aiofastnet

aiofastnet is a very efficient C/Cython drop-in reimplementation of asyncio's loop Transport/Protocol layer. You can use it with your current event loop (asyncio loops, uvloop, winloop, etc.) to improve overall networking performance.

There are multiple ways to enable aiofastnet; the simplest is to install it before calling asyncio.run().

import aiofastnet

...
# Call this before asyncio.run(...)
aiofastnet.install_policy()

Are you using aiohttp, asyncpg, websockets, uvicorn, or any other library that relies on asyncio networking? They become faster if you enable aiofastnet. The difference is especially noticeable when SSL is used.

Benchmark

The benchmark below compares echo round-trips over loopback for TCP and SSL. The exact gains depend on workload, message sizes, CPU, OpenSSL version, and how much of your total runtime is spent in transport/SSL plumbing.

Source: examples/benchmark.py

In these benchmarks, aiofastnet is up to 2.7x faster than standard asyncio and up to 1.6x faster than uvloop for TLS connections.

aiofastnet is fully compatible with free-threaded Python builds and scales as expected when multiple event loops run in parallel across multiple threads.

Source: examples/benchmark_threaded.py

How is this possible?

aiofastnet provides drop-in, highly efficient C/Cython replacements for asyncio's:

asyncio libraries use these loop primitives to establish communication channels. The current implementations in both asyncio and uvloop are not optimal, especially for SSL/TLS connections.

Calling aiofastnet.install_policy() replaces these primitives with aiofastnet's efficient implementations. From that moment on, any library that uses asyncio networking will use aiofastnet.

aiofastnet is not a different event loop. It works on top of stock asyncio loops or uvloop by using low-level primitives such as add_reader and add_writer. It has no background threads and does not use unscalable tricks such as calling synchronous recv/send syscalls from another thread. Essentially, it provides the same kind of internal implementation you would find in asyncio and uvloop, but with much better optimization.

As a cherry on top, aiofastnet supports Kernel TLS on Linux.

Why Use aiofastnet

Faster hot path. Transport and SSL internals are reimplemented in Cython/C to reduce overhead on long-lived connections.
Drop-in API. Keep using the standard asyncio streams or transport/protocol model and familiar loop-level networking operations.
Works with the event loop you already use. aiofastnet works with stock asyncio loops, uvloop, and winloop.
Particularly strong for SSL-heavy workloads. aiofastnet uses OpenSSL directly and avoids extra memory copying and unnecessary Python plumbing in the data path.
Kernel TLS support on Linux. Enable native sendfile for SSL. aiohttp can send FileResponse more efficiently over secure connections.
Safer transport write() / writelines() behavior. If the socket cannot accept everything immediately, only bytes and memoryview objects backed by bytes are retained without copying. Other objects, including bytearray and non-bytes exporters, are copied before being queued. Unlike standard asyncio transports, this avoids sending corrupted data if application code modifies the underlying buffer after write() / writelines() returns.
Better SSL backpressure semantics. Buffer sizes and write limits reflect what is actually queued across the stack.
Works for library authors. WebSocket, HTTP, RPC, proxy, database, broker, and custom protocol libraries can expose the same API while giving users a faster transport layer.

Quickstart

Install from PyPI:

$ pip install aiofastnet

aiofastnet requires Python 3.9 or greater.

For applications, the easiest way to enable aiofastnet is to use an event loop factory.

import asyncio
import aiofastnet

async def main():
    ...

asyncio.run(main(), loop_factory=aiofastnet.loop_factory())

If you use another event loop implementation, pass its loop factory:

import asyncio
import uvloop
import aiofastnet

asyncio.run(main(), loop_factory=aiofastnet.loop_factory(uvloop.new_event_loop))

For older applications that still configure asyncio through event loop policies, install the aiofastnet policy wrapper before creating loops:

import asyncio
import aiofastnet

aiofastnet.install_policy()
asyncio.run(main())

Event loop policies are deprecated in Python 3.14 and are scheduled for removal in Python 3.16, so prefer loop_factory() for new code.

