Using cURL for Web Scraping: The Complete Guide (2025)

Want to learn how to scrape websites using cURL? In this guide, we’ll cover all the basics of cURL for web scraping, including:

• Running basic cURL commands to fetch data from websites
• Using proxies and headers to avoid blocks
• Pairing cURL with Python and ScraperAPI for advanced scraping, including JavaScript rendering

Let’s dive in!

What is cURL in Web Scraping?

cURL (or Client URL) is a versatile command-line tool for sending HTTP requests and retrieving web data like HTML and JSON. Its simplicity, flexibility, and cross-platform compatibility make it a favorite among developers.

With features like proxies, HTTP headers, and user-agent management, cURL suits beginners and experienced developers who want to quickly obtain data or test scraping APIs.

Leveraging cURL for Effective Data Extraction

cURL is popular in web scraping because it’s simple, flexible, and gives you complete control over each request.

With curl commands, you can tailor each data request to meet specific needs, which is especially helpful when scraping sites that require custom settings or need to be approached cautiously to avoid blocks.

Here’s when cURL is a good choice for your scraping project:

• Quick Data Fetching: Perfect when you need to retrieve data quickly without setting up a complex tool or interface.
• Customizable Requests: If your project requires managing HTTP headers, setting a unique user-agent, or using proxies to prevent blocks, cURL makes it easy to adjust these settings.
• Working with APIs: Ideal for scraping sites that offer data through APIs. With cURL, you can easily retrieve structured JSON or XML data directly from the source.

For smaller tasks or straightforward API requests, cURL is usually all you need. But for more advanced projects—like scraping complex web pages or handling JavaScript; you might find it helpful to pair cURL with tools like ScraperAPI or integrate it with Python for automation and enhanced control.

Fetching Web Data with cURL Commands

Now, let’s get hands-on with cURL to gather data from a website. In this section, I’ll guide you through everything from installing cURL to running basic curl commands that let you scrape data. 

1. Installing cURL

Before using cURL, ensure it’s installed on your system:

• Linux: Run sudo apt-get install curl.
• MacOS: Use Homebrew with brew install curl.
• Windows: Pre-installed on Windows 10+; download from the cURL website for older versions.

Verify installation by running curl in your terminal. If installed, you’ll see a message like:

curl: try 'curl --help' or 'curl --manual' for more information

With cURL installed and working, you’re ready to start scraping!

2. Basic cURL Syntax

The most basic form of a cURL command looks like this:

curl [URL]

By default, this command retrieves the raw HTML content of the provided target URL and displays it directly in your terminal. For example, to fetch the content of the Books to Scrape homepage, you can use:

curl https://books.toscrape.com

3. Enhancing cURL with Options

cURL offers several options to customize your requests and handle responses more effectively:

• -o <output_file> : Saves the output to a specified file.
• -O : Saves the output as the file’s original name from the server.
• -L : Follows HTTP redirects automatically.
• -i : Includes HTTP response headers in the output.
• -k : Allows requests to SSL sites without verifying certificates.
• -v : Enables verbose output for debugging.
• -H : Adds custom headers to the request.

For example, to save the HTML from Books to Scrape while following redirects, use:

curl -L https://books.toscrape.com -o books.html

These options make cURL a flexible tool for fetching and saving web content, setting the foundation for more advanced scraping tasks.

4. Saving HTML from a Website

To grab the HTML from Books to Scrape and save it to a file called books.html, you can use the -o option:

curl -o books.html https://books.toscrape.com

This command instructs cURL to fetch the page’s HTML and save it to the file books.html in your current working directory.

5. Using Proxies with cURL

If you plan to make frequent requests, using a proxy can help you avoid blocks by changing your IP address with each request. 

Here’s how to set up a proxy with cURL:

curl -x http://yourproxy:port https://books.toscrape.com/ > books.html

Replace http://yourproxy:port with your proxy details. This approach helps mask your IP, making it less likely for the website to block your requests.

