Olostep vs ScrapeGraph: A Comprehensive Technical Comparison

Overview

Olostep and ScrapeGraph both aim to simplify web scraping through AI-powered extraction, but they take different approaches to solving common scraping challenges. Both platforms offer REST APIs and support various output formats, but their architectures, pricing models, and feature sets differ significantly.

API Architecture and Integration

Olostep

Olostep provides a straightforward REST API with several specialized endpoints. The platform separates concerns into distinct operations:

import requests
 
# Example: Web scraping with LLM extraction
url = "https://api.olostep.com/v1/scrapes"
payload = {
    "url_to_scrape": "https://example.com",
    "formats": ["json"],
    "llm_extract": {
        "prompt": "extract name, position, history"
    }
}
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())

Olostep's API structure is endpoint-based, meaning different operations use different routes (/scrapes, /crawls, /answers, /maps). This granular approach gives you explicit control over what operation you're performing.

ScrapeGraph

ScrapeGraph uses a client-based approach with a more Pythonic interface:

from scrapegraph_py import Client
 
# Initialize the client
client = Client(api_key="YOUR_API_KEY")
 
# SmartScraper request
response = client.smartscraper(
    website_url="https://example.com",
    user_prompt="Extract information"
)
print(response)

Feature Comparison

Data Extraction Capabilities

Olostep offers multiple specialized endpoints:

• Scraping - Extract data from single pages with LLM-powered prompts
• Crawling - Multi-page crawls with depth and page limits
• Q&A - Natural language question answering against websites
• Site Maps - Generate maps of website structure with latency tracking

The Q&A endpoint is particularly interesting, allowing you to ask questions directly about website content without writing extraction prompts.

ScrapeGraph provides multiple specialized scrapers:

• SmartScraper - Single-page extraction with user prompts
• Sitemap Extractor - Extract and manage sitemap URLs

Code Examples in Action

Multi-Page Crawling

Olostep's approach:

import requests
import time
 
API_URL = 'https://api.olostep.com/v1'
API_KEY = 'YOUR_API_KEY'
headers = {
    'Content-Type': 'application/json',
    'Authorization': f'Bearer {API_KEY}'
}
 
def initiate_crawl():
    data = {
        "start_url": "https://example.com",
        "include_urls": ["/**"],
        "exclude_urls": [],
        "include_external": False,
        "max_depth": 3,
        "max_pages": 10
    }
    response = requests.post(f'{API_URL}/crawls', headers=headers, json=data)
    return response.json()
 
def get_crawl_info(crawl_id):
    response = requests.get(f'{API_URL}/crawls/{crawl_id}', headers=headers)
    return response.json()
 
def get_crawled_list(crawl_id, formats=None):
    params = {'formats': formats}
    response = requests.get(
        f'{API_URL}/crawls/{crawl_id}/pages',
        headers=headers,
        params=params
    )
    return response.json()
 
# Initiate the crawl
crawl = initiate_crawl()
crawl_id = crawl['id']
 
# Wait for completion
while True:
    info = get_crawl_info(crawl_id)
    if info['status'] == 'completed':
        break
    time.sleep(5)
 
# Retrieve results
formats = ["html", "markdown"]
crawl_list = get_crawled_list(crawl_id, formats=formats)
 
for page in crawl_list['pages']:
    print(f"URL: {page['url']}")
    print(f"Content: {page.get('markdown_content', 'No content')}")

Olostep requires manual polling with status checks. You initiate a crawl, then periodically check its status until completion.

ScrapeGraph's approach:

from scrapegraph_py import Client
 
client = Client(api_key="YOUR_API_KEY")
 
# SmartCrawler handles polling internally
response = client.smartcrawler(
    website_url="https://example.com",
    user_prompt="Extract data",
    max_depth=1,
    max_pages=10,
    sitemap=True
)
print(response)

ScrapeGraph abstracts the polling complexity. You call the method and wait for results, with internal handling of the async workflow.

Sitemap Extraction

ScrapeGraph's dedicated sitemap feature:

from scrapegraph_py import Client
from dotenv import load_dotenv
 
load_dotenv()
client = Client(api_key="YOUR_API_KEY")
 
try:
    print("Extracting sitemap from https://example.com...")
    response = client.sitemap(website_url="https://example.com")
    
    print(f"✅ Found {len(response.urls)} URLs\n")
    
    print("First 10 URLs:")
    for i, url in enumerate(response.urls[:10], 1):
        print(f"   {i}. {url}")
    
    if len(response.urls) > 10:
        print(f"   ... and {len(response.urls) - 10} more URLs")
    
    # Save to file
    with open("sitemap_urls.txt", "w") as f:
        for url in response.urls:
            f.write(url + "\n")
    
    print("\n💾 URLs saved to: sitemap_urls.txt")
except Exception as e:
    print(f"❌ Error: {str(e)}")
finally:
    client.close()

ScrapeGraph provides a dedicated, simple method for sitemap extraction with built-in file handling.

Key Differences

When to Choose Each

Choose Olostep if:

• You need language-agnostic scraping (Node.js, Go, Java, etc.)
• You want to ask natural language questions about websites
• You prefer explicit control over API calls
• You need fine-grained crawling configuration Choose ScrapeGraph if:
• You value ease of integration and readable code
• You prefer abstraction over manual HTTP management
• You want sitemap extraction built-in

Performance and Reliability

Both platforms claim fast response times and high reliability. Olostep exposes latency metrics directly (you can measure request time), while ScrapeGraph abstracts this away but focuses on providing a stable, easy-to-use interface.

Conclusion

Neither platform is objectively "better"—they're optimized for different use cases. Olostep shines for teams building polyglot systems and those needing advanced features like Q&A capabilities. ScrapeGraph excels for Python-centric teams who value simplicity and integrated features like markdown conversion.

The best choice depends on your specific needs:

• Your primary programming language
• Whether you need language-agnostic support
• Your preference for explicit control vs. abstraction
• Which specific features matter most to your project

Both platforms represent significant improvements over traditional web scraping approaches, and either is a solid investment for modern data extraction workflows.

Frequently Asked Questions

Which platform is better for beginners? Do these tools handle JavaScript-heavy websites?

Both platforms handle JavaScript rendering, but approach it differently. Learn more about handling heavy JavaScript in our dedicated guide.

How do these compare to traditional scraping with Scrapy?

Both Olostep and ScrapeGraph use AI to reduce the manual work required with traditional tools like Scrapy. Read our Scrapy alternative guide and traditional vs AI scraping comparison to understand the differences.

Is web scraping with these tools legal?

Related Resources

Explore more comparisons and guides to find the perfect scraping solution:

Platform Comparisons

• ScrapeGraph vs Browserbase - Browser automation comparison
• ScrapeGraph vs Exa - Search-based scraping comparison
• ScrapeGraph vs Diffbot - Enterprise scraping solutions

Getting Started

• JavaScript Web Scraping - Node.js implementation

Advanced Features

• Building AI Agents - Agent-based scraping

Use Cases

• Job Posting Scraping - Employment data

Integration Guides

• CrewAI Integration - Multi-agent systems

Note: This comparison is based on publicly available API documentation and code examples. For the most current feature lists and pricing, refer to the official documentation at olostep.com and scrapegraphai.com.