Scrape Web Content for LLM Consumption

LLMs are hungry. They want context. They want documents. They want the web. denkbot.dog converts URLs into clean text that LLMs can actually consume — no HTML tags, no boilerplate, no "please accept our cookies" walls. Just the content. Feed the model. The dog fetches.

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What you'd use this for

Building RAG systems, giving LLMs real-time web context, generating content summaries from URLs, feeding research documents into AI pipelines, and LLM-powered data extraction.

How it works

example

// Feed web content to your LLM
const { text } = await fetch('https://api.denkbot.dog/scrape', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    url: 'https://arxiv.org/abs/example',
    renderJs: false,
  }),
}).then(r => r.json())

const completion = await openai.chat.completions.create({
  messages: [
    { role: 'system', content: 'You are a research assistant.' },
    { role: 'user', content: `Summarize this: ${text.slice(0, 8000)}` },
  ],
  model: 'gpt-4o',
})