--- title: "Scrapegraph-ai" type: "tool" slug: "scrapegraphai-scrapegraph-ai" canonical_url: "https://www.graphcanon.com/tools/scrapegraphai-scrapegraph-ai" github_url: "https://github.com/ScrapeGraphAI/Scrapegraph-ai" homepage_url: "https://scrapegraphai.com" stars: 28143 forks: 2711 primary_language: "Python" license: "MIT" categories: ["data-retrieval", "llm-frameworks", "developer-tools", "inference-serving"] tags: ["large-language-model", "scraping-python", "llm", "ai-scraping", "ai-crawler", "web-data-extraction"] updated_at: "2026-07-07T18:22:19.390412+00:00" --- # Scrapegraph-ai > Python scraper based on AI ScrapeGraphAI is a Python library that leverages LLMs and direct graph logic to facilitate web scraping, including website and document data extraction. ## Facts - Repository: https://github.com/ScrapeGraphAI/Scrapegraph-ai - Homepage: https://scrapegraphai.com - Stars: 28,143 · Forks: 2,711 · Open issues: 1 · Watchers: 157 - Primary language: Python - License: MIT - Last pushed: 2026-06-25T07:51:20+00:00 ## Categories - [Data & Retrieval](/categories/data-retrieval.md) - [LLM Frameworks](/categories/llm-frameworks.md) - [Developer Tools](/categories/developer-tools.md) - [Inference & Serving](/categories/inference-serving.md) ## Tags large-language-model, scraping-python, llm, ai-scraping, ai-crawler, web-data-extraction ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [JavaGuide](/tools/snailclimb-javaguide.md) - Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发 (★ 156,863) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [firecrawl](/tools/firecrawl-firecrawl.md) - The API to search, scrape, and interact with the web at scale. (★ 147,117) ## README (excerpt) ```text ## 🚀 **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**](https://scrapegraphai.com/?utm_source=github&utm_medium=readme&utm_campaign=oss_cta&ut#m_content=top_banner)! 🚀 --- # 🕷️ ScrapeGraphAI: You Only Scrape Once

ScrapeGraphAI

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ScrapeGraphAI%2FScrapegraph-ai | Trendshift

[ScrapeGraphAI](https://scrapegraphai.com) 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](https://scrapegraphai.com) **Integrations**: - **API**: [Documentation](https://docs.scrapegraphai.com/introduction) - **SDKs**: [Python](https://docs.scrapegraphai.com/sdks/python), [Node](https://docs.scrapegraphai.com/sdks/javascript) - **LLM Frameworks**: [Langchain](https://docs.scrapegraphai.com/integrations/langchain), [Llama Index](https://docs.scrapegraphai.com/integrations/llamaindex), [Crew.ai](https://docs.scrapegraphai.com/integrations/crewai), [Agno](https://docs.scrapegraphai.com/integrations/agno), [CamelAI](https://github.com/camel-ai/camel) - **Low-code Frameworks**: [Pipedream](https://pipedream.com/apps/scrapegraphai), [Bubble](https://bubble.io/plugin/scrapegraphai-1745408893195x213542371433906180), [Zapier](https://zapier.com/apps/scrapegraphai/integrations), [n8n](http://localhost:5001/dashboard), [Dify](https://dify.ai), [Toolhouse](https://app.toolhouse.ai/mcp-servers/scrapegraph_smartscraper) - **MCP server**: [Link](https://smithery.ai/server/@ScrapeGraphAI/scrapegraph-mcp) ## 🚀 Quick install The reference page for Scrapegraph-ai is available on the official page of PyPI: [pypi](https://pypi.org/project/scrapegraphai/). ```bash 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. ```python 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 = SmartScraperGr ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/scrapegraphai-scrapegraph-ai`](/api/graphcanon/tools/scrapegraphai-scrapegraph-ai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_