Best Python HTTP Clients for Web Scraping in 2025

We’ll start by understanding what HTTP clients are and why they’re so important for web scraping. Then, we’ll dive into how to use them effectively. By the end of this article, you’ll have a clear idea of how to get started with building your web scraper using Python.

What is a Python HTTP Client?

A Python HTTP client is a tool that allows you to send HTTP requests, such as GET or POST, to web servers to retrieve information. In simple terms, it helps you fetch raw HTML from a webpage. However, this raw HTML is often messy and hard to read, which is why it’s usually combined with parsing libraries like Beautiful Soup or lxml to make the data more understandable.

It’s important to note that HTTP clients might not work with all websites. For example, if you’re trying to scrape data from sites that use JavaScript to load content, you’ll need a headless browser library like Selenium or Puppeteer.

In web scraping, HTTP clients are often used with proxy servers. Changing your IP address and location can be crucial because many websites use anti-bot measures to prevent automated data collection. Using a proxy can help you avoid these protections and successfully scrape the information you need.

Why is Python Requests So Popular?

Requests is a popular choice for both experienced web scrapers and beginners. It stands out because it’s easy to use and requires less code compared to other HTTP clients.

One of the advantages of using Requests is that you don’t have to manually add query strings to the URL. It’s built on Python’s urllib3 library, allowing you to make requests within a session.

If the website you’re targeting has an API, Requests lets you connect directly to it, making it easier to access specific data. A key feature of Requests is its built-in JSON decoder, which allows you to retrieve and decode JSON data with just a few lines of code.

Requests also automatically follows HTTP redirects and decodes content based on the response headers, which is helpful when dealing with compressed data. It includes SSL verification and connection timeouts as well.

Requests is versatile in handling tasks like managing cookies, headers, and errors during web scraping. However, it’s important to note that Requests is synchronous by default, meaning it can’t send multiple requests at the same time.

Here’s a simple example of making a GET request using Requests:

import requests
def main():
url = 'https://example.com'
response = requests.get(url)

Choosing the right Python HTTP client is crucial for efficient web scraping. Whether you’re a beginner or handling complex, high-concurrency tasks, different libraries offer unique strengths. Let’s explore the top Python HTTP clients, helping you choose the best tool for your web scraping needs.

1. urllib3 — Efficient HTTP Client for Managing Multiple Requests

The urllib3 library is another powerful Python HTTP client, known for its speed and ability to handle multiple requests at once. While it may not be as user-friendly as Requests, it offers several features that make it a popular choice for web scraping.

One of the key strengths of urllib3 is that it’s designed to be thread-safe. This means you can use techniques like multithreading to break down your web scraping tasks into several threads, allowing you to scrape multiple pages simultaneously. This ability to handle concurrent requests makes urllib3 fast and efficient.

Another advantage of urllib3 is connection pooling. Instead of opening a new connection for each request, the library allows you to reuse an existing connection by calling the Get() function. This feature improves performance and reduces the computing resources needed compared to Requests. You can handle multiple requests over a single connection, which speeds up the process.

However, connection pooling has a drawback — it doesn’t support cookies, so you’ll need to pass them as a header value manually.

In addition, urllib3 supports SSL/TLS encryption, and you can specify connection timeouts and set up retries. The library also handles redirects and retries automatically.

Here’s a simple example of using urllib3 to send a GET request:

import urllib3
http = urllib3.PoolManager()
url = 'https://www.example.com'
response = http.request('GET', url)

2. HTTPX — Asynchronous HTTP Client with HTTP/2 Support

HTTPX is a versatile HTTP client that’s great for various web scraping tasks. By default, it supports synchronous API calls, but it also allows for asynchronous web scraping, which is preferred for better performance. Asynchronous requests let you manage connections like WebSockets and handle tasks more efficiently.

One standout feature of HTTPX is its support for HTTP/2. This version of the protocol can help reduce the chances of getting blocked compared to HTTP/1. With HTTP/2, a single TCP connection can load multiple resources simultaneously, making it more difficult for websites to track your browsing activity. This is a unique feature among the libraries we’ve discussed.

