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marimo

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Open source · Apache-2.0

marimo is a reactive notebook for Python that replaces Jupyter's cell-execution-order chaos with a dependency graph.

updated 2026-07-01

marimo

What it is

marimo is a reactive notebook for Python that replaces Jupyter's cell-execution-order chaos with a dependency graph. When you change a variable in one cell, every downstream cell that depends on it re-runs automatically — and cells with no dependency on the change don't. The notebook itself is a .py file, not JSON, so it diffs cleanly in git, works with your existing linters/formatters, and can be imported as a regular Python module. The same file runs as a script from the CLI, executes as a notebook in the browser, or deploys as an interactive web app with no extra glue code.

The core pitch: Jupyter notebooks are notoriously bad at reproducibility because hidden state (cells run out of order, stale variables) makes "restart and run all" a gamble. marimo's reactive execution model eliminates hidden state by construction.

Who builds it and why

marimo is developed by a dedicated startup (marimo, Inc.), not a side project or a single maintainer's weekend hack. It's a VC-backed company, and the tool is the product — this isn't a loss-leader for consulting or a research artifact abandoned after a paper ships. 308 contributors on GitHub signals real external adoption and community contribution, not just an internal team pushing commits. The motivation is straightforward: notebooks are the default interface for data science and ML experimentation, and Jupyter's execution model has well-known reproducibility and tooling gaps that marimo is built specifically to close.

Production readiness signal

21,655 GitHub stars and a recent commit (July 2026 in the source data) indicate active development and a healthy pace of releases — 0.23.11 suggests frequent iteration, though the sub-1.0 version number means the API isn't guaranteed stable yet. Apache-2.0 licensing is production-friendly with no copyleft concerns. CNCF maturity is not publicly available — marimo isn't a CNCF project, so don't expect that governance signal here; evaluate it on GitHub activity and release cadence instead. 308 contributors is a strong sign for a tool at this stage, comparable to well-established open-source data tooling. There's no public SLA or enterprise support tier disclosed at time of writing — treat this as a fast-moving open-source project, not a vendor product with contractual guarantees.

Who should use this

  • Teams doing exploratory data analysis or ML experimentation who are tired of Jupyter's "worked in my notebook, broke in CI" problem.
  • Anyone who needs notebooks to live in git with real diffs and real code review, not opaque JSON blobs.
  • Teams that want to go from notebook to internal app (dashboards, demos, data tools) without rewriting in Streamlit or Dash.
  • Python-first shops already comfortable with standard tooling (ruff, mypy, pytest) who want their notebooks to play by the same rules.

Who should NOT use this

  • Teams deeply invested in the Jupyter ecosystem — specific extensions, JupyterLab plugins, or nbconvert pipelines that have no marimo equivalent.
  • Multi-language notebook users (R, Julia, Scala via kernels) — marimo is Python-only.
  • Anyone needing long-term notebook stability guarantees — pre-1.0 versioning means breaking changes are still plausible.
  • Large teams with existing heavy investment in .ipynb-based CI/CD, nbdime diffing workflows, or Papermill-based orchestration — migration cost may outweigh the benefit.
  • Use cases needing enterprise support contracts today — not publicly available as an offering yet, so regulated or support-dependent environments should ask directly before committing.

Alternatives

  • Jupyter/JupyterLab — the incumbent standard; larger ecosystem and extension library, but no reactivity and JSON-based files that don't diff well.
  • Observable — reactive notebooks like marimo but JavaScript-native; better fit if your stack isn't Python-centric.
  • Streamlit — not a notebook at all, but the closest comparison for "Python script to interactive app" if you don't need the notebook authoring experience.

Pricing

Fully open source under Apache-2.0. No paid tier, no gated features disclosed. Self-host and run it yourself — there's no cost to adopt beyond your own infrastructure and engineering time.

Frequently asked

What is marimo?+
marimo is a reactive notebook for Python that replaces Jupyter's cell-execution-order chaos with a dependency graph. When you change a variable in one cell, every downstream cell that depends on it re-runs automatically — and cells with no dependency on the change don't. The notebook itself is a .
Who builds marimo?+
marimo is developed by a dedicated startup (marimo, Inc.), not a side project or a single maintainer's weekend hack. It's a VC-backed company, and the tool is the product — this isn't a loss-leader for consulting or a research artifact abandoned after a paper ships.
Is marimo production ready?+
21,655 GitHub stars and a recent commit (July 2026 in the source data) indicate active development and a healthy pace of releases — 0.23.11 suggests frequent iteration, though the sub-1.0 version number means the API isn't guaranteed stable yet. Apache-2.0 licensing is production-friendly with no copyleft concerns.
Who should use marimo?+
Teams doing exploratory data analysis or ML experimentation who are tired of Jupyter's "worked in my notebook, broke in CI" problem. Anyone who needs notebooks to live in git with real diffs and real code review, not opaque JSON blobs.
Who should not use marimo?+
Teams deeply invested in the Jupyter ecosystem — specific extensions, JupyterLab plugins, or nbconvert pipelines that have no marimo equivalent. Multi-language notebook users (R, Julia, Scala via kernels) — marimo is Python-only. Anyone needing long-term notebook stability guarantees — pre-1.
What are the alternatives to marimo?+
Jupyter/JupyterLab — the incumbent standard; larger ecosystem and extension library, but no reactivity and JSON-based files that don't diff well. Observable — reactive notebooks like marimo but JavaScript-native; better fit if your stack isn't Python-centric.
How much does marimo cost?+
Fully open source under Apache-2.0. No paid tier, no gated features disclosed. Self-host and run it yourself — there's no cost to adopt beyond your own infrastructure and engineering time.