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Molt documentation

A lightweight macOS scratchpad for fast, stateful Python computation.
Get startedUse Molt

Molt gives macOS users four always-ready notebook tabs for calculations, data checks, small experiments, and other short Python tasks. Each tab has its own Python process, so code is stateful within a tab and isolated from the other three.

What Molt is good for

Section titled “What Molt is good for”

Use Molt when opening a full notebook environment would add more friction than value:

  • Evaluate an expression and keep the result nearby.
  • Build a small calculation across several cells.
  • Inspect Python objects with normal repr output.
  • Keep four independent scratch contexts available at once.
  • Run code with packages already installed in a chosen Python interpreter or virtual environment.

Molt stores cell source between launches. It deliberately does not restore outputs, execution counts, or live Python variables, so every app launch begins with fresh kernels around your saved code.

Find what you need

Section titled “Find what you need”
  • Getting started covers installation, Gatekeeper, Python setup, and a first successful calculation.
  • Guides show how to work with cells, kernels, environments, saved work, and settings.
  • Concepts explain tab isolation, stateful execution, persistence boundaries, and architecture.
  • Reference records controls, shortcuts, configuration, schemas, APIs, requirements, and current limits.
  • Contributing covers development, testing, architecture, and release preparation.
Section titled “Project links”