Very cool!
We’ve been exploring a similar direction too, but with a plain REPL and a much thinner tool surface. In our case, it’s basically one tool for sending input, with interrupts and restarts handled through that same path. Marimo seems to expose much richer notebook structure and notebook-manipulation semantics, which is a pretty different point in the design space.
It seems like the tradeoff is between keeping the interaction model simple and the context small, versus introducing notebook structure earlier so the model works toward an artifact at the same time it iterates and explores. Curious how you think about that balance.
Thanks for sharing!
I think the tradeoff is less stark than it seems. Our tool surface is also basically just "run Python", but the model may additionally use a semi-private API ("code mode") within that execution context to inspect and modify the notebook itself. So the notebook structure isn't something the model has to manage. Marimo's reactivity handles the graph automatically, and the model gets artifact construction as a side effect of exploration.
Where I'd imagine the approaches diverge more is in what the runtime gives back. In a plain REPL, the model carries state in its context. In marimo, it can offload state to the notebook and query it back: inspect variables, read cell outputs, check the dependency graph. That turns the notebook into working memory the model can use without it all living in the conversation. Over longer sessions especially, that matters.
interesting, i also went the same direction with https://github.com/danieltanfh95/replsh because i needed to support existing legacy python codebases.