Développement
Scrapling
Adaptive web scraping framework in Python — from a single request to a full-scale crawl
★ 86,3 k étoilesBSD-3-Clause
Voir le guide →Scrapegraph-ai est un projet de développement open source, créé par ScrapeGraphAI (ScrapeGraphAI/Scrapegraph-ai sur GitHub). Il compte 31 623 étoiles sur GitHub et 3 221 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 6 octobre 2026.
Scrape any website with a prompt: LLM-powered scraping pipelines in a few lines of Python
Decide what you want to achieve with Scrapegraph-ai and check the README on GitHub for requirements before installing.
Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).
Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.
Work through the README's quickstart example end to end, then adapt it to your own project or data.
Describe the data you want in plain language; the LLM builds the extraction for you.
The flagship graph scrapes a page and returns structured output from a single prompt and URL.
Additional pipelines cover searching, multi-page scraping and other extraction patterns documented in the docs.
Works with different LLM providers and models through a simple config object.
Extracted, structured content drops neatly into RAG and data pipelines.
A hosted ScrapeGraph API and SDKs exist if you outgrow local runs (the open-source library remains MIT).
Get the library source and examples.
git clone https://github.com/ScrapeGraphAI/Scrapegraph-ai.git
cd Scrapegraph-aiInstall ScrapeGraphAI from PyPI.
pip install scrapegraphaiCreate a SmartScraperGraph with your model config, a prompt and a URL, then call run() as in the README usage example.
# graph = SmartScraperGraph(prompt='Extract the product names and prices', source='https://example.com', config=graph_config)
# result = graph.run() # see README Usage for the full snippetScrapegraph-ai is maintained by the ScrapeGraphAI organization/account on GitHub, together with open-source contributors, in the public repository ScrapeGraphAI/Scrapegraph-ai.
Yes — the code is open source under the MIT license. You can use, modify and redistribute it under that license's terms.
The library is free, but extraction is LLM-driven — you need your own LLM API key (or a local model), and you pay that provider's token costs.
Classic scrapers are free per run but break when layouts change; ScrapeGraphAI trades a small LLM cost per page for prompts that survive redesigns.
Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.
Adaptive web scraping framework in Python — from a single request to a full-scale crawl
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