Marimo
Marimo is an open-source reactive notebook for Python that changes the paradigm of interactive computing. Unlike traditional notebooks where cells are executed sequentially and hidden state can accumulate, Marimo automatically keeps track of dependencies between cells. When you modify a cell, only the cells that depend on it are re-executed, ensuring that the notebook's state is always consistent and reproducible. This reactive model eliminates common bugs like undefined variables, stale outputs, and broken pipelines. Marimo notebooks are stored as pure Python (.py) files, making them fully compatible with version control systems like Git. This means you can diff, merge, and review notebook changes just like any other code. The plain-text format also allows you to run notebooks as regular Python scripts from the command line, or deploy them as interactive web apps with `marimo run`. The editor is designed with both developers and AI in mind. It features modern code completion, inline documentation, and an AI chat panel that can assist with coding. You can interactively query databases with SQL directly inside the notebook, and the results are displayed live. Marimo also supports building rich, interactive UI elements like sliders and charts, turning notebooks into shareable dashboards. Marimo is highly flexible: use it as a traditional notebook for exploratory analysis, as a script for batch processing, or as an app for end-user interaction. It is easy to install via pip and runs locally in your browser, with support for VS Code integration. The project is actively maintained and has a growing community around reproducible, literate programming in Python.
- Category
- Code
- Pricing
- Open Source
Problem solved
Data scientists and developers often struggle with traditional notebooks because execution order and hidden state lead to reproducibility failures. Marimo solves this by making notebooks reactive: cells are automatically structured as a directed acyclic graph, and any change triggers a re-execution of only the affected cells. Combined with a pure Python file format, this guarantees that the notebook always reflects a consistent, deterministic state, and it can be easily versioned, shared, and deployed.
Use cases
- Exploratory data analysis with live reactive outputs
- Building interactive dashboards and data apps
- Writing reproducible research papers and reports
- Teaching Python and data science with live coding
- Creating and sharing SQL-backed data pipelines
- Prototyping machine learning models with interactive controls
Audience
- Developers
- Data Scientists
- ML Engineers
- Researchers
Pros
- Reactive execution guarantees consistency and reproducibility
- Notebooks stored as plain .py files enable seamless version control
- Can be used as a notebook, script, or deployed as an app
- Built-in AI assistant and modern code editor features
- Open source with active development and growing community
Cons
- Reactive paradigm may require a mindset shift for users accustomed to traditional notebooks
- Some Python libraries that rely on imperative execution patterns may not work perfectly
- Documentation and ecosystem are still maturing compared to Jupyter
Official website: https://github.com/marimo-team/marimo
Canonical LambdaBase page: https://www.lambdabase.com/tools/marimo