If the event loop already exists, patch it directly:

import asyncio
import aiofastnet

async def main():
    aiofastnet.patch_loop()
    ...

asyncio.run(main())

Library authors should call aiofastnet APIs directly instead of patching a loop owned by their users. The API mirrors stdlib asyncio: pass the running loop and use your existing protocol factory.

import asyncio
import aiofastnet

class EchoClientProtocol(asyncio.Protocol):
    def connection_made(self, transport):
        self.transport = transport
        self.transport.write(b"hello")

    def data_received(self, data):
        print("received:", data)
        self.transport.close()

async def main():
    loop = asyncio.get_running_loop()

    transport, protocol = await aiofastnet.create_connection(
        loop,
        EchoClientProtocol,
        "127.0.0.1",
        9000,
    )

    ...

asyncio.run(main())

For servers, replace loop.create_server(...) with aiofastnet.create_server(loop, ...) in the same way.

aiofastnet also exposes start_tls and sendfile:

transport = await aiofastnet.start_tls(
    loop,
    transport,
    protocol,
    ssl_context,
    server_side=False,
    server_hostname="example.com",
)

await aiofastnet.sendfile(loop, transport, fileobj)

When aiofastnet Is a Good Fit

aiofastnet is most attractive when you already rely on asyncio's transport/protocol APIs directly or indirectly and one or more of these are true:

You have long-lived TCP or TLS connections.
You run protocol-heavy services where transport overhead is visible in CPU profiles.
You maintain a library and want better performance without changing your public API.
You care about consistent write() / writelines() buffer behavior.
You want SSL flow control to reflect the whole buffered stack, not only part of it.

When Not To Use aiofastnet

aiofastnet is not the right default for every networking project:

If your workload is dominated by very short-lived connections, you should expect little or no gain. Currently, aiofastnet focuses on optimizing the data path after connection establishment.

Platform Compatibility

aiofastnet is built and tested on Linux, macOS, and Windows. It works with the standard asyncio event loop, uvloop, and winloop.

On Windows it works with SelectorEventLoop and winloop. With ProactorEventLoop it falls back to asyncio's native connection and server implementation, because the proactor loop does not provide the add_reader() / add_writer() hooks required by aiofastnet's custom transport implementation. For transports created through aiofastnet, a compatibility wrapper preserves the documented write() / writelines() buffer-safety behavior.

For TLS connections, aiofastnet has two SSL engines:

Direct OpenSSL engine. Used when Python's _ssl module is dynamically linked against OpenSSL's libssl / libcrypto shared libraries. This is the case for pretty much any Python distribution: actions/setup-python, conda, pyenv, system Python, etc. Calls OpenSSL functions like SSL_read/SSL_write directly. It is the fastest path and allows to use KTLS.
Fallback SSL engine. Used when Python's _ssl module is statically linked against OpenSSL. This is the case for Python shipped with uv. The standard ssl module is used instead of direct OpenSSL calls. It does not support KTLS and is slower than the direct engine, but still significantly faster than asyncio's and uvloop's SSL transports.

You can check which engine aiofastnet uses with:

$ python -c "import aiofastnet; print(aiofastnet.OPENSSL_DYN_LIBS)"

If this prints None, aiofastnet is using the fallback SSL engine. TCP and Unix-domain socket transports still use aiofastnet's native implementation.

If maximum TLS performance or KTLS support matters, do not use Python shipped with uv.

$ uv venv --no-managed-python --python python

Asyncio Compatibility

aiofastnet is a drop-in replacement and is expected to behave like the corresponding asyncio API, with one notable exception on the direct SSL path: aiofastnet implements its own SSL object, and transport.get_extra_info("ssl_object") returns it instead of ssl.SSLObject. When the fallback SSL engine is used, get_extra_info("ssl_object") returns the standard ssl.SSLObject.

For the direct SSL engine, the following public methods and attributes of SSLObject are absent:

do_handshake()
read()
write()
unwrap()
pending()
selected_npn_protocol()
verify_client_post_handshake()
session

Kernel TLS (KTLS) support on Linux

On Linux, aiofastnet can use OpenSSL's KTLS support for TLS connections. KTLS is beneficial if any of the following is true:

Static files need to be sent over a TLS connection and sendfile() can be used. In that case the kernel can read data directly instead of forcing the application to copy file contents through userspace.
Some high-end NICs support hardware TLS offload, which leads to huge CPU savings.

