GitHub Open Source! GPT-Crawler: Crawl website knowledge base with one click and build your own AI brain!

Written by
Caleb Hayes
Updated on:July-08th-2025
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Crawling websites with one click, building your AI brain! GPT-Crawler makes data collection simple.

Core content:
1. GPT-Crawler: Automatically crawling website knowledge base with one click
2. Intelligent cleaning, multi-format output, adapting to mainstream AI frameworks
3. Deployment in 5 minutes, performance crushing traditional solutions

Yang Fangxian
Founder of 53AI/Most Valuable Expert of Tencent Cloud (TVP)

Knowledge base AI tools have become very popular recently, but data collection is too troublesome? BuilderIO  directly throws out a super-powerful solution - GPT-Crawler ! With just one command, any website can be automatically turned into a structured knowledge base, and it can be fed to ChatGPT and RAG!



Why are developers going crazy?

One-click crawling : Enter the URL to automatically crawl the page (support deep crawling/PDF/documents) Intelligent cleaning : Automatically filter out ads, footers and other noise, retain the core content Multi-format output : Markdown/JSON/OpenAI compatible format, ready to use out of the box Privacy worry-free : run locally, data will never be transmitted 5-minute deployment : one Docker command takes off directly




The hardcore highlights that the technical party loves most

1. Zero configuration and brute force is easy to use

export  const defaultConfig: Config = {
  // ? Core configuration item disassembly
  url:  "https://www.builder.io/c/docs/developers" , // Seed URL (required)
  match:  "https://www.builder.io/c/docs/**" , // Wildcard matching rule
  selector: `.docs-builder-container`, // CSS selector for precise content extraction
  maxPagesToCrawl: 50, // Anti-crash safety valve
  outputFileName:  "output.json"                   // Output file name
};

(You don’t even need to remember the parameters, even a novice can easily get started)

2. Optimized for AI • Automatically generate semantic metadata (title/keywords/abstract) • Perfectly adapt to RAG frameworks such as LangChain and LlamaIndex

3. Outperforming its peers

Task Type
Traditional solutions are time-consuming
GPT-Crawler time
Enterprise website crawling
3 hours
8 minutes
Technical documentation processing
Manual cleaning required
Automatic structuring