6. Automating Proxy Management with ScraperAPI

If you’re scraping frequently, handling proxies can be a hassle. ScraperAPI makes it easy with a proxy port method that takes care of IP rotation, CAPTCHA bypass, and retries for you. This way, your cURL requests go through ScraperAPI’s proxy front-end, and all the hard work is managed automatically.

Here’s how to use ScraperAPI in proxy mode with cURL:

curl -x "http://scraperapi:YOUR_API_KEY@proxy-server.scraperapi.com:8001" -k "https://books.toscrape.com/"

What’s happening here:

• -x sets up the proxy server using ScraperAPI’s address.
• Replace YOUR_API_KEY with your actual API key for authentication.
• -k skips SSL verification, which is recommended for smooth connections.

With this setup, ScraperAPI manages IP addresses and CAPTCHAs in the background so you can focus on your web scraping.

Note: Don’t have an API key? Create a free ScraperAPI account to get a unique API key and 5,000 API credits.

Dynamic Web Scraping with cURL

Some websites load their content dynamically using JavaScript, meaning the data you’re after may not be directly available in the HTML source. In these cases, simply using cURL to fetch the page’s HTML might leave you with empty sections where the content should be. This is where JavaScript rendering comes in.

With ScraperAPI, you can enable JavaScript rendering to ensure you capture all the content, even on dynamic sites.

When to Use JavaScript Rendering

You’ll typically need JavaScript rendering for pages that rely heavily on client-side scripting to display information. Here’s how to tell if a page uses JavaScript to load its content:

1. Inspect the HTML Source: If you don’t see the data you’re after directly in the page source (like product details or prices), but you can see it when you load the page in a browser, it’s likely being injected with JavaScript.
2. Look for Empty HTML Tags: Sites that use JavaScript to load data often have empty tags (like divs or spans) in the source code. These tags are placeholders that JavaScript fills in after the page loads.
3. Check for AJAX Calls: Many JavaScript-heavy sites use AJAX to load content dynamically. You can look for requests to fetch data from APIs within the browser’s network tab. If you see these, it’s a sign the page is using JavaScript to populate content.

Using JavaScript Rendering with ScraperAPI

To handle JavaScript-heavy pages, enable JavaScript rendering in ScraperAPI. Here’s how to set it up with cURL:

curl -x "http://scraperapi.render=true:YOUR_API_KEY@proxy-server.scraperapi.com:8001" -k "https://example.com"

In this command:

• render=true tells ScraperAPI to load the page entirely, including any JavaScript-injected content.

Javascript Rendering in ScraperAPI Proxy Mode

ScraperAPI’s proxy mode lets you add JavaScript rendering in the curl command. Just add parameters to the username section:

curl -x "http://scraperapi.render=true:YOUR_API_KEY@proxy-server.scraperapi.com:8001" -k "https://books.toscrape.com/"

You can even combine multiple features, like enabling JavaScript and setting a country code:

curl -x "http://scraperapi.render=true.country_code=us:YOUR_API_KEY@proxy-server.scraperapi.com:8001" -k "https://books.toscrape.com/"

By enabling JavaScript rendering, you ensure that the cURL request pulls in the complete data from dynamic pages, making it ideal for scraping modern websites that rely on JavaScript for key information. 

Async Scraping in cURL: POST Requests and Proxies

Scraping websites one request at a time works for small projects, but what if you need to gather data from thousands of pages quickly? That’s where async scraping comes in.

By running multiple requests simultaneously, you can dramatically speed up your workflow. ScraperAPI’s Async Scraper pairs perfectly with cURL, making large-scale scraping simple and efficient.

Submitting an Async Scraping Job with cURL

ScraperAPI’s Async Scraper lets you submit a scraping job using a simple POST request. Here’s how you can send a job:

curl -X POST -H "Content-Type: application/json" \
-d '{"apiKey": "YOUR_API_KEY", "url": "https://example.com"}' \
"https://async.scraperapi.com/jobs"

In this command:

• -X POST specifies that you’re sending a POST request.
• -H "Content-Type: application/json" sets the content type to JSON.
• -d provides the request body, including:
  • Your API key for authentication.
  • The URL of the webpage you want to scrape.
  • Your API key for authentication.
  • The URL of the webpage you want to scrape.
  • Your API key for authentication.
  • The URL of the webpage you want to scrape.

