Eric J. Ma dives into agents and notebooks with Use coding agents to write Marimo notebooks.
In this blog post, I share how combining AI coding assistants with Marimo notebooks can supercharge your Python development and data science workflows. I walk through handy features like the —watch flag for live updates, the marimo check command for code quality, and even advanced options like MCP and built-in AI editing. Curious how you can automate and speed up your notebook workflow while keeping your code clean?
If you’re like me, you might find coding with AI assistants somewhat addictive. And if you’re like me, you might also like to write code in Marimo notebooks, the modern alternative to Jupyter that offers better reproducibility and cleaner Python development.
Turns out there’s a way to put these two together for automated Python development and data science workflows, creating a powerful combination for rapid prototyping and iterative coding.
Previously, I noted how JupyterAI has an interesting notebook editing model. I still have respect for what’s going on there. But Marimo seems to be pushing really hard in this space. There’s something to be said for startup energy.
AI-assisted coding
marimo is an AI-native editor, with support for full-cell AI code generation:
- generating new cells from a prompt
- refactoring existing cells from a prompt
- generating entire notebooks
as well as inline autocompletion (like GitHub Copilot).
marimo’s AI assistant is specialized for working with data: unlike traditional assistants that only have access to the text of your program, marimo’s assistant has access to the values of variables in memory, letting it code against your dataframe and database schemas.
This guide provides an overview of these features and how to configure them.
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