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Overview

ScriptCreatorGraph defines a scraping pipeline for generating web scraping scripts. Instead of extracting data, it generates Python code that can scrape the specified information from a website.

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Class Signature

class ScriptCreatorGraph(AbstractGraph):
    def __init__(
        self,
        prompt: str,
        source: str,
        config: dict,
        schema: Optional[Type[BaseModel]] = None,
    )

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Constructor Parameters

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prompt
str
required
The natural language prompt describing what information the script should extract.
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source
str
required
The target website URL or local HTML file that the generated script will scrape.
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config
dict
required
Configuration parameters for the graph. Must include:
  • llm: LLM configuration (e.g., {"model": "openai/gpt-4o"})
  • library: The scraping library to use (“beautifulsoup”, “playwright”, “selenium”)
Optional parameters:
  • verbose (bool): Enable detailed logging
  • headless (bool): Run browser in headless mode
  • additional_info (str): Extra context for script generation
  • loader_kwargs (dict): Parameters for page loading
  • storage_state (str): Browser state file path
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schema
Type[BaseModel]
default:"None"
Optional Pydantic model defining the expected output structure of the generated script.

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Attributes

​
prompt
str
The extraction prompt for the script.
​
source
str
The target website URL or local directory path.
​
config
dict
Configuration dictionary for the graph.
​
schema
BaseModel
Optional output schema for the generated script.
​
llm_model
object
The configured language model instance.
​
library
str
The scraping library specified for code generation.
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input_key
str
Either “url” or “local_dir” based on the source type.

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Methods

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run()

Executes the script generation process and returns the generated code. 
def run(self) -> str
​
return
str
The generated Python scraping script as a string.

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Basic Usage

from scrapegraphai.graphs import ScriptCreatorGraph

graph_config = {
    "llm": {
        "model": "openai/gpt-4o",
        "api_key": "your-api-key"
    },
    "library": "beautifulsoup"
}

script_creator = ScriptCreatorGraph(
    prompt="Extract all product names and prices",
    source="https://example.com/products",
    config=graph_config
)

generated_script = script_creator.run()
print(generated_script)

# Save the script to a file
with open("scraper.py", "w") as f:
    f.write(generated_script)

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Library Options

config = {
    "llm": {"model": "openai/gpt-4o"},
    "library": "beautifulsoup"  # Fast, simple, great for static HTML
}

script_creator = ScriptCreatorGraph(
    prompt="Extract article titles and publication dates",
    source="https://example.com/blog",
    config=config
)

script = script_creator.run()
Generated script will use:
  • requests for HTTP requests
  • BeautifulSoup for HTML parsing
  • CSS selectors or XPath for element selection
config = {
    "llm": {"model": "openai/gpt-4o"},
    "library": "playwright"  # For JavaScript-heavy sites
}

script_creator = ScriptCreatorGraph(
    prompt="Extract product prices after page loads",
    source="https://example.com/dynamic-products",
    config=config
)

script = script_creator.run()
Generated script will use:
  • playwright for browser automation
  • Async/await patterns
  • Wait conditions for dynamic content

​
Selenium (For Browser Automation)

config = {
    "llm": {"model": "openai/gpt-4o"},
    "library": "selenium"  # Alternative browser automation
}

script_creator = ScriptCreatorGraph(
    prompt="Click the 'Load More' button and extract all items",
    source="https://example.com/infinite-scroll",
    config=config
)

script = script_creator.run()
Generated script will use:
  • selenium for browser control
  • WebDriver for browser interaction
  • Explicit waits for elements

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Structured Output with Schema

from pydantic import BaseModel, Field
from typing import List

class Product(BaseModel):
    name: str = Field(description="Product name")
    price: float = Field(description="Product price")
    description: str = Field(description="Product description")
    in_stock: bool = Field(description="Availability status")

class ProductList(BaseModel):
    products: List[Product]

config = {
    "llm": {"model": "openai/gpt-4o"},
    "library": "beautifulsoup"
}

script_creator = ScriptCreatorGraph(
    prompt="Extract product information",
    source="https://example.com/products",
    config=config,
    schema=ProductList
)

script = script_creator.run()
# Generated script will output data matching the ProductList schema

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Graph Workflow

The ScriptCreatorGraph uses the following node pipeline: 
FetchNode → ParseNode → GenerateScraperNode
  1. FetchNode: Fetches the target web page
  2. ParseNode: Parses the HTML structure (without full parsing)
  3. GenerateScraperNode: Generates the scraping script based on the page structure

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Advanced Usage

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With Additional Context

config = {
    "llm": {"model": "openai/gpt-4o"},
    "library": "playwright",
    "additional_info": """
        The site uses lazy loading. 
        Wait for all images to load before extracting data.
        Use headless mode for production.
    """
}

script_creator = ScriptCreatorGraph(
    prompt="Extract all image URLs and captions",
    source="https://example.com/gallery",
    config=config
)

