Crawl4ai
Overview
Crawl4ai is an AI-powered web scraping framework designed to extract structured data from websites efficiently. It combines traditional HTML parsing with AI to handle dynamic content, extract text intelligently, and clean and structure data from complex web pages.
When to Use This Skill
Use when Codex needs to:
- Extract structured data from web pages (products, articles, forms, tables, etc.)
- Scrape websites with dynamic content or complex JavaScript
- Clean and normalize extracted data from various HTML structures
- Work with APIs or web services that return HTML
- Handle CORS limitations by scraping directly
- Process web content at scale with reliability
Trigger phrases:
- "Extract data from this website"
- "Scrape this page for [specific data]"
- "Parse this HTML"
- "Get data from [URL]"
- "Extract structured information from [website]"
- "Scrape [website] for [data type]"
- "Web scrape [URL]"
Quick Start
Basic Usage
from crawl4ai import AsyncWebCrawler, BrowserMode
async def scrape_page(url):
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
browser_mode=BrowserMode.LATEST,
headless=True
)
return result.markdown, result.clean_html
Extracting Structured Data
from crawl4ai import AsyncWebCrawler, JsonModeScreener
import json
async def extract_products(url):
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
screenshot=True,
javascript=True,
bypass_cache=True
)
# Extract product data
products = []
for item in result.extracted_content:
if item['type'] == 'product':
products.append({
'name': item['name'],
'price': item['price'],
'url': item['url']
})
return products
Common Tasks
Web Scraping Basics
Scenario: User wants to scrape a website for all article titles.
from crawl4ai import AsyncWebCrawler
async def scrape_articles(url):
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
javascript=True,
verbose=True
)
# Extract article titles from HTML
articles = result.extracted_content if result.extracted_content else []
titles = [item.get('name', item.get('text', '')) for item in articles]
return titles
Trigger: "Scrape this site for article titles" or "Get all titles from [URL]"
Dynamic Content Handling
Scenario: Website loads data via JavaScript.
from crawl4ai import AsyncWebCrawler
async def scrape_dynamic_site(url):
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
javascript=True, # Wait for JS execution
wait_for="body", # Wait for specific element
delay=1.5, # Wait time after load
headless=True
)
return result.markdown
Trigger: "Scrape this dynamic website" or "This page needs JavaScript to load data"
Structured Data Extraction
Scenario: Extract specific fields like prices, descriptions, etc.
from crawl4ai import AsyncWebCrawler
async def extract_product_details(url):
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
screenshot=True,
js_code="""
const products = document.querySelectorAll('.product');
return Array.from(products).map(p => ({
name: p.querySelector('.name')?.textContent,
price: p.querySelector('.price')?.textContent,
url: p.querySelector('a')?.href
}));
"""
)
return result.extracted_content
Trigger: "Extract product details from this page" or "Get price and name from [URL]"
HTML Cleaning and Parsing
Scenario: Clean messy HTML and extract clean text.
from crawl4ai import AsyncWebCrawler
async def clean_and_parse(url):
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
remove_tags=['script', 'style', 'nav', 'footer', 'header'],
only_main_content=True
)
# Clean and return markdown
clean_text = result.clean_html
return clean_text
Trigger: "Clean this HTML" or "Extract main content from this page"
Advanced Features
Custom JavaScript Injection
async def custom_scrape(url, custom_js):
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
js_code=custom_js,
js_only=True # Only execute JS, don't download resources
)
return result.extracted_content
Session Management
from crawl4ai import AsyncWebCrawler
async def multi_page_scrape(base_url, urls):
async with AsyncWebCrawler() as crawler:
results = []
for url in urls:
result = await crawler.arun(
url=url,
session_id=f"session_{url}",
bypass_cache=True
)
results.append({
'url': url,
'content': result.markdown,
'status': result.success
})
return results
Best Practices
- Always check if the site allows scraping - Respect robots.txt and terms of service
- Use appropriate delays - Add delays between requests to avoid overwhelming servers
- Handle errors gracefully - Implement retry logic and error handling
- Be selective with data - Extract only what you need, don't dump entire pages
- Store data reliably - Save extracted data in structured formats (JSON, CSV)
- Clean URLs - Handle redirects and malformed URLs
Error Handling
async def robust_scrape(url):
try:
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=url,
timeout=30000 # 30 seconds timeout
)
if result.success:
return result.markdown, result.extracted_content
else:
print(f"Scraping failed: {result.error_message}")
return None, None
except Exception as e:
print(f"Scraping error: {str(e)}")
return None, None
Output Formats
Crawl4ai supports multiple output formats:
- Markdown: Clean, readable text (
result.markdown) - Clean HTML: Structured, cleaned HTML (
result.clean_html) - Extracted Content: Structured JSON data (
result.extracted_content) - Screenshot: Visual representation (
result.screenshot) - Links: All links found on page (
result.links)
Resources
scripts/
Python scripts for common crawling operations:
scrape_single_page.py- Basic scraping utilityscrape_multiple_pages.py- Batch scraping with paginationextract_from_html.py- HTML parsing helperclean_html.py- HTML cleaning utility
references/
Documentation and examples:
api_reference.md- Complete API documentationexamples.md- Common use cases and patternserror_handling.md- Troubleshooting guide
