GitHub repo: https://github.com/marimo-team/marimo
In marimo, a notebook’s code, outputs, and program state are always consistent. Run a cell and marimo reacts by automatically running the cells that reference its declared variables. Delete a cell and marimo scrubs its variables from program memory, eliminating hidden state. Our reactive runtime is based on static analysis, so it’s performant. If you’re worried about accidentally triggering expensive computations, you can disable specific cells from auto-running.
marimo comes with UI elements like sliders, a dataframe transformer, and interactive plots that are automatically synchronized with Python [3]. Interact with an element and the cells that use it are automatically re-run with its latest value. Reactivity makes these UI elements more useful and ergonomic than Jupyter’s ipywidgets.
Every marimo notebook can be run as a script from the command line, with cells executed in a topologically sorted order, or served as an interactive web app, using the marimo CLI.
We’re a team of just two developers. We chose to develop marimo because we believe that the Python community deserves a better programming environment to do research and communicate it; experiment with code and share it; and learn computational science and teach it. We’ve seen lots of research start in Jupyter notebooks (much of my own has), only to fail to reproduce; lots of promising prototypes built that were never made real; and lots of tutorials written that failed to engage students.
marimo has been developed with the close input of scientists and engineers, and with inspiration from many tools, including Pluto.jl and streamlit. We open-sourced it recently because we feel it’s ready for broader use. Please try it out (pip install marimo && marimo tutorial intro). We’d appreciate your feedback!
[1] https://github.com/marimo-team/marimo