asynkit: A toolkit for Python coroutines

asynkit provides advanced control over Python's asyncio module, offering tools for eager execution, fine-grained scheduling, and powerful coroutine manipulation.

Helper tools for controlling coroutine execution, such as CoroStart and Monitor
Utility classes such as GeneratorObject
Coroutine helpers such coro_iter() and the awaitmethod() decorator
Helpers to run async code from non-async code, such as await_sync() and aiter_sync()
Scheduling helpers for asyncio, and extended event-loop implementations
eager_task_factory support for global eager task execution (Python 3.12 API, backward compatible)
@eager decorator for selective eager execution of coroutines
Experimental support for Priority Scheduling of Tasks
Other experimental features such as task_interrupt()
Limited support for anyio and trio.

Installation

pip install asynkit

asynkit has a custom C extension to optimize some primitives. If available for your platform it will be automatically installed. If you want to force compilation from source for your platform use:

pip install --no-binary=asynkit asynkit

Wheel selection:

Binary wheels (Windows, macOS, Linux × Python 3.10-3.14): High-performance C extension provides 8% better throughput and near-native latency
Source distribution: For platforms without pre-built wheels, or when using --no-binary flag

Build environment variables:

ASYNKIT_FORCE_CEXT=1: Force C extension compilation and fail if it cannot be built (ensures no silent fallback to pure Python)
ASYNKIT_DISABLE_CEXT=1: Disable C extension completely, use pure Python implementation
ASYNKIT_DEBUG=1: Build C extension with debug symbols and assertions

Example:

# Force C extension build (fail if compilation fails)
ASYNKIT_FORCE_CEXT=1 pip install --no-binary=asynkit asynkit

# Build with debug symbols for development
ASYNKIT_DEBUG=1 pip install --no-binary=asynkit asynkit

Check your installation:

import asynkit

info = asynkit.get_implementation_info()
print(f"Using: {info['implementation']}")  # "C extension" or "Pure Python"

Features

🚀 Eager Execution: Start coroutines immediately, not when awaited
🏭 Eager Task Factory: Global eager execution for all tasks (Python 3.12 API, backward compatible)
🔧 Cross-Version Compatibility: Seamless eager execution across all Python versions with automatic monkeypatching
⚡ Advanced Scheduling: Priority tasks, queue control, task switching
🔧 Coroutine Control: Low-level inspection and manipulation
🔬 Experimental Features: Task interruption, custom timeouts
🔌 Backend Support: Full asyncio and anyio support, limited trio support

Coroutine Tools

`eager()` - lower latency IO

ℹ️ Note: Python 3.12+ introduced native eager task execution via asyncio.eager_task_factory. See docs/eager_tasks.md for a detailed comparison of Python's built-in eager tasks and asynkit's eager() feature. Performance analysis shows asynkit's C extension achieves near-native latency (0.80μs vs 0.74μs) with 8% better throughput than pure Python.

Did you ever wish that your coroutines started right away, and only returned control to the caller once they become blocked? Like the way the async and await keywords work in the C# language?

Now they can. Just decorate or convert them with acynkit.eager:

@asynkit.eager
async def get_slow_remote_data():
    result = await execute_remote_request()
    return result.important_data


async def my_complex_thing():
    # kick off the request as soon as possible
    future = get_slow_remote_data()
    # The remote execution may now already be in flight. Do some work taking time
    intermediate_result = await some_local_computation()
    # wait for the result of the request
    return compute_result(intermediate_result, await future)

By decorating your function with eager, the coroutine will start executing right away and control will return to the calling function as soon as it suspends, returns, or raises an exception. In case it is suspended, a Task is created and returned, ready to resume execution from that point.

Notice how, in either case, control is returned directly back to the calling function, maintaining synchronous execution. In effect, conventional code calling order is maintained as much as possible. We call this depth-first-execution.

This allows you to prepare and dispatch long running operations as soon as possible while still being able to asynchronously wait for the result.

asynkit.eager can also be used directly on the returned coroutine:

log = []


async def test():
    log.append(1)
    await asyncio.sleep(0.2)  # some long IO
    log.append(2)


async def caller(convert):
    del log[:]
    log.append("a")
    future = convert(test())
    log.append("b")
    await asyncio.sleep(0.1)  # some other IO
    log.append("c")
    await future


# do nothing
asyncio.run(caller(lambda c: c))
assert log == ["a", "b", "c", 1, 2]

# Create a Task
asyncio.run(caller(asyncio.create_task))
assert log == ["a", "b", 1, "c", 2]

# eager
asyncio.run(caller(asynkit.eager))
assert log == ["a", 1, "b", "c", 2]

eager() is actually a convenience function, invoking either coro_eager() or func_eager() (see below) depending on context. Decorating your function makes sense if you always intend To await its result at some later point. Otherwise, just apply it at the point of invocation in each such case.

