The Best Python HTTP Clients for Web Scraping

Discover the top Python HTTP clients, their features, and best use cases for web scraping in 2024.
13 min read
Best Python HTTP Clients blog image

HTTP clients are helpful Python libraries that enable your code to send requests to web servers or APIs and receive responses. They make it easy to send various types of HTTP requests (GET, POST, PUT, DELETE, etc.), fetch data, submit data, or perform actions on websites or APIs.

When it comes to web scraping, these clients are often used alongside HTML parsing libraries like Beautiful Soup or html5lib.

In this article, you’ll take a look at some of the best Python HTTP clients, including Requests, urllib3, Uplink, GRequests, HTTPX, and aiohttp. You’ll evaluate each based on their features, ease of use, documentation and support, and popularity. At the end of this article, you’ll have a better idea of which library is best for your use case.


Let’s start with the most popular Python HTTP client: the Requests library, with an impressive 30 million downloads each week.

Here’s an example of how you can handle HTTP requests and responses with Requests:

import requests

print("Testing `requests` library...")
resp = requests.get('', params={"foo": "bar"})
if resp.status_code == 200:     # success
    print(f"Response Text: {resp.text} (HTTP-{resp.status_code})")
else:   # error
    print(f"Error: HTTP-{resp.status_code}")

Note that provides sample responses for testing various HTTP methods.

In this code snippet, the requests.get(...) method takes the desired URL and a single query parameter, foo. You can pass any query parameters using the params argument.

With Requests and other HTTP clients, you don’t need to manually add query strings to your URLs or encode your data; the library handles it for you. To send JSON data, you pass a Python dictionary using the data argument, and you can receive the JSON response directly with resp.json().

The Requests library automatically handles HTTP redirects (3xx) by default, which is useful in web scraping to access content from redirected URLs. It also supports Secure Sockets Layer (SSL) connections.

Under the hood, Requests uses urllib3 to manage low-level HTTP tasks, like connection pooling and SSL verification, offering a higher-level, more Pythonic API for developers.

Additionally, Requests supports session management, allowing you to persist parameters, like cookies, headers, or authentication tokens, across multiple requests. This is particularly useful for web scraping tasks where maintaining such parameters is essential for accessing restricted content.

The Requests library also supports streaming, which is helpful for web scraping tasks that involve large responses, such as downloading files or processing streaming data from APIs.

To efficiently process response data without loading it all into memory, you can use methods like iter_content() or iter_lines():

import requests

print("Testing `requests` library with streaming...")
resp = requests.get('', stream=True)
for chunk in resp.iter_content(chunk_size=1024):

However, the Requests library lacks built-in asynchronous capabilities and built-in caching support (although it’s available via requests-cache). Additionally, Requests does not support HTTP/2, and it’s unlikely to be added soon, as indicated in this discussion.

Note: HTTP/2 is the newer version of the HTTP protocol, designed to be faster and more efficient than HTTP/1.1. It allows multiple requests and responses over a single Transmission Control Protocol (TCP) connection through multiplexing, resulting in reduced client-server connections and faster page loads. However, support for HTTP/2 is still limited.

The Requests library simplifies Python’s HTTP interactions. It excels due to its simple methods, concise syntax, automatic connection pooling for efficiency, and built-in JSON decoding. Its ease of use, session management, streaming capabilities, and extensive documentation make it a popular choice for developers.


urllib3 is a well-tested and widely used library for making HTTP requests—not just by developers but by many other HTTP clients as well. It offers useful features and customization options for handling low-level HTTP requests in web scraping.

Here’s a basic example that uses urllib3 to make an HTTP request:

import urllib3

print("Testing `urllib3` library...")
http = urllib3.PoolManager()    # PoolManager for connection pooling
resp = http.request('GET', '', fields={"foo": "bar"})

if resp.status == 200:     # success
    print(f"Response: {'utf-8')} (HTTP-{resp.status})")
else:    # error
    print(f"Error: HTTP-{resp.status}")

In this code snippet, urllib3.PoolManager() creates a pool of connections that can be reused for multiple requests, improving performance by avoiding the overhead of establishing new connections for each request. Along with the URL, you can pass the required query parameters using its fields argument.

One notable feature of urllib3 is its ability to handle streaming responses, allowing you to process large amounts of data efficiently without loading it all into memory. This is useful for downloading large files or consuming streaming APIs during web scraping.

urllib3 supports automatic redirection by default and has built-in SSL support. However, it lacks built-in asynchronous capabilities, caching support, and session management (such as cookies), and it does not support HTTP/2.

While urllib3’s handling of connection pooling makes it less user-friendly than the Requests library, urllib3 uses simple scripting syntax, unlike some clients that require a class-based approach or decorators, making urllib3 useful for basic HTTP interactions. Additionally, urllib3 comes with well-maintained documentation.