If you only send regular data (not static files) and do not have a high-end NIC with TLS offload, enabling KTLS may actually cause a slight performance degradation. In terms of CPU cost, it does not matter whether encryption/decryption happens in the kernel or in userspace, but the kernel tls module has to do extra bookkeeping. Also, aiofastnet can batch data and reduce the number of syscalls when KTLS is not used.

KTLS requires support from all of these layers:

The tls kernel module is loaded.
The direct SSL engine. KTLS does not work with uv Python.
OpenSSL built with KTLS support on a machine with suitable kernel headers.
An ssl.SSLContext with ssl.OP_ENABLE_KTLS enabled.
A TLS version and cipher suite supported by the kernel TLS implementation.

To load the kernel module:

$ sudo modprobe tls

To check if the direct SSL engine is used, aiofastnet should be able to find dynamic OpenSSL libraries:

$ python -c "import aiofastnet; print(aiofastnet.OPENSSL_DYN_LIBS)"

To enable KTLS on Python SSL contexts (available in Python 3.12+):

import ssl

server_context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
server_context.options |= ssl.OP_ENABLE_KTLS

client_context = ssl.create_default_context()
client_context.options |= ssl.OP_ENABLE_KTLS

The OpenSSL library used by Python must itself have KTLS support. Sometimes Python distributions ship their own OpenSSL libraries, and those libraries may have been built on older systems where KTLS was not available. That can be true even when the host running your program has a newer kernel.

If your Python distribution bundles an older or non-KTLS OpenSSL, one practical option is to locate the bundled libssl and libcrypto files and replace them with symbolic links to a newer system OpenSSL build that supports KTLS. This is often useful with Conda Python, which commonly ships its own OpenSSL libraries inside the environment.

KTLS support by kernel version is outlined here.

Check out aiohttp_ktls_fileresponse.py and aiohttp_ws_speedup.py for examples showing how you can speed up aiohttp (or any other asyncio application).

Some other useful links:

Free-Threaded Python

aiofastnet is compatible with free-threaded Python builds such as python3.14t. The extension modules are built to work without forcing the legacy GIL back on, so separate event loops may run in separate threads.

The repository includes several free-threading examples:

examples/benchmark_threaded.py runs multiple echo client/server pairs in parallel to compare single-loop and multi-threaded execution.
examples/echo_server_threaded.py starts one listening echo server per thread on the same port with reuse_port=True so the kernel can distribute incoming connections across worker threads.
examples/echo_client_threaded.py starts one echo client per thread and drives them in parallel against the shared server port.

Transport objects remain thread-affine. Methods such as write(), writelines(), close(), pause_reading(), and similar transport operations must be called from the same thread that established the connection. Calling transport methods directly from a different thread raises RuntimeError.

This matches the general asyncio model: loops and loop-owned objects are not meant to be used concurrently from arbitrary threads. aiofastnet does not add internal transport locking for cross-thread access.

To use aiofastnet correctly with multithreading:

Create a separate event loop in each worker thread.
Create connections and servers inside the loop thread that will own them.
Keep all direct transport interaction on that same thread.
If another thread needs to send data or close a transport, schedule that work onto the owning loop with loop.call_soon_threadsafe(...) instead of touching the transport directly.

In other words, free-threaded Python lets multiple loops make progress in parallel, but each individual connection is still owned by exactly one loop and one thread.

Building From Source

Clone the repository:

$ git clone git@github.com:tarasko/aiofastnet.git
$ cd aiofastnet

Create and activate a virtual environment:

$ python3 -m venv aiofn-dev
$ source aiofn-dev/bin/activate

Install test dependencies:
```
$ pip install -r requirements-test.txt
```

Build extensions in place and run tests:

$ python setup.py build_ext --dev --inplace
$ pytest -s -v

Run the benchmark:
```
$ python -m examples.benchmark
```

Run tests:

$ pytest -s -v
$ pytest -s -v -k test_echo[uvloop-tcp-buffered-6291456] --asyncio-debug --log-cli-level DEBUG

Build coverage report:

Building for coverage testing requires enabling line tracing in cython, which significantly slows down extension modules. It is disabled by default. You would need to rebuild specifically with coverage support.

python setup.py build_ext --inplace --dev --with-coverage
pytest -s -v --cov=aiofastnet --cov-report=html

Contributing

Contributions are welcome!