It may be prudent to ensure that the result of eager() does not continue running if it will never be awaited, such as in the case of an error. You can use the cancelling() context manager for this:

with cancelling(eager(my_method())) as v:
    await some_method_which_may_raise()
    await v

As a convenience, eager_ctx() will perform the above:

with eager_ctx(my_method()) as v:
    await some_method_which_may_raise()
    await v

`coro_eager()`, `func_eager()`

coro_eager() is the magic coroutine wrapper providing the eager behaviour:

It copies the current context
It initializes a CoroStart() object for the coroutine, starting it in the copied context.
If it subsequently is done() It returns CoroStart.as_future(), otherwise it creates and returns a Task (using asyncio.create_task by default.)

The result is an awaitable which can be either directly awaited or passed to asyncio.gather(). The coroutine is executed in its own copy of the current context, just as would happen if it were directly turned into a Task.

func_eager() is a decorator which automatically applies coro_eager() to the coroutine returned by an async function.

`eager_task_factory` and `create_eager_factory()` - Global Eager Execution

For applications that want to apply eager execution globally (similar to Python 3.12's asyncio.eager_task_factory), asynkit provides task factory functions that make all tasks created with asyncio.create_task() execute eagerly. This implementation provides the same API as Python 3.12's native factory while being backward compatible with older Python versions.

Using the Pre-created Factory

import asyncio
import asynkit

# Set up eager execution for all tasks (Python 3.12 API, works with 3.10+)
loop = asyncio.get_running_loop()
loop.set_task_factory(asynkit.eager_task_factory)


# Now all tasks execute eagerly
async def my_coroutine():
    return "immediate_result"


task = asyncio.create_task(my_coroutine())
# Task starts executing immediately, not when awaited

Creating Custom Factories

import asynkit

# Create a custom eager factory (preserving existing factory)
loop = asyncio.get_running_loop()
old_factory = loop.get_task_factory()
eager_factory = asynkit.create_eager_factory(old_factory)
loop.set_task_factory(eager_factory)

ℹ️ Note on current_task() behavior: When using eager task execution, asyncio.current_task() returns the parent task or a temporary ghost task before the first blocking call, and the actual task afterwards. This ensures compatibility with framework detection libraries. See Current Task Behavior for details.

Python 3.14+ Compatibility

asynkit also provides a create_task() function with the same eager_start parameter as Python 3.14+:

# Works on all Python versions (3.10+)
task = asynkit.create_task(my_coroutine(), eager_start=True)  # Eager
task = asynkit.create_task(my_coroutine(), eager_start=False)  # Standard

Performance Benefits

Eager task factories provide significant performance improvements for task startup latency:

Standard asyncio: ~2.0 microseconds minimum per-task delay (scales with application complexity)
Eager factories: ~0.6-1.1 microseconds consistent execution time
Improvement: 1.8x faster minimum latency, much larger improvements in real applications
asynkit C extension: 8% better throughput than pure Python, achieving 93% of native Python 3.14 performance with cross-platform compatibility

Important: Eager execution provides predictable performance - latency remains constant regardless of work done between task creation and await, while non-eager latency scales with application complexity.

See docs/eager_task_factory_performance.md for detailed performance analysis comparing asynkit's implementation with Python 3.14's native eager_task_factory.

When to Use Task Factories vs. Decorators

Use Case	Recommendation
Global optimization	`eager_task_factory`
Legacy code migration	`eager_task_factory`
Selective optimization	`@asynkit.eager` decorator
Fine-grained control	`@asynkit.eager` decorator
Python 3.10/3.11 support	Either (both work)
Python 3.13+ migration	`eager_task_factory` (compatible API)

Cross-Version Compatibility

Automatic Eager Execution Monkeypatching

For the ultimate in cross-version compatibility, asynkit provides automatic monkeypatching that enables modern eager execution APIs on all Python versions:

import asyncio
import asynkit.compat

# Enable comprehensive eager task execution across all Python versions
asynkit.compat.enable_eager_tasks()

# Now these APIs work identically on Python 3.10-3.14+:

# 1. asyncio.eager_task_factory is available everywhere
loop = asyncio.get_running_loop()
loop.set_task_factory(asyncio.eager_task_factory)  # Global eager execution

# 2. asyncio.create_task() supports eager_start parameter everywhere
task = asyncio.create_task(my_coroutine(), eager_start=True)  # Eager execution
task = asyncio.create_task(my_coroutine(), eager_start=False)  # Standard behavior

How It Works

The enable_eager_tasks() function automatically detects your Python version and applies the optimal implementation:

Python Version	Implementation Strategy
Python < 3.12	Adds `asyncio.eager_task_factory` using asynkit's implementation, enhances `create_task()` with `eager_start` parameter
Python 3.12-3.13 without native `eager_start`	Uses native `eager_task_factory` with temporary factory swapping for `eager_start=True`
Python 3.14+ with native `eager_start`	Uses native Python implementations directly (no monkeypatching needed)

Benefits

🔄 Drop-in Compatibility: Identical APIs across all Python versions
⚡ Optimal Performance: Automatically uses the fastest available implementation
🛡️ Future-Proof: Gracefully adapts to new Python versions
🧹 Clean Restoration: disable_eager_tasks() restores original behavior

Example: Cross-Version Migration

# This code works identically on Python 3.10, 3.11, 3.12, 3.13, and beyond!
import asyncio
import asynkit.compat


def setup_eager_execution():
    """Set up eager execution using the best available method."""
    asynkit.compat.enable_eager_tasks()

    # Global eager execution
    loop = asyncio.get_running_loop()
    loop.set_task_factory(asyncio.eager_task_factory)


async def main():
    setup_eager_execution()

    # Per-task control works everywhere
    eager_task = asyncio.create_task(fetch_data(), eager_start=True)
    standard_task = asyncio.create_task(fetch_data(), eager_start=False)

    # Both APIs provide identical behavior across Python versions
    results = await asyncio.gather(eager_task, standard_task)
    return results


# Works on any Python 3.10+ installation
asyncio.run(main())

This makes asynkit the definitive solution for eager task execution across the entire Python ecosystem, providing a smooth migration path from legacy Python versions to the latest releases.

