What is web scraping with an LLM Agent?

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With the rise of LLM Agents, the web scraping is becoming smarter and more autonomous. This evolution is transforming the way we access and use online data.

It's perfectly possible to do web scraping with an LLM, by giving it clear instructions in natural language. Cristina for Alucare.fr

What is web scraping with an LLM Agent?

📌 As a reminder, the web scraping is to extract information automatically from websites.

This type of collection is often carried out using classic methods based on precise rules. These involve selectors such as XPath or CSS, which indicate exactly where to find information on the page.

🔥 With the arrival of LLM AgentsWeb scraping is undergoing a real paradigm shift.

What is an LLM Agent?

It is a program that combines a advanced language model (LLM) to understand human language.

👉 So, instead of just giving technical instructions like with XPath or CSS, you can tell the agent what you want in normal language. He is responsible for finding and collect data for you.

Role of the LLM Agent in Web Scraping

An LLM (Large Language Model) Agent is a program that uses an advanced language model to interpret human instructions and automate data retrieval from the web. — An LLM (Large Language Model) Agent is a program that uses an advanced language model to interpret human instructions and automate data extraction on the web. ©Christina for Alucare.fr

The LLM Agent plays several roles in web scraping:

Understanding instructions user's natural language expression.
Identify and navigate automatically in the various web page structures.
Extract, transform and organize data independently.
Adapting to changes on the site without modifying the rules by hand.

Here are some specific examples of how LLM agents are used in web scraping:

✅ Price and product feature extraction.
✅ Monitoring customer reviews.
✅ Retrieval of articles or news items.
✅ Automatic collection of financial or stock market data.

How does an LLM agent work in web scraping?

An LLM Agent follows a lifecycle to extract data from the web.

Objective (Prompt)

The user defines the task in plain language. For example: “Find the price and description of this item.”.

Planning (LLM)

The agent breaks down the task into concrete actions. For example, they decide to visit the page, click on a tab, or scroll down a list.

Execution (Actions)

The agent navigates the site, clicks on buttons, scrolls through the page, and interacts with the elements necessary to achieve the objective.

Extraction (LLM)

The agent identifies and extracts the relevant data.

Check and loop

The agent checks the result and can repeat the process to refine the extraction or correct errors.

Find out how to use an LLM Agent for web scraping in this step-by-step tutorial.

Step 1: Preparing the environment

Installation of necessary libraries (Python, frameworks, etc.).

# Linux / macOS
python3 -m venv .venv
source .venv/bin/activate

# Windows (PowerShell)
python -m venv .venv
.venv\Scripts\Activate.ps1

# Install libs
pip install requests beautifulsoup4 httpx python-dotenv

Step 2: Target selection

Select a web page to scrape and identify important information.

# Example of target URL to scrape
url = "https://example.org/produits"

# Information to be extracted :
# - Page title
# - Main product name
# - Displayed price
# - Links to other products
<html>
  <head>
    <title>Store Example - Products</title>
  </head>
  <body>
    <h1>Our products</h1>
    <div class="product">
      <h2>Product A</h2>
      <span class="price">29.99€</span>
    </div>
    <a href="/en/produit-b/">See Product B</a>
  </body>
</html>

Step 3: Formulating the prompt

Write clear and precise instructions for the agent.

System:
You're an LLM agent specializing in web scraping.
Your mission is to analyze and organize data extracted from a web page.

User:
Here's the parsed HTML content:
<h1>Our products</h1>
Product A - €29.99
Product B - 45.00€

Tasks :
1. Summarize the main content.
2. Give a JSON format containing {product_name, price}.
3. Suggest 2 relevant CSS selectors.

Step 4: Running the script

Run the process and observe the result.

