How to Scrape the Web Using LLMs: A Complete Guide

How to Scrape the Web Using LLMs: A Complete Guide

Introduction

Web scraping is evolving. With Large Language Models (LLMs), we can now extract data more intelligently and handle complex scenarios that traditional scrapers struggle with. This guide shows you how to use Critique Labs API to build sophisticated web scraping solutions.

Why Use LLMs for Web Scraping?

  1. Intelligent Data Extraction

    • Understanding context and relationships
    • Handling dynamic content
    • Natural language processing capabilities
  2. Adaptive Scraping

    • Automatically adjusts to site changes
    • Handles different page layouts
    • Understands semantic meaning
  3. Cost-Effective

    • No need for expensive infrastructure
    • Pay-as-you-go pricing
    • Scalable for any size project
  4. Community and Support

    • Access to a growing community of developers
    • Regular updates and improvements
    • Active support from the Critique team

Our API combines the power of LLMs with robust web scraping capabilities, so you only need to provide your custom API endpoint and the LLM will handle the rest:

Example API call

1function fetchData() {
2    const url = "https://api.critique-labs.ai/v1/published-service/real-time-stock-sentiment-analysis";
3    const data = { "stock_symbol": "string" } ; // replace with actual inputs
4    const headers = {
5        'Content-Type': 'application/json',
6        'X-API-Key': '<YOUR API KEY HERE>'
7    };
8
9    fetch(url, {
10        method: 'POST',
11        headers: headers,
12        body: JSON.stringify(data)
13    })
14    .then(response => response.json())
15    .then(output => {
16        if (output.error) {
17            throw new Error(output.error);
18        }
19        // Output in your specified format
20        const formattedOutput = output.response;
21        // The sources used to generate this output
22        const sources = output.context;
23
24        console.log(formattedOutput);
25        console.log(sources);
26    })
27    .catch(error => {
28        console.error("Error:", error);
29    });
30}
31
32fetchData();
33

Coming Soon: Agentic Web Researcher

We're developing an advanced AI agent that can:

  • Navigate websites autonomously
  • Follow complex research instructions
  • Validate and cross-reference information
  • Generate structured reports

Start Building Your API →