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GitHub Trending AI · 10/7/2026, 10:15:20 AM

marimo Hits 23k Stars: Reactive Python Notebook Replacing Jupyter

By marimo-team
68AI Score
Executive Summary

Open-source project marimo has reached 23k GitHub stars, positioning itself as a 'reactive' Python notebook. It resolves state inconsistency in traditional notebooks by automatically tracking cell dependencies and stores code as pure .py files for Git compatibility. The project aims to consolidate features from Jupyter, Streamlit, and ipywidgets, allowing notebooks to be deployed directly as web apps or scripts.

SOURCE COVERAGEOriginal coverage

A reactive Python notebook that's reproducible, git-friendly, and deployable as scripts or apps.

Docs · Discord · Examples · Gallery · YouTube

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marimo is a reactive Python notebook: run a cell or interact with a UI element, and marimo automatically runs dependent cells (or <a href="#expensive-notebooks">marks them as stale</a>), keeping code and outputs consistent. marimo notebooks are stored as pure Python (with first-class SQL support), executable as scripts, and deployable as apps.

Highlights.

PYTHON
pip install marimo && marimo tutorial intro

Get started instantly with molab, our free online notebook. Or jump to the quickstart for a primer on our CLI.

A reactive programming environment

marimo guarantees your notebook code, outputs, and program state are consistent. This solves many problems associated with traditional notebooks like Jupyter.

A reactive programming environment. Run a cell and marimo reacts by automatically running the cells that reference its variables, eliminating the error-prone task of manually re-running cells. Delete a cell and marimo scrubs its variables from program memory, eliminating hidden state.

<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/reactive.gif" width="700px" />

<a name="expensive-notebooks"></a>

Compatible with expensive notebooks. marimo lets you configure the runtime to be lazy, marking affected cells as stale instead of automatically running them. This gives you guarantees on program state while preventing accidental execution of expensive cells.

Synchronized UI elements. Interact with UI elements like sliders, dropdowns, dataframe transformers, and chat interfaces, and the cells that use them are automatically re-run with their latest values.

<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/readme-ui.gif" width="700px" />

Interactive dataframes. Page through, search, filter, and sort millions of rows blazingly fast, no code required.

<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/docs-df.gif" width="700px" />

Generate cells with data-aware AI. Collaborate on marimo notebooks with your favorite agent, such as Claude Code, Codex, or OpenCode, using marimo pair. Or, generate code in the marimo editor with an AI assistant that is highly specialized for working with data, with context about your variables in memory. Customize the system prompt, bring your own API keys, or use local models.

<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/readme-generate-with-ai.gif" width="700px" />

Query data with SQL. Build SQL queries that depend on Python values and execute them against dataframes, databases, lakehouses, CSVs, Google Sheets, or anything else using our built-in SQL engine, which returns the result as a Python dataframe.

<img src="https://raw.githubusercontent.com/marimo-team/marimo/main/docs/_static/readme-sql-cell.png" width="700px" />

Your notebooks are still pure Python, even if they use SQL.

Dynamic markdown. Use markdown parametrized by Python variables to tell dynamic stories that depend on Python data.

Built-in package management. marimo has built-in support for all major package managers, letting you install packages on import. marimo can even [serialize package requirements](https://

(注:更多技术实现细节与完整 API 文档请查阅原项目 README)