10 Best Python HTTP Clients in 2025 (Compared & Tested)

When building apps, automation scripts, or tools that interact with the internet, you need a way to talk to web servers. HTTP clients help Python developers send and receive data over the web using HTTP requests. Python HTTP clients are essential tools when you’re making HTTP requests to APIs, doing web scraping, or syncing data between systems.

Having the best Python HTTP clients on hand makes a big difference when handling web scraping tasks, sending asynchronous requests, or calling APIs.

Selection Criteria

Not all HTTP clients are built the same. Some shine in performance, others make coding easier. Here’s how we evaluated our choices among Python HTTP clients:

• Performance. Fast, reliable response times for HTTP requests.
• Async support. Handles asynchronous HTTP requests and concurrent requests easily.
• Documentation. Clear docs save time and confusion.

Tools that do well across these points can often be considered as some of the best Python HTTP clients out there.

Top 10 Python HTTP Clients

1. Requests

The Requests library is the gold standard for synchronous HTTP requests, and has a simple, user-friendly API. Requests are ideal for quick scripts, simple API interactions, and any general-purpose synchronous tasks where developer productivity is most important.

Pros:

• Its API is exceptionally intuitive, readable, and beginner-friendly.
• It’s robust, tried-and-tested, and supported by a massive community and ecosystem.

Cons:

• It’s purely synchronous, making it inefficient for high-concurrency tasks.
• It lacks native support for modern protocols like HTTP/2.

2. Httpx

A modern, next-gen client that offers a requests-compatible API with support for both synchronous and asynchronous operations. It’s ideal for new general-purpose applications, as it allows projects to start synchronously and adopt async patterns later without changing libraries.

Pros:

• Its dual sync/async API provides maximum flexibility for evolving project requirements.
• It natively supports modern features like HTTP/2 and is fully type-annotated.

Cons:

• Its ecosystem of third-party plugins is less mature than the one for requests.
• It can be marginally slower than pure async libraries in extremely high-concurrency benchmarks.

3. Aiohttp

A high-performance, async-native library providing both an HTTP client and a web server framework for the asyncio ecosystem. It’s the top choice for building high-concurrency, asyncio-based apps like web scrapers, API gateways, and real-time services using WebSockets.

Pros:

• Delivers top-tier performance for I/O-bound workloads under high concurrency.
• Offers first-class, integrated support for both client and server WebSockets.

Cons:

• It has a steep learning curve and requires a good understanding of asyncio.
• The API is verbose for simple, single requests compared to synchronous libraries.

4. Urllib3

The low-level, high-performance library that powers requests and focuses on connection pooling, thread safety, and reliability. It’s best for library developers or applications that require fine-grained, low-level control over network connections and retry logic.

Pros:

• Offers excellent performance and efficiency due to advanced connection pooling.
• Provides a highly configurable and powerful retry mechanism for resilience against network failures.

Cons:

• The API is significantly more verbose and less intuitive compared to higher-level libraries.
• Requires manual handling for common tasks like JSON encoding and decoding.

5. Pycurl

A high-speed Python interface for libcurl, offering maximum performance, multi-protocol support, and low-level control. It’s essential for specialized applications where absolute peak performance, multi-protocol support (FTP, SMTP, etc.), or fine-grained control over the network stack is required.

Pros:

• It’s generally the fastest HTTP client available due to its thin C-based wrapper.
• Inherits the vast feature set of libcurl, including extensive protocol support and deep configuration options.

Cons:

• The API is notoriously complex, verbose, and considered un-Pythonic.
• Installation can be complex, often requiring manual installation of system-level dependencies.

6. Uplink

A unique, declarative client that turns REST API definitions into clean, reusable Python classes. It’s perfect for building clean, maintainable, and reusable client wrappers for well-defined RESTful APIs, abstracting away HTTP boilerplate.

Pros:

• Drastically reduces boilerplate code, making API interaction cleaner and more readable.
• It’s flexible, allowing requests or aiohttp to be used as the backend for sync or async operations.

Cons:

• It’s highly specialized for structured REST APIs and not suited for general web scraping.
• The layer of abstraction reduces direct, low-level control over the request-response cycle.

7. Httplib2

A mature synchronous client distinguished by its powerful, built-in support for HTTP caching. It’s the perfect choice for applications that repeatedly request the same resources and can benefit from an intelligent, specification-compliant caching mechanism.

Pros:

• Its primary strength is a superior, built-in caching system that respects server headers.
• It’s a feature-complete library covering a wide range of HTTP specifications.

Cons:

• Its API is lower-level and more complex than modern alternatives like the requests library.
• It’s an older library that has been largely superseded in popularity for new projects.

8. Grequests

A library that combines the simple API of requests with the gevent concurrency model for asynchronous requests. It’s a potential option for adding simple concurrency to existing request-based scripts, but it should be used with caution due to its age and lack of maintenance.

Pros:

• Provides a very familiar API for developers already proficient with requests.
• Achieves concurrency for a batch of requests with a simple map() function call.

Cons:

• It relies on gevent and monkey-patching, and an outdated paradigm in the modern asyncio ecosystem.
• The library is not actively maintained, and its own developers recommend alternatives.

9. Tornado

The integrated asynchronous HTTP client is designed to work seamlessly within the Tornado web framework. It’s the most natural choice for making outbound HTTP requests from within an application already built on the Tornado framework.

Pros:

• Offers seamless integration with the Tornado IOLoop and concurrency model.
• It’s a mature, battle-tested, and performance client for high-concurrency environments.

Cons:

• It’s tightly coupled to the Tornado framework and not designed as a general-purpose client.
• Its design originally predates async/await, and while it now supports those features, some legacy patterns remain.

10. Treq

A requests-like client library explicitly built for the Twisted event-driven networking engine. It’s the definitive high-level client for making HTTP requests in any project built on the Twisted framework, simplifying its complex networking APIs.

Pros:

• Provides a familiar, requests-inspired API within the complex Twisted ecosystem.
• It’s the idiomatic choice for Twisted projects, integrating perfectly with its Deferreds.

Cons:

• Its use is entirely coupled to the Twisted framework, making it unsuitable for any other context.

Comparison Table

How to Choose the Right HTTP Client

Picking the right tool depends on your project. For simple tasks, the Requests library is a safe choice. It’s the go-to for many Python HTTP developers working on APIs or light web scraping tasks.

If you’re handling multiple HTTP requests or heavy asynchronous requests, go with aiohttp or httpx. These are some of the best Python HTTP clients when it comes to speed and asynchronous support.

Conclusion

There are many Python HTTP clients available, but the best choice depends on what you’re building. If you’re sending a few HTTP requests, stick with the basics like the Requests library. If you’re handling multiple requests or doing advanced web scraping, pick one with async support or performance perks.

Some tools offer modern features and flexibility, while others give comfort and simplicity. The right balance depends on your goals, team, and codebase.