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Overview

This guide will show you how to:
  • Start a crawl specifically targeting Stripe’s blog posts
  • Monitor the crawl progress
  • Retrieve and process the crawled content

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Crawling Stripe’s Blog Pages

To crawl Stripe’s blog pages, use the crawls endpoint with pattern matching to target specific blog URLs. This will fetch the full HTML content of each page, which you can then process to extract the information you need. 
import requests
import time
import json
from datetime import datetime

# Configuration
API_URL = 'https://api.olostep.com/v1'
API_KEY = '<your_olostep_api_key>'
HEADERS = {
    'Content-Type': 'application/json',
    'Authorization': f'Bearer {API_KEY}'
}

# Record start time for crawl duration tracking
crawl_start_time = time.time()

print(f"[{datetime.now().strftime('%H:%M:%S')}] Starting Stripe blog crawl...")

# Start a crawl focused on Stripe's engineering blog posts
# You can adjust the patterns based on your specific interests
payload = {
    "start_url": "https://stripe.com/blog",
    "include_urls": ["/blog/engineering/**"],  # Focus on engineering posts
    "max_pages": 25  # Limit to 25 pages for this example
}

# Start the crawl
print("Starting crawl of Stripe's engineering blog posts...")
response = requests.post(f'{API_URL}/crawls', headers=HEADERS, json=payload)
data = response.json()
crawl_id = data['id']
print(f"Crawl started with ID: {crawl_id}")

# Monitor crawl progress
while True:
    status_response = requests.get(f'{API_URL}/crawls/{crawl_id}', headers=HEADERS)
    status_data = status_response.json()
    print(f"Crawl status: {status_data['status']} - Pages crawled: {status_data.get('pages_count', 0)}")
    
    if status_data['status'] == 'completed' or status_data['status'] == 'failed':
        break
        
    # Wait 5 seconds before checking again
    time.sleep(5)

# Calculate and display crawl duration
crawl_duration = time.time() - crawl_start_time
print(f"[{datetime.now().strftime('%H:%M:%S')}] Crawl completed in {crawl_duration:.2f} seconds")

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Converting Blog Content to Markdown

One powerful way to use the crawled content is to convert it to markdown format, which is ideal for feeding into LLMs or creating a knowledge base. Here’s how to retrieve and convert the blog content to markdown: 
import requests
import time
import json
from datetime import datetime
import os
from concurrent.futures import ThreadPoolExecutor, as_completed

# Configuration
API_URL = 'https://api.olostep.com/v1'
API_KEY = '<your_olostep_api_key>'
HEADERS = {
    'Content-Type': 'application/json',
    'Authorization': f'Bearer {API_KEY}'
}

# Function to retrieve content with markdown format
def retrieve_content(retrieve_id, formats):
    params = {
        "retrieve_id": retrieve_id,
        "formats": json.dumps(formats)
    }
    response = requests.get(f"{API_URL}/retrieve", headers=HEADERS, params=params)
    return response.json()

# Continuing from the previous crawl example
if status_data['status'] == 'completed':
    print(f"\nCrawl completed! Retrieved {status_data['pages_count']} pages.")
    pages_response = requests.get(f'{API_URL}/crawls/{crawl_id}/pages', headers=HEADERS)
    pages_data = pages_response.json()
    
    # Create output directory if it doesn't exist
    os.makedirs("output", exist_ok=True)
    
    # Prepare to collect markdown content
    markdown_pages = []
    total_pages = len(pages_data['pages'])
    
    # Process pages in parallel to get markdown content
    with ThreadPoolExecutor(max_workers=10) as executor:
        # Create futures for content retrieval
        future_to_page = {
            executor.submit(retrieve_content, page['retrieve_id'], ["markdown"]): page
            for page in pages_data['pages']
        }
        
        # Process results as they complete
        for i, future in enumerate(as_completed(future_to_page), 1):
            page = future_to_page[future]
            url = page['url']
            print(f"Processing {i}/{total_pages}: {url}")
            
            try:
                content_data = future.result()
                if content_data and "markdown_content" in content_data:
                    markdown_pages.append({
                        'url': url,
                        'title': page['title'],
                        'markdown_content': content_data['markdown_content']
                    })
                    print(f"✓ Markdown content retrieved for {url}")
                else:
                    print(f"⚠ No markdown content for {url}")
            except Exception as e:
                print(f"❌ Error retrieving content for {url}: {str(e)}")
    
    # Save all markdown content to a single file
    output_file = "output/stripe_blog_markdown.md"
    
    with open(output_file, "w", encoding="utf-8") as f:
        for page in markdown_pages:
            # Write page header with title and URL
            f.write(f"URL: {page['url']}\n\n")
            
            # Write the markdown content
            f.write(f"{page['markdown_content']}\n\n")
            
            # Add separator between pages
            f.write("---\n\n")
            
            print(f"✓ Added markdown content from {page['url']}")

    print(f"\n✅ Process complete! All markdown content has been saved to '{output_file}'")
    print(f"Total pages processed: {len(markdown_pages)}")
else:
    print(f"Crawl failed with status: {status_data['status']}")

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Example Markdown Output

The resulting markdown file will contain all the crawled blog content in a clean, structured format: 
URL: https://stripe.com/blog/using-ml-to-detect-and-respond-to-performance-degradations

## Using ML to detect and respond to performance degradations

By Jane Smith, Senior Engineer at Stripe

At Stripe, we process millions of API requests every day...

---

URL: https://stripe.com/blog/building-robust-payment-systems

## Building a robust payment system

By John Doe, Engineering Manager

Reliability is at the core of Stripe's infrastructure...

---

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Next Steps

Now that you’ve successfully crawled and extracted content from Stripe’s blog, you can:
  1. Expand your crawl: Modify the include_urls parameter to crawl other sections of Stripe’s blog
  2. Implement regular updates: Set up a scheduled job to periodically crawl for new content
  3. Perform deeper analysis: Use NLP tools to extract insights from the blog content
  4. Build a search engine: Create a searchable database of Stripe’s blog content
  5. Feed into LLMs: Use the markdown content as context for LLMs to answer questions about Stripe’s engineering practices
Using Olostep’s content crawling capabilities, you can build powerful tools for monitoring and analyzing any website’s content strategy.
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