36% of 10M Jupyter notebooks on GitHub not reproducible. Marimo guarantees code/outputs/state consistency via reactive execution.
Marimo
activeReactive Python notebook that replaces Jupyter. Pure .py files, reactive DAG execution, dual-mode (notebook → app). 19.8K stars, 1.9M monthly PyPI downloads, 261 contributors.

Where it wins
Reactive DAG execution — only re-runs affected cells, guarantees code/outputs/state consistency
Pure .py file format — git-friendly diffs, natively readable by coding agents
Dual-mode: works as notebook AND deploys as app from same file
1.9M monthly PyPI downloads — explosive growth trajectory
261 contributors, daily commits, very active development
PyCon US 2025 talk — major conference validation
Independent switching stories from credible sources (Towards Data Science, Oracle engineering team)
Migration path from Jupyter: `marimo convert notebook.ipynb -o notebook.py`
Where to be skeptical
GitHub doesn't render .py files like .ipynb — no inline preview
Extension ecosystem smaller than Jupyter's
Less enterprise sales presence than Hex
Editorial verdict
Best-in-class reactive notebook — the Jupyter replacement that actually works. Reactive execution guarantees reproducibility. Pure .py format is natively agent-readable. The natural choice for agent-assisted data workflows.
Source
Videos
Reviews, tutorials, and comparisons from the community.
marimo: A Notebook that Compiles Python (PyCon US 2025)
marimo concepts
Related

Streamlit
90The dominant Python data app framework. 44K stars, 31.8M monthly PyPI downloads, acquired by Snowflake for $800M. Ecosystem giant for deploying data apps — the standard answer for sharing Python analysis as a web app.

Observable Framework
84Static site generator for data apps with D3.js lineage. Full web dev power (HTML, CSS, JS, React). 3.4K stars, 16.7K npm monthly downloads, 360 pts on HN.

Data Formulator
82AI-powered data visualization tool from Microsoft Research. Interactive AI agents iterate on chart design from raw data. 15.1K stars, MIT license, very active development.

Evidence
78BI-as-code platform: SQL + Markdown → polished data reports. No JS/Python required. YC S21, 6K stars, 76 contributors. Git-versioned reports for SQL-first analysts.
Public evidence
Enterprise validation from a major cloud vendor's internal team switching to Marimo.
One of the highest-scoring notebook announcements on HN. Sustained attention with 10 separate stories over 2 years.
Major conference acceptance signals serious technical credibility.
Consistent praise for reactive execution, pure .py files, git-friendliness. Main limitation: GitHub doesn't render .py like .ipynb.
Raw GitHub source
GitHub README peek
Constrained peek so you can sanity-check the source material without leaving the site.
marimo is a reactive Python notebook: run a cell or interact with a UI element, and marimo automatically runs dependent cells (or <a href="#expensive-notebooks">marks them as stale</a>), keeping code and outputs consistent. marimo notebooks are stored as pure Python (with first-class SQL support), executable as scripts, and deployable as apps.
Highlights.
- 🚀 batteries-included: replaces
jupyter,streamlit,jupytext,ipywidgets,papermill, and more - ⚡️ reactive: run a cell, and marimo reactively runs all dependent cells or <a href="#expensive-notebooks">marks them as stale</a>
- 🖐️ interactive: bind sliders, tables, plots, and more to Python — no callbacks required
- 🐍 git-friendly: stored as
.pyfiles - 🛢️ designed for data: query dataframes, databases, warehouses, or lakehouses with SQL, filter and search dataframes
- 🤖 AI-native: connect agent CLIs like Claude Code to notebooks, or use our editor's built-in AI features
- 🔬 reproducible: no hidden state, deterministic execution, built-in package management
- 🏃 executable: execute as a Python script, parameterized by CLI args
- 🛜 shareable: deploy as an interactive web app or slides, run in the browser via WASM
- 🧩 reusable: import functions and classes from one notebook to another
- 🧪 testable: run pytest on notebooks
- ⌨️ a modern editor: GitHub Copilot, AI assistants, vim keybindings, variable explorer, and more
- 🧑💻 use your favorite editor: run in VS Code or Cursor, or edit in neovim, Zed, or any other text editor
pip install marimo && marimo tutorial intro
Get started instantly with molab, our free online notebook. Or jump to the quickstart for a primer on our CLI.
A reactive programming environment
marimo guarantees your notebook code, outputs, and program state are consistent. This solves many problems associated with traditional notebooks like Jupyter.
A reactive programming environment. Run a cell and marimo reacts by automatically running the cells that reference its variables, eliminating the error-prone task of manually re-running cells. Delete a cell and marimo scrubs its variables from program memory, eliminating hidden state.
<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/reactive.gif" width="700px" /><a name="expensive-notebooks"></a>
Compatible with expensive notebooks. marimo lets you configure the runtime to be lazy, marking affected cells as stale instead of automatically running them. This gives you guarantees on program state while preventing accidental execution of expensive cells.
Synchronized UI elements. Interact with UI elements like sliders, dropdowns, dataframe transformers, and chat interfaces, and the cells that use them are automatically re-run with their latest values.
<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/readme-ui.gif" width="700px" />Interactive dataframes. Page through, search, filter, and sort millions of rows blazingly fast, no code required.
<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/docs-df.gif" width="700px" />Generate cells with data-aware AI. Collaborate on marimo notebooks with your favorite agent, such as Claude Code, Codex, or OpenCode, using marimo pair. Or, generate code in the marimo editor with an AI assistant that is highly specialized for working with data, with context about your variables in memory. Customize the system prompt, bring your own API keys, or use local models.
<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/readme-generate-with-ai.gif" width="700px" />Query data with SQL. Build SQL queries that depend on Python values and execute them against dataframes, databases, lakehouses, CSVs, Google Sheets, or anything else using our built-in SQL engine, which returns the result as a Python dataframe.
<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/readme-sql-cell.png" width="700px" />Your notebooks are still pure Python, even if they use SQL.
Dynamic markdown. Use markdown parametrized by Python variables to tell dynamic stories that depend on Python data.
Built-in package management. marimo has built-in support for all major package managers, letting you install packages on import. marimo can even serialize package requirements in notebook files, and auto install them in isolated venv sandboxes.
Deterministic execution order. Notebooks are executed in a deterministic order, based on variable references instead of cells' positions on the page. Organize your notebooks to best fit the stories you'd like to tell.
Performant runtime. marimo runs only those cells that need to be run by statically analyzing your code.
Batteries-included. marimo comes with GitHub Copilot, AI assistants, Ruff code formatting, HTML export, fast code completion, a VS Code extension, an interactive dataframe viewer, and many more quality-of-life features.