Installation

ipyflow ships as a single PyPI distribution that pulls in the ipyflow-core backend, the JupyterLab/Notebook 7 extension, and a Jupyter kernelspec:

pip install ipyflow

This registers a kernel named Python 3 (ipyflow). In JupyterLab or Notebook 7 pick it from the Launcher, or switch an open notebook to it via Kernel → Change Kernel. Because ipyflow is a strict superset of ipykernel, existing notebooks run unchanged – you simply gain the dataflow features on top.

Requirements

ipyflow supports CPython 3.7+ (declared support goes back to 3.6; CI exercises 3.7 through 3.14). The frontend features target JupyterLab 3/4 and Notebook 7.

Installing the kernelspec manually

pip install ipyflow installs the kernelspec for you. If you need to (re)install it into a specific environment – for example inside a container, or after moving a virtualenv – invoke the installer module directly:

python -m ipyflow.install --sys-prefix

--sys-prefix installs into the active environment’s prefix (the usual choice inside a virtualenv or conda env); omit it to install into the user location, or pass --prefix to target an arbitrary directory. This is the same entry point the packaging invokes, exposed as ipyflow.install.

Using ipyflow outside JupyterLab

The reactive UI (dependency dots, reactive re-execution, autosave) requires the JupyterLab/Notebook 7 extension. On surfaces where that extension is not yet available – Colab, VS Code, a plain terminal IPython, or a raw Jupyter Console – you can still load ipyflow’s tracer and use its full dataflow API by loading it as an IPython extension:

%pip install ipyflow
%load_ext ipyflow

%load_ext ipyflow swaps in ipyflow’s kernel/shell machinery for the current session (see ipyflow.load_ipython_extension()), after which deps, users, code, %flow, %%memoize, and the rest behave as documented here. Reactive execution and the visual highlights, however, remain a frontend-driven feature.

Trying it without installing

A zero-install build runs entirely in the browser via JupyterLite (Pyodide):

https://ipyflow.github.io/ipyflow/lab/index.html?path=demo.ipynb

It is the quickest way to get a feel for reactive execution before adopting ipyflow locally.

Next: Your first reactive notebook walks through building a small reactive notebook and inspecting the dataflow graph it produces.