# lorey/mlscraper

🤖 Scrape data from HTML websites automatically by just providing examples

Repository: https://github.com/lorey/mlscraper
Canonical: https://ross.abutalabs.com/products/mlscraper
Homepage: https://pypi.org/project/mlscraper/
Language: Python
License Family: other
Topics: scraping, crawling, html, machine-learning, extraction-engine, scraper, crawler, crawler-python
Last push: 2024-03-17T08:12:02+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2225, "days_push": 899, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1385, forks 92 (observed 2026-08-28T04:04:34.852129+00:00)

## What it is
mlscraper is a Python library that automatically extracts structured data from HTML pages using machine learning. Instead of writing CSS selectors or XPath rules manually, you provide a few example outputs and it learns the extraction rules to apply to new pages.

## Use cases
- scrape structured data from html pages without writing css selectors
- extract data from websites by providing example values
- train a scraper from sample data and apply it to new pages
- automatically find extraction rules for html dom nodes
- convert html pages into clean dictionaries of data
- build a web scraper without manual selector engineering

## When to choose
- you want to scrape data from similar HTML pages but don't want to hand-write selectors
- you have a few example outputs and want the rules figured out automatically
- you prefer an open-source Python alternative to point-and-click scraping services
- you need to extract data repeatedly from pages with consistent structure

## When to avoid
- you need to scrape JavaScript-rendered single-page applications rather than static HTML
- you need a no-code browser-based scraping tool with a visual interface
- you have no example data available to train the extractor
- you need a battle-tested 1.0 release, since the stable PyPI version is still 0.x

## Facets
- artifact type: library
- maturity: active
- function: web-scraping, machine-learning, parser, data-science
- domain: crawlers, web-development, machine-learning, data-science
- platform: python, cross-platform
- tags: html-extraction, automatic-scraping, example-based-training, structured-data-extraction

## Member repositories
- lorey/mlscraper (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:34.852129+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 2026-08-30T04:39:57.362451+00:00, confidence not recorded.
  - readme: https://github.com/lorey/mlscraper (fetched 2026-08-28T04:04:34.852129+00:00, sha 3e29b486a147)
  - homepage: https://pypi.org/project/mlscraper/ (fetched 2026-08-29T11:55:34.900285+00:00, sha 4b4e8fead74a)
  - registry_pypi: https://pypi.org/pypi/mlscraper/json (fetched 2026-08-29T11:55:34.909537+00:00, sha 49ceb33e03f1)
- Data as of 2026-08-30T08:39:29.467469+00:00.