marimo

Overview

marimo is an open-source Python notebook built around reactive execution and notebooks stored as Python files. It is best suited to people who want reproducible data or experimentation workflows that can move between an editor, Git, scripts, and interactive apps. Its strongest case is a free, flexible local notebook; molab adds hosted compute, but public-by-default notebooks and time-limited sessions make it a selective fit for cloud work.

Key features

When a cell runs, marimo runs dependent cells or marks them stale. That makes it easier to see when outputs need refreshing instead of trusting results left behind by an earlier state. Deterministic execution and package requirements stored with notebook files also help keep work reproducible, although users still need to manage their project dependencies and review code before running it.

Storing notebooks as pure Python files is a practical advantage for developers: files can be versioned with Git, run as scripts, and reused as modules. The same work can be deployed as an interactive web app or executed in a browser through WebAssembly. That flexibility suits Python projects that need to grow beyond exploration, but marimo is a poor match for workflows centered on a language other than Python or SQL.

Sliders, dropdowns, interactive dataframe tables, and selectable plots let readers explore results without editing code. SQL cells work across dataframes, CSVs, Google Sheets, and databases, including Polars, Pandas, PyArrow, DuckDB, SQLite, Postgres, and MySQL. AI notebook features connect to OpenAI, Anthropic, Gemini, Ollama, and OpenAI-compatible APIs; these options are useful for teams already using those services, but do not change the product’s Python-first focus.

Editor support includes extensions for VS Code and PyCharm; the VS Code extension also works with Cursor and other VS Code forks. Project package management supports pip, uv, Poetry, Pixi, and Rye, while notebook sandboxes support uv and Pixi. Those choices fit varied Python setups, though the breadth of options may require teams to settle on a consistent project workflow.

Security controls reduce some risks: marimo says code does not run without an explicit user action, and edit-mode notebooks load statically rather than executing as Python modules. Token authentication is enabled by default for remote edit access, while run mode can use token flags or custom middleware. These safeguards do not make execution harmless: notebook code can run arbitrary code, so only run trusted notebooks or review them first.

Pricing

The open-source notebook is the clear choice for anyone comfortable running locally or self-hosting: it costs nothing and avoids molab’s hosted-session limits. molab suits lightweight cloud experiments when public notebooks and sessions of up to 12 hours are acceptable. For notebooks created after August 26, 2026, files larger than 1 GB are not saved when a notebook shuts down, which matters for work built around large outputs or data files. marimo cloud is aimed at users who need on-demand hosted resources, but it remains in closed beta and has custom pricing.

Platforms

marimo supports API access, extensions, Linux, macOS, Windows, the web, and self-hosting. It supports Python and SQL, with hybrid execution, GPU compute, and notebook formats including Python (.py), Jupyter Notebook (.ipynb), HTML, PDF, and Markdown. That range makes it adaptable to local development and sharing, but it does not promise version history.

Who it's for

marimo fits data scientists, analysts, data and analytics engineers, ML and AI engineers, educators, and web developers who want Python work to remain executable, reviewable, and reusable. It is less compelling for people who need private-by-default hosted notebooks, long-running cloud sessions, or a notebook environment organized around another language. Installation issues can be raised through GitHub or Discord, and general inquiries can go to [email protected].

Pros and cons

• Pros: Reactive execution and deterministic behavior help keep code and outputs in sync.
• Pros: Python-file notebooks work naturally with Git and can be run as scripts or reused as modules.
• Pros: Interactive controls, SQL integrations, and multiple editor and package-manager options support varied data workflows.
• Cons: molab notebooks are public by default, sessions last at most 12 hours, and large files over 1 GB are not saved after shutdown for notebooks created after August 26, 2026.
• Cons: Running a notebook can execute arbitrary code, so convenience does not replace code review or trust.
• Cons: marimo cloud is in closed beta and uses custom pricing, so it is not a straightforward paid upgrade.

Alternatives

Choose JupyterLab instead if you want free, open-source notebook software installed locally with pip. Pick Deepnote if a hosted notebook with a free forever tier, up to 3 editors, and up to 5 projects better suits collaborative work. Paperspace Notebooks is an alternative for readers seeking a free tier with included storage and CPU and GPU machines, while Datalore offers a free cloud tier with capped machine hours, storage, parallel notebooks, and workspace capacity.

Pluto.jl is another free, open-source notebook option. Apache Zeppelin is a free, open-source notebook alternative. SageMath is a free open-source alternative. Dnote is a free alternative. Browse more options in Notebook Software or Coding Playgrounds.

Verdict

Choose marimo if your work is Python-centered and you want reproducible notebooks that behave like maintainable code, with Git-friendly files and a free local option. Look elsewhere if hosted privacy, sessions beyond 12 hours, or a different primary language matters more than that workflow.