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Welcome to the ScrapeGraphAI examples collection. These hands-on examples demonstrate real-world use cases and help you get started quickly with the library.

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What You’ll Learn

Through these examples, you’ll discover how to:
  • Extract data from websites using natural language prompts
  • Scrape multiple pages simultaneously
  • Process local documents (CSV, PDF, text)
  • Integrate web search capabilities
  • Define custom schemas for structured output
  • Handle various data formats and sources

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Available Examples

Basic Scraping

Get started with simple web scraping using SmartScraperGraph

Multi-Page Scraping

Learn to scrape multiple URLs in a single operation

Local Documents

Extract data from CSV files and text documents

Search Integration

Combine web search with intelligent scraping

Custom Schemas

Define Pydantic schemas for structured, validated output

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Example Categories

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Web Scraping

Extract data from websites using AI-powered natural language queries. Perfect for news sites, e-commerce, social media, and more.

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Document Processing

Process local files including CSV, JSON, XML, and PDF documents. Extract structured information from unstructured text.

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Search & Discovery

Combine search engines with scraping to find and extract information across the web automatically.

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Advanced Patterns

Learn advanced techniques like custom schemas, multi-source scraping, and data validation.

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Prerequisites

Before running these examples, make sure you have:
  1. Installed ScrapeGraphAI 
    pip install scrapegraphai
  2. Set up API keys Create a .env file with your API credentials: 
    OPENAI_API_KEY=your_key_here
  3. Installed dependencies 
    pip install python-dotenv playwright
playwright install

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Code Structure

All examples follow a consistent pattern:
1

Import dependencies

Load required modules and environment variables
2

Configure the graph

Define LLM settings and scraping parameters
3

Create graph instance

Initialize the appropriate graph type with your prompt and source
4

Run and process

Execute the graph and handle the results

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Running the Examples

Each example is self-contained and can be run directly: 
python example_name.py
Make sure your API keys are properly configured in your .env file before running any examples.

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Need Help?

If you encounter issues:

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Next Steps

Start with Basic Scraping to learn the fundamentals, then explore more advanced examples as you build confidence with the library.

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