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For Authenticated Pages

config = {
    "llm": {"model": "openai/gpt-4o"},
    "library": "playwright",
    "storage_state": "./auth_state.json",  # Browser state with auth cookies
    "additional_info": "The page requires authentication. Use the provided session state."
}

script_creator = ScriptCreatorGraph(
    prompt="Extract user dashboard data",
    source="https://example.com/dashboard",
    config=config
)

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Example: Generated BeautifulSoup Script

# Input
script_creator = ScriptCreatorGraph(
    prompt="Extract all article titles and links",
    source="https://example.com/blog",
    config={"llm": {"model": "openai/gpt-4o"}, "library": "beautifulsoup"}
)

script = script_creator.run()

# Output (example)
"""
import requests
from bs4 import BeautifulSoup

def extract_data(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')
    
    articles = []
    for article in soup.find_all('article', class_='post'):
        title = article.find('h2', class_='title').text.strip()
        link = article.find('a', class_='read-more')['href']
        articles.append({'title': title, 'link': link})
    
    return articles

if __name__ == '__main__':
    url = 'https://example.com/blog'
    data = extract_data(url)
    print(data)
"""

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Example: Generated Playwright Script

# Input
script_creator = ScriptCreatorGraph(
    prompt="Extract product prices after clicking 'Show All'",
    source="https://example.com/products",
    config={"llm": {"model": "openai/gpt-4o"}, "library": "playwright"}
)

script = script_creator.run()

# Output (example)
"""
import asyncio
from playwright.async_api import async_playwright

async def extract_data(url):
    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=True)
        page = await browser.new_page()
        await page.goto(url)
        
        # Click show all button
        await page.click('button.show-all')
        await page.wait_for_selector('.product-price', state='visible')
        
        # Extract prices
        products = await page.query_selector_all('.product')
        data = []
        for product in products:
            name = await product.query_selector('.name')
            price = await product.query_selector('.price')
            data.append({
                'name': await name.inner_text(),
                'price': await price.inner_text()
            })
        
        await browser.close()
        return data

if __name__ == '__main__':
    url = 'https://example.com/products'
    data = asyncio.run(extract_data(url))
    print(data)
"""

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Use Cases

  1. Code Generation: Automatically generate scraping scripts
  2. Learning: Understand how to scrape specific websites
  3. Prototyping: Quickly create scraper prototypes
  4. Documentation: Generate example code for documentation
  5. Template Creation: Create reusable scraping templates

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Accessing Generated Code

script = script_creator.run()

# Get the generated code
print("Generated Script:")
print(script)

# Save to file
with open("generated_scraper.py", "w") as f:
    f.write(script)

# Access full state
final_state = script_creator.get_state()
generated_code = final_state.get("answer")
parsed_html = final_state.get("parsed_doc")

print(f"Code length: {len(generated_code)} characters")

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Execution Information

script = script_creator.run()

# Get execution metrics
exec_info = script_creator.get_execution_info()
for node_info in exec_info:
    print(f"Node: {node_info['node_name']}")
    print(f"  Time: {node_info['exec_time']:.2f}s")
    print(f"  Tokens: {node_info['total_tokens']}")
    print(f"  Cost: ${node_info['total_cost_USD']:.4f}")

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Testing Generated Scripts

import subprocess
import tempfile
import os

# Generate script
script = script_creator.run()

# Save to temporary file
with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
    f.write(script)
    script_path = f.name

try:
    # Test the generated script
    result = subprocess.run(
        ['python', script_path],
        capture_output=True,
        text=True,
        timeout=30
    )
    
    print("Script output:")
    print(result.stdout)
    
    if result.returncode != 0:
        print("Script errors:")
        print(result.stderr)
finally:
    # Clean up
    os.unlink(script_path)

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Error Handling

try:
    script = script_creator.run()
    
    if not script or len(script) < 100:
        print("Generated script seems incomplete")
    else:
        print(f"Successfully generated {len(script)} characters of code")
        
except Exception as e:
    print(f"Error during script generation: {e}")

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Best Practices

  1. Be specific in prompts: Clearly describe what data to extract
  2. Choose appropriate library:
    • BeautifulSoup for static HTML
    • Playwright/Selenium for dynamic content
  3. Test generated scripts: Always test before production use
  4. Review code: Manually review generated code for edge cases
  5. Use schema: Define schemas for type-safe output

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Limitations

  • Generated code may need manual refinement
  • Complex scraping logic might not be perfect
  • CAPTCHA or anti-bot measures not automatically handled
  • Generated code quality depends on LLM capabilities

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