llm-friendly-web-crawler

name	llm-friendly-web-crawler
description	Usa al crawlear web LLM-friendly con crawl4ai.
version	2.0.0
tags	["crawler","crawl4ai","web","llm","scraping","markdown","pydantic"]
related_skills	["firecrawl-web-scraping","adaptive-web-scraping","crawlee-web-scraping","browser-use-ai"]

Crawl4AI — crawling web LLM-friendly

⚠️ Corrección 2026-09-05 (auditoría): la clase CrawlerStrategy NO existe y result.markdown es un objeto, no un string. API real = BrowserConfig/CrawlerRunConfig + strategies JsonCssExtractionStrategy/LLMExtractionStrategy.

Repo: https://github.com/unclecode/crawl4ai (Python, ~81K⭐).

When to Use

• Cuando pidas crawling LLM-friendly: extraer contenido de una web en markdown/JSON listo para meter en un LLM.

Uso (API real)

from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, JsonCssExtractionStrategy, LLMExtractionStrategy

async with AsyncWebCrawler() as crawler:
    result = await crawler.arun("https://ejemplo.com", config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS))
    md = result.markdown.raw_markdown        # markdown es un OBJETO con .raw_markdown / .fit_markdown
    print(md[:500])

Extracción estructurada:

schema = {...}
strategy = JsonCssExtractionStrategy(schema, verbose=True)   # o LLMExtractionStrategy(...)

Pitfalls

• No existe CrawlerStrategy — usa JsonCssExtractionStrategy/LLMExtractionStrategy.
• result.markdown es un objeto: .raw_markdown/.fit_markdown, no [:500] a secas.
• El core es AsyncWebCrawler + CrawlerRunConfig (o BrowserConfig).

Verificación

• await crawler.arun(url) y leer result.markdown.raw_markdown. Para extracción con schema, JsonCssExtractionStrategy.