_rel": 20, "gap_med": 0, "n_releases_24m": 98}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2998, forks 198 (observed 2026-08-28T04:07:37.277769+00:00)

## What it is
Deepnote is an open-source, AI-first drop-in replacement for Jupyter notebooks with a human-readable YAML format, block-based architecture (SQL, charts, inputs, agent blocks), and reactive execution. It runs locally in VS Code and other editors via the Deepnote Toolkit, and scales to Deepnote Cloud for real-time collaboration, AI agents, scheduling, and deployable data apps.

## Use cases
- replace Jupyter with a modern AI-powered data notebook
- run SQL and Python in the same notebook against Snowflake or BigQuery
- build interactive dashboards and data apps from notebooks
- schedule notebooks to run ETL pipelines on a cron basis
- collaborate with a team on data analysis in real time
- convert existing ipynb notebooks to a git-friendly format
- use an AI agent to explore data and generate SQL queries
- define consistent metric definitions with a semantic layer

## When to choose
- you want a Jupyter-compatible notebook with better UI, SQL blocks, and AI assistance
- you need version-control-friendly notebook files with clean diffs
- you want to publish analyses as shareable apps or scheduled reports
- your team needs real-time collaborative data science with 80+ data integrations

## When to avoid
- you need a fully self-hosted collaborative cloud experience, which requires Deepnote Cloud
- you depend on the classic .ipynb ecosystem and tooling without conversion
- you only need lightweight scripting without notebooks or data integrations
- AI features require paid plans and send notebook content to third-party model providers

## Facets
- artifact type: application
- maturity: active
- function: data-science, data-visualization, machine-learning, etl, charts, scheduling, agent-framework, llm-inference, developer-tools
- domain: data-science, data-visualization, analytics, machine-learning, artificial-intelligence, developer-tools
- platform: cross-platform, python, cloud, self-hosted
- tags: notebook, jupyter-alternative, data-notebook, sql-blocks, collaborative-analytics, data-apps, vscode-extension, yaml-notebook-format, reactive-execution, data-integrations, sql, data-engineering, web, desktop, nodejs

## Member repositories
- deepnote/deepnote (main) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.277769+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:30:36.594499+00:00, confidence not recorded.
  - readme: https://github.com/deepnote/deepnote (fetched 2026-08-28T04:07:37.277769+00:00, sha 5a35d5aeaa1e)
  - homepage: https://deepnote.com/?utm_source=github&utm_medium=github&utm_campaign=github&utm_content=readme_main (fetched 2026-08-29T09:46:22.628844+00:00, sha 111f3b3730c5)
  - site_page: https://deepnote.com/docs/etl-elt-pipelines (fetched 2026-08-29T09:46:22.649930+00:00, sha 1b9b473f1adb)
  - site_page: https://deepnote.com/docs/data-catalogs (fetched 2026-08-29T09:46:22.651805+00:00, sha 11720ec9bbed)
  - site_page: https://deepnote.com/docs/deepnote-ai (fetched 2026-08-29T09:46:22.638286+00:00, sha 144f537116fe)
  - site_page: https://deepnote.com/docs/data-apps (fetched 2026-08-29T09:46:22.640239+00:00, sha 8885bb32d498)
  - site_page: https://deepnote.com/docs/notebooks (fetched 2026-08-29T09:46:22.642654+00:00, sha 4afe451e395f)
  - site_page: https://deepnote.com/docs/scheduling (fetched 2026-08-29T09:46:22.644476+00:00, sha 6d2b69776c41)
  - site_page: https://deepnote.com/docs/semantic-layer (fetched 2026-08-29T09:46:22.646207+00:00, sha 6a0376670a69)
  - site_page: https://deepnote.com/docs/integrations (fetched 2026-08-29T09:46:22.647958+00:00, sha 6a778c58efc8)
- Data as of 2026-08-30T08:39:29.467469+00:00.