High-performance HTML parsing library with BeautifulSoup4-compatible API
Project description
CutySoup
High-performance HTML parsing for Python with a BeautifulSoup4-compatible API (Gumbo + C++ via pybind11).
Why CutySoup
If you like the BeautifulSoup4 API but need more throughput, CutySoup moves the heavy work (parsing and common queries) into a native extension while keeping the same mental model: find, find_all, select, get_text, navigation, and DOM manipulation.
Status: 100% BeautifulSoup4 API compatibility with 148/148 tests passing in this repo.
Install
pip install cutysoup
If you're installing from source (or no wheel exists for your platform), you'll need a C++ toolchain and CMake.
Platforms: macOS, Linux, and Windows (binary wheels when available; otherwise builds from source).
Quickstart
from cutysoup import CutySoup
soup = CutySoup("<html><body><p class='intro'>Hello</p></body></html>")
print(soup.p.text) # Hello
print(soup.find("p", class_="intro"))
print(soup.select_one("p.intro"))
Performance
Benchmarks vary by hardware, Python version, and document shape:
cutysoup==0.1.0(this repo build)beautifulsoup4==4.13.4- Python
3.8.20
Command:
uv run python benchmarks/portable_benchmark.py --sizes l
For the l fixture (about 639 KB HTML, 2000 repeated <article> blocks), the mean speedups vs BeautifulSoup4 (html.parser) were:
| Operation | Speedup |
|---|---|
parse |
7.5x |
parse_and_scrape_articles(loop) |
3.8x |
find_all_tag(p) |
178x |
find_all_class(.text) |
1762x |
select_descendant(article .link) |
7.3x |
href_batch(all a href) |
18.8x |
To reproduce the full table (including s and m sizes) and write a JSON report:
uv run python benchmarks/portable_benchmark.py --sizes s,m,l --json-out /tmp/cutysoup_portable_benchmark.json
Extra APIs (Optional)
CutySoup includes some convenience helpers that are not part of BeautifulSoup4 but are useful for performance-sensitive scraping:
from cutysoup import CutySoup
soup = CutySoup('<a href="/one">One</a><a href="/two">Two</a>')
print(soup.get_all_hrefs()) # ['/one', '/two']
There is also a structured extraction helper (soup.query(...)) for extracting data using CSS selectors and transforms. See docs/CSQL_GUIDE.md.
Development
Recommended (creates an env and builds the extension):
uv sync
Run tests:
uv run pytest tests/
Local build script (alternative):
python dev_build.py
License
MIT
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