`await_sync(), aiter_sync()` - Running coroutines synchronously

If you are writing code which should work both synchronously and asynchronously, you can now write the code fully async and then run it synchronously in the absence of an event loop. As long as the code doesn't block (await unfinished futures) and doesn't try to access the event loop, it can successfully be executed. This helps avoid writing duplicate code.

async def async_get_processed_data(datagetter):
    data = datagetter()  # an optionally async callback
    data = await data if isawaitable(data) else data
    return process_data(data)


# raises SynchronousError if datagetter blocks
def sync_get_processed_data(datagetter):
    return asynkit.await_sync(async_get_processed_data(datagetter))

This sort of code might previously have been written thus:

# A hybrid function, _may_ return an _awaitable_
def hybrid_get_processed_data(datagetter):
    data = datagetter()
    if isawaitable(data):
        # return an awaitable helper closure
        async def helper():
            data = await data
            return process_data(data)

        return helper
    return process_data(data)  # duplication


async def async_get_processed_data(datagetter):
    r = hybrid_get_processed_data(datagetter)
    return await r if isawaitable(r) else r


def sync_get_processed_data(datagetter):
    r = hybrid_get_processed_data(datagetter)
    if isawaitable(r):
        raise RuntimeError("callbacks failed to run synchronously")
    return r

The above pattern, writing async methods as sync and returning async helpers, is common in library code which needs to work both in synchronous and asynchronous context. Needless to say, it is very convoluted, hard to debug and contains a lot of code duplication where the same logic is repeated inside async helper closures.

Using await_sync() it is possible to write the entire logic as async methods and then simply fail if the code tries to invoke any truly async operations. If the invoked coroutine blocks, a SynchronousError is raised from a SynchronousAbort exception which contains a traceback. This makes it easy to pinpoint the location in the code where the async code blocked. If the code tries to access the event loop, e.g. by creating a Task, a RuntimeError will be raised.

The syncfunction() decorator can be used to automatically wrap an async function so that it is executed using await_sync():

>>> @asynkit.syncfunction
... async def sync_function():
...     async def async_function():
...         return "look, no async!"
...     return await async_function()
...
>>> sync_function()
'look, no async!'
>>>

the asyncfunction() utility can be used when passing synchronous callbacks to async code, to make them async. This, along with syncfunction() and await_sync(), can be used to integrate synchronous code with async middleware:

@asynkit.syncfunction
async def sync_client(sync_callback):
    middleware = AsyncMiddleware(asynkit.asyncfunction(sync_callback))
    return await middleware.run()

Using this pattern, one can write the middleware completely async, make it also work for synchronous code, while avoiding the hybrid function antipattern.

`aiter_sync()`

A helper function is provided, which turns an AsyncIterable into a generator, leveraging the await_sync() method:

async def agen():
    for v in range(3):
        yield v


assert list(aiter_sync(agen())) == [1, 2, 3]

This is useful if using patterns such as GeneratorObject in a synchronous application.

`CoroStart`

This class manages the state of a partially run coroutine and is what what powers the coro_eager() and await_sync() functions. When initialized, it will start the coroutine, running it until it either suspends, returns, or raises an exception. It can subsequently be awaited to retrieve the result.

Similarly to a Future, it has these methods:

done() - returns True if the coroutine finished without blocking. In this case, the following two methods may be called to get the result.
result() - Returns the return value of the coroutine or raises any exception that it produced.
exception() - Returns any exception raised, or None otherwise.

But more importantly it has these:

__await__() - A magic method making it directly awaitable. If it has already finished, awaiting this coroutine is the same as calling result(), otherwise it awaits the original coroutine's continued execution
as_coroutine() - A helper which returns a proper coroutine object to await the CoroStart
as_future() - If done(), returns a Future holding its result, otherwise, a RuntimeError is raised.
as_awaitable() - If done(), returns as_future(), else returns self. This is a convenience method for use with functions such as asyncio.gather(), which would otherwise wrap a completed coroutine in a Task.

In addition it has:

aclose() - If not done(), will throw a GeneratorError into the coroutine and wait for it to finish. Otherwise does nothing.
athrow(exc) - If not done(), will throw the given error into the coroutine and wait for it to raise or return a value.
close() and throw(exc) - Synchronous versions of the above, will raise RuntimeError if the coroutine does not immediately exit.

