This is an overview of the top Python HTTP clients, their features, and best use cases for web scraping in 2025:
Requests stands as the most widely used Python HTTP client, boasting an impressive 30 million weekly downloads.
Here's an example of how you can handle HTTP requests and responses with Requests, using httpbin.org that provides sample responses for testing various HTTP methods:
import requests
print("Testing `requests` library...")
resp = requests.get('https://httpbin.org/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}")With the requests.get(...) method, you can easily pass URL parameters using the params argument without manually adding query strings.
Requests automatically handles query string encoding, JSON data processing, and HTTP redirects. It also provides built-in SSL support. Under the hood, it leverages urllib3 for low-level HTTP operations while offering a more intuitive interface.
Session management is another powerful feature, allowing you to maintain cookies, headers, and authentication across multiple requests—essential for accessing protected content during web scraping.
For handling large responses efficiently, Requests offers streaming capabilities:
import requests
print("Testing `requests` library with streaming...")
resp = requests.get('https://httpbin.org/stream/10', stream=True)
for chunk in resp.iter_content(chunk_size=1024):
print(chunk.decode('utf-8'))Requests does have some limitations: it lacks native asynchronous capabilities and HTTP/2 support, with no plans to add the latter according to this discussion. While it doesn't include built-in caching, you can use extensions like requests-cache.
Note: HTTP/2 improves upon HTTP/1.1 with features like multiplexing, which allows multiple requests over a single TCP connection for faster page loads.
Requests remains popular due to its straightforward syntax, automatic connection pooling, JSON handling, and comprehensive documentation.
urllib3 serves as a robust foundation for HTTP requests, powering many other HTTP clients while offering direct access to low-level HTTP functionality.
Here's a basic example using urllib3:
import urllib3
print("Testing `urllib3` library...")
http = urllib3.PoolManager() # PoolManager for connection pooling
resp = http.request('GET', 'https://httpbin.org/get', fields={"foo": "bar"})
if resp.status == 200: # success
print(f"Response: {resp.data.decode('utf-8')} (HTTP-{resp.status})")
else: # error
print(f"Error: HTTP-{resp.status}")The urllib3.PoolManager() creates reusable connection pools that enhance performance by eliminating the overhead of establishing new connections for each request.
urllib3 excels at handling streaming responses, making it ideal for processing large datasets without memory overload. It also supports automatic redirects and SSL connections.
However, urllib3 lacks built-in asynchronous capabilities, caching, session management, and HTTP/2 support.
While urllib3's connection pooling implementation is more complex than Requests, it maintains a straightforward scripting syntax and provides well-maintained documentation.
Consider urllib3 for basic web scraping tasks where you need power without session management.
Uplink offers a distinctive approach to HTTP requests through class-based interfaces, particularly useful for API-focused web scraping.
Here's how you can use Uplink to interact with an API:
import uplink
@uplink.json
class JSONPlaceholderAPI(uplink.Consumer):
@uplink.get("/posts/{post_id}")
def get_post(self, post_id):
pass
def demo_uplink():
print("Testing `uplink` library...")
api = JSONPlaceholderAPI(base_url="https://jsonplaceholder.typicode.com")
resp = api.get_post(post_id=1)
if resp.status_code == 200: # success
print(f"Response: {resp.json()} (HTTP-{resp.status_code})")
else: # error
print(f"Error:HTTP-{resp.status_code}")This example defines a JSONPlaceholderAPI class that inherits from uplink.Consumer. The @uplink.get decorator creates an HTTP GET request with a dynamic post_id parameter in the endpoint path.
Uplink handles SSL connections and automatic redirects. It also supports the unique Bring Your Own HTTP Library feature.
The library lacks built-in support for streaming responses, asynchronous requests, caching (though it can use requests-cache), and HTTP/2.
While Uplink provides good documentation, it's not actively maintained (last release was 0.9.7 in March 2022). Its class-based approach appeals to object-oriented developers but may feel less intuitive for those preferring Python's scripting style.
Choose Uplink when your scraping focuses primarily on RESTful API endpoints rather than HTML pages.
GRequests extends the popular Requests library with asynchronous capabilities, allowing simultaneous data fetching from multiple sources.
