Scrapegraph-ai README

The project's README from github.com/ScrapeGraphAI/Scrapegraph-ai. As one markdown file: README.md.

🚀 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

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License: MIT

ScrapeGraphAI%2FScrapegraph-ai | Trendshift

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..

Web data extraction at scale? Try ScrapeGraphAI cloud

You can find more informations at the following link

Integrations:

🚀 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.

Pipeline Name Description
SmartScraperGraph Single-page scraper that only needs a user prompt and an input source.
SearchGraph Multi-page scraper that extracts information from the top n search results of a search engine.
SpeechGraph Single-page scraper that extracts information from a website and generates an audio file.
ScriptCreatorGraph Single-page scraper that extracts information from a website and generates a Python script.
SmartScraperMultiGraph Multi-page scraper that extracts information from multiple pages given a single prompt and a list of sources.
ScriptCreatorMultiGraph Multi-page scraper that generates a Python script for extracting information from multiple pages and sources.

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 as OpenAI, Groq, Azure, Gemini, MiniMax and more, or local models using Ollama.

Remember to have Ollama installed and download the models using the ollama pull command, if you want to use local models.

📖 Documentation

Open In Colab

The documentation for ScrapeGraphAI can be found here.

🆚 Open Source vs Managed API

ScrapeGraphAI comes in two flavours: this open-source library, which you run yourself, and the managed cloud API (used via the Python and JS/TS SDKs). This table explains the difference so you can pick the right one.

Open Source (scrapegraphai) Managed API (scrapegraph-py / scrapegraph-js)
What it is A Python library you run yourself A hosted cloud service you call via SDK
Where it runs Your own infrastructure (self-hosted) ScrapeGraphAI cloud
LLM Bring your own (OpenAI, Groq, Gemini, Azure, local via Ollama) Managed for you
Browser / JS rendering You configure it (Playwright) Managed (stealth, auto/fast/js modes)
Proxies & anti-bot Your responsibility Included
Scaling & maintenance Your responsibility Fully managed
Cost model LLM tokens + your own infra Pay-as-you-go credits
Auth Your own LLM keys SGAI_API_KEY
Capabilities Graph pipelines (SmartScraper, Search, Speech, ScriptCreator…) Scrape, Extract, Search, Crawl, Monitor, History
Setup effort More configuration Minimal — API key + one call
License MIT SDK is MIT; the API service is paid

Choose the open-source library if you want full control, on-prem/self-hosted data, local LLMs (Ollama), or fine-grained cost tuning — and you're happy to manage browsers, proxies and scaling yourself.

Choose the managed API if you want zero infrastructure, managed JS rendering & anti-bot, built-in Crawl and scheduled Monitor jobs, and the fastest path to production — billed per credit.

🏆 Sponsors

NodeMaven

Codes for ScrapeGraphAI users: SCRAPEGRAPH35 (35% off mobile and residential proxies), SCRAPEGRAPH40 (40% off ISP / static proxies).

🤝 Contributing

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

Please see the contributing guidelines.

My Skills My Skills My Skills

🔗 ScrapeGraph API & SDKs

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

API Banner

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

SDK Language GitHub Link
Python SDK Python scrapegraph-py
Node.js SDK Node.js scrapegraph-js

The Official API Documentation can be found here.

📈 Telemetry

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 documentation here.

❤️ Contributors

Contributors

🎓 Citations

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/ScrapeGraphAI/Scrapegraph-ai},
    note = {A Python library for scraping leveraging large language models}
  }

Authors

Contact Info
Marco Vinciguerra Linkedin Badge
Lorenzo Padoan Linkedin Badge

📜 License

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

Acknowledgements

Made with ❤️ by ScrapeGraph AI

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Scrapegraph-ai (ScrapeGraphAI/Scrapegraph-ai)

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31.8K ·GitHub
Scrapegraph-ai
Что это такое?ScrapeGraphAI — это Python-библиотека для веб-скрейпинга, которая использует LLM и логику графов, чтобы собирать скрейпинг-пайплайны для сайтов и локальных документов (XML, HTML, JSON, Markdown). Вы просто говорите, какую информацию хотите извлечь, а всё остальное она делает сама.
Это библиотека, которую я запускаю сам, или облачный сервис, к которому я обращаюсь?Это Python-библиотека, которую вы запускаете на своей инфраструктуре, используя свои ключи LLM. Есть ещё управляемый облачный API с SDK для Python и JS, но это отдельный платный сервис, а не этот репозиторий.
Как добавить её в свой проект?Установите её с PyPI через pip — в README советуют делать это внутри виртуального окружения, чтобы избежать конфликтов с другими библиотеками. В репозитории также есть файлы Docker и docker-compose.
Мне нужен API-ключ OpenAI или можно работать на локальных моделях?И так, и так: работает с LLM API — OpenAI, Groq, Azure, Gemini, MiniMax и другими — или полностью с локальными моделями через Ollama. Для локальных моделей нужно, чтобы Ollama была установлена, а модель скачана командой 'ollama pull'.
В чём подвох — браузерами и защитой от ботов придётся заниматься самому?Да — в open-source-библиотеке рендеринг JS через Playwright, прокси, обход защиты от ботов, масштабирование и поддержка — всё на вашей совести. Управляемый API всё это включает, оплата за кредиты.
Она сообщает куда-то, как я её использую?По умолчанию она собирает анонимную телеметрию использования. Отключить её можно, задав переменную окружения SCRAPEGRAPHAITELEMETRYENABLED в значение false.
Какие скрейпинг-пайплайны в неё уже встроены?Шесть графовых пайплайнов: SmartScraperGraph (одна страница из промпта + источника), SearchGraph (первые n результатов поиска), SpeechGraph (извлекает информацию и возвращает аудиофайл), ScriptCreatorGraph (генерирует Python-скрипт), плюс версии Multi для SmartScraper и ScriptCreator — для списков страниц с параллельными вызовами LLM.
С какого пайплайна лучше начать?SmartScraperGraph — он самый ходовой и требует только пользовательский промпт и URL источника, возвращая извлечённые данные в виде словаря.
Когда выбирать её, а не платный облачный API ScrapeGraph?Библиотеку — когда нужен полный контроль, данные на своей стороне, локальные модели Ollama или тонкая настройка расходов, и вы не против сами разбираться с браузерами, прокси и масштабированием. Облачный API выигрывает, когда нужна нулевая инфраструктура, управляемый рендеринг JS и защита от ботов, а также самый быстрый путь в продакшен.
С какими инструментами и фреймворками она интегрируется?LLM-фреймворки (Langchain, Llama Index, Crew.ai, Agno, CamelAI), low-code-платформы (Pipedream, Bubble, Zapier, n8n, Dify, Toolhouse), MCP-сервер и официальные SDK для Python и Node.
What are similar projects?firecrawl, maxun, Scrapling, crawl4ai, wigolo, MediaCrawler, crawlee, scrapy
Which topics is it in?large-language-model, ai-search, web-crawler, web-scraper, webscraping, n8n, web-search, data-extraction, scraper, scraping, crawler, python-library
Creator
ScrapeGraphAI
Size
16.9 MB
Stars
31.8K
Updated
CommitOct 06Releasev2.3.1Oct 06
Age
2y
Commits · 90 Days