[Bug]: target_element does influence link extraction

[Joorrit]

crawl4ai version

0.5.0.post8

Expected Behavior

As stated in the docs:

With target_elements, the markdown generation and structural data extraction focus on those elements, but other page elements (like links, images, and tables) are still extracted from the entire page.

So i expect to get the same amount of links, when using target_elements and when not using target_elements.

Current Behavior

Without the target_elements i am getting 727 links returned. With the config target_elements=["#main"] i am getting 410 links returned.

Interestly some of the links that are missing are included in the main div.

Is this reproducible?

Yes

Inputs Causing the Bug

Steps to Reproduce

1. Excecute the code snippet
2. Comment out the line target_elements=["#main"]
3. Excecute the code snippet again

Code snippets

import asyncio
from crawl4ai import *

async def main():
    config = CrawlerRunConfig(
            target_elements=["#main"], # Comment this line out
        )

    async with AsyncWebCrawler() as crawler:
        source_url = "https://www.schorndorf.de/de/stadt-buerger/rathaus/buergerservice/dienstleistungen"
        result = await crawler.arun(
            url=source_url,
            config=config
        )
        links = result.links.get("internal", [])
        print(len(links))


if __name__ == "__main__":
    asyncio.run(main())

OS

Windows

Python version

3.12.0

Browser

No response

Browser version

No response

Error logs & Screenshots (if applicable)

python .\minimal_repo.py [INIT].... → Crawl4AI 0.5.0.post8 [FETCH]... ↓ https://www.schorndorf.de/de/stadt-buerger/rathaus... | Status: True | Time: 1.68s [SCRAPE].. ◆ https://www.schorndorf.de/de/stadt-buerger/rathaus... | Time: 0.332s [COMPLETE] ● https://www.schorndorf.de/de/stadt-buerger/rathaus... | Status: True | Total: 2.02s 727

python .\minimal_repo.py [INIT].... → Crawl4AI 0.5.0.post8 [FETCH]... ↓ https://www.schorndorf.de/de/stadt-buerger/rathaus... | Status: True | Time: 1.58s [SCRAPE].. ◆ https://www.schorndorf.de/de/stadt-buerger/rathaus... | Time: 0.183s [COMPLETE] ● https://www.schorndorf.de/de/stadt-buerger/rathaus... | Status: True | Total: 1.77s 410

[aravindkarnam]

Root Cause Analysis: Link Count Discrepancy with Target Elements

Issue

When using target_elements, we observed significantly fewer extracted links (403) compared to without it (716), indicating unintended interaction between content targeting and link extraction. This affected both our BeautifulSoup and lxml implementations.

Root Cause

The issue was caused by shared references to DOM nodes. When elements were selected for content_element using body.select(), they remained the same objects in memory as those in the original document. Later, when certain elements were removed with element.decompose() during processing, these nodes were removed from both locations simultaneously, resulting in fewer links being found when using target elements.

Solution

We implemented similar solutions for both implementations:

1. BeautifulSoup: Reparsed the HTML for each target selector using BeautifulSoup(html, "html.parser")
2. lxml: Created fresh DOM trees using lhtml.fromstring() for each selector

We chose reparsing over deepcopy() for better performance with large documents, as parsing engines are highly optimized for this task.

This approach successfully decoupled content targeting from link extraction in both implementations, ensuring consistent link counts regardless of target element settings.

[aravindkarnam]

@Joorrit Thanks for catching this bug. I've applied fix for this in bug fix branch for this month. We'll target this for next release, in the mean time, you can pull in the patch from here.

[tedvalson]

I just tried the deepcopy fix above: https://github.com/unclecode/crawl4ai/commit/d2648eaa39d4232b3de6a27a1170b5fef8ecc389 It worked for me. Figured I'd let you know.

I cannot speak to the efficiency compared to reparsing or other methods, that would require some benchmarking work.