LLM Scraper — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for LLM Scraper. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 13, 2026 • Sentiment updated: Apr 14, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As seen on Reddit, 'HTML to Markdown with CSS selector & XPath annotations for LLM Scraper' is a popular topic, and on HackerNews, a user stated 'The LLM scraper bots ignore robots.txt'

Pros & Cons

What People Love

Helpful data collection, Reddit users praise the versatility of LLM scrapers, GitHub users appreciate the open-source nature of some LLM scraper projects

Common Complaints

Server overloading, Ignored robots.txt

Biggest Positive: Helpful data collection

Biggest Negative: LLM scrapers overload servers

Why LLM Scraper Stands Out

LLM Scraper stands out from alternatives due to its unique combination of Playwright framework, Zod schema support, and streaming mode, which enable robust and efficient webpage data extraction. Its support for multiple LLM providers, including OpenAI, Anthropic, and Google, allows users to choose the best model for their specific use case. Additionally, LLM Scraper's code-generation feature and ability to handle various formatting modes make it an attractive choice for developers looking to simplify their webpage scraping and data processing workflows.

Built With

Build a webpage scraper that extracts structured data using LLMs — LLM Scraper's Playwright framework and Zod schema support enable robust data extraction, Build a custom browser automation script that integrates with LLMs — LLM Scraper's code-generation feature and JSON Schema support simplify the process, Build a research agent that reads webpages and summarizes content — LLM Scraper's streaming mode and support for multiple LLM providers facilitate real-time data processing, Build a data pipeline that ingests webpage data and applies LLM-based transformations — LLM Scraper's ability to handle various formatting modes and custom content loading enables flexible data processing, Build a monitoring tool that tracks webpage changes using LLMs — LLM Scraper's support for multiple LLM models and ability to generate reusable Playwright scripts streamline the monitoring process

Getting Started

  1. Install the required dependencies with `npm i zod playwright llm-scraper`
  2. Initialize your LLM provider using `npm i @ai-sdk/openai` and `const llm = openai('gpt-4o')`
  3. Create a new LLMScraper instance with `const scraper = new LLMScraper(llm)`
  4. Define a schema to extract contents into using `const schema = z.object({ ... })`
  5. Try running the scraper with `const { data } = await scraper.run(page, Output.object({ schema }), { format: 'html' })` to verify it works

About

Turn any webpage into structured data using LLMs

Category & Tags

Category: data

Tags: ai, artificial-intelligence, browser, browser-automation, gpt, gpt-4, langchain, llama, llm, openai, playwright, puppeteer, scraper

Market Context

Competitive data collection tools market