marimo is a reactive Python notebook environment designed to eliminate the inconsistency and hidden state problems of traditional Jupyter notebooks. Unlike conventional notebooks that execute cells in top-down order, marimo uses static analysis to automatically re-run dependent cells when variables change—ensuring reproducibility and deterministic execution. It stores notebooks as pure Python files (.py), making them fully compatible with git, version control, and standard development workflows. This approach bridges the gap between exploratory data analysis and production-ready applications by allowing users to turn a notebook into a deployable web app, script, or slide deck with no code changes. Built for data scientists, ML engineers, and developers who value reproducibility and modern tooling, marimo integrates AI-assisted coding, SQL querying, interactive UIs, and package management directly into the notebook experience.
What You Get
- Reactive execution - Cells automatically re-run based on variable dependencies, eliminating manual cell reruns and hidden state issues. Delete a cell and its variables are removed from memory.
- Pure Python notebooks - All notebooks are saved as standard .py files, making them git-friendly and compatible with any Python editor or CI/CD pipeline.
- Built-in SQL support - Write SQL queries that reference Python variables and execute them against dataframes, databases, CSVs, Google Sheets, or lakehouses—all returning pandas DataFrames.
- Interactive UI elements - Bind sliders, dropdowns, dataframes, and chat interfaces directly to Python code without callbacks. Interactions trigger automatic cell re-execution.
- AI-powered coding assistant - Generate cells or entire notebooks using a data-aware AI assistant with context about in-memory variables. Supports custom prompts, local models, and API key integration.
- Deploy as web apps - Convert any notebook into a standalone interactive web app with
marimo run your_notebook.py, hiding code and exposing only UI elements.
- Execute as scripts - Run notebooks directly from the command line using
python your_notebook.py, with CLI parameter support for automation and pipelines.
- Package management integration - Install packages on import, auto-generate requirements.txt, and run notebooks in isolated virtual environments with built-in dependency resolution.
- VS Code & neovim support - Use marimo as a full-featured extension in VS Code, Cursor, neovim, Zed, or any text editor with live editing and variable exploration.
- Reproducible & testable - Enforce deterministic execution order based on dependencies, not cell position. Run pytest directly on notebooks for automated testing.
- WASM browser support - Run marimo notebooks entirely in the browser without a server via WebAssembly, enabling offline and secure usage.
Common Use Cases
- Building a reproducible data analysis pipeline - A data scientist uses marimo to create a notebook that queries SQL databases, filters large datasets with interactive UIs, and generates visualizations—all while ensuring every output is traceable to its source code via git.
- Deploying an internal dashboard without a frontend team - A machine learning engineer converts a Jupyter notebook into a web app with
marimo run to share model predictions with stakeholders, using sliders to adjust parameters and real-time updates without writing HTML or Flask code.
- Transitioning from Jupyter to production-ready code - A team converts legacy .ipynb files using
marimo convert your_notebook.ipynb > your_notebook.py, then adds package requirements and tests to integrate the notebook into their CI/CD pipeline.
- Team collaboration on data projects - Multiple analysts work on the same notebook in VS Code with Git, using marimo’s reactive system to avoid inconsistent outputs and automatically resolve variable dependencies when merging branches.
- Rapid prototyping with AI assistance - A researcher uses the built-in AI assistant to generate SQL queries for a new dataset and auto-create visualization code, then iterates interactively using sliders to refine results before exporting as a script.
Under The Hood
Marimo is a modern, interactive data science platform that merges Jupyter-like interactivity with contemporary web technologies, enabling real-time collaboration and seamless integration between frontend and backend components. It supports a flexible architecture that accommodates both monorepo and distributed deployment models.
Architecture
Marimo adopts a modular, multi-layered architecture that cleanly separates presentation, business logic, and data handling. This design promotes scalability and maintainability across its components.
- Emphasis on clear separation of concerns between frontend and backend layers
- Supports both monorepo and distributed deployment patterns for flexibility
- Integrates reactive state management to synchronize frontend and backend states
Tech Stack
Marimo leverages a diverse tech stack centered on Python for backend functionality and TypeScript/React for the frontend, ensuring a robust and extensible development environment.
- Built with Python as the core backend language and TypeScript/React for the frontend
- Utilizes modern UI libraries such as Radix UI, shadcn/ui, and Material UI for rich interfaces
- Employs Vite for fast development and Turbo for monorepo management
- Integrates Vitest and Playwright for comprehensive unit and end-to-end testing
Code Quality
Marimo demonstrates a mature codebase with strong testing practices and consistent error handling across modules.
- Comprehensive test coverage, especially in frontend components, with extensive end-to-end and unit tests
- Consistent application of error handling patterns to manage exceptions and edge cases
- Strong adherence to linting and formatting standards with Biome and CI/CD pipeline support
- Moderate technical debt present in legacy configuration and some outdated patterns
What Makes It Unique
Marimo introduces a distinctive blend of interactive Python development and modern web frameworks, offering unique capabilities for real-time collaboration and reactive state handling.
- Combines Jupyter-like interactivity with modern frontend frameworks for a seamless user experience
- Implements innovative reactive state management that synchronizes changes across frontend and backend
- Enables real-time collaboration through a unified architecture that bridges data science and web development