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Python Asyncio by amey-thakur — Agent Skill

Name: Python Asyncio
Author: Amey-Thakur

Python Asyncio

Decide whether asyncio pays off, keep the event loop unblocked, and structure concurrency with TaskGroup and disciplined cancellation. Use when writing async Python or diagnosing a hung, slow, or silently-failing event loop.

Amey-Thakur 5c9db6b 2.8 KB Updated Sep 17, 2026

File contents

Python asyncio

Asyncio wins for high-concurrency I/O waiting, not for CPU work and not for a handful of calls. Its whole model rests on one rule: never block the event loop, because one blocking call stalls every other task.

Method

Confirm async is the right tool. Use it when you have many concurrent I/O-bound operations (hundreds of sockets, requests, or DB calls) that spend their time waiting. For CPU-bound work use processes; for a few sequential calls, plain synchronous code is simpler and faster to reason about. See python-concurrency for the decision table.
Keep the loop pure I/O. Any synchronous call that blocks (a requests call, time.sleep, heavy computation, a blocking driver) freezes all tasks. Push it off the loop with asyncio.to_thread(fn, ...) for I/O or a process pool for CPU. Enable PYTHONASYNCIODEBUG=1 or pass debug=True to asyncio.run to log slow callbacks.
Structure concurrency with TaskGroup. On 3.11+, async with asyncio.TaskGroup() as tg: tg.create_task(...) awaits all children and cancels siblings if one fails, surfacing errors as an ExceptionGroup. It replaces bare gather, which leaves orphaned tasks running when one task raises and you forget return_exceptions.
Use gather only for its shape. gather(*coros) fits a fixed fan-out where you want results positionally and are ready to handle partial failure explicitly. Do not fire tasks with create_task and drop the reference; the loop may garbage-collect them mid-flight. Hold references or use a TaskGroup.
Treat cancellation as cooperative. Cancelling raises CancelledError at the next await; always re-raise it rather than swallowing it in a broad except. Guard cleanup with finally, and bound external calls with asyncio.timeout() so a stuck peer cannot hang a task forever.
Never nest or block the loop from sync code. One asyncio.run per program entry; calling it from inside a running loop raises. To bridge sync callers, use asyncio.run at the top only, and never loop.run_until_complete inside async code.

Boundaries

Async does not speed up CPU-bound work; the GIL still serializes it. Reach for multiprocessing instead.
One blocking library call defeats the model. If a dependency has no async client, wrap it in a thread or pick a different tool.
Mixing async and threads shares no automatic safety; the loop is not thread-safe, so hand work across with run_coroutine_threadsafe.

---name: python-asynciodescription: Decide whether asyncio pays off, keep the event loop unblocked, and structure concurrency with TaskGroup and disciplined cancellation. Use when writing async Python or diagnosing a hung, slow, or silently-failing event loop.---# Python asyncioAsyncio wins for high-concurrency I/O waiting, not for CPU work and not fora handful of calls. Its whole model rests on one rule: never block the eventloop, because one blocking call stalls every other task.## Method1. **Confirm async is the right tool.** Use it when you have many concurrent I/O-bound operations (hundreds of sockets, requests, or DB calls) that spend their time waiting. For CPU-bound work use processes; for a few sequential calls, plain synchronous code is simpler and faster to reason about. See python-concurrency for the decision table.2. **Keep the loop pure I/O.** Any synchronous call that blocks (a `requests` call, `time.sleep`, heavy computation, a blocking driver) freezes all tasks. Push it off the loop with `asyncio.to_thread(fn, ...)` for I/O or a process pool for CPU. Enable `PYTHONASYNCIODEBUG=1` or pass `debug=True` to `asyncio.run` to log slow callbacks.3. **Structure concurrency with TaskGroup.** On 3.11+, `async with asyncio.TaskGroup() as tg: tg.create_task(...)` awaits all children and cancels siblings if one fails, surfacing errors as an ExceptionGroup. It replaces bare `gather`, which leaves orphaned tasks running when one task raises and you forget `return_exceptions`.4. **Use gather only for its shape.** `gather(*coros)` fits a fixed fan-out where you want results positionally and are ready to handle partial failure explicitly. Do not fire tasks with `create_task` and drop the reference; the loop may garbage-collect them mid-flight. Hold references or use a TaskGroup.5. **Treat cancellation as cooperative.** Cancelling raises `CancelledError` at the next await; always re-raise it rather than swallowing it in a broad `except`. Guard cleanup with `finally`, and bound external calls with `asyncio.timeout()` so a stuck peer cannot hang a task forever.6. **Never nest or block the loop from sync code.** One `asyncio.run` per program entry; calling it from inside a running loop raises. To bridge sync callers, use `asyncio.run` at the top only, and never `loop.run_until_complete` inside async code.## Boundaries- Async does not speed up CPU-bound work; the GIL still serializes it. Reach for multiprocessing instead.- One blocking library call defeats the model. If a dependency has no async client, wrap it in a thread or pick a different tool.- Mixing async and threads shares no automatic safety; the loop is not thread-safe, so hand work across with `run_coroutine_threadsafe`.

Frequently asked questions