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aiofastnet-1.0.3.tar.gz (706.6 kB view details)

Uploaded Aug 3, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

aiofastnet-1.0.3-cp314-cp314t-win_arm64.whl (488.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tWindows ARM64

aiofastnet-1.0.3-cp314-cp314t-win_amd64.whl (561.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tWindows x86-64

aiofastnet-1.0.3-cp314-cp314t-win32.whl (494.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tWindows x86

aiofastnet-1.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl (655.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tmusllinux: musl 1.2+ x86-64

aiofastnet-1.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl (643.8 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tmusllinux: musl 1.2+ ARM64

aiofastnet-1.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (647.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tmanylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

aiofastnet-1.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (634.7 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tmanylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

aiofastnet-1.0.3-cp314-cp314t-macosx_11_0_arm64.whl (570.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tmacOS 11.0+ ARM64

aiofastnet-1.0.3-cp314-cp314t-macosx_10_15_x86_64.whl (587.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.14tmacOS 10.15+ x86-64

aiofastnet-1.0.3-cp314-cp314-win_arm64.whl (453.0 kB view details)

Uploaded Aug 3, 2026 CPython 3.14Windows ARM64

aiofastnet-1.0.3-cp314-cp314-win_amd64.whl (512.0 kB view details)

Uploaded Aug 3, 2026 CPython 3.14Windows x86-64

aiofastnet-1.0.3-cp314-cp314-win32.whl (447.8 kB view details)

Uploaded Aug 3, 2026 CPython 3.14Windows x86

aiofastnet-1.0.3-cp314-cp314-musllinux_1_2_x86_64.whl (633.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.14musllinux: musl 1.2+ x86-64

aiofastnet-1.0.3-cp314-cp314-musllinux_1_2_aarch64.whl (611.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.14musllinux: musl 1.2+ ARM64

aiofastnet-1.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (625.3 kB view details)

Uploaded Aug 3, 2026 CPython 3.14manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

aiofastnet-1.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (601.7 kB view details)

Uploaded Aug 3, 2026 CPython 3.14manylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

aiofastnet-1.0.3-cp314-cp314-macosx_11_0_arm64.whl (533.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.14macOS 11.0+ ARM64

aiofastnet-1.0.3-cp314-cp314-macosx_10_15_x86_64.whl (549.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.14macOS 10.15+ x86-64

aiofastnet-1.0.3-cp313-cp313-win_arm64.whl (438.3 kB view details)

Uploaded Aug 3, 2026 CPython 3.13Windows ARM64

aiofastnet-1.0.3-cp313-cp313-win_amd64.whl (503.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.13Windows x86-64

aiofastnet-1.0.3-cp313-cp313-win32.whl (440.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.13Windows x86

aiofastnet-1.0.3-cp313-cp313-musllinux_1_2_x86_64.whl (631.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.13musllinux: musl 1.2+ x86-64

aiofastnet-1.0.3-cp313-cp313-musllinux_1_2_aarch64.whl (601.9 kB view details)

Uploaded Aug 3, 2026 CPython 3.13musllinux: musl 1.2+ ARM64

aiofastnet-1.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (623.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.13manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

aiofastnet-1.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (591.6 kB view details)

Uploaded Aug 3, 2026 CPython 3.13manylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

aiofastnet-1.0.3-cp313-cp313-macosx_11_0_arm64.whl (530.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.13macOS 11.0+ ARM64

aiofastnet-1.0.3-cp313-cp313-macosx_10_13_x86_64.whl (548.8 kB view details)

Uploaded Aug 3, 2026 CPython 3.13macOS 10.13+ x86-64

aiofastnet-1.0.3-cp312-cp312-win_arm64.whl (439.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.12Windows ARM64

aiofastnet-1.0.3-cp312-cp312-win_amd64.whl (505.6 kB view details)

Uploaded Aug 3, 2026 CPython 3.12Windows x86-64

aiofastnet-1.0.3-cp312-cp312-win32.whl (441.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.12Windows x86

aiofastnet-1.0.3-cp312-cp312-musllinux_1_2_x86_64.whl (636.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.12musllinux: musl 1.2+ x86-64

aiofastnet-1.0.3-cp312-cp312-musllinux_1_2_aarch64.whl (605.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.12musllinux: musl 1.2+ ARM64

aiofastnet-1.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (628.6 kB view details)

