marimo-team/marimo

A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure...

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Summary Information

Updated 15 minutes ago
Added to GitGenius on May 7th, 2025
Created on August 14th, 2023
Open Issues & Pull Requests: 582 (+1)
Number of forks: 1,236
Total Stargazers: 22,475 (+0)
Total Subscribers: 69 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 4.9 hours
Mean response time: 14.5 days
90th percentile: 21.2 days
Tracked items: 2,830

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 96% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "upstream" is answered fastest, typically in about 2 hours, while "good first issue (typescript)" waits about 2 days. 56% of tracked open issues have had no activity in three months. Only 7% of issues opened in the past year have been closed.

Charts & Analytics

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Issue Activity (beta)

Open issues: 518
New in 7 days: 14
Closed in 7 days: 14
Avg open age: 186 days
Stale 30+ days: 447
Stale 90+ days: 347

Recent activity

Opened in 7 days: 12
Closed in 7 days: 12
Comments in 7 days: 9
Events in 7 days: 32

Top labels

  • bug (1,622)
  • enhancement (854)
  • needs discussion (168)
  • help wanted (110)
  • documentation (101)
  • good first issue (100)
  • good first issue (typescript) (65)
  • upstream (63)

Detailed Description

marimo is a reactive Python notebook designed to address fundamental limitations of traditional notebook environments like Jupyter. Stored as pure Python files, marimo notebooks are reproducible, version-controllable with git, executable as scripts, and deployable as interactive web applications. The project positions itself as a batteries-included replacement for multiple tools including Jupyter, Streamlit, jupytext, ipywidgets, and Papermill.

The core innovation in marimo is its reactive execution model. When a user runs a cell or interacts with a UI element, marimo automatically executes all dependent cells to maintain consistency between code and outputs. This eliminates the hidden state problems endemic to traditional notebooks where cells can be executed out of order, leaving the program in an inconsistent state. The system can also be configured to mark affected cells as stale rather than automatically executing them, accommodating expensive computations while still maintaining guarantees about program state.

marimo includes comprehensive data handling capabilities with first-class SQL support. Users can write SQL queries that depend on Python variables and execute them against dataframes, databases, lakehouses, CSVs, Google Sheets, and other data sources. The built-in SQL engine returns results as Python dataframes, keeping notebooks pure Python despite SQL integration. Interactive dataframe features allow users to page through, search, filter, and sort millions of rows without writing code.

The editor is modern and AI-native, supporting GitHub Copilot, AI assistants specialized for data work, vim keybindings, a variable explorer, and code completion. marimo can be used through its web-based editor, VS Code via an official extension, or any text editor with file watching. The system includes built-in package management supporting all major package managers, with the ability to install packages on import and serialize requirements directly in notebook files.

The project maintains active discussion with 129 items tagged as needing discussion.

The repository overlaps with contributors from github/gh-aw, solo-io/gloo, and microsoft/vscode, suggesting cross-pollination with command-line tools, API gateway projects, and the VS Code ecosystem.

marimo notebooks are executable as Python scripts parameterized by command-line arguments, deployable as interactive web apps or slides, and runnable in browsers via WebAssembly. Functions and classes can be imported from one notebook to another, enabling code reuse. The system supports pytest testing of notebooks and includes dynamic markdown parametrized by Python variables for narrative-driven data exploration. Built-in features encompass HTML export, fast code completion, and an interactive dataframe viewer, positioning marimo as a comprehensive environment for reproducible research, data analysis, and interactive application development.