Every developer has lived through the classic Jupyter tragedy: you run cell 12, tweak cell 3, accidentally re-execute cell 7 out of order, and suddenly your notebook claims your model achieved 104% accuracy on data that hasn't loaded yet. You commit the resulting .ipynb file to Git, triggering an unreadable diff of 4,000 lines of base64 image strings and escape-character soup that brings your code reviewer to tears.
Enter marimo-team/marimo, an open-source, reactive Python notebook designed specifically to eliminate hidden state while doubling as an interactive web application framework.
┌───────────────────────────┐
│ marimo UI Slider: value │
└─────────────┬─────────────┘
│ (triggers re-eval)
▼
┌───────────────────────────┐
│ Cell 2: df_filtered = │
│ df[df['age'] > value] │
└─────────────┬─────────────┘
│ (cascades downstream)
▼
┌───────────────────────────┐
│ Cell 3: plot(df_filtered)│
└───────────────────────────┘
What Is marimo?
Marimo is an open-source, reactive notebook environment for Python that stores code as pure .py files rather than serialised JSON.
Instead of relying on arbitrary, manual cell execution order, marimo models the relationship between your cells as a Directed Acyclic Graph (DAG). When you modify a variable in one cell, marimo automatically runs any downstream cells that depend on it—precisely how a spreadsheet recalculates dependent formulas. Delete a variable, and marimo scrubs its memory clean, preventing ghost variables from lingering in memory.
Why the Python Ecosystem Is Raving About It
Across developer subreddits, YouTube tool reviews, and AI community discussions, the consensus is simple: the classic notebook paradigm is fundamentally ill-suited for modern software engineering workflows. Marimo tackles the three biggest headaches in data science and AI prototyping:
- Pure Python Storage: A marimo notebook is simply a valid Python script. You can run it via
python notebook.py, import functions from it directly into standard modules, format it with Ruff, and review clean, line-by-line diffs in standard Git pull requests. - Deterministic Reactivity: No more "Restart Kernel and Run All Cells" just to verify your logic works. The DAG execution engine guarantees reproducibility by keeping UI elements and downstream computations completely synchronised.
- App Deployment Out of the Box: With a single CLI switch, a marimo notebook transitions from an exploratory development environment into a production-ready, interactive web app (similar to Streamlit or Dash), complete with zero-setup sliders, dropdowns, and interactive tables.
Architecture & Feature Comparison
| Feature | Classic Jupyter (.ipynb) | Streamlit | marimo |
|---|---|---|---|
| Storage Format | Bloated JSON | Pure Python (.py) | Pure Python (.py) |
| Execution Model | Manual, out-of-order | Top-to-bottom rerun | Reactive DAG |
| Hidden State Risk | High (stale memory) | None | None |
| Git Mergeability | Miserable | Seamless | Seamless |
| Interactive UI | Requires plugins (ipywidgets) | Native components | Native, reactive components |
| WASM / Static Export | Limited (JupyterLite) | Not native | Yes (runs client-side via Pyodide) |
Getting Started: Installation and Setup
Getting started takes seconds via your package manager of choice:
# Using pip
pip install marimo
# Or using uv (recommended for ultra-fast environments)
uv pip install marimo
To create a fresh notebook or edit an existing one:
marimo edit analysis.py
This command launches a local web server with marimo’s web editor. If you want to serve your completed analysis as a read-only interactive dashboard without editor chrome:
marimo run analysis.py
Practical Example: Building a Reactive Dashboard
Here is how clean and declarative marimo looks when saved to a standard .py file. Each logical cell is enclosed in a decorated function:
import marimo
app = marimo.App(width="medium")
@app.cell
def __():
import marimo as mo
import polars as pl
return mo, pl
@app.cell
def __(mo):
# Built-in UI components bind directly to reactive variables
slider = mo.ui.slider(start=10, stop=100, step=5, value=25, label="Threshold")
slider
return (slider,)
@app.cell
def __(mo, slider):
# Downstream execution runs automatically whenever `slider.value` updates
data_limit = slider.value
mo.md(f"Currently filtering dataset to top **{data_limit}** records.")
return (data_limit,)
Notice the function signatures: marimo inspects variable definitions and dependencies automatically to construct the graph. You never have to manually wire callbacks or handle state stores.
Key Takeaways
- Built-in AI & SQL tooling: Marimo natively supports duckdb SQL queries inside cells and includes an integrated AI code assistant directly within the editor interface.
- WASM Export: You can compile your notebooks into standalone static HTML pages powered by WebAssembly, letting stakeholders run your models and queries locally inside their browsers without spinning up an expensive cloud backend.
- Developer ergonomics: Native linting, auto-completion, vim keybindings, and GitHub Copilot support work right out of the box.
If you are tired of debugging broken notebook states and dreading merge conflicts on raw JSON blobs, marimo is the modern Python workspace you should be building with today.