This means that a context manager such as aclosing() can be used to ensure that the coroutine is cleaned up in case of errors before it is awaited:

# start foo() and run until it blocks
async with aclosing(CoroStart(foo())) as coro:
    ...  # do things, which may result in an error
    return await coro

CoroStart can be provided with a contextvars.Context object, in which case the coroutine will be run using that context.

Context helper

coro_await() is a helper function to await a coroutine, optionally with a contextvars.Context object to activate:

var1 = contextvars.ContextVar("myvar")


async def my_method():
    var1.set("foo")


async def main():
    context = contextvars.copy_context()
    var1.set("bar")
    await asynkit.coro_await(my_method(), context=context)
    # the coroutine didn't modify _our_ context
    assert var1.get() == "bar"
    # ... but it did modify the copied context
    assert context.get(var1) == "foo"

This is similar to contextvars.Context.run() but works for async functions. This function is implemented using CoroStart

`awaitmethod()`

This decorator turns the decorated method into a Generator as required for __await__ methods, which must only return Iterator objects.

This makes it simple to make a class instance awaitable by decorating an async __await__() method.

class Awaitable:
    def __init__(self, cofunc):
        self.cofunc = cofunc
        self.count = 0

    @asynkit.awaitmethod
    async def __await__(self):
        await self.cofunc()
        return self.count
        self.count += 1


async def main():
    async def sleeper():
        await asyncio.sleep(1)

    a = Awaitable(sleeper)
    assert (await a) == 0  # sleep once
    assert (await a) == 1  # sleep again


asyncio.run(main())

Unlike a regular coroutine (the result of calling a coroutine function), an object with an __await__ method can potentially be awaited multiple times.

The method can also be a classmethod or staticmethod:

class Constructor:
    @staticmethod
    @asynkit.awaitmethod
    async def __await__():
        await asyncio.sleep(0)
        return Constructor()


async def construct():
    return await Constructor

`awaitmethod_iter()`

An alternative way of creating an await method, it uses the coro_iter() method to to create a coroutine iterator. It is provided for completeness.

`coro_iter()`

This helper function turns a coroutine function into an iterator. It is primarily intended to be used by the awaitmethod_iter() function decorator.

`cancelling()`

This context manager automatically calls the cancel()method on its target when the scope exits. This is convenient to make sure that a task is not left running if it never to be awaited:

with cancelling(asyncio.Task(foo())) as t:
    function_which_may_fail()
    return await t

Monitors and Generators

Monitor

A Monitor object can be used to await a coroutine, while listening for out of band messages from the coroutine. As the coroutine sends messages, it is suspended, until the caller resumes awaiting for it.

async def coro(monitor):
    await monitor.oob("hello")
    await asyncio.sleep(0)
    await monitor.oob("dolly")
    return "done"


async def runner():
    m = Monitor()
    c = coro(m)
    while True:
        try:
            print(await m.aawait(c))
            break
        except OOBData as oob:
            print(oob.data)

which will result in the output

hello
dolly
done

For convenience, the Monitor can be bound so that the caller does not have to keep the coroutine around. Calling the monitor with the coroutine returns a BoundMonitor:

async def coro(m):
    await m.oob("foo")
    return "bar"


m = Monitor()
b = m(coro(m))
try:
    await b
except OOBData as oob:
    assert oob.data == "foo"
assert await b == "bar"

Notice how the BoundMonitor can be awaited directly, which is the same as awaiting b.aawait(None).

The caller can pass in data to the coroutine via the aawait(data=None) method and it will become the return value of the Monitor.oob() call in the coroutine. Monitor.athrow() can similarly be used to raise an exception out of the Montitor.oob() call. Neither data nor an exception can be sent the first time the coroutine is awaited, only as a response to a previous OOBData exception.

A Monitor can be used when a coroutine wants to suspend itself, maybe waiting for some external condition, without resorting to the relatively heavy mechanism of creating, managing and synchronizing Task objects. This can be useful if the coroutine needs to maintain state. Additionally, this kind of messaging does not require an event loop to be present and can can be driven using await_sync() (see below.)

Consider the following scenario. A parser wants to read a line from a buffer, but fails, signalling this to the monitor:

async def readline(m, buffer):
    l = buffer.readline()
    while not l.endswith("\n"):
        await m.oob(None)  # ask for more data in the buffer
        l += buffer.readline()
    return l


async def manager(buffer, io):
    m = Monitor()
    a = m(readline(m, buffer))
    while True:
        try:
            return await a
        except OOBData:
            try:
                buffer.fill(await io.read())
            except Exception as exc:
                await a.athrow(exc)

In this example, readline() is trivial, but if it were a stateful parser with hierarchical invocation structure, then this pattern allows the decoupling of IO and the parsing of buffered data, maintaining the state of the parser while the caller fills up the buffer.

Any IO exception is sent to the coroutine in this example. This ensures that it cleans up properly. Alternatively, aclose() could have been used:

m = Monitor()
with aclosing(m(readline(m, buffer))) as a:
    # the aclosing context manager ensures that the coroutine is closed
    # with `await a.aclose()`
    # even if we don't finish running it.
    ...

