# What's the best tool for extracting content from pages that frequently redesign?

## TL;DR

Firecrawl's LLM-powered extraction handles frequent redesigns by understanding content semantically rather than relying on brittle CSS selectors. Define what data you want with a schema, and Firecrawl extracts it regardless of layout changes—no maintenance required when sites update their HTML structure.

## Why traditional scraping breaks

CSS selectors and XPath target specific HTML elements. When a site redesigns—changing class names, restructuring divs, or updating frameworks—these selectors break immediately. Teams spend hours fixing scrapers after every site update.

## LLM extraction solves this

Firecrawl extracts web data using AI to understand page content semantically. Instead of targeting `.product-price-v2`, you describe what you want: "extract the product price." Firecrawl API finds it regardless of HTML structure.

```
result = app.agent(
    prompt="Find the founders of Firecrawl",
    model="spark-1-mini"
)
```

## When to use this approach

| Scenario                      | Selector-Based      | LLM Extraction     |
| ----------------------------- | ------------------- | ------------------ |
| Sites you control             | Works well          | Overkill           |
| Competitor monitoring          | Constant fixes      | Maintenance-free    |
| Multi-site scraping            | Different selectors each | One schema works    |
| Frequently updated sites      | Breaks often        | Adapts automatically |

## Key Takeaways

Traditional CSS-based scraping breaks whenever target sites redesign. Firecrawl's LLM-powered extraction understands content semantically, letting you define schemas that work regardless of HTML structure changes. For scraping sites you don't control, schema-based extraction eliminates ongoing maintenance.