The endpoint https://async.scraperapi.com/jobs processes the job on ScraperAPI’s backend. Once submitted, you’ll receive a unique statusUrl to track your job’s progress.

Tracking the Job Status

To monitor the status of your scraping job, use the statusUrl provided in the response:

curl "https://async.scraperapi.com/jobs/JOB_ID"

When the job is complete, the status changes to finished, and the response will include the scraped content:

{
  "id": "JOB_ID",
  "status": "finished",
  "url": "https://example.com",
  "response": {
    "headers": {
      "content-type": "text/html; charset=utf-8"
    },
    "body": "<!doctype html>...</html>",
    "statusCode": 200
  }
}

Sending POST Requests with a Body

For requests that require sending additional data (e.g., forms or APIs), you can include a method parameter and a request body:

curl -X POST -H "Content-Type: application/json" 
-d '{"apiKey": "YOUR_API_KEY", "url": "https://example.com", "method": "POST", "body": "var1=value1&var2=value2"}' 
"https://async.scraperapi.com/jobs"

• method: Specifies the HTTP method to use (e.g., POST).
• body: Sends additional data, such as form fields or API parameters.

This flexibility allows you to handle dynamic scraping scenarios, such as submitting search forms or interacting with APIs.

When scraping large datasets, managing proxies manually can quickly become overwhelming. ScraperAPI simplifies this process by automating:

• IP Rotation: Every request is routed through a different IP address, reducing the risk of blocks or bans.
• HTTP Headers: ScraperAPI automatically adjusts headers like user-agent strings to mimic organic browser activity.
• Retries and CAPTCHA Solving: Failed requests are automatically retried, and CAPTCHAs are solved without manual intervention.

This automation makes ScraperAPI perfect for handling large-scale scraping projects, even those involving millions of URLs. You can focus entirely on collecting and analyzing data by eliminating the need to manage infrastructure.

By pairing cURL with ScraperAPI’s Async Scraper, you can build a scraping pipeline that is fast, scalable, and reliable—perfect for tackling even the most complex web scraping projects

cURL in Web APIs: Syntax and Use Cases

cURL makes it easy to scrape data from APIs in structured formats like JSON. Use a basic cURL command to fetch data:

curl "https://api.example.com/data?key=YOUR_API_KEY&param=value"

Key Components:

• Base URL: The API’s endpoint (e.g., https://api.example.com/data).
• Parameters: Query parameters like key and param to customize the request.

The API typically responds with structured data, such as JSON, which you can quickly process. 

If the API requires authentication, you can include an authorization header in your cURL request:

curl -H "Authorization: Bearer YOUR_API_KEY" "https://api.example.com/data"

This ensures secure access to the API while enabling you to retrieve data for authenticated users.

Example: Scraping Amazon Product Data with ScraperAPI

ScraperAPI’s Amazon Product Page API makes retrieving structured data from Amazon, including product details, pricing, and variants, easy. Here’s how to use it with cURL:

curl "https://api.scraperapi.com/structured/amazon/product?api_key=YOUR_API_KEY&asin=B07FTKQ97Q&country=us&tld=com"

• api_key : Your ScraperAPI authentication key.
• asin : The Amazon Standard Identification Number unique to the product (e.g., B07FTKQ97Q).
• country : The two-letter country code for geo-targeting (e.g., us for the United States).
• tld : The top-level domain of the Amazon marketplace (e.g., com for amazon.com).

The API processes the product page and returns data in JSON format. With the power of cURL and ScraperAPI, you can scale your data extraction processes, save time, and focus on analyzing results.