A standalone parser can also be simply implemented by two helper methods, start() and try_await().

async def stateful_parser(monitor, input_data):
    while input_short(input_data):
        input_data += await monitor.oob()  # request more
    # continue parsing, maybe requesting more data
    return await parsed_data(monitor, input_data)


m: Monitor[Tuple[Any, bytes]] = Monitor()
initial_data = b""
p = m(stateful_parser(m, b""))
await p.start()  # set the parser running, calling oob()

# feed data until a value is returned
while True:
    parsed = await p.try_await(await get_more_data())
    if parsed is not None:
        break

This pattern can even be employed in non-async applications, by using the await_sync() method instead of the await keyword to drive the Monitor.

For a more complete example, have a look at example_resp.py

GeneratorObject

A GeneratorObject builds on top of the Monitor to create an AsyncGenerator. It is in many ways similar to an asynchronous generator constructed using the generator function syntax. But whereas those return values using the yield keyword, a GeneratorObject has an ayield() method, which means that data can be sent to the generator by anyone, and not just by using yield, which makes composing such generators much simpler.

The GeneratorObject leverages the Monitor.oob() method to deliver the ayielded data to whomever is iterating over it:

async def generator(gen_obj):
    # yield directly to the generator
    await gen_obj.ayield(1)

    # have someone else yield to it
    async def helper():
        await gen_obj.ayield(2)

    await asyncio.create_task(helper())


async def runner():
    gen_obj = GeneratorObject()
    values = [val async for val in gen_obj(generator(gen_obj))]
    assert values == [1, 2]

The GeneratorObject, when called, returns a GeneratorObjectIterator which behaves in the same way as an AsyncGenerator object. It can be iterated over and supports the asend(), athrow() and aclose() methods.

A GeneratorObject is a flexible way to asynchronously generate results without resorting to Task and Queue objects. What is more, it allows this sort of generating pattern to be used in non-async programs, via aiter_sync():

def sync_runner():
    gen_obj = GeneratorObject()
    values = [val for val in aiter_sync(gen_obj(generator(gen_obj)))]
    assert values == [1, 2]

Scheduling tools

A set of functions are provided to perform advanced scheduling of Task objects with asyncio. They work with the built-in event loop, and also with any event loop implementing the AbstractSchedulingLoop abstract base class, such as the SchedulingMixin class which can be used to extend the built-in event loops.

Scheduling functions

`sleep_insert(pos)`

Similar to asyncio.sleep() but sleeps only for pos places in the runnable queue. Whereas asyncio.sleep(0) will place the executing task at the end of the queue, which is appropriate for fair scheduling, in some advanced cases you want to wake up sooner than that, perhaps after a specific task.

`task_reinsert(task, pos)`

Takes a runnable task (for example just created with asyncio.create_task() or similar) and reinserts it at a given position in the queue. Similarly as for sleep_insert(), this can be useful to achieve certain scheduling goals.

`task_switch(task, *, insert_pos=None)`

Immediately moves the given task to the head of the ready queue and switches to it, assuming it is runnable. If insert_pos is not None, the current task will be put to sleep at that position, using sleep_insert(). Otherwise the current task is put at the end of the ready queue. If insert_pos == 1 the current task will be inserted directly after the target task, making it the next to be run. If insert_pos == 0, the current task will execute before the target.

`task_is_blocked(task)`

Returns True if the task is waiting for some awaitable, such as a Future or another Task, and is thus not on the ready queue.

`task_is_runnable(task)`

Roughly the opposite of task_is_blocked(), returns True if the task is neither done() nor blocked and awaits execution.

`create_task_descend(coro)`

Implements depth-first task scheduling.

Similar to asyncio.create_task() this creates a task but starts it running right away, and positions the caller to be woken up right after it blocks. The effect is similar to using asynkit.eager() but it achieves its goals solely by modifying the runnable queue. A Task is always created, unlike eager, which only creates a task if the target blocks.

Runnable task helpers

A few functions are added to help working with tasks.

The following identity applies:

asyncio.all_tasks() == (
    asynkit.runnable_tasks() | asynkit.blocked_tasks() | asyncio.current_task()
)

`runnable_tasks(loop=None)`

Returns a set of the tasks that are currently runnable in the given loop

`blocked_tasks(loop=None)`

Returns a set of the tasks that are currently blocked on some future in the given loop.

Event Loop tools

Also provided is a mixin for the built-in event loop implementations in python, providing some primitives for advanced scheduling of tasks. These primitives are what is used by the scheduling functions above, and so custom event loop implementations can provide custom implementations of these methods.

`SchedulingMixin` mixin class

This class adds some handy scheduling functions to the event loop. The are intended to facilitate some scheduling tricks, particularly switching to tasks, which require finding items in the queue and re-inserting them at an early position. Nothing is assumed about the underlying implementation of the queue.

queue_len() - returns the length of the ready queue
queue_find(self, key, remove) - finds and optionally removes an element in the queue
queue_insert_pos(self, pos, element) - inserts an element at position pos the queue
call_pos(self, pos, ...) - schedules a callback at position pos in the queue

Concrete event loop classes

Concrete subclasses of Python's built-in event loop classes are provided.

