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Ferret is a web scraping system. It aims to simplify data extraction from the web for UI testing, machine learning, analytics and more. ferret allows users to focus on the data. It abstracts away the technical details and complexity of underlying technologies using its own declarative language. It is extremely portable, extensible, and fast.


  • Declarative language
  • Support of both static and dynamic web pages
  • Embeddable
  • Extensible

Ferret is always implemented in Python through pyfer

Autoscraper project is made for automatic web scraping to make scraping easy. It gets a url or the html content of a web page and a list of sample data which we want to scrape from that page. This data can be text, url or any html tag value of that page. It learns the scraping rules and returns the similar elements. Then you can use this learned object with new urls to get similar content or the exact same element of those new pages.

Autoscraper is minimalistic and auto-generative approach to web scraping. For example, here's a scraper that finds all titles on a page:

from autoscraper import AutoScraper

url = ''

# We can add one or multiple candidates here.
# You can also put urls here to retrieve urls.
wanted_list = ["What are metaclasses in Python?"]

scraper = AutoScraper()
result =, wanted_list)



Example Use

// Example scraper for Google in Ferret:
LET google = DOCUMENT("", {
    driver: "cdp",
    userAgent: "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.87 Safari/537.36"

HOVER(google, 'input[name="q"]')
INPUT(google, 'input[name="q"]', @criteria, 30)
CLICK(google, 'input[name="btnK"]')

WAITFOR EVENT "navigation" IN google

WAIT_ELEMENT(google, "#res")

LET results = ELEMENTS(google, X("//*[text() = 'Search Results']/following-sibling::*/*"))

FOR el IN results
    RETURN {
        title: INNER_TEXT(el, 'h3')?,
        description: INNER_TEXT(el, X("//em/parent::*")),
        url: ELEMENT(el, 'a')?.attributes.href

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