Marimo: The Reactive Python Notebook That Fixes Everything Wrong With Jupyter
Marimo solves Jupyter's reproducibility problem with DAG-based reactivity - edit any cell and all dependents re-run automatically, and notebooks are stored as plain Python files.
Jupyter notebooks have one fundamental flaw: hidden state. You can run cells out of order, delete a cell but keep its variables in memory, and share a notebook that works on your machine but fails for everyone else because they ran cells in a different order.
The fix requires a different mental model. Marimo approaches notebooks as a directed acyclic graph (DAG) - cells have explicit dependencies, and the execution order is always deterministic.
How Marimo's Reactivity Works
# Cell 1: define a variable
df = pd.read_csv("sales.csv")
# Cell 2: depends on df - runs automatically when Cell 1 changes
summary = df.groupby("region")["revenue"].sum()
# Cell 3: depends on summary - runs automatically when Cell 2 changes
chart = summary.plot(kind="bar")
Edit Cell 1 (load a different CSV) → Cell 2 and Cell 3 re-run automatically. This is the DAG. Marimo tracks dependencies at the variable level, not the cell level.
Team workspace
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import marimo as mo
import polars as pl
# Create an interactive slider
threshold = mo.ui.slider(0, 100, value=50, label="Revenue threshold")
threshold # display it
# This cell re-runs when threshold changes
df = pl.read_csv("data.csv")
filtered = df.filter(pl.col("revenue") > threshold.value)
mo.ui.table(filtered)
Marimo UI elements (mo.ui.*) are reactive - change a slider and all cells that reference its value re-run. This works without IPython widgets or JavaScript.
Notebooks as Pure Python Files
This is a significant advantage for teams. Every Marimo notebook is a valid .py file:
# Development mode (edit and run interactively)
marimo edit my_notebook.py
# App mode (read-only, deployable web app)
marimo run my_notebook.py
# Convert existing Jupyter notebook
marimo convert my_notebook.ipynb > my_notebook.py
marimo run serves the notebook as a web app where users interact with UI elements but cannot edit cells. This is the equivalent of Streamlit for Marimo notebooks.
Marimo vs Jupyter vs Observable
Jupyter
Marimo
Observable
Reactivity
Manual
Automatic DAG
Automatic
File format
JSON
Python
JavaScript
Version control
Difficult
Easy (plain .py)
Easy
Type checking
No
Yes
No
Deploy as app
nbconvert/Voilà
marimo run
Observable
When to Use Marimo
Marimo is best for: reproducible research notebooks, data exploration that becomes a report, internal tools that non-technical users will interact with, and any team that has suffered from Jupyter hidden-state bugs. Keep Jupyter for: existing notebooks in ecosystems that depend on the .ipynb format, or when colleagues are not ready to change their workflow.
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// written byFIG. AUTH-01
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Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
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