SchedulingSelectorEventLoop is a subclass of asyncio.SelectorEventLoop with the SchedulingMixin
SchedulingProactorEventLoop is a subclass of asyncio.ProactorEventLoop with the SchedulingMixin on those platforms that support it.

Creating scheduling event loops

The recommended way to use scheduling event loops is with the scheduling_loop_factory() function (Python 3.13+):

import asyncio
import asynkit

# Modern approach (Python 3.13+)
asyncio.run(main(), loop_factory=asynkit.scheduling_loop_factory)

For Python 3.12 and earlier, or when using asyncio.Runner:

import asyncio
import asynkit

# Using asyncio.Runner (Python 3.11+)
with asyncio.Runner(loop_factory=asynkit.scheduling_loop_factory) as runner:
    runner.run(main())

Event Loop Policy (Legacy)

⚠️ Note: Event loop policies are deprecated as of Python 3.14 and will be removed in Python 3.16.

For compatibility with Python 3.10-3.11, or for code that hasn't migrated away from the policy system, a policy class is provided:

SchedulingEventLoopPolicy is a subclass of asyncio.DefaultEventLoopPolicy which instantiates either of the above event loop classes as appropriate.

Use this either directly:

# Legacy approach (Python 3.10-3.11)
asyncio.set_event_loop_policy(asynkit.SchedulingEventLoopPolicy())
asyncio.run(main())

or with a context manager:

with asynkit.event_loop_policy():
    asyncio.run(main())

Priority Scheduling

FIFO scheduling

Since the beginning, scheduling of Tasks in asyncio has always been FIFO, meaning "first-in, first-out". This is a design principle which provides a certain fairness to tasks, ensuring that all tasks run and a certain predictability is achieved with execution. FIFO is maintained in the following places:

In the Event Loop, where tasks are executed in the order in which they become runnable
In locking primitives (such as asyncio.Lock or asyncio.Condition) where tasks are able to acquire the lock or get notified in the order in which they arrive.

All tasks are treated equally.

The `asynkit.experimental.priority` module

🧪 Note: This is currently an experimental feature.

In pre-emptive system, such as scheduling of threads or processes there is usually some sort of priority involved too, to allow designating some tasks as more important than others, thus requiring more rapid servicing, and others as having lower priority and thus be relegated to background tasks where other more important work is not pending.

The asynkit.experimental.priority module now allows us to do something similar.

You can define the priority of Task objects. A task defining the effective_priority() method returning a float will get priority treatment in the following areas:

When awaiting a PriorityLock or PriorityCondition
When waiting in to be executed by a PrioritySelectorEventLoop or a PriorityProactorEventLoop.

The floating point priority value returned by effective_priority() is used to determine the task's priority, with lower values giving higher priority (in the same way that low values are sorted before high values). If this method is missing, the default priority of 0.0 is assumed. The Priority enum class can be used for some basic priority values, defining Priority.HIGH as -10.0 and Priority.LOW as 10.0. In case of identical priority values, FIFO order is respected.

The locking primitives provided are fully compatible with the standard locks in asyncio and also fully support the experimental task interruption feature.

`PriorityTask`

This is an asyncio.Task subclass which implements the effective_priority() method. It can be constructed with a priority keyword or a priority_value attribute. It also participates in Priority Inheritance.

`PriorityLock`

This is a asyncio.Lock subclass which respects the priorities of any Task objects attempting to acquire it. It also participates in Priority Inheritance.

`PriorityCondition`

This is an asyncio.Condition subclass which respects the priorities of any Task objects awaiting to be woken up. Its default lock is of type PriorityLock.

`DefaultPriorityEventLoop`

This is an asyncio.AbstractEventLoop subclass which respects the priorities of any Task objects waiting to be executed. It also provides all the scheduling extensions from AbstractSchedulingLoop. It also participates in Priority Inheritance.

This is either a PrioritySelectorEventLoop or a PriorityProactorEventLoop, both instances of the PrioritySchedulingMixin class.

Priority Inversion

A well known problem with priority scheduling is the so-called Priority Inversion problem. This implementation addresses that by two different means:

Priority Inheritance

A PriorityTask keeps track of all the PriorityLock objects it has acquired, and a PriorityLock keeps track of all the asyncio.Task objects waiting to acquire it. A PriorityTask's effective_priority() method will be the highest effective_priority of any task waiting to acquire a lock held by it. Thus, a high priority-task which starts waiting for a lock which is held by a low-priority task, will temporarily propagate its priority to that task, so that ultimately, the PrioritySchedulingMixin event loop with ensure that the previously low-priority task is now executed with the higher priority.

This mechanism requires the co-operation of both the tasks, locks and the event-loop to properly function.

Priority Boosting

The PrioritySchedulingMixin will regularly do "queue maintenance" and will identify Tasks that have sat around in the queue for many cycles without being executed. It will randomly "boost" the priority of these tasks in the queue, so that they have a chance to run.

This mechanism does not require the co-operation of locks and tasks to work, and is in place as a safety mechanism in applications where it is not feasible to replace all instances of Locks and Tasks with their priority_inheritance-aware counterparts.

