import marimo

__generated_with = "0.1.78"
app = marimo.App()

@app.cell
def __():
    # Reactive variable - changes automatically propagate
    import marimo as mo
    return mo,

@app.cell
def __(mo):
    # Slider controls a value
    slider = mo.ui.slider(start=1, stop=10, value=5, label="Multiplier")
    slider
    return slider,

@app.cell
def __(mo, slider):
    # This cell reacts to slider changes
    value = slider.value
    mo.md(f"## Current value: {value}")
    return value,

@app.cell
def __(value):
    # This cell also reacts automatically
    squared = value ** 2
    print(f"{value} squared is {squared}")
    return squared,

@app.cell
def __(mo, squared):
    # Visual output updates reactively
    mo.md(f"**Result:** {squared}")
    return

if __name__ == "__main__":
    app.run()

For years, data scientists and Python developers have been trapped in a paradox: the very notebooks designed to facilitate exploration—Jupyter and its predecessors—have become notorious for creating irreproducible, tangled messes. Hidden state, out-of-order execution, and files bloated with binary outputs have made collaboration and version control a nightmare. New data from GitHub reveals a massive shift: over 18,000 developers have starred marimo, a reactive notebook that rethinks the paradigm from the ground up. This isn't just another editor; it's a systematic solution to the core problems that have plagued computational notebooks for a decade.

What Is Marimo and Why the Surge?

Marimo is an open-source, reactive notebook for Python. At first glance, it resembles familiar tools, but its architecture represents a fundamental departure. The key innovation is reactivity: cells automatically update when their dependencies change, eliminating hidden state and guaranteeing that the notebook's output always reflects its code in order. This alone solves the single biggest source of non-reproducibility. Furthermore, marimo notebooks are stored as pure Python files (`.py`), making them instantly compatible with `git`, code formatters, linters, and any standard Python toolchain.

The project's traction is undeniable. With over 18,000 stars and trending consistently, its growth signals a collective frustration with the status quo and a readiness for a tool that prioritizes correctness and collaboration from the start. The description—"run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git"—isn't just marketing; it's a direct address of the multifaceted workflow pains experienced in research, data analysis, and ML engineering.

Beyond Reactivity: An AI-Native, Multi-Modal Workbench

Marimo's reactivity is its engine, but its modern feature set is what makes it a comprehensive workbench. It moves beyond being a mere notebook to become a unified interface for diverse tasks.

The Pure Python Advantage

Storing notebooks as `.py` files is a game-changer for professional workflows. It means:

• Flawless Git Integration: Diffs are clean and meaningful, showing code changes, not JSON blobs or binary output.
• Script Execution: Any marimo notebook can be run headless with `python notebook.py`, making it perfect for production pipelines.
• Tooling Compatibility: Black, isort, ruff, and IDEs work out of the box.

Built-In SQL and Data App Deployment

Marimo bakes in capabilities that typically require cumbersome extensions. An integrated SQL cell allows querying databases directly, with results flowing seamlessly into Python variables for further analysis. More impressively, any notebook can be instantly deployed as a interactive web app using `marimo run`. This bridges the gap between exploration and sharing, allowing teams to create internal tools or dashboards without a separate framework.

AI-Native Editing

The editor is designed for the modern LLM-assisted workflow. Its clean, fast interface and understanding of the reactive graph make it an ideal partner for AI code generation, ensuring suggestions maintain consistency across the notebook's state.

The Technical Shift: From Stateful Document to Reactive Program

Traditional notebooks are essentially stateful documents where cells are independent commands sent to a kernel. Marimo redefines a notebook as a single, reactive Python program. The UI cells are different views into this program. When you define a variable in one cell, marimo's runtime tracks its dependencies. If you change an upstream cell, every dependent cell is automatically and efficiently re-executed.

This model has profound implications:

• No More "Restart & Run All" Roulette: The notebook is always in a correct, consistent state.
• Implicit Documentation: The reactivity graph visually documents data flow.
• Performance: Only the necessary subgraph of computations is rerun.

Context: The Growing Cost of Non-Reproducibility

The excitement around marimo isn't happening in a vacuum. Studies and surveys have repeatedly highlighted a "reproducibility crisis" in data science. A 2020 analysis suggested that a staggering percentage of published research using Jupyter notebooks contained errors due to out-of-order execution. In industry, the cost is measured in wasted engineering hours debugging "it worked on my machine" issues and the inability to reliably audit or build upon past work. Marimo directly attacks these pain points by making the reproducible, linear script the primary artifact, while preserving the interactive, exploratory feel of a notebook.

Implications and What's Next

The rapid adoption of marimo signals a maturation in the data tooling ecosystem. Developers are prioritizing robustness and integration over isolated features. For teams, it promises reduced onboarding friction and higher-quality, maintainable analytical code. For individuals, it offers a smoother path from idea to shareable app.

The project's roadmap will likely focus on enhancing its collaborative features, expanding its visualization library, and deepening integrations with cloud platforms and LLMs. The core premise—that exploration and production readiness shouldn't require different tools—is powerfully compelling.

Conclusion: A Pragmatic Step Forward

Marimo is not claiming to be an AI that writes perfect code or a magical solution to all complexity. Instead, it offers a pragmatic, well-engineered response to very real, quantified problems in data work. The data from GitHub—18,000+ stars and climbing—shows that the community is voting with its attention for tools that enforce good practices by design. For any Python developer tired of reconciling notebook chaos, marimo represents a credible and powerful opportunity to reset their workflow on a foundation of reproducibility, clarity, and pure Python.

The Takeaway: If your work involves Python for analysis, ML, or research, marimo is no longer a niche experiment. Its traction demonstrates it's a viable, production-ready tool that fundamentally fixes the broken parts of the notebook paradigm. The barrier to trying it is low—it's a `pip install` away—and the potential payoff in saved time and reduced frustration is high.