Here's an example demonstrating GRequests in action:
import grequests
print("Testing `grequests` library...")
# Fetching data from multiple URLs
urls = [
'https://www.python.org/',
'http://httpbin.org/get',
'http://httpbin.org/ip',
]
responses = grequests.map((grequests.get(url) for url in urls))
for resp in responses:
print(f"Response for: {resp.url} ==> HTTP-{resp.status_code}")This code sends three concurrent GET requests using grequests.map(...) and collects the responses. GRequests leverages gevent to handle asynchronous operations without requiring complex concurrency management.
GRequests supports automatic redirects, SSL connections, and streaming responses. It lacks built-in HTTP/2 support and caching (though it can use requests-cache).
The library simplifies asynchronous requests with an intuitive API similar to Requests, eliminating the need for complex async/await patterns. However, its documentation is minimal due to its small codebase (213 lines in version 0.7.0) and limited development activity.
Consider GRequests when you need to gather data from multiple sources simultaneously with minimal complexity.
HTTPX represents a modern evolution of Python HTTP clients, designed as a Requests replacement with added asynchronous support and improved performance.
Here's an example of HTTPX's asynchronous capabilities:
import httpx
import asyncio
async def fetch_posts():
async with httpx.AsyncClient() as client:
response = await client.get('https://jsonplaceholder.typicode.com/posts')
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
asyncio.run(httpx_demo())This code defines an asynchronous function fetch_posts() that retrieves data from an API using httpx.AsyncClient(). The httpx_demo() function awaits the results and processes them.
HTTPX stands out with its built-in HTTP/2 support, enabling faster loading of multiple resources over a single connection and making browser fingerprinting more difficult during web scraping.
To use HTTP/2 with HTTPX:
import httpx
client = httpx.Client(http2=True)
response = client.get("https://http2.github.io/")
print(response)Note that HTTP/2 support requires additional installation:
pip install httpx[http2]HTTPX also provides excellent streaming support for handling large responses efficiently:
with httpx.stream("GET", "https://httpbin.org/stream/10") as resp:
for text in resp.iter_text():
print(text)While HTTPX doesn't include built-in caching, you can integrate Hishel.
Unlike Requests, HTTPX doesn't follow redirects by default but can be configured to do so:
import httpx
# test http --> https redirect
response = httpx.get('http://github.com/', follow_redirects=True)Despite its asynchronous features adding some complexity, HTTPX provides simple methods for both synchronous and asynchronous requests. Its popularity continues to grow, supported by comprehensive documentation and an active community.
HTTPX is ideal for projects requiring a feature-rich HTTP client with asynchronous capabilities.
aiohttp focuses exclusively on asynchronous programming, excelling in high-performance web scraping scenarios requiring concurrent, non-blocking requests.
Here's how to use aiohttp for concurrent scraping:
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 = [
'https://www.python.org/',
'http://httpbin.org/get',
'http://httpbin.org/ip',
]
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
asyncio.run(demo_aiohttp())This code creates an asynchronous function fetch_data() that establishes a session and sends a GET request. The demo_aiohttp() function creates tasks for multiple URLs and executes them concurrently using asyncio.gather().
aiohttp handles HTTP redirects automatically and supports streaming responses for efficient memory management when processing large files. It also offers a wide range of third-party middleware and extensions.
Additionally, aiohttp can function as a development server, though this article focuses on its client capabilities.
The library lacks HTTP/2 support and built-in caching, though you can add caching with libraries like aiohttp-client-cache.
aiohttp's asynchronous nature makes it more complex than simpler clients like Requests, requiring a solid understanding of asynchronous programming. However, it's quite popular with 14.7K GitHub stars and numerous third-party extensions. It also provides comprehensive documentation.
Choose aiohttp for real-time data scraping tasks like monitoring stock prices or tracking live events.
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 |
Each HTTP client in this review offers unique advantages. Requests, Uplink, and GRequests provide simplicity, with Requests maintaining the highest popularity. Meanwhile, aiohttp and HTTPX continue gaining traction due to their asynchronous capabilities. Your specific needs will determine which option best suits your project.
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