Here is an example of simple code with Python using Requests, BeautifulSoup and an LLM API:

import requests
import json

# Simulates the LLM agent function that schedules and executes actions
def execute_llm_agent(prompt, url_target):
    # Here, the agent uses the prompt to "decide" which actions to take.
    print(f "LLM agent: I'm scanning the {url_target} page for data. My goal: '{prompt}'")
    
    # 1. Analysis and Planning (simulated)
    print("Agent LLM : I plan my strategy...")
    
    # Agent could generate selectors, navigation instructions, etc.
    # Ex: the agent decides to search for '' and '' items with the 'price' class.
    
    # 2. execution and retrieval
    response = requests.get(url_target)
    # The agent "understands" the HTML structure and extracts the relevant data.
    # In a real agent, this part would be driven by the LLM.
    extracted_data = {
        "page_title": "Shop Example - Products", # Dynamically extracted
        "product_A": "Product A", # Dynamically extracted
        "price_A": "29.99€" # Dynamically extracted
    }
    
    # 3. Verification and organization
    print("LLM agent: I've found the data. I'm organizing it in JSON format.")
    
    # The agent uses its reasoning ability to format the final result.
    resultat_json = json.dumps({
        "products": [
            {
                "product_name": extracted_data["product_A"],
                "price": extracted_data["prix_A"]
            }
        ]
    }, indent=2)
    
    return resultat_json

# Run agent with user's goal
prompt_user = "Find the product name and price on the page."
url_of_site = "https://example.com"

extract_data = execute_llm_agent(prompt_user, url_from_site)
print("Agent's final result:")
print(extract_data)

Comparing web scraping tools with LLM Agents

To get the most out of web scraping with LLM Agents, it's important to know the different tools available and their specific features.

🌐 Tool / Framework	🤖 LLM approach	✅ Highlights	❌ Weak points
Bright Data	Web data and tools platform with LLM integration	Robust infrastructure, complete solutions, high resilience	Potentially high cost for large volumes, complexity for beginners
Apify + LLM	Integrating LLM into an existing framework	Very powerful, manages infrastructure	Requires more technical knowledge
ScrapeGraphAI	Graph-based, highly visual	Easy to use, no coding required	May be less flexible for complex tasks
In-house solutions“	Direct use of LLM PLCs	Maximum flexibility, total control	High cost and complexity, requires coding

FAQs

What's the difference between an LLM and a web scraping API?

✔ Un LLM is a language model capable of understanding and generating text in human language. It can be used to interpret web pages and guide extraction.

✔ One Web scraping API, on the other hand, is a ready-to-use tool that directly provides the extracted data. It often has built-in features such as IP rotation or CAPTCHA management.

Which LLM agent should I choose for web scraping?

When choosing an LLM Agent, here are a few criteria to consider:

✅ La size and complexity of the task.
✅ The budget available.
✅ La language and domain data.
✅ La compatibility with your environment technology.

What are the challenges of web scraping with LLMs?

Before using an LLM Agent, it is best to be aware of the limitations and potential difficulties:

Cost of use API calls to LLMs can be expensive, especially for large-scale tasks.
Performance and speed : LLM inference is slower than executing predefined selectors.
Precision and robustness The result depends heavily on the quality of the prompt. The LLM can “make mistakes” or “hallucinate,” and a slight change in layout can disrupt the agent.
Technical constraints JavaScript-based sites, anti-bot protection (Cloudflare), and CAPTCHA remain difficult to manage.

How do you manage errors and blockages (CAPTCHA, anti-bot protection) with an LLM agent?

Some specialized services, such as Bright Data offer integrated solutions to overcome these bottlenecks. This makes the process of scraping with an LLM Agent smoother and more reliable.

Bright Data automatically bypasses blocks and captchas, making scraping easier and more efficient. ©Christina for Alucare.fr

Is web scraping with an LLM legal?

The legality of web scraping depends on context and country. In general, it all depends on how the data is used and whether it is protected by rights.

💬 In short, LLM Agents are transforming web scraping by making it more flexible and accessible, even if technical challenges remain. And you, what do you think of this evolution?