Uploaded Aug 3, 2026 CPython 3.12manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

aiofastnet-1.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (595.8 kB view details)

Uploaded Aug 3, 2026 CPython 3.12manylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

aiofastnet-1.0.3-cp312-cp312-macosx_11_0_arm64.whl (534.7 kB view details)

Uploaded Aug 3, 2026 CPython 3.12macOS 11.0+ ARM64

aiofastnet-1.0.3-cp312-cp312-macosx_10_13_x86_64.whl (553.0 kB view details)

Uploaded Aug 3, 2026 CPython 3.12macOS 10.13+ x86-64

aiofastnet-1.0.3-cp311-cp311-win_arm64.whl (451.3 kB view details)

Uploaded Aug 3, 2026 CPython 3.11Windows ARM64

aiofastnet-1.0.3-cp311-cp311-win_amd64.whl (509.8 kB view details)

Uploaded Aug 3, 2026 CPython 3.11Windows x86-64

aiofastnet-1.0.3-cp311-cp311-win32.whl (449.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.11Windows x86

aiofastnet-1.0.3-cp311-cp311-musllinux_1_2_x86_64.whl (636.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.11musllinux: musl 1.2+ x86-64

aiofastnet-1.0.3-cp311-cp311-musllinux_1_2_aarch64.whl (614.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.11musllinux: musl 1.2+ ARM64

aiofastnet-1.0.3-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (629.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.11manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

aiofastnet-1.0.3-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (605.3 kB view details)

Uploaded Aug 3, 2026 CPython 3.11manylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

aiofastnet-1.0.3-cp311-cp311-macosx_11_0_arm64.whl (537.9 kB view details)

Uploaded Aug 3, 2026 CPython 3.11macOS 11.0+ ARM64

aiofastnet-1.0.3-cp311-cp311-macosx_10_9_x86_64.whl (550.9 kB view details)

Uploaded Aug 3, 2026 CPython 3.11macOS 10.9+ x86-64

aiofastnet-1.0.3-cp310-cp310-win_arm64.whl (450.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.10Windows ARM64

aiofastnet-1.0.3-cp310-cp310-win_amd64.whl (505.3 kB view details)

Uploaded Aug 3, 2026 CPython 3.10Windows x86-64

aiofastnet-1.0.3-cp310-cp310-win32.whl (450.1 kB view details)

Uploaded Aug 3, 2026 CPython 3.10Windows x86

aiofastnet-1.0.3-cp310-cp310-musllinux_1_2_x86_64.whl (635.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.10musllinux: musl 1.2+ x86-64

aiofastnet-1.0.3-cp310-cp310-musllinux_1_2_aarch64.whl (614.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.10musllinux: musl 1.2+ ARM64

aiofastnet-1.0.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (628.6 kB view details)

Uploaded Aug 3, 2026 CPython 3.10manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

aiofastnet-1.0.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (605.4 kB view details)

Uploaded Aug 3, 2026 CPython 3.10manylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

aiofastnet-1.0.3-cp310-cp310-macosx_11_0_arm64.whl (539.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.10macOS 11.0+ ARM64

aiofastnet-1.0.3-cp310-cp310-macosx_10_9_x86_64.whl (551.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.10macOS 10.9+ x86-64

aiofastnet-1.0.3-cp39-cp39-win_arm64.whl (452.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.9Windows ARM64

aiofastnet-1.0.3-cp39-cp39-win_amd64.whl (507.3 kB view details)

Uploaded Aug 3, 2026 CPython 3.9Windows x86-64

aiofastnet-1.0.3-cp39-cp39-win32.whl (451.8 kB view details)

Uploaded Aug 3, 2026 CPython 3.9Windows x86

aiofastnet-1.0.3-cp39-cp39-musllinux_1_2_x86_64.whl (637.0 kB view details)

Uploaded Aug 3, 2026 CPython 3.9musllinux: musl 1.2+ x86-64

aiofastnet-1.0.3-cp39-cp39-musllinux_1_2_aarch64.whl (616.5 kB view details)