How to Avoid Getting Blocked Using cURL for Web Scraping

When scraping websites, anti-scraping measures like CAPTCHAs, rate limits, or outright blocks can disrupt your workflow. Frequent requests from the same IP address or poorly configured HTTP headers often trigger these. Pairing cURL with ScraperAPI simplifies this process by automating proxy rotation, managing HTTP headers, and solving CAPTCHAs, making large-scale scraping smoother and more reliable.

Using ScraperAPI with cURL

Here’s a basic example of a ScraperAPI request using curl:

curl "http://api.scraperapi.com?api_key=YOUR_API_KEY&url=https://example.com"

What ScraperAPI handles for you:

• Rotates IPs automatically for every request to avoid blocks
• Retries failed requests without additional coding
• Handles CAPTCHAs in the background

With this setup, you can focus entirely on scraping data while ScraperAPI handles the heavy lifting.

Advanced ScraperAPI Features for Web Scraping

ScraperAPI’s advanced features, like JavaScript rendering, geotargeting, and Structured Data Endpoints (SDEs), make it a powerful tool for handling dynamic or complex websites.

Let’s take a look at some of them:

Javascript Rendering

Many modern websites rely on JavaScript to load key content after the initial page load. ScraperAPI enables JavaScript rendering to capture all dynamic content. Simply add the render=true parameter:

curl "http://api.scraperapi.com?api_key=YOUR_API_KEY&url=https://books.toscrape.com&render=true"

ScraperAPI’s Render Instruction Set (RIS) allows you to interact with web pages by automating actions like filling out forms, clicking buttons, and scrolling. Below is an example of scraping search results on Wikipedia:

Example JSON Instruction Set:

[
  {
    "type": "input",
    "selector": { "type": "css", "value": "#searchInput" },
    "value": "sunglasses"
  },
  {
    "type": "click",
    "selector": { "type": "css", "value": "#search-form button[type='submit']" }
  },
  {
    "type": "wait_for_selector",
    "selector": { "type": "css", "value": "#results" }
  }
]

Send this instruction set with cURL:

curl "https://api.scraperapi.com/?url=https://www.wikipedia.org" -H "x-sapi-api_key: <YOUR_API_KEY>" -H "x-sapi-render: true" -H 'x-sapi-instruction_set: [{"type": "input", "selector": {"type": "css", "value": "#searchInput"}, "value": "sunglasses"}, {"type": "click", "selector": {"type": "css", "value": "#search-form button[type=\"submit\"]"}}, {"type": "wait_for_selector", "selector": {"type": "css", "value": "#content"}}]'

Additional RIS Actions:

• Scroll: Automatically scroll through infinite-scrolling pages.
• Loop: Repeat actions like scrolling multiple times.
• Wait: Pause until specific elements load, ensuring you scrape fully rendered content.

Geotargeting

ScraperAPI also allows you to scrape location-specific content by adding a country_code parameter:

curl "http://api.scraperapi.com?api_key=YOUR_API_KEY&url=https://example.com&country_code=us"

• country_code=us : Sends the request from a U.S.-based IP address.
• Replace us with other two-letter country codes (e.g., uk for the United Kingdom, de for Germany).

This feature is perfect for scraping localized content, such as region-specific pricing, inventory, or promotions.

Structured Data Endpoints (SDEs)

ScraperAPI’s Structured Data Endpoints (SDEs) allow you to retrieve structured JSON data directly from supported platforms like Amazon. Instead of parsing raw HTML manually, you receive clean, pre-parsed data.

Example request for Amazon product data:

curl "https://api.scraperapi.com/structured/amazon/product?api_key=YOUR_API_KEY&asin=B07FTKQ97Q"

Key Parameters:

• api_key: Your ScraperAPI authentication key.
• asin: The Amazon product identifier (e.g., B07FTKQ97Q).

This feature is handy for e-commerce analysis, eliminating the need for custom HTML parsing.

