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ScrapeGraphAI is aweb 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!

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 followinglink

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

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 🐱

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

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

fromscrapegraphai.graphsimportSmartScraperGraph# Define the configuration for the scraping pipelinegraph_config= {"llm": {"model":"ollama/llama3.2","model_tokens":8192    },"verbose":True,"headless":False,}# Create the SmartScraperGraph instancesmart_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 pipelineresult=smart_scraper_graph.run()importjsonprint(json.dumps(result,indent=4))

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.

For each of these graphs there is the multi version. It allows to make calls of the LLM in parallel.

It is possible to use different LLM through APIs, such asOpenAI,Groq,Azure andGemini, or local models usingOllama.

Remember to haveOllama installed and download the models using theollama pull command, if you want to use local models.

The documentation for ScrapeGraphAI can be foundhere.Check out also the Docusaurushere.

Feel free to contribute and join our Discord server to discuss with us improvements and give us suggestions!

Please see thecontributing guidelines.

If you are looking for a quick solution to integrate ScrapeGraph in your system, check out our powerful APIhere!

We offer SDKs in both Python and Node.js, making it easy to integrate into your projects. Check them out below:

The Official API Documentation can be foundhere.

We collect anonymous usage metrics to enhance our package's quality and user experience. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable SCRAPEGRAPHAI_TELEMETRY_ENABLED=false. For more information, please refer to the documentationhere.

If you have used our library for research purposes please quote us with the following reference:

  @misc{scrapegraph-ai,    author = {Lorenzo Padoan, Marco Vinciguerra},    title = {Scrapegraph-ai},    year = {2024},    url = {https://github.com/VinciGit00/Scrapegraph-ai},    note = {A Python library for scraping leveraging large language models}  }

	Contact Info
Marco Vinciguerra
Lorenzo Padoan

ScrapeGraphAI is licensed under the MIT License. See theLICENSE file for more information.

• We would like to thank all the contributors to the project and the open-source community for their support.
• ScrapeGraphAI is meant to be used for data exploration and research purposes only. We are not responsible for any misuse of the library.

Made with ❤️ byScrapeGraph AI

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