🚀 Looking for an even faster and simpler way to scrape at scale (only 5 lines of code)? Check out our enhanced version at ScrapeGraphAI.com! 🚀

🕷️ ScrapeGraphAI: You Only Scrape Once

ScrapeGraphAI is a web scraping python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.).

Just say which information you want to extract and the library will do it for you!

🚀 Integrations

ScrapeGraphAI offers seamless integration with popular frameworks and tools to enhance your scraping capabilities. Whether you're building with Python or Node.js, using LLM frameworks, or working with no-code platforms, we've got you covered with our comprehensive integration options..

You can find more informations at the following link

Integrations:

API: Documentation
SDKs: Python, Node
LLM Frameworks: Langchain, Llama Index, Crew.ai, Agno, CamelAI
Low-code Frameworks: Pipedream, Bubble, Zapier, n8n, Dify, Toolhouse
MCP server: Link

🚀 Quick install

The reference page for Scrapegraph-ai is available on the official page of PyPI: pypi.

pip install scrapegraphai

# IMPORTANT (for fetching websites content)
playwright install

Note: it is recommended to install the library in a virtual environment to avoid conflicts with other libraries 🐱

💻 Usage

There are multiple standard scraping pipelines that can be used to extract information from a website (or local file).

The most common one is the SmartScraperGraph, which extracts information from a single page given a user prompt and a source URL.

from scrapegraphai.graphs import SmartScraperGraph

# Define the configuration for the scraping pipeline
graph_config = {
    "llm": {
        "model": "ollama/llama3.2",
        "model_tokens": 8192,
        "format": "json",
    },
    "verbose": True,
    "headless": False,
}

# Create the SmartScraperGraph instance
smart_scraper_graph = SmartScraperGraph(
    prompt="Extract useful information from the webpage, including a description of what the company does, founders and social media links",
    source="https://scrapegraphai.com/",
    config=graph_config
)

# Run the pipeline
result = smart_scraper_graph.run()

import json
print(json.dumps(result, indent=4))

[!NOTE] For OpenAI and other models you just need to change the llm config!
graph_config = {
   "llm": {
       "api_key": "YOUR_OPENAI_API_KEY",
       "model": "openai/gpt-4o-mini",
   },
   "verbose": True,
   "headless": False,
}

The output will be a dictionary like the following:

{
    "description": "ScrapeGraphAI transforms websites into clean, organized data for AI agents and data analytics. It offers an AI-powered API for effortless and cost-effective data extraction.",
    "founders": [
        {
            "name": "",
            "role": "Founder & Technical Lead",
            "linkedin": "https://www.linkedin.com/in/perinim/"
        },
        {
            "name": "Marco Vinciguerra",
            "role": "Founder & Software Engineer",
            "linkedin": "https://www.linkedin.com/in/marco-vinciguerra-7ba365242/"
        },
        {
            "name": "Lorenzo Padoan",
            "role": "Founder & Product Engineer",
            "linkedin": "https://www.linkedin.com/in/lorenzo-padoan-4521a2154/"
        }
    ],
    "social_media_links": {
        "linkedin": "https://www.linkedin.com/company/101881123",
        "twitter": "https://x.com/scrapegraphai",
        "github": "https://github.com/ScrapeGraphAI/Scrapegraph-ai"
    }
}

There are other pipelines that can be used to extract information from multiple pages, generate Python scripts, or even generate audio files.

Scrapegraph-ai v2.1.7