Another advantage of HTTPX is its built-in support for streaming responses, which is useful when downloading large datasets without loading everything into memory at once.

HTTPX also automatically decodes JSON responses, making it easier to handle data returned from your requests.

In terms of performance, HTTPX is faster than Requests but slower than the Aiohttp library. However, it doesn’t follow redirects automatically.

Here’s an example of making a GET request using HTTPX:

import httpx
import asyncio
async def main():
url = 'https://example.com'
async with httpx.AsyncClient() as client:
response = await client.get(url)
asyncio.run(main())

3. aiohttp — Powerful Asynchronous Web Scraping Library

aiohttp is an excellent choice for asynchronous web scraping, especially when you need to handle a large number of requests simultaneously.

Built on the asyncio library, aiohttp supports asynchronous I/O operations, allowing it to manage multiple requests at the same time without blocking your main program. This means your scraper can continue working on other tasks while waiting for responses.

Similar to Requests, aiohttp supports standard HTTP methods and can handle different types of requests and responses.

Beyond web scraping, aiohttp is also used to develop web applications and APIs that can handle high volumes of asynchronous connections. This is particularly useful if you want to create custom APIs or manage HTTP requests in environments requiring high concurrency.

Additionally, aiohttp offers session management, enabling you to maintain state across requests. You can manage cookies, store session data, handle authentication, and even customize request headers with plugins or middleware.

Here’s an example of making an asynchronous request with aiohttp:

import aiohttp
import asyncio
async def main():
async with aiohttp.ClientSession() as session:
async with session.get("https://example.com") as response:
print(await response.text())
asyncio.run(main())

4. httplib2 — Ideal for Caching HTTP Responses

While httplib2 might not be as well-known as the Requests library, it offers some valuable features that make it a solid choice for web scraping. One of its key benefits is built-in caching, which allows you to store HTTP responses and avoid making unnecessary requests. This is especially useful if you want to prevent overloading the target website’s servers or avoid IP blocks due to too many connections.

httplib2 also automatically follows and handles 3XX redirects on GET requests, making it easier to navigate through different web pages. Another advantage of httplib2 is its built-in support for handling cookies, which is essential for maintaining session data during web scraping.

Although httplib2 is synchronous by default, it supports the keep-alive header, enabling you to send multiple requests over the same connection. Additionally, httplib2 can automatically handle data compression based on response headers, which helps speed up your scraper by avoiding the slowdown caused by downloading uncompressed data.

Here’s an example of making a GET request with httplib2:

import httplib2
http = httplib2.Http()
url = 'https://example.com'
response, content = http.request(url, 'GET')

5. GRequests — Asynchronous Extension for the Requests Library

GRequests is an extension of the popular Requests library, designed to handle asynchronous requests. It’s a user-friendly tool that works well alongside Requests.

Built on Python’s asynchronous libraries like Gevent, GRequests allows you to send multiple HTTP requests simultaneously. This makes it a great choice for speeding up web scraping tasks.

One of the main advantages of GRequests is that it can easily be integrated into existing projects that already use the Requests library. The best part is that you don’t need to rewrite your entire codebase to start using it.

If you’re already comfortable with Requests, switching to GRequests is simple. It uses similar syntax and methods, making it easy to adapt. However, it’s worth noting that GRequests isn’t as popular or actively maintained as some other libraries.

Here’s an example of making a GET request with GRequests:

import grequests
urls = ['https://example.com', 'https://example.org']
requests = (grequests.get(url) for url in urls)
responses = grequests.map(requests)

Conclusion

When choosing the best Python HTTP client for web scraping, it’s important to match the tool with your specific needs. If you’re new to web scraping or need something straightforward, I’d recommend starting with Requests because it’s simple and effective. For projects that require handling many requests at once, aiohttp or HTTPX are better options because they can manage multiple connections simultaneously. If you need connection pooling or caching, urllib3 is a solid choice. If you’re already using Requests but want to add asynchronous capabilities, GRequests can help you scale up easily.

Got any questions or clients to suggest? Let me know in the comments, thanks for reading 🙂