Ross ROSS = Recommend OSS · open-source software intelligence for agents

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/ observed · 2026-08-28

github.com/deepnote/deepnote · homepage · TypeScript · Apache-2.0 (permissive) observed · 2026-08-28

Health v2 · maintenance only

79/100

  • Activity 99
  • Release rhythm 85
  • Longevity 24
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 0
  • age_days: 338
  • days_rel: 20
  • days_push: 7
  • n_releases_24m: 98

Full methodology

Adoption not part of the score

2998 stars · 198 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

application · maturity active

data-science data-visualization machine-learning etl charts scheduling agent-framework llm-inference developer-tools data-science data-visualization analytics machine-learning artificial-intelligence developer-tools cross-platform python cloud self-hosted 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

10 sources

Member repositories

RepositoryRoleHealth v2
deepnote/deepnotemain79

For agents

markdown · JSON · MCP: product_card(name="deepnote/deepnote")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem