Python SDK

from pydantic import BaseModel, Field  class ArticleData(BaseModel):  title: str = Field(description="The article title")  author: str = Field(description="The author's name")  publish_date: str = Field(description="Article publication date")  content: str = Field(description="Main article content")  category: str = Field(description="Article category")  response = client.smartscraper(  website_url="https://example.com/blog/article",  user_prompt="Extract the article information",  output_schema=ArticleData )  print(f"Title: {response.title}") print(f"Author: {response.author}") print(f"Published: {response.publish_date}") 

from typing import List from pydantic import BaseModel, Field  class Employee(BaseModel):  name: str = Field(description="Employee's full name")  position: str = Field(description="Job title")  department: str = Field(description="Department name")  email: str = Field(description="Email address")  class Office(BaseModel):  location: str = Field(description="Office location/city")  address: str = Field(description="Full address")  phone: str = Field(description="Contact number")  class CompanyData(BaseModel):  name: str = Field(description="Company name")  description: str = Field(description="Company description")  industry: str = Field(description="Industry sector")  founded_year: int = Field(description="Year company was founded")  employees: List[Employee] = Field(description="List of key employees")  offices: List[Office] = Field(description="Company office locations")  website: str = Field(description="Company website URL")  # Extract comprehensive company information response = client.smartscraper(  website_url="https://example.com/about",  user_prompt="Extract detailed company information including employees and offices",  output_schema=CompanyData )  # Access nested data print(f"Company: {response.name}") print("\nKey Employees:") for employee in response.employees:  print(f"- {employee.name} ({employee.position})")  print("\nOffice Locations:") for office in response.offices:  print(f"- {office.location}: {office.address}") 

from scrapegraph_py import Client from pydantic import BaseModel, Field  class ProductInfo(BaseModel):  name: str = Field(description="Product name")  price: str = Field(description="Product price")  description: str = Field(description="Product description")  availability: str = Field(description="Product availability status")  client = Client(api_key="your-api-key")  # Enable enhanced JavaScript rendering for a React-based e-commerce site response = client.smartscraper(  website_url="https://example-react-store.com/products/123",  user_prompt="Extract product details including name, price, description, and availability",  output_schema=ProductInfo,  render_heavy_js=True # Enable for React/Vue/Angular sites )  print(f"Product: {response['result']['name']}") print(f"Price: {response['result']['price']}") print(f"Available: {response['result']['availability']}") 

from pydantic import BaseModel, Field from typing import List  class ProductInfo(BaseModel):  name: str = Field(description="Product name")  description: str = Field(description="Product description")  price: str = Field(description="Product price")  features: List[str] = Field(description="List of key features")  availability: str = Field(description="Availability information")  response = client.searchscraper(  user_prompt="Find information about iPhone 15 Pro",  output_schema=ProductInfo )  print(f"Product: {response.name}") print(f"Price: {response.price}") print("\nFeatures:") for feature in response.features:  print(f"- {feature}") 

from typing import List from pydantic import BaseModel, Field  class MarketPlayer(BaseModel):  name: str = Field(description="Company name")  market_share: str = Field(description="Market share percentage")  key_products: List[str] = Field(description="Main products in market")  strengths: List[str] = Field(description="Company's market strengths")  class MarketTrend(BaseModel):  name: str = Field(description="Trend name")  description: str = Field(description="Trend description")  impact: str = Field(description="Expected market impact")  timeframe: str = Field(description="Trend timeframe")  class MarketAnalysis(BaseModel):  market_size: str = Field(description="Total market size")  growth_rate: str = Field(description="Annual growth rate")  key_players: List[MarketPlayer] = Field(description="Major market players")  trends: List[MarketTrend] = Field(description="Market trends")  challenges: List[str] = Field(description="Industry challenges")  opportunities: List[str] = Field(description="Market opportunities")  # Perform comprehensive market research response = client.searchscraper(  user_prompt="Analyze the current AI chip market landscape",  output_schema=MarketAnalysis )  # Access structured market data print(f"Market Size: {response.market_size}") print(f"Growth Rate: {response.growth_rate}")  print("\nKey Players:") for player in response.key_players:  print(f"\n{player.name}")  print(f"Market Share: {player.market_share}")  print("Key Products:")  for product in player.key_products:  print(f"- {product}")  print("\nMarket Trends:") for trend in response.trends:  print(f"\n{trend.name}")  print(f"Impact: {trend.impact}")  print(f"Timeframe: {trend.timeframe}") 

from scrapegraph_py import Client  client = Client(api_key="your-api-key")  # Enable markdown mode for cost-effective content gathering response = client.searchscraper(  user_prompt="Latest developments in artificial intelligence",  num_results=3,  extraction_mode=False # Enable markdown mode (2 credits per page vs 10 credits) )  # Access the raw markdown content markdown_content = response['markdown_content'] reference_urls = response['reference_urls']  print(f"Markdown content length: {len(markdown_content)} characters") print(f"Reference URLs: {len(reference_urls)}")  # Process the markdown content print("Content preview:", markdown_content[:500] + "...")  # Save to file for analysis with open('ai_research_content.md', 'w', encoding='utf-8') as f:  f.write(markdown_content)  print("Content saved to ai_research_content.md")