chompjsvsscrapling
chompjs can be used in web scrapping for turning JavaScript objects embedded in pages into valid Python dictionaries.
In web scraping this is particularly useful for parsing Javascript variables like:
python
import chompjs
js = """
var myObj = {
myMethod: function(params) {
// ...
},
myValue: 100
}
"""
chompjs.parse_js_object(js, json_params={'strict': False})
{'myMethod': 'function(params) {\n // ...\n }', 'myValue': 100}
In practice this can be used to extract hidden JSON data like data from <script id=__NEXT_DATA__> elements
from nextjs (and similar) websites. Unlike json.loads command chompjs can ingest json documents that contain
javascript natives like functions making it a super easy way to scrape hidden web data objects.
Scrapling is an adaptive web scraping framework for Python that introduces "self-healing" selectors — selectors that can track and find elements even when the website's DOM structure changes. This solves one of the biggest maintenance headaches in web scraping: broken selectors after website updates.
Key features include:
- Self-healing selectors Scrapling uses smart element matching that can identify target elements even after the page structure changes. It builds a fingerprint of the element based on multiple attributes (text, position, siblings, attributes) and uses fuzzy matching to relocate it.
- Multiple parsing backends Supports different parsing engines including lxml (fast) and a custom engine, allowing you to choose the right balance of speed and features.
- Scrapy-like Spider API Provides a familiar Spider class pattern for organizing crawling logic, similar to Scrapy but with the added benefit of adaptive selectors.
- CSS and XPath selectors Full support for CSS selectors and XPath, plus the adaptive matching system on top.
- Type hints and modern Python Built with full type annotations and 92% test coverage for reliability.
- Async support Supports asynchronous crawling for efficient concurrent scraping.
Scrapling gained massive traction in 2025 as one of the most starred new Python scraping libraries. It is particularly useful for scraping targets that frequently update their HTML structure, where traditional selector-based scrapers would break.