fast-scrape

PyPI Python License

8x faster HTML parsing than BeautifulSoup4. Rust-powered with 3000x faster CSS selector queries.

Installation

pip install fast-scrape
Alternative package managers 
# uv (recommended - 10-100x faster)
uv pip install fast-scrape

# Poetry
poetry add fast-scrape

# Pipenv
pipenv install fast-scrape

[!IMPORTANT] Requires Python 3.10 or later.

Quick start

from scrape_rs import Soup

soup = Soup("<html><body><div class='content'>Hello, World!</div></body></html>")

div = soup.find("div")
print(div.text)  # Hello, World!

Usage

Find elements 
from scrape_rs import Soup

soup = Soup(html)

# Find first element by tag
div = soup.find("div")

# Find all elements
divs = soup.find_all("div")

# CSS selectors
for el in soup.select("div.content > p"):
    print(el.text)
Element properties 
element = soup.find("a")

text = element.text  # Get text content
html = element.inner_html  # Get inner HTML
href = element.get("href")  # Get attribute
Batch processing 
from scrape_rs import parse_batch

# Process multiple documents in parallel
documents = [html1, html2, html3]
soups = parse_batch(documents)

for soup in soups:
    print(soup.find("title").text)

[!TIP] Use parse_batch() for processing multiple documents. Uses all CPU cores automatically.

Type hints

Full IDE support with type stubs:

from scrape_rs import Soup, Tag


def extract_links(soup: Soup) -> list[str]:
    return [a.get("href") for a in soup.select("a[href]")]

Performance

Massive performance improvements across all operations:

Parse speed comparison
File size fast-scrape BeautifulSoup4 lxml Speedup
1 KB 11 µs 0.23 ms 0.31 ms 20-28x faster
100 KB 2.96 ms 31.4 ms 28.2 ms 9.5-10.6x faster
1 MB 15.5 ms 1247 ms 1032 ms 66-80x faster

Throughput: 64 MB/s on 1MB files — handles large documents efficiently.

Query performance
Operation fast-scrape BeautifulSoup4 Speedup
find("div") 208 ns 16 µs 77x
find(".class") 20 ns 797 µs 40,000x
find("#id") 20 ns 799 µs 40,000x
select("div > p") 24.7 µs 4.361 ms 176x

CSS selectors dominate: Class and ID selectors run in nanoseconds vs microseconds.

Memory efficiency (100MB HTML)
Library Memory Efficiency
fast-scrape 145 MB 1x baseline
lxml 2,100 MB 14.5x larger
BeautifulSoup4 3,200 MB 22x larger

Result: 14-22x more memory-efficient than Python competitors.

Architecture optimizations:

  • SIMD-accelerated class matching — 2-10x faster selector execution
  • Zero-copy serialization — 50-70% memory reduction in HTML output
  • Batch processing — Parallel parsing uses all CPU cores automatically

Parsing & Selection (Servo browser engine):

Streaming Parser (Cloudflare):

  • lol_html — High-performance streaming HTML parser with constant-memory event-driven API

Related packages

Platform Package
Rust scrape-core
Node.js @fast-scrape/node
WASM @fast-scrape/wasm

License

MIT OR Apache-2.0

Project links

Data verified by PyPI on Aug 12, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
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GitHub Statistics

Data verified by PyPI on Aug 12, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License, MIT License (MIT OR Apache-2.0)

Requires

Python >=3.10

Tags

html parser scraping css-selectors dom

Classifiers

Report project as malware

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This release

0.2.11 This release

30 files

0.2.10

30 files

0.2.9

30 files

0.2.8

30 files

0.2.7

29 files

0.2.6

28 files

0.2.5

28 files

0.2.4

28 files

0.2.3

28 files

0.2.2

28 files

0.2.1

28 files

0.2.0

28 files

0.1.6

28 files

0.1.5

28 files

0.1.4

28 files

0.1.3

28 files

0.1.2

28 files

0.1.1

28 files

0.1.0

2 files

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