Marimo

Marimo is a reactive, open-source Python notebook that stores as pure .py files, auto-reruns every dependent cell when you change one, and deploys as an interactive web app with a single command. We rate it 84/100 — the cleanest, most reproducible notebook experience we have tested in 2026, and the obvious pick for anyone tired of stale-state Jupyter bugs, although a recent pre-auth RCE vulnerability and a still-maturing ecosystem keep it from a 90.

What is Marimo?

Marimo is a next-generation notebook for Python, built from scratch by Akshay Agrawal (ex-Google Brain, Stanford PhD) and Myles Scolnick (ex-Palantir) in 2022. The pitch is simple: kill the "hidden state" problem that has plagued Jupyter for a decade. In Marimo, cells form a DAG; run one cell and everything downstream reruns (or is marked stale), so what you see is what would actually run if you re-opened the notebook tomorrow. Variables can only be defined once, out-of-order execution is impossible, and notebooks serialize as plain Python — so git diff actually works.

On October 30, 2025, Marimo Inc. was acquired by CoreWeave for an undisclosed amount. CoreWeave has committed to keeping the notebook permissively-licensed and open-source, and the latest release (0.23.2, April 20, 2026) was shipped just days ago. The project is at 20,500+ GitHub stars, downloaded hundreds of thousands of times a month, and already in production at Cloudflare, Shopify and BlackRock.

Key Features of Marimo

• Reactive dataflow execution: Marimo statically analyses your code to build a dependency graph between cells. Run one cell and it automatically re-executes every downstream cell — like a spreadsheet, but in Python. No more "restart kernel and run all" rituals.
• Pure-Python file format: Every notebook is a valid, executable .py file. That means git diff is legible, merge conflicts are resolvable, and you can run any notebook with python notebook.py from the CLI.
• Interactive UI without callbacks: mo.ui.slider(), mo.ui.dropdown(), mo.ui.dataframe() — bind a widget to a variable and every cell that reads it updates reactively. No @observe, no event handlers.
• Built-in SQL cells: Write SQL against DuckDB, SQLite, Postgres or MySQL in a first-class cell that returns a dataframe. Results flow into Python cells exactly like any other variable.
• Deploy as an app or script: marimo run notebook.py serves the notebook as a read-only interactive web app; marimo export ships it as a WASM bundle that runs entirely in the browser.
• AI-native editor: Built-in code generation, GitHub Copilot autocomplete, and the recently-launched marimo pair skill which lets Claude Code or similar agents drive the notebook, inspect live variables, and rewrite cells.
• Integrated package management: Declares dependencies inline using PEP 723 script metadata, so notebooks are self-contained and reproducible by uv or pip.

What Users Say About Marimo

Reaction on Hacker News has been overwhelmingly positive, with the original Show HN thread crossing 500 points and top comments repeatedly praising the reproducibility story. On Reddit, r/Python and r/MachineLearning users highlight the git-friendly .py format as "the single feature that made me switch." Towards Data Science called it "a genuine replacement, not a wrapper" in early 2026.

The honest criticism is also consistent. Multiple Jupyter Discourse threads note that GitHub does not yet render .py notebooks visually the way it renders .ipynb, so sharing a read-only view on GitHub is worse. Some data scientists miss the ability to run cells truly out of order for exploratory hacking. And on April 8, 2026, Sysdig disclosed a pre-authentication RCE in the terminal WebSocket endpoint; it was patched within 10 hours, but anyone exposing a Marimo server to the public internet without auth should double-check their version is ≥ 0.23.0.

Marimo Pricing

The Marimo notebook itself is free and open-source under the Apache 2.0 license. There is no paid tier, no user cap, and no license key. The cloud-hosted offering, molab at molab.marimo.io, is currently free in public beta; enterprise pricing for CoreWeave-integrated deployments is negotiated directly.

Plan	Price	Key Details
Marimo OSS	$0	Self-host, unlimited notebooks, Apache 2.0 license, all features
molab (cloud)	$0 (beta)	Cloud-hosted shareable notebooks, browser-only
CoreWeave Enterprise	Contact	Integrated with CoreWeave GPU cloud, SSO, audit logs

Who Should Use Marimo?

Best for: ML engineers, data scientists and AI builders who live in notebooks all day and keep getting burned by stale-state bugs; teams who want notebooks to live in git alongside the rest of their Python code; anyone building interactive dashboards or demos on top of a Python pipeline.

Not ideal for: Teams deeply invested in .ipynb tooling (Papermill pipelines, JupyterHub, Obsidian-style nbdev workflows), or anyone who relies on GitHub's native .ipynb rendering for casual sharing.

Pros and Cons

Pros:

• Reactivity kills the single worst failure mode of Jupyter (hidden state)
• Pure-Python file format is a massive win for code review and CI
• Interactive widgets are trivially easy — easier than Streamlit for many use cases
• Apache 2.0, truly free, and now has CoreWeave-funded long-term backing
• Ships as WASM — you can run notebooks in a browser with no server

Cons:

• GitHub still does not render .py notebooks as rich previews
• Smaller ecosystem of extensions than Jupyter's decade-long head start
• Strict single-definition rule frustrates some exploratory workflows
• Recent pre-auth RCE (patched in 0.23.0) is a reminder that deploying Marimo publicly requires care

Alternatives to Marimo

The obvious alternative is JupyterLab — mature, ubiquitous, and the safe choice for anyone who does not value reactivity. Deepnote and Hex offer hosted reactive notebooks with built-in collaboration, but they are closed-source SaaS with per-seat pricing. Observable is brilliant if you are happy in JavaScript instead of Python. For pure scripting-as-documents, take a look at Val Town.

Verdict: Is Marimo Worth It?

Yes, for most Python data and ML teams. Marimo solves a real, painful problem — notebook non-reproducibility — without asking you to adopt a new language or give up your favourite editor keybindings. The CoreWeave acquisition removes the last real objection ("will this startup survive?"), and the OSS license means you can adopt it today without vendor risk. The ecosystem is still smaller than Jupyter's, and if your team's workflow is tightly coupled to .ipynb tooling the switch will hurt for a few weeks. But the payoff in reproducibility and git-sanity is real. At 84/100, Marimo is the notebook we would pick for a new project in 2026.

Frequently Asked Questions

Is Marimo free?

Yes. Marimo is open-source under the Apache 2.0 license. The cloud-hosted molab service is also free in public beta. Only enterprise CoreWeave-integrated deployments have negotiated pricing.

What platforms does Marimo support?

Marimo runs anywhere Python runs: macOS, Windows, Linux, and browsers via WASM/Pyodide. The editor is a web app served by the Marimo server, so any modern browser works as the client.

How does Marimo compare to Jupyter?

Marimo is reactive (cells auto-rerun based on a dependency graph), while Jupyter is imperative (you run cells manually in any order). Marimo stores notebooks as .py files that are git-friendly; Jupyter stores them as .ipynb JSON files that are hard to diff. Jupyter has a larger ecosystem and native GitHub rendering; Marimo has stronger reproducibility guarantees.

Is Marimo open source?

Yes, Apache 2.0. The source is available at github.com/marimo-team/marimo and CoreWeave has committed to keeping the notebook permissively-licensed after the October 2025 acquisition.

Can Marimo notebooks be deployed as web apps?

Yes. Run marimo run notebook.py to serve the notebook as a read-only interactive web app, or marimo export html-wasm to compile to a static WASM bundle that runs entirely in the browser with no server.