If you want a powerful library and don’t need session management, urllib3 is great for simple web scraping tasks.


Uplink is a powerful yet lesser-known Python HTTP client. It simplifies interactions with RESTful APIs using class-based interfaces, making it particularly useful for web scraping that involves API calls.

Take a look at this sample code using Uplink to call an API endpoint:

import uplink

class JSONPlaceholderAPI(uplink.Consumer):
    def get_post(self, post_id):

def demo_uplink():
    print("Testing `uplink` library...")
    api = JSONPlaceholderAPI(base_url="")
    resp = api.get_post(post_id=1)
    if resp.status_code == 200:     # success
        print(f"Response: {resp.json()} (HTTP-{resp.status_code})")
    else:   # error

This code snippet defines a JSONPlaceholderAPI class that inherits from the uplink.Consumer. It uses the @uplink.get decorator to create an HTTP GET request for the JSONPlaceholder API to retrieve a specific post. The post_id parameter is dynamically included in the endpoint with this decorator: @uplink.get("/posts/{post_id}"). The site simulates a REST API, providing JSON responses for testing and development.

Uplink supports SSL and automatically handles redirects to fetch the final response. It also offers advanced features such as Bring Your Own HTTP Library.

However, Uplink does not have built-in support for streaming responses, asynchronous requests, caching (though it can use requests-cache), and HTTP/2.

Uplink provides adequate documentation for its powerful features, but it is not actively maintained (the last release was 0.9.7 in March 2022) and is not very popular. While its class-based approach and decorator syntax may appeal to object-oriented developers, it offers only moderate ease of use for those who prefer Python’s scripting style.

Users typically choose Uplink when they need to gather data primarily from different RESTful API endpoints and not HTML pages.


GRequests is an extension of the well-known Requests library, adding support for asynchronous requests. It allows concurrent fetching of data from multiple websites or APIs.

Unlike sequential requests, which wait for each response before sending the next, GRequests improves efficiency by sending requests simultaneously. This is especially beneficial for fetching data from multiple websites or APIs.

Take a look at this example:

import grequests

print("Testing `grequests` library...")
# Fetching data from multiple URLs
urls = [

responses = for url in urls))
for resp in responses:
    print(f"Response for: {resp.url} ==> HTTP-{resp.status_code}")

In this code, GRequests concurrently sends three GET requests using to different URLs and gathers the responses in a list named responses. Then, it iterates over these responses to print them. Internally, GRequests uses gevent, a coroutine-based networking library, for asynchronous HTTP requests. This allows you to send multiple HTTP requests simultaneously without managing complex concurrency. One practical application of this could be scraping news from different websites for a particular topic or category.

GRequests also supports features such as automatic handling of redirects, SSL support, and processing streaming responses without loading them all into memory at once. However, keep in mind that GRequests does not have built-in HTTP/2 support or caching capabilities, although it can use requests-cache.

GRequests simplifies asynchronous HTTP requests with intuitive methods similar to its base library, Requests. It eliminates the need for complex async/await constructs to handle concurrency, making it easy to use. However, its documentation is minimal due to its tiny codebase (213 lines in its 0.7.0 version) and reduced development activity. These factors contribute to its lower popularity.

You should consider GRequests for its easy-to-use asynchronous capabilities when you need to gather data from multiple sources simultaneously.


HTTPX is a modern and feature-rich HTTP client for Python, widely used for all types of web scraping projects. It’s designed to replace Python’s Requests library while providing asynchronous support and better performance.

The following example demonstrates an asynchronous HTTP GET request using HTTPX:

import httpx
import asyncio

async def fetch_posts():
    async with httpx.AsyncClient() as client:
        response = await client.get('')
        return response.json()

async def httpx_demo():
    print("Testing `httpx` library...")
    posts = await fetch_posts()
    for idx, post in enumerate(posts):
        print(f"Post #{idx+1}: {post['title']}")

# async entry point to execute the code

This code defines an asynchronous function called fetch_posts(), which retrieves dummy blog posts from the API using httpx.AsyncClient(). Another asynchronous function, httpx_demo(), waits for fetch_posts() to return those posts and then prints their titles in a loop. Finally, serves as the entry point for executing httpx_demo() asynchronously.

Besides its built-in support for asynchronous HTTP clients, HTTPX also offers built-in HTTP/2 support. This allows faster loading of multiple resources concurrently over a single TCP connection, making it more difficult for websites to track your browser fingerprint during web scraping.

To send an HTTP/2 request, simply set the http2=True parameter when creating an HTTPX client:

import httpx

client = httpx.Client(http2=True)
response = client.get("")

Keep in mind that to use HTTP/2, you need to install HTTPX with its http2 support:

pip install httpx[http2]

Additionally, HTTPX provides excellent support for streaming responses, allowing you to efficiently handle large responses or data streams without loading the entire response into memory.

