# jsvine/markovify

A simple, extensible Markov chain generator.

Repository: https://github.com/jsvine/markovify
Canonical: https://ross.abutalabs.com/products/markovify
Language: Python
License: MIT
License Family: permissive
Last push: 2024-04-30T19:12:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4261, "days_push": 855, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3403, forks 347 (observed 2026-08-28T04:08:02.965815+00:00)

## What it is
Markovify is a simple, extensible Python library for building Markov chain models from text corpora and generating random semi-plausible sentences from them. It is pure Python, supports JSON-serializable models, and offers highly customizable text parsing and sentence generation.

## Use cases
- generate random sentences from a text corpus
- build a Markov chain text model in Python
- create a Twitter bot that tweets Markov-generated text
- generate fake headlines or song lyrics from existing text
- cache and reuse trained Markov models as JSON
- customize sentence generation rules for a specific corpus

## When to choose
- you want a lightweight, pure-Python Markov chain text generator with minimal dependencies
- you need simple, extensible sentence generation from a large, well-punctuated corpus
- you want to serialize trained models to JSON for later reuse

## When to avoid
- you need modern neural language models with coherent, context-aware output
- your corpus is small or poorly punctuated and you cannot preprocess it
- you need high-performance generation over massive datasets

## Facets
- artifact type: library
- maturity: stable
- function: nlp, data-generation, machine-learning
- domain: -
- platform: python, cross-platform
- tags: markov-chain, text-generation, sentence-generation, text-modeling, natural-language-processing, algorithms

## Member repositories
- jsvine/markovify (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:02.965815+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 2026-08-29T18:38:38.078011+00:00, confidence not recorded.
  - readme: https://github.com/jsvine/markovify (fetched 2026-08-28T04:08:02.965815+00:00, sha 06192e946b73)
  - registry_pypi: https://pypi.org/pypi/markovify/json (fetched 2026-08-29T09:32:49.627238+00:00, sha 280eafd04792)
- Data as of 2026-08-30T08:39:29.467469+00:00.