ScraperAI - AI RAG and Search Tool

Overview

ScraperAI is an open-source, AI-assisted web-scraping toolkit that uses large language models (for example, ChatGPT-style LLMs) to simplify extraction workflows. According to the project's GitHub repository, ScraperAI automatically detects data elements on pages, generates XPATH selectors, handles pagination, and produces reusable scraping recipes, reducing manual selector engineering and boilerplate crawling logic. It supports multiple scraping backends — for example Selenium and custom crawler integrations — so users can adapt the tool to dynamic sites and bespoke crawling stacks. The project is intended for developers and data engineers who want to accelerate building scrapers with LLM-guided element detection and recipe reuse. The repository (GPL-3.0 licensed) shows active maintenance, and the architecture emphasizes modular scraping methods, pagination handling, and repeatable recipes that can be persisted and re-run against target sites. According to the GitHub repository, the project is community-driven and designed to plug into LLM APIs for element inference and recipe generation.

GitHub Statistics

• Stars: 236
• Forks: 30
• Contributors: 3
• License: GPL-3.0
• Primary Language: HTML
• Last Updated: 2025-09-18T19:02:17Z
• Latest Release: 0.0.3

According to the GitHub repository, ScraperAI has 236 stars, 30 forks, and 3 contributors, and is licensed under GPL-3.0. The project had a recent commit on 2025-09-18, indicating ongoing maintenance. The modest contributor count suggests a small core team with a growing user community; stars and forks show interest but also room for wider adoption. The GPL license requires derived work to remain open-source, which can attract contributors who prefer strong copyleft licenses.

Installation

Install via pip:

git clone https://github.com/scraperai/scraperai.git

cd scraperai

python -m venv .venv && source .venv/bin/activate

pip install -r requirements.txt

pip install -e .

Key Features

• LLM-guided automatic detection of data elements on web pages
• Automatic XPATH generation for identified page elements
• Built-in pagination handling to traverse multi-page listings
• Reusable scraping recipes to save and rerun extraction workflows
• Support for Selenium-based scraping backends for dynamic pages
• Pluggable custom crawler integrations for bespoke crawling needs
• Designed to integrate with LLM APIs (e.g., ChatGPT-style models) for inference

Community

The project has a small but active community—236 GitHub stars, 30 forks, and 3 contributors. Recent commits (last recorded 2025-09-18) show maintenance. Contributions, issues, and pull requests are managed through the GitHub repository; prospective users should consult the repo for up-to-date installation steps, requirements, and contribution guidelines.

Key Information

• Category: RAG and Search
• Type: AI RAG and Search Tool