Here’s an example of streaming text response using HTTPX:

with"GET", "") as resp:
   for text in resp.iter_text():

While HTTPX doesn’t include built-in caching capabilities, you can use Hishel instead.

HTTPX doesn’t follow HTTP redirects by default, but you can enable this with the follow_redirects parameter:

import httpx

# test http --> https redirect
response = httpx.get('', follow_redirects=True)

Although its asynchronous features add some complexity, HTTPX provides simple methods for HTTP communication and supports easy-to-use synchronous requests. This makes it accessible for both beginners and experienced developers. Additionally, its usage is growing, thanks to its extensive documentation and an active community of developers building tools for HTTPX integration.

If you’re looking for a feature-rich, asynchronous HTTP client, consider HTTPX.


Similar to HTTPX, aiohttp offers built-in asynchronous support for HTTP requests. However, aiohttp is designed exclusively for asynchronous programming, allowing it to excel in scenarios requiring concurrent and non-blocking requests. This makes it well-suited for high-performance web scraping projects and it’s easy to use with proxies.

Here’s how you can use aiohttp to scrape multiple URLs concurrently:

import asyncio
import aiohttp

async def fetch_data(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()    

async def demo_aiohttp():
    print("Testing `aiohttp` library...")
    urls = [
    tasks = [fetch_data(url) for url in urls]
    responses = await asyncio.gather(*tasks)
    for resp_text in responses:
        print(f"Response: {resp_text}")

# async entry point to execute the code

The asynchronous function fetch_data(...) creates an aiohttp.ClientSession() and sends a GET request to the specified URL. It then creates a list of tasks for each URL and executes them concurrently with asyncio.gather(...). Once all tasks are completed, the scraped data (in this case, the response text) is collected and printed. The actual execution is initiated with

aiohttp automatically handles HTTP redirects and supports streaming responses, ensuring efficient management of large files or data streams without excessive memory usage. It also offers flexibility with a wide range of third-party middleware and extensions.

Additionally, aiohttp can serve as a development server if necessary, although this article focuses solely on its HTTP client functionality.

However, aiohttp lacks support for HTTP/2 and built-in caching capabilities. Nevertheless, you can integrate caching using libraries such as aiohttp-client-cache when needed.

aiohttp can be more complex to use than simpler HTTP clients like Requests, especially for beginners. Its asynchronous nature and additional features require a good understanding of asynchronous programming in Python. However, it’s quite popular, with 14.7K stars on GitHub and numerous third-party libraries built on top of it. aiohttp also offers comprehensive documentation for developers.

If you’re looking for full-fledged asynchronous support, consider aiohttp. Its asynchronous performance makes it ideal for real-time data scraping tasks, such as monitoring stock prices or tracking live events like elections as they unfold.

Check out the following table for a quick overview of the top Python HTTP clients:

Requests urllib3 Uplink GRequests HTTPX aiohttp
Ease of Use Easy Easy-to-Moderate Moderate Easy Moderate Moderate
Automatic Redirects Yes Yes Yes Yes Needs Enabling Yes
SSL Support Yes Yes Yes Yes Yes Yes
Asynchronous Capability No No No Yes Yes Yes
Streaming Responses Yes Yes No Yes Yes Yes
HTTP/2 Support No No No No Yes No
Caching Support Via: requests-cache No Via:requests-cache Via:requests-cache Via: Hishel Via: aiohttp-client-cache


In this article, you learned all about a few popular Python HTTP clients, including Requests, urllib3, Uplink, GRequests, HTTPX, and aiohttp, each with unique features like simplicity, async support, streaming, and HTTP/2.

While Requests, Uplink, and GRequests are known for their simplicity, aiohttp and HTTPX offer powerful async capabilities. Although Requests remains the most popular, aiohttp and HTTPX are gaining traction due to their async abilities. Ultimately, you’ll need to review each one to choose the one that suits your needs.

When it comes to web scraping in real life, you’ll need to consider more than just your HTTP client, such as bypassing anti-bot measures and using proxies. Thankfully, Bright Data can help.

Bright Data makes web scraping easier with tools like the Web Scraper IDE, which offers ready-made JavaScript functions and templates, and the Web Unlocker, which bypasses CAPTCHAs and anti-bot measures. The Bright Data Scraping Browser integrates with Puppeteer, Playwright, and Selenium for multistep data collection. Additionally, the Bright Data proxy networks and services allow access from different locations. These tools handle complex tasks, such as managing proxies and solving CAPTCHAs, so you can focus on getting the data you need.

Start your free trial today and experience everything Bright Data has to offer.

No credit card required