🕷️ ScrapeGraphAI: You Only Scrape Once

What is the project about?

ScrapeGraphAI is a Python library for web scraping that utilizes Large Language Models (LLMs) and graph logic to simplify and automate the process of extracting data from websites and local documents (HTML, XML, JSON, Markdown, etc.).

What problem does it solve?

Traditional web scraping often involves writing custom code for each website and dealing with complex HTML structures. ScrapeGraphAI simplifies this by allowing users to specify what data they want, and the library figures out how to extract it, reducing the need for manual, website-specific scraping logic. It aims to make web scraping more accessible and efficient. It also solves the problem of maintaining scrapers when websites change.

What are the features of the project?

• LLM-Powered Scraping: Uses LLMs to understand website structure and extract data based on natural language prompts.
• Graph-Based Approach: Employs a graph structure to manage the scraping process, making it more robust and adaptable.
• Multiple Scraping Pipelines: Offers pre-built pipelines for various scraping tasks (single-page, multi-page, search-based, script generation, audio generation).
• Support for Multiple LLMs: Compatible with various LLM providers, including OpenAI, Groq, Azure, Gemini, and local models via Ollama.
• Headless/Headed Browser Control: Can run with or without a visible browser window.
• Parallel Processing: "Multi" versions of graphs allow for parallel LLM calls, speeding up scraping.
• SDKs: Offers Python and Node.js SDKs for easy integration.
• API: Provides a powerful API for quick integration.

What are the technologies used in the project?

• Python: The core programming language.
• Large Language Models (LLMs): OpenAI, Groq, Azure, Gemini, Ollama (for local models).
• Playwright: For browser automation and fetching website content.
• Graph Data Structures: For managing the scraping workflow.
• Pylint: For code linting.
• CodeQL: For code quality and security analysis.

What are the benefits of the project?

• Simplified Scraping: Reduces the complexity of web scraping by using natural language prompts.
• Increased Efficiency: Automates much of the scraping process, saving time and effort.
• Adaptability: The graph-based approach and LLM integration make it more resilient to website changes.
• Flexibility: Supports various LLMs and scraping scenarios.
• Faster Development: Pre-built pipelines and SDKs accelerate development.
• Cost-Effective: Can potentially reduce the cost of scraping by optimizing the process.

What are the use cases of the project?

• Data Extraction for AI Agents: Gathering data to train or feed AI models.
• Data Analysis: Collecting data for market research, competitive analysis, and other analytical tasks.
• Content Aggregation: Pulling information from multiple sources to create a unified dataset.
• Automated Data Entry: Extracting data and populating databases or spreadsheets.
• Website Monitoring: Tracking changes on websites.
• Research: Gathering data for academic or scientific studies.
• Generating Python Scripts: Creating custom scraping scripts based on website content.
• Generating Audio Files: Creating audio summaries of web page content.