Uploaded Aug 3, 2026 CPython 3.9musllinux: musl 1.2+ ARM64

aiofastnet-1.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (630.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.9manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

aiofastnet-1.0.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (607.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.9manylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

aiofastnet-1.0.3-cp39-cp39-macosx_11_0_arm64.whl (541.2 kB view details)

Uploaded Aug 3, 2026 CPython 3.9macOS 11.0+ ARM64

aiofastnet-1.0.3-cp39-cp39-macosx_10_9_x86_64.whl (553.8 kB view details)

Uploaded Aug 3, 2026 CPython 3.9macOS 10.9+ x86-64

File details

Details for the file aiofastnet-1.0.3.tar.gz.

File metadata

Download URL: aiofastnet-1.0.3.tar.gz
Upload date: Aug 3, 2026
Size: 706.6 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for aiofastnet-1.0.3.tar.gz
Algorithm	Hash digest
SHA256	`c5104dcb3353f2d6c478c3f388c47bd34119dd6aa3dec2a8af09ea85ee5cf79c`
MD5	`32cf68ac504e8de52e548f01ea0e6210`
BLAKE2b-256	`3ecd2e045a6c18b93263bb74db4d7ff226c088a5f84ffa26ddbfd02453936b34`

Algorithm	Hash digest
SHA256	`4891cf4940f6eb80b31d2283d29907fa089c7690c25cfcfc1c07a0e34b657dc0`
MD5	`b251f8a4fc6a0163914acd0f2a250bc3`
BLAKE2b-256	`5d15bd124de7ffdf0acf1d2af6e8bb9ee9a5ce4f50fee3f3fb7c23e7ee716fbf`

Algorithm	Hash digest
SHA256	`50ce485ac9c38405322b886eaf2e22fb66a64f1a1fa479b6feaabe62f82823cb`
MD5	`5ca2eeb6bd0fce028e0eaf46b9ba731c`
BLAKE2b-256	`5ac3f9bddf41b1ad5a3e2860c4b2ec5f87ce8322a8c7c8cc4f3823433fdcbfc1`

Algorithm	Hash digest
SHA256	`aea4bb48b1937dd79f84c28de707db2c99f374d336ea1a0d6eed21e6ff904fc7`
MD5	`9072165c93c9f63c94b1d2f29d8cd834`
BLAKE2b-256	`c946a27db5a3b76f839c28ea9071e17534ef07c9428889e4e93e419b4e4a08e4`

Algorithm	Hash digest
SHA256	`d4fb68c3a58356a317bba1b0b58bb2d1f33c78601f43fbedd8df710f93775c56`
MD5	`dd2c344f05add340e21da4d498f26e87`
BLAKE2b-256	`6da06f51bd2018be8d085288d656f1b7f1df1afedc5841487b92b5be4cccb2d3`

Algorithm	Hash digest
SHA256	`1416ae22400993dbbb7f7466349ec95f74c403322e87626c38eaee0331def464`
MD5	`4f8d9c906ae7c93a929f75f7454dcc01`
BLAKE2b-256	`3a7e6b4218c95890dc9434a075f843ec4fa8f7b85218fb1b68d792f0de4765c6`

Algorithm	Hash digest
SHA256	`868e408b4ad004a8b45bfc981f1c324cc70f38e2d85f44142d880cf1151cdddb`
MD5	`e8bc8d77c3876a6824ab0c5fe4e2244c`
BLAKE2b-256	`3105c7ccb564e73da0e22a3dc168630bed015add355ddd50394ddcf88d031185`

Algorithm	Hash digest
SHA256	`4d0396bf217a981981204d2d73cea54a02e47f24a01a80402c6322f8444a26aa`
MD5	`95d1b8a4861db0ecb896ccbbcfd4845d`
BLAKE2b-256	`c501d9102d664e943b108e97886ef1ff01466710ddc6b881b15cf9ee6e638a76`

Algorithm	Hash digest
SHA256	`9f5ddd160b68c483f6bc7ea22b4d565f748c77050fb17a51f74c152bb3d24815`
MD5	`3151078875f06f93a749b3606b38af6f`
BLAKE2b-256	`223c7c3e8ad1b73b8d2eb8666e9759f90bfba3fa604934b128fcd8ecfc3e668d`