How to use Priority Scheduling

To make use of Priority scheduling, you need to use either the priority scheduling event loop (e.g. DefaultPriorityEventLoop) or a priority-aware synchronization primitive, i.e. PriorityLock or PriorityCondition. In addition, you need Task objects which support the effective_priority() method, such as PriorityTask

It is possible to get priority behaviour from locks without having a priority event loop, and vice versa. But when using the priority event loop, it is recommended to use the accompanying lock and task classes which co-operate to provide priority inheritance.

A good first step, in your application, is to identify tasks that perform background work, such as housekeeping tasks, and assign to them the Priority.LOW priority.

Subsequently you may want to identify areas of your application that require more attention than others. For a web-application's URL handler may elect to temporarily raise the priority (change PriorityTask.priority_value) for certain endpoints to give them better response.

This is new territory and it remains to be seen how having priority scheduling in a co-operative`` environment such as asyncio` actually works in practice.

Coroutine helpers

A couple of functions are provided to introspect the state of coroutine objects. They work on both regular async coroutines, classic coroutines (using yield from) and async generators.

coro_is_new(coro) - Returns true if the object has just been created and hasn't started executing yet
coro_is_suspended(coro) - Returns true if the object is in a suspended state.
coro_is_done(coro) - Returns true if the object has finished executing, e.g. by returning or raising an exception.
coro_get_frame(coro) - Returns the current frame object of the coroutine, if it has one, or None.

`anyio` support

The library has been tested to work with anyio 4.11.0+ on Python 3.10-3.13, including the trio backend (trio 0.31.0+). However, not all features are supported on the trio backend. For full asynkit functionality, use the asyncio backend.

Backend Compatibility

Feature	asyncio backend	trio backend
Basic `anyio` integration	✅ Full support	✅ Full support
`create_eager_task_group()` (non-blocking)	✅ Full support	✅ Full support
`create_eager_task_group()` (with blocking)	✅ Full support	❌ Not supported
Event loop extensions	✅ Full support	❌ Not supported
`CoroStart` with Tasks	✅ Full support	❌ Not supported

Using with asyncio backend

When using the asyncio backend, the module asynkit.experimental.anyio can be used to provide "eager"-like behaviour to task creation. It will return an EagerTaskGroup context manager:

from asynkit.experimental.anyio import create_eager_task_group
from anyio import run, sleep


async def func(task_status):
    print("hello")
    task_status.started("world")
    await sleep(0.01)
    print("goodbye")


async def main():
    async with create_eager_task_group() as tg:
        start = tg.start(func)
        print("fine")
        print(await start)
    print("world")


run(main, backend="asyncio")

This will result in the following output:

hello
fine
world
goodbye
world

The first part of the function func is run even before calling await on the result from EagerTaskGroup.start()

Similarly, EagerTaskGroup.start_soon() will run the provided coroutine up to its first blocking point before returning.

Why trio has limitations

trio differs significantly from asyncio in its architectural approach, which limits asynkit compatibility:

Different Task models:

asyncio uses a Future-based model where task scheduling is performed by the event loop
trio uses a Task-centric model where scheduling happens inside synchronization primitives

Impact on eager execution:

When CoroStart is used with Tasks (such as in EagerTaskGroup), it works by intercepting a Future being passed up via the await protocol before it reaches the event loop. This allows asynkit to schedule the continuation in a new Task context.

This approach works perfectly with asyncio because:

The event loop never sees the Future until as_coroutine() has been called and awaited
All task scheduling happens at the event loop level
Resuming in a different Task context works seamlessly

However, trio performs Task-based validation both before sleeping and upon waking:

Synchronization primitives like Event.wait() or sleep() record which Task called them
Upon waking, trio validates that the same Task is resuming
If eager execution causes the continuation to run in a different Task, trio detects this and raises errors

Cancel scope corruption:

Additionally, trio's cancel scopes track Task identity. When eager execution crosses Task boundaries, cancel scopes may become corrupted, leading to assertion errors.

Recommendation: Use the asyncio backend with anyio to access asynkit's eager execution features. The non-eager features of asynkit work with both backends.

Experimental features

Some features are currently available experimentally. They may work only on some platforms or be experimental in nature, not stable or mature enough to be officially part of the library.

Task Interruption

See docs/task_interruption.md for detailed documentation.

⚠️ Note: Task interruption with _PyTask objects on Python 3.14.0 requires the patch_pytask() compatibility function due to separated C and Python asyncio implementations. This function is automatically called by create_pytask() to synchronize both implementations for proper current_task() behavior. C Tasks (from asyncio.create_task()) have limited interrupt support.

Methods are provided to raise exceptions on a Task. This is somewhat similar to task.cancel() but different:

The caller specifies the exception instance to be raised on the task.
The target task is made to run immediately, precluding interference with other operations.
The exception does not propagate into awaited objects. In particular, if the target task is awaiting another task, the wait is interrupted, but that other task is not otherwise affected.

A task which is blocked, waiting for a future, is immediately freed and scheduled to run. If the task is already scheduled to run, i.e. it is new, or the future has triggered but the task hasn't become active yet, it is still awoken with an exception.

Please note the following cases:

The Python asyncio library in places assumes that the only exception that can be raised out of awaitables is CancelledError. In particular, there are edge cases in asyncio.Lock, asyncio.Semaphore and asyncio.Condition where raising something else when acquiring these primitives will leave them in an incorrect state.