Example response:

{

 "name": "Animal Adventure | Sweet Seats | Pink Owl Children's Plush Chair",

 "product_information": {

   "product_dimensions": "14 x 19 x 20 inches",

   "color": "Pink",

   "material": "95% Polyurethane Foam, 5% Polyester Fibers",

   "style": "Pink Owl",

   "product_care_instructions": "Machine Wash",

   "number_of_items": "1",

   "brand": "Animal Adventure",

   "fabric_type": "Polyurethane",

   "unit_count": "1.0 Count",

   "item_weight": "1.9 pounds",

   "asin": "B06X3WKY59",

   "item_model_number": "49559",

   "manufacturer_recommended_age": "18 months - 4 years",

   "best_sellers_rank": [

     "#9,307 in Home & Kitchen (See Top 100 in Home & Kitchen)",

     "#69 in Kids' Furniture"

   ],
.......

Why Use ScraperAPI for Avoiding Blocks?

By pairing cURL with ScraperAPI’s advanced features, you can:

• Navigate JavaScript-heavy websites and dynamic content with render=true
• Automate complex interactions using RIS
• Scrape region-specific data for global insights using geotargeting
• Save time and effort with SDEs, bypassing the need for manual HTML parsing

ScraperAPI’s smart proxy rotation, CAPTCHA handling, and retries make it the ultimate solution for efficient, large-scale web scraping.

cURL with Python for Web Scraping

While cURL is excellent for sending HTTP requests and retrieving raw data, it can be limited when processing and organizing that data. Combining cURL with Python can create a powerful synergy for web scraping. Together, they enable you to:

• Streamline Workflows: Use cURL for simple HTTP requests and Python for advanced parsing and analysis.
• Handle Dynamic Content: Python libraries like BeautifulSoup and Scrapy make extracting and interacting with JavaScript-heavy pages easier.
• Scale Efficiently: Python’s flexibility allows you to automate and manage large scraping projects easily.

This combination gives you the best of both worlds: cURL’s command-line simplicity and Python’s programming power, enabling efficient and flexible scraping solutions.

Using cURL in Python

To integrate cURL with Python, you can use Python’s built-in subprocess module to execute cURL commands programmatically. This approach lets you fetch raw HTML or data directly from the web, which you can process further in Python.

Here’s an example of how to execute a basic cURL command in Python:

import subprocess

# cURL command to fetch HTML from a website
url = "https://books.toscrape.com"
curl_command = ["curl", "-s", url]

# Execute the cURL command and capture the output
result = subprocess.run(curl_command, capture_output=True, text=True)

# Print the fetched HTML
html_content = result.stdout
print(html_content)

In this code:

• subprocess.run() executes the cURL command
• capture_output=True captures the response from the server.
• result.stdout stores the raw HTML content fetched by cURL.

At this point, you have the HTML content stored in the html_content variable, ready for parsing.

Pro Tip 💡

Python has pretty good built-in libraries for opening URLs, like urllib or request (to send get requests). If you have a python program, you should avoid running cURL in subprocesses because it would use unnecessary CPU and memory resources. For example, if you had 1000 URLs to scrape, it would create 1000 new processes for the lifetime of each scrape. I believe it is really not a recommended thing to run cURL from basically any programming language. In my opinion, it is really not a recommended thing to run cURL from basically any programming language.

Peter Bastyi, Senior Engineer at ScraperAPI

Parsing HTML with BeautifulSoup

Once the raw HTML is retrieved, Python’s BeautifulSoup library can parse and extract specific elements from the page, such as titles, prices, or links. This transforms unstructured HTML into easily accessible data.

Here’s how you can extract book titles and prices from the HTML:

from bs4 import BeautifulSoup

# Example HTML content fetched by cURL
html_content = """
<!doctype html>
<html>
  <body>
    <div class="product">
      <h2 class="title">Book Title 1</h2>
      <p class="price">$20.99</p>
    </div>
    <div class="product">
      <h2 class="title">Book Title 2</h2>
      <p class="price">$15.49</p>
    </div>
  </body>
</html>
"""

# Parse the HTML content
soup = BeautifulSoup(html_content, "html.parser")

# Extract titles and prices
products = soup.find_all("div", class_="product")
for product in products:
    title = product.find("h2", class_="title").text
    price = product.find("p", class_="price").text
    print(f"Title: {title}, Price: {price}")

Output:

Title: Book Title 1, Price: $20.99
Title: Book Title 2, Price: $15.49

This workflow illustrates how cURL and Python work together: cURL fetches the data, and Python organizes it for analysis.