Algorithm	Hash digest
SHA256	`3803dc3a4103d50f95635fbbf3e2aadb25aa5aa96f0c1f871b5516af37e79c13`
MD5	`8619758ce774ffb783f600cf71630885`
BLAKE2b-256	`3ceecb9086c2236eff360078b17cc3b8114520bfcd0eb98f9127a0636414cab4`

Algorithm	Hash digest
SHA256	`b235596b1bb9b6bc8642f344ccecafa8e85b42fe372cb6af288365139d128665`
MD5	`bbf41312185cee5f8fbd46e0728a6d31`
BLAKE2b-256	`5bfca5a748c3c41e3fc1738c15008f7e3bbec995b3cc5e3a7c358da77e2898ce`

Algorithm	Hash digest
SHA256	`0522febb3ed88fcadc28c862b3929be7935ff78b2672c91e4094b1a2e8c14919`
MD5	`fa069a9e67b437731341734c54792bcc`
BLAKE2b-256	`d636f55510ef4dfec8de3eed6e5e0f2267ba4d5c5890f488318d8f079ab5432f`

Algorithm	Hash digest
SHA256	`fa9f9e5eb37efcb09c47bb7fd91225de5d9b7cb9e96e82f9e5bdf96d3162c052`
MD5	`da71709ee2880c52df90559f3d7420e3`
BLAKE2b-256	`28e62aa9e569a898f81fd4e7060eadd8bd5f48b9eeefffd34877482e30f3e381`

Algorithm	Hash digest
SHA256	`7dfecf85bcac6806ef758d92f3bac9a0c05f97582d24804d4c7a57532aaef79c`
MD5	`dcc9d5487897cca55ca2b898cff1ac26`
BLAKE2b-256	`54df8885ecbb833edc42b8892a272982868318a63ed9fedae2f845c9e71a99bc`

Algorithm	Hash digest
SHA256	`a0d6fd538978f47f2dd126bc1160470795bfe18c643c27c598fdc0e64343a4d8`
MD5	`0c5dcb58f2043a5a387265e0e0338bc0`
BLAKE2b-256	`50c01f6ae90ad3f3973ac64e18b961fed192a92802cb0cce25de1bfa43bf545a`

Algorithm	Hash digest
SHA256	`12986736cf9241021adcb2b7bd2602ec9f6e20f697e57e16114ad448d42acfed`
MD5	`a812a4b799597a52f7a30808ea4370a1`
BLAKE2b-256	`685fc2c8d6a6f839845007f4bd664273561f34fbf602256007c6d9277cdfbb54`

Algorithm	Hash digest
SHA256	`4e4febf6054442e738f2491f2532ecbfeb794da2502152fa12580e48e16a2519`
MD5	`1e0d628511a0e1c6380ce92df80f1c21`
BLAKE2b-256	`f0f883719ad91040f09eada327b7181359f2dd2954297f0547d0f7be44356892`

Algorithm	Hash digest
SHA256	`443c527e275c790fa792d6a279ef86a007375dad40479cc706c266e62266024d`
MD5	`859cea5e4df5f240ae542974ffdd14c8`
BLAKE2b-256	`80b9cdde370ac8dd5ad86165f4b7a2469fd5dda5c8bb579dffa9b2109b60cdea`

Algorithm	Hash digest
SHA256	`58035f60a43fa45482cb36c2f247b158eeebad2f39aaa0f9f9b0973ca9fc6f6e`
MD5	`844b0309a30d15f4651b4cb44a930c86`
BLAKE2b-256	`6a03926dd4da3dd303feb110a1dab7a8edb263b2236a534dc7d5b55084b9d484`

Algorithm	Hash digest
SHA256	`ca0e31ce006901ee3ee168e8ed26278c8278162e1de469da00ddfc183ac49832`
MD5	`b740a79827bb96eb872ac2e6dd606eec`
BLAKE2b-256	`01040bcb4105d9f93e20ff14b430738264a82d034ef7ca8805489938c33958db`

Algorithm	Hash digest
SHA256	`768ea5235783493e2c369b5740610354369ca4e9095d218274dfbaefb0d5114c`
MD5	`52f5701062f14e6218fa4c3c5cac8fe7`
BLAKE2b-256	`a5e100b9f7311c79c2b6d549cbfe3a5647ae6ccce30d1b0000b7fbd96eeed1e2`

aiofastnet 1.0.3

Benchmark

How is this possible?

