# grangier/python-goose

Html Content / Article Extractor, web scrapping lib in Python

Repository: https://github.com/grangier/python-goose
Canonical: https://ross.abutalabs.com/products/python-goose
Language: HTML
License: Apache-2.0
License Family: permissive
Last push: 2026-03-10T10:24:55+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 71, release rhythm 35, longevity 100
- inputs: {"age_days": 5351, "days_push": 176, "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 4106, forks 779 (observed 2026-08-28T04:08:35.660266+00:00)

## What it is
Python-Goose is a Python library that extracts the main body text, metadata, top image, and embedded videos from news article web pages. It is a rewrite of the Java/Scala Goose article extractor with language-aware parsing.

## Use cases
- extract main article text from news pages
- get the top image from an article url
- scrape meta description and meta tags
- extract embedded youtube or vimeo videos from articles
- parse articles in multiple languages
- build a readability-style clean reader view

## When to choose
- you need to extract clean article content and metadata from news-style pages in Python
- you want automatic main-image detection from articles

## When to avoid
- you need general-purpose web crawling or scraping of non-article pages
- you need a actively developed modern extractor with ongoing updates

## Facets
- artifact type: library
- maturity: maintenance
- function: web-scraping, parser, nlp
- domain: web-development, crawlers
- platform: python, cross-platform
- tags: article-extraction, html-parsing, content-extraction, readability, natural-language-processing

## Member repositories
- grangier/python-goose (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:35.660266+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 2026-08-29T18:23:13.652931+00:00, confidence not recorded.
  - readme: https://github.com/grangier/python-goose (fetched 2026-08-28T04:08:35.660266+00:00, sha 64a6d29809b7)
- Data as of 2026-08-30T08:39:29.467469+00:00.