pyscrappy 1.1.0

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PyScrappy: robust, all-in-one Python web scraping toolkit

PyScrappy is a Python toolkit for web scraping that works out of the box. Point it at any URL and get structured data back — or use built-in scrapers for Wikipedia, IMDB, Yahoo Finance, news feeds, and more.

Key features

• Generic scraper — give it any URL, get back structured text, links, images, tables, and metadata
• Auto-pagination — automatically follows "next page" links
• JS rendering — optional Playwright backend for JavaScript-heavy sites
• Custom selectors — pass CSS selectors to extract exactly what you need
• Built-in scrapers — Wikipedia, IMDB, Yahoo Finance, news (RSS), image search, Amazon, LinkedIn
• Clean API — every scraper returns a ScrapeResult with .to_dataframe() and .to_json()
• Retry & rate-limiting — built-in exponential backoff and per-domain rate limiting
• Type-safe — full type hints, py.typed marker

Installation

pip install pyscrappy

Optional extras:

# Browser support (for JS-rendered pages)
pip install 'pyscrappy[browser]'
playwright install chromium

# DataFrame support
pip install 'pyscrappy[dataframe]'

# MCP server (use PyScrappy's scrapers as AI-agent tools)
pip install 'pyscrappy[mcp]'

# Everything
pip install 'pyscrappy[all]'

Quick start

Scrape any URL (one-liner)

from pyscrappy import scrape

result = scrape("https://en.wikipedia.org/wiki/Web_scraping")
print(result.data[0]["metadata"]["title"])
print(result.data[0]["text"]["word_count"])

Custom CSS selectors

from pyscrappy import GenericScraper

with GenericScraper() as gs:
    result = gs.scrape(
        url="https://news.ycombinator.com",
        selectors={"title": ".titleline a", "score": ".score"},
    )
    for item in result.data:
        print(item["title"], item.get("score", ""))

Wikipedia

from pyscrappy import WikipediaScraper

with WikipediaScraper() as ws:
    result = ws.scrape(query="Python (programming language)", mode="summary")
    print(result.data[0]["text"])

Stock data

from pyscrappy import StockScraper

with StockScraper() as ss:
    result = ss.scrape(symbol="AAPL", mode="history", period="1mo")
    df = result.to_dataframe()
    print(df.head())

IMDB

from pyscrappy import IMDBScraper

with IMDBScraper() as scraper:
    result = scraper.scrape(genre="sci-fi", max_pages=2)
    df = result.to_dataframe()
    print(df[["title", "year", "rating"]])

News (RSS feeds)

from pyscrappy import NewsScraper

with NewsScraper() as ns:
    result = ns.scrape(feed_url="https://rss.nytimes.com/services/xml/rss/nyt/World.xml")
    for article in result.data[:5]:
        print(article["title"])

Image search

from pyscrappy import ImageSearchScraper

with ImageSearchScraper() as iss:
    result = iss.scrape(query="golden retriever", max_images=10, download_to="./dogs")

Configuration

from pyscrappy import ScraperConfig, GenericScraper

config = ScraperConfig(
    timeout=20.0,            # request timeout in seconds
    max_retries=3,           # retry failed requests
    rate_limit=2.0,          # seconds between requests per domain
    proxy="http://...",      # HTTP/SOCKS proxy
    headless=True,           # browser runs headless
    render_js="auto",        # auto-detect if JS rendering is needed
)

with GenericScraper(config) as gs:
    result = gs.scrape(url="https://example.com")

YouTube

from pyscrappy import YouTubeScraper

with YouTubeScraper() as scraper:
    result = scraper.scrape(query="python tutorial", max_results=10)
    for video in result.data:
        print(video["title"], video.get("views", ""))

SoundCloud

from pyscrappy import SoundCloudScraper

with SoundCloudScraper() as scraper:
    result = scraper.scrape(query="lo-fi beats", max_results=10)

E-Commerce (Alibaba, Flipkart, Snapdeal)

from pyscrappy import AlibabaScraper, FlipkartScraper, SnapdealScraper

with FlipkartScraper() as scraper:
    result = scraper.scrape(query="laptop", max_pages=2)
    df = result.to_dataframe()

Food Delivery (Swiggy, Zomato)

from pyscrappy import SwiggyScraper, ZomatoScraper

# These are JS-heavy — use render_js=True for best results
with SwiggyScraper() as scraper:
    result = scraper.scrape(city="bangalore", render_js=True)

Built-in scrapers

MCP server (use PyScrappy from an AI agent)

PyScrappy ships an optional Model Context Protocol server, so an AI agent (e.g. Claude) can call PyScrappy's scrapers as tools and get structured web data back.

pip install 'pyscrappy[mcp]'

This installs a pyscrappy-mcp command (a stdio MCP server). You can also run it with python -m pyscrappy.mcp.

Register with Claude Code

claude mcp add pyscrappy pyscrappy-mcp

Register with Claude Desktop

Add to your claude_desktop_config.json and restart the app:

{
  "mcpServers": {
    "pyscrappy": {
      "command": "pyscrappy-mcp"
    }
  }
}

Tip: Claude Desktop does not inherit your shell PATH. If pyscrappy-mcp is not found, use the absolute path to the command (e.g. the one printed by which pyscrappy-mcp).

Available tools

Once registered, just ask the agent naturally — e.g. "use pyscrappy to get the latest headlines from bbc.co.uk and the AAPL stock quote."

Dependencies

Required: httpx, beautifulsoup4, lxml

Optional: playwright (JS rendering), pandas (DataFrames), mcp (MCP server)

License

MIT

Contributing

All contributions welcome. See Issues.

This package is for educational and research purposes.