Why Use aiofastnet

Quickstart

When aiofastnet Is a Good Fit

When Not To Use aiofastnet

Platform Compatibility

Asyncio Compatibility

Kernel TLS (KTLS) support on Linux

Free-Threaded Python

Building From Source

Contributing

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

Requires

Tags

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

Algorithm	Hash digest
SHA256	`a0ab818fed1152adf377a86f26f79e9f953c04dd9218968d7435d01d138779a0`
MD5	`344b936a38f84dc5d353fb1aed0797d6`
BLAKE2b-256	`cdb050480bb8dfcb1c843fb6f516e08a5a1ba6d82afb0bd36563e8fc6eb3cf01`

Algorithm	Hash digest
SHA256	`bb993b6268c72c13740ffcf10a7ccd6582fe7230eeee49b54e8f6ed48ecf56fb`
MD5	`da1cb2953c11677670ce5a907322d9b9`
BLAKE2b-256	`861af7e15744e54a880b5e42f6cdca22e46bb580044f74102410f2b9dd634527`

Algorithm	Hash digest
SHA256	`ef4b3c98f4bfe76c993cda8377498ed0d5209f0813dd7f6d170b4c4d77173440`
MD5	`671a30ee063dbba9333367ca0ec21991`
BLAKE2b-256	`4d44b445bfbe971cdcb5597d4b093b5e1708fa4ba025b342c2138e704d8bd0ad`

Algorithm	Hash digest
SHA256	`1f8d4981270dcc9f671be200223c924afa92cd4e5824b3e9ab950107c0f12597`
MD5	`6092cf7b6c5388fb706f00513aa4d219`
BLAKE2b-256	`63ec33f4de5205c2fedfbe47bb138860b86bcf57224a349424fef3398fe8b2ef`

Algorithm	Hash digest
SHA256	`b8bf40146b07a569b9f0d2b4019f74717118f4a0787027663647be9e0527e274`
MD5	`b3ce86882cb86bbd7115b22ce168918e`
BLAKE2b-256	`08a660217a804d80ad97406dfbcc58f1ac46f50459cc59b9e2f58d93fd05d30e`

Algorithm	Hash digest
SHA256	`b6fb0d29b27c8a0ac519f90212ae562a80cf6e1f0f9da252a8bb4c6fe95acc43`
MD5	`8ecf98151e783b059a9ce28178c03171`
BLAKE2b-256	`e877a39fd4b3ba6211526144d3014442c52123c1dcac145f0bcb04a5a1a40337`

Algorithm	Hash digest
SHA256	`a0d775e52c7f14eca78c1fa39716ea7a0e6f16ebe8081b132cacb0a71ed6c1f1`
MD5	`b4521de6bc8641f4dff2e4baea59af5e`
BLAKE2b-256	`b33d2577a71d44c15c2dd17a1b46e8d2644b94a592cba3edcf0b08e9e292cd61`

Algorithm	Hash digest
SHA256	`87d2dff2e923bbaf04572f527a213e8f41d410e74648bcc637509dde2fb96465`
MD5	`43aae576712ae61a7e0731bc0a8d1315`
BLAKE2b-256	`3b5ab1269b5e5ec7f2bc9326535b563e2dd9909d9e6488f1a28eae85fcab0e47`

Algorithm	Hash digest
SHA256	`68992d15d110085c12b62dec4c523f37c38bca085967b5d1dfa798eca1dbebb4`
MD5	`dbf6892c4e78cd82c9ae7c7e2bc6c59e`
BLAKE2b-256	`a1ce8d4bf84811f2868a480ab1885cdfb8e2df576c4f785976475ef14c1c32d3`

Algorithm	Hash digest
SHA256	`fc7d58a1bf8819da56a36f2816d4a269df2d16d61fd7af295363de8b5e743b58`
MD5	`afa564630f9bad8961cbac83dcc089de`
BLAKE2b-256	`1f2207b286bb658da30692443e5e1595c60f1786ca7e51cb07f7f207d8651cb7`