Tasks (create_task) and gather

Run coroutines concurrently using create_task and asyncio.gather, then collect results safely.

1 min readupdated 6 Oct 2026

Why tasks

A Task is a scheduled coroutine.

You create it
The event loop runs it concurrently with others

create_task

Python

import asyncio
 
 
async def job(i: int) -> str:
    await asyncio.sleep(0.2)
    return f"done {i}"
 
 
async def main():
    t1 = asyncio.create_task(job(1))
    t2 = asyncio.create_task(job(2))
 
    # await results
    print(await t1)
    print(await t2)
 
 
asyncio.run(main())

sketch create_task schedules; it does not start p5.js

A measured surprise: with two tasks created and a print between, the print runs FIRST. create_task hands the coroutine to the loop and returns immediately -- the coroutine body does not begin until the loop next gets control, which is at your first await. Observed order was: after create_task, start 1, start 2, end 1, end 2.

create_task schedules — it does not start

The coroutine body does not begin when you call create_task. The task is handed to the event loop, and the loop only gets control when you next await. Put a print between two create_task calls and it runs first:

Python

import asyncio
 
order = []
 
 
async def note(i):
    order.append(("start", i))
    await asyncio.sleep(0.05)
    order.append(("end", i))
 
 
async def main():
    t1 = asyncio.create_task(note(1))
    t2 = asyncio.create_task(note(2))
    order.append(("after create_task", None))     # <- runs before either task
    await t1
    await t2
    print(order)
 
 
asyncio.run(main())
# [('after create_task', None), ('start', 1), ('start', 2), ('end', 1), ('end', 2)]

Verified — that is the real output, not a sketch of one. The practical consequence: a task you create and never await (or never keep a reference to) may be garbage-collected before it finishes, which is why TaskGroup exists.

asyncio.gather

gather is a convenient way to run many coroutines and collect results.

Python

import asyncio
 
 
async def job(i: int) -> int:
    await asyncio.sleep(0.1)
    return i * i
 
 
async def main():
    results = await asyncio.gather(*(job(i) for i in range(10)))
    print(results)
 
 
asyncio.run(main())

sketch Sequential await against gather, with the measured times p5.js

Ten jobs, each awaiting asyncio.sleep(0.1). Awaiting them one at a time takes the sum; handing them all to asyncio.gather takes about as long as the slowest one. Measured on CPython 3.14.4: 1.091 s sequential against 0.109 s gathered, a 10x difference, and both produce exactly the same list. The bars run in real time -- the animation is not sped up.

Error behavior

If one coroutine fails, gather cancels others by default.

You can keep errors as values:

Python

import asyncio
 
 
async def ok():
    return 1
 
 
async def bad():
    raise ValueError("boom")
 
 
async def main():
    res = await asyncio.gather(ok(), bad(), return_exceptions=True)
    print(res)
 
 
asyncio.run(main())

🧪 Try It Yourself

Exercise 1 – Create a Task

exerciseYour turn

script.py

Output

Press Run to see what your code prints.

Exercise 2 – Gather Multiple Tasks

exerciseYour turn

script.py

Output

Press Run to see what your code prints.

Exercise 3 – Task Name and Done

exerciseYour turn

script.py

Output

Press Run to see what your code prints.

Tasks (create_task) and gather

Why tasks

create_task

asyncio.gather

Error behavior

🧪 Try It Yourself

Exercise 1 – Create a Task

Exercise 2 – Gather Multiple Tasks

Exercise 3 – Task Name and Done

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