# ipyflow/ipyflow

A reactive Python kernel for Jupyter notebooks.

Repository: https://github.com/ipyflow/ipyflow
Canonical: https://ross.abutalabs.com/products/ipyflow
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: jupyter, tracing, static-analysis, static-code-analysis, lineage, developer-tools, pypi, python, nbsafety, jupyter-notebooks, notebooks, highlighting, dataflow, ipyflow, jupyterlab, reactivity
Last push: 2026-08-20T23:33:32+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 56, longevity 100
- inputs: {"age_days": 2423, "days_push": 13, "days_rel": 83, "gap_med": 200, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1275, forks 25 (observed 2026-08-28T04:04:12.978585+00:00)

## What it is
IPyflow is a reactive Python kernel for JupyterLab and Notebook 7 that tracks dataflow relationships between symbols and cells during interactive sessions. It works as a drop-in replacement for ipykernel, automatically re-executing the minimal set of out-of-sync upstream and downstream cells so notebook state matches a clean top-to-bottom run.

## Use cases
- keep jupyter notebook cells in sync automatically
- re-execute dependent cells when a variable changes in a notebook
- track dataflow dependencies between jupyter notebook cells
- avoid stale outputs and hidden state bugs in jupyter
- highlight outdated cells after editing in jupyter
- reactive programming in python notebooks
- drop-in replacement kernel for ipykernel with dependency awareness

## When to choose
- You iterate interactively in Jupyter and want out-of-sync cells to re-run automatically with minimal re-execution
- You need precise dependency inference down to subscript and attribute level, not just whole-variable tracking
- You want full ipykernel backwards compatibility plus dataflow lineage and safety highlighting

## When to avoid
- Your cells perform heavy or irreversible side effects (long training runs, deployments) that must never re-execute automatically
- You use non-Python Jupyter kernels
- You want a zero-overhead kernel without tracing and static analysis overhead

## Facets
- artifact type: library
- maturity: active
- function: tracing, developer-tools
- domain: developer-tools, data-science
- platform: python, cross-platform
- tags: jupyter, jupyter-kernel, jupyterlab, notebooks, ipykernel, reactive-programming, dataflow-analysis, static-analysis, lineage, reactivity, cell-dependencies

## Member repositories
- ipyflow/ipyflow (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:12.978585+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:03:01.802304+00:00, confidence not recorded.
  - readme: https://github.com/ipyflow/ipyflow (fetched 2026-08-28T04:04:12.978585+00:00, sha da1ddbf35d65)
  - registry_pypi: https://pypi.org/pypi/ipyflow/json (fetched 2026-08-29T12:14:10.951747+00:00, sha bf3b48129304)
- Data as of 2026-08-30T08:39:29.467469+00:00.