Resource: Follow our in-depth tutorial on scraping web data with Python.

Building Advanced Workflows with Scrapy

For more advanced projects that require scalability, Scrapy is a Python-based framework designed for large-scale web scraping. While cURL is helpful for debugging requests or testing APIs, Scrapy excels at handling dynamic workflows, scheduling requests, and managing proxies.To get started with Scrapy:

1. Install Scrapy:

pip install scrapy

2. Create a Scrapy Project:

scrapy startproject myproject

3. Generate a Spider:

scrapy genspider scraper https://books.toscrape.com/

4. Replace the generated code with this:

import scrapy
class Spider(scrapy.Spider):
    name = "books"
    allowed_domains = ["books.toscrape.com"]
    start_urls = ["https://books.toscrape.com"]

    def parse(self, response):
        for product in response.css("article.product_pod"):
            yield {
                "title": product.css("h3 a::attr(title)").get(),
                "price": product.css("p.price_color::text").get(),
            }

5. Run the Spider:

scrapy crawl spider -o books.json

The output will be saved in a JSON file (books.json):

[
    {"title": "Book Title 1", "price": "$20.99"},
    {"title": "Book Title 2", "price": "$15.49"}
]

Scrapy’s built-in tools simplify tasks like handling pagination, managing concurrent requests, and integrating proxies, making it a powerful tool for large-scale scraping.

Resource: Dive deeper into web scraping with Scrapy.

Combining cURL with Python Libraries

In addition to Scrapy and BeautifulSoup, you can combine cURL with Python to debug or enhance your scraping workflows. For example:

• Use cURL to inspect HTTP headers or raw API responses.
• Pass cURL outputs to Python for further processing using libraries like pandas for data manipulation.

Here’s an example of combining cURL and pandas to save scraped data as a CSV file:

import subprocess
import pandas as pd
from bs4 import BeautifulSoup

# Step 1: Fetch HTML with cURL
url = "https://books.toscrape.com"
curl_command = ["curl", "-s", url]
result = subprocess.run(curl_command, capture_output=True, text=True)
html_content = result.stdout

# Step 2: Parse HTML with BeautifulSoup
soup = BeautifulSoup(html_content, "html.parser")
data = []
for product in soup.select("article.product_pod"):
    title = product.h3.a["title"]
    price = product.select_one("p.price_color").text
    data.append({"title": title, "price": price})

# Step 3: Save data to a CSV file with pandas
df = pd.DataFrame(data)
df.to_csv("books.csv", index=False)
print("Data saved to books.csv")

By integrating cURL with Python, you can build efficient and scalable scraping workflows.

While cURL handles simple HTTP requests, Python’s libraries—like BeautifulSoup and Scrapy—add powerful tools for parsing, automating, and managing data.

Conclusion

That’s a wrap on mastering web scraping with cURL! This guide has given you the skills to extract data efficiently, from basic cURL commands to advanced workflows with ScraperAPI’s powerful features.

Whether you’re handling dynamic content, geo-targeted data, or large-scale scraping projects, you now have the tools to tackle any challenge.

In this article, you learned how to:

• Run basic cURL commands to fetch raw HTML or API data
• Avoid blocks with proxies, user agents, and ScraperAPI’s automation
• Scrape dynamic websites using JavaScript rendering and the Render Instruction Set (RIS)
• Target region-specific content with geotargeting parameters
• Retrieve structured data with ScraperAPI’s Structured Data Endpoints (SDEs)
• Integrate Python to parse, process, and scale your scraping workflows

By automating tasks like CAPTCHA solving, IP rotation, and retries, ScraperAPI lets you focus on extracting insights and building real-world solutions.

Take these skills, experiment, and start building real-world projects—the data awaits you!

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