Therefore, we provide a base class, InterruptError, deriving from CancelledError which should be used for interrupts in general.

However, asyncio.Condition in Python 3.13 and earlier has a buggy implementation that will not correctly pass on such subclasses during wait() in all cases. The finally block that re-acquires the lock only catches CancelledError, not its subclasses. This was fixed in Python 3.13+ with improved exception handling that properly catches CancelledError and subclasses (CPython PR #112201).

For compatibility with older Python versions, a safer InterruptCondition class is provided that handles CancelledError subclasses correctly. On Python 3.13+, InterruptCondition is simply an alias to the standard asyncio.Condition for optimal performance.
Even subclasses of CancelledError will be converted to a new CancelledError instance when not handled in a task, and awaited.
These functions currently are only work reliably with Task object implemented in Python. Modern implementation often have a native "C" implementation of Task objects and they contain inaccessible code which cannot be used by the library. In particular, the Task.__step method cannot be explicitly scheduled to the event loop. For that reason, a special create_pytask() helper is provided to create a suitable python Task instance.
However: This library does go through extra hoops to make it usable with C Tasks. It almost works, but with two caveats:
- CTasks which have plain TaskStepMethWrapper callbacks scheduled cannot be interrupted. These are typically tasks executing await asyncio.sleep(0) or freshly created tasks that haven't started executing.
- The CTask's _fut_waiting member cannot be cleared from our code, so there exists a time where it can point to a valid, not-done, Future, even though the Task is about to wake up. This will make methods such as task_is_blocked() return incorrect values. It will get cleared when the interrupted task starts executing, however. All the more reason to use task_interrupt() over task_throw() since the former allows no space for code to see the task in such an intermediate state.

`task_throw()`

def task_throw(task: Task, exc: BaseException):
    pass

This method will make the target Task immediately runnable with the given exception pending.

If the Task was runnable due to a previous call to task_throw(), this will override that call and its exception.
Because of that, this method should probably not be used directly. It is better to ensure that the target takes delivery of the exception right away, because there is no way to queue pending exceptions and they do not add up in any meaningful way. Prefer to use task_interrupt() below.
This method will fail if the target task has a pending cancellation, that is, it is in the process of waking up with a pending CancelledError. Cancellation is currently asynchronous, while throwing exceptions is intended to be synchronous.

`task_interrupt()`

async def task_interrupt(task: Task, exc: BaseException):
    pass

An async version of task_throw(). When awaited, task_interrupt() is called, followed by a task_switch() to the target. Once awaited, the exception has been raised on the target task.

By ensuring that the target task runs immediately, it is possible to reason about task execution without having to rely on external synchronization primitives and the cooperation of the target task. An interrupt is never pending on the task (as a cancellation can be) and therefore it cannot cause collisions with other interrupts.

async def test():
    async def task():
        await asyncio.sleep(1)

    create_pytask(task)
    await asyncio.sleep(0)
    assert task_is_blocked(task)
    await task_interrupt(task, InterruptException())
    assert task.done()  # the error has already been raised.
    try:
        await task
    except CancelledError:  # original error is substituted
        pass
    else:
        assert False, "never happens"

`create_pytask()`

Similar to asyncio.create_task() but will create a pure Python Task which can safely be used as the target for task_throw()and task_interrupt(). Because of implementation issues, regular C Task objects, as returned by asyncio.create_task(), cannot be interrupted in all cases, in particular when doing an await asyncio.sleep(0) or directly after having been created.

`task_timeout()`

This is a context manager providing a timeout functionality, similar to asyncio.timeout(). By leveraging task_throw() and a custom BaseException subclass, TimeoutInterrupt, the logic becomes very simple and there is no unintended interaction with regular task cancellation().

Name		Name	Last commit message	Last commit date
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.github		.github
docs		docs
examples		examples
scripts		scripts
src/asynkit		src/asynkit
tests		tests
.clang-format		.clang-format
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGES.md		CHANGES.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
setup.py		setup.py
uv.lock		uv.lock

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asynkit: A toolkit for Python coroutines

Installation

Features

Coroutine Tools

eager() - lower latency IO

coro_eager(), func_eager()

eager_task_factory and create_eager_factory() - Global Eager Execution

Using the Pre-created Factory

Creating Custom Factories

Python 3.14+ Compatibility

Performance Benefits

When to Use Task Factories vs. Decorators

Cross-Version Compatibility

Automatic Eager Execution Monkeypatching

How It Works

Benefits

Example: Cross-Version Migration

await_sync(), aiter_sync() - Running coroutines synchronously

aiter_sync()

CoroStart

Context helper

awaitmethod()

awaitmethod_iter()

coro_iter()

cancelling()

Monitors and Generators

Monitor

GeneratorObject

Scheduling tools

Scheduling functions

sleep_insert(pos)

task_reinsert(task, pos)

task_switch(task, *, insert_pos=None)

task_is_blocked(task)

task_is_runnable(task)

create_task_descend(coro)

Runnable task helpers

runnable_tasks(loop=None)

blocked_tasks(loop=None)