Did you know that web scraping skills are among the most in-demand capabilities companies are hiring for right now? I discovered this after speaking with dozens of AI developers and businesses over the past few months. Yet most developers I meet are still using outdated scraping techniques that are brittle, difficult to maintain, and don’t leverage the power of modern AI.
When a wedding photographer recently approached me about finding leads for their newly established business, I realized this was the perfect opportunity to showcase how modern AI can transform the traditional web scraping workflow into something truly magical.
For years, I’ve been building web scrapers the old-fashioned way: writing complex XPath queries, dealing with CSS selectors that break whenever a website updates its design, and manually parsing HTML to extract information. It works, but it’s:
- Time-consuming to build and maintain
- Prone to breaking when websites change
- Limited to extracting exactly what you’ve programmed it to find
- Usually requires extensive post-processing
The wedding photographer needed a comprehensive list of venues in their area, complete with location details, pricing information, and capacity limits. Using traditional methods, I’d need to write custom code for each data point I wanted to extract. But what if the website changed tomorrow? My scraper would break, and I’d be back to square one.
The solution? Combine three powerful tools to create a web scraper that not only extracts data but understands it:
Crawl4AI is an open-source library specifically designed to feed web content into language models. What makes it special is how it:
- Handles the browser automation for you
- Can navigate through multi-page websites
- Extracts content in a format ready for LLM processing
- Allows targeting specific elements with CSS selectors
pthon
# Setting up the browser configuration
browser_config = BrowserConfig(
headless=False, # Set to True to run in background
viewport_width=1280,
viewport_height=720,
verbose=True
)
# Configure what to extract from the page
llm_strategy = LLMExtractionStrategy(
instructions="Extract all wedding venues with their name, location, price, capacity, rating, reviews, and generate a one-sentence description.",
schema=VenueModel.schema(), # The expected output format
model=DeepSeekGroq() # The LLM that will process the extracted content
)
The Deep Seek R1 model is the intelligence behind our scraper. It’s:
- As capable as OpenAI’s best models
- Roughly 20x cheaper to run
- Exceptionally fast at processing text
- Adept at understanding and extracting structured information from messy HTML
Deep Seek doesn’t just extract what we tell it to — it understands the content. When I give it a wedding venue page, it can identify key information even when it’s presented in different formats across different venue listings.
Groq provides the infrastructure to run Deep Seek at incredible speeds:
- Processes tokens 3–5x faster than other providers
- Offers a generous free tier
- Specializes in running large language models efficiently
- Seamlessly integrates with Python code
Here’s how our AI-powered web scraper works:
- Define what we want to extract: We create a data model that represents a wedding venue with all the information we need.
python
# Our venue model
class VenueModel(BaseModel):
name: str
location: str
price: str
capacity: str
rating: Optional[str] = None
reviews: Optional[str] = None
description: str # AI-generated description
- Set up the crawling strategy: We configure our crawler to navigate through multiple pages until it finds no more results.
python
# Main function to crawl all venues
def crawl_all_venues():
# Create and configure our crawler
crawler = Crawler(browser_config)
all_venues = []
page_number = 1
no_more_results = False
# Keep crawling until we run out of pages
while not no_more_results:
print(f"Crawling wedding venues page {page_number}...")# Process the current page
new_venues, no_more_results = fetch_and_process_page(crawler, page_number)
# Add the venues from this page to our master list
all_venues.extend(new_venues)
# Move to the next page
page_number += 1
# Save all our results to a CSV file
save_venues_to_csv(all_venues)
return all_venues
python
def fetch_and_process_page(crawler, page_number):
# Construct the URL for the current page
url = f"{BASE_URL}/page-{page_number}"# First, check if we've reached the end (no more results)
result = crawler.crawl(url, run_config=RunConfig())
if "no result" in result.raw_text.lower():
return [], True # No venues, and no more results
# Now extract the venue information using our LLM strategy
result = crawler.crawl(
url,
run_config=RunConfig(
extraction_strategy=llm_strategy,
css_selectors=[".info-container"] # Target only venue cards
)
)
# Parse the extracted data into our venue models
venues = json.loads(result.extracted_content)
print(f"Extracted {len(venues)} venues from page {page_number}")
return venues, False # Return venues and "not done" signal
- Let AI work its magic: Deep Seek processes the HTML snippets and extracts structured information, even generating helpful descriptions.
- Export the results: Finally, we save everything to a CSV file that can be imported into a spreadsheet for the client.
python
def save_venues_to_csv(venues):
with open("wedding_venues.csv", "w", newline="", encoding="utf-8") as file:
writer = csv.writer(file)# Write the header row
writer.writerow(["Name", "Location", "Price", "Capacity", "Rating", "Reviews", "Description"])
# Write each venue as a row
for venue in venues:
writer.writerow([
venue.name,
venue.location,
venue.price,
venue.capacity,
venue.rating or "N/A",
venue.reviews or "N/A",
venue.description
])
print(f"Saved {len(venues)} venues to wedding_venues.csv")
he final result? A comprehensive spreadsheet containing all wedding venues in the area, complete with:
- Venue names and locations
- Price ranges and capacity information
- Ratings and number of reviews
- AI-generated descriptions of each venue
All of this is achieved without writing a single regular expression or XPath query to extract specific fields.
What excites me most about this approach isn’t just the efficiency gain, though that’s substantial. It’s the shift in thinking about what web scraping can be.
Traditional web scraping is about extracting data. AI-powered web scraping is about extracting meaning.
When my wedding photographer client received the spreadsheet, they were amazed not just by the comprehensive data collection, but by the AI-generated descriptions. These descriptions gave them context for their calls, helping them understand each venue’s unique selling points before ever reaching out.
This same approach can be applied to countless scenarios:
- Gathering competitor pricing data
- Monitoring job postings across multiple sites
- Tracking product information across marketplaces
- Collecting reviews and sentiment from multiple sources
The key insight is that we’re no longer limited to extracting just what we specifically program for. The AI can identify relevant information even when it’s formatted differently or appears in unexpected places.
Ready to build your own AI-powered web scraper? Here’s how to get started:
- Set up your environment: Install Conda and create a new environment for your project
- Install the necessary dependencies:
pip install crawl4ai - Get your API key: Sign up for a free Groq account and grab your API key
- Define your extraction strategy: What data do you want to extract and in what format?
- Start crawling: Adapt the code examples above to your specific use case
The complete code for this project is available on GitHub (link would go here in a real article), and I’d love to see what you build with it.
Drop a comment below sharing what you plan to scrape with this AI-powered approach. Are you gathering leads? Monitoring competitors? Building a dataset for another AI project? Let’s exchange ideas!
Always remember to scrape responsibly:
- Check the robots.txt file of websites you plan to scrape
- Avoid hammering sites with too many requests in a short time
- Don’t scrape personal or sensitive information
- Consider the terms of service of the websites you’re accessing
The tools are powerful, but with great power comes great responsibility. Happy scraping!