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For a known set of related agent calls, use asyncio.TaskGroup when they should share a lifetime and a failure boundary; use asyncio.gather() when you want results in input order and will manage errors yourself. Add a semaphore or fixed worker pool to limit concurrency, use as_completed() to handle early results, apply timeouts as cancellation, and move blocking synchronous I/O off the event loop with asyncio.to_thread().
The examples below target Python 3.11 or later unless noted. These are practical ways to combine documented asyncio APIs, not a formal Python taxonomy.
1. Use TaskGroup for related agent calls
A task group is a good default when several calls belong to one operation—for example, gathering independent research from multiple agents before producing a response. The async with block owns the child tasks: it waits for them before exiting. If a child raises an exception, the group cancels its remaining tasks and reports failures as an exception group.
import asyncio
async def ask_agent(name, prompt):
return await call_agent(name, prompt)
async def run_agents(prompt):
async with asyncio.TaskGroup() as tg:
research = tg.create_task(ask_agent("research", prompt))
critique = tg.create_task(ask_agent("critique", prompt))
return research.result(), critique.result()Read task results after the context exits, when the group has finished. Task groups were added in Python 3.11. Their explicit ownership is useful for nested work because child failures do not leave sibling tasks running in the background. See the Python documentation for coroutines and tasks.
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2. Use gather for a compact fan-out with ordered results
asyncio.gather() is concise when you have a known collection of awaitables and want successful results in the same order as the inputs, regardless of which call finishes first.
async def run_agents(prompts):
return await asyncio.gather(
*(ask_agent(f"agent-{i}", prompt)
for i, prompt in enumerate(prompts))
)Its default failure behavior differs from a task group: the first exception is propagated to the waiter, but other submitted awaitables continue running. A child failure therefore does not automatically stop its peers. Choose gather() when that behavior fits your error policy; cancelling the gather operation itself cancels unfinished submitted awaitables. For related work that should stop together on failure, use TaskGroup instead. These semantics are documented in the Python task reference.
3. Use a semaphore to cap access to a resource
When an external service or shared resource should only handle a limited number of in-flight calls, put an asyncio.Semaphore around the protected operation. This limits concurrent entries into that section, not the number of tasks you create overall.
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async def run_agents(prompts, limit=5):
semaphore = asyncio.Semaphore(limit)
async def limited_call(prompt):
async with semaphore:
return await ask_agent("worker", prompt)
return await asyncio.gather(
*(limited_call(prompt) for prompt in prompts)
)Use a positive limit appropriate to the resource. A semaphore is a concurrency gate, not a rate-per-time-window limiter and not a worker lifecycle: tasks waiting to acquire it still exist. The asyncio API index lists both Semaphore and BoundedSemaphore among its synchronization primitives; check the reference for the Python version you deploy. See the asyncio high-level API index.
4. Use a queue and fixed workers for incremental workloads
If jobs arrive over time, or the input is too large to create a task for every item at once, use an asyncio.Queue with a fixed number of worker tasks. A bounded queue applies backpressure: when it fills, producers wait until workers make room. Python’s asyncio API index describes queues as a way to distribute work among tasks, as well as for connection pools and pub/sub patterns.
import asyncio
async def worker(queue):
while True:
job = await queue.get()
try:
if job is None: # shutdown signal
return
await process_agent_job(job)
finally:
queue.task_done()
async def process_jobs(jobs, worker_count=5):
queue = asyncio.Queue(maxsize=worker_count * 2)
async with asyncio.TaskGroup() as tg:
for _ in range(worker_count):
tg.create_task(worker(queue))
for job in jobs:
await queue.put(job)
await queue.join()
# One sentinel per worker, so each can exit cleanly.
for _ in range(worker_count):
await queue.put(None)
await queue.join()Each successful get() must be paired with task_done(), including for shutdown signals; join() waits until all queued items have been marked done. The TaskGroup keeps workers owned by the operation, and sentinels give them a normal exit after jobs drain. If a worker fails, the group cancels its peers and the failure surfaces from the group. Queue behavior and lifecycle details are in the asyncio API index.
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5. Use as_completed when early results matter
asyncio.as_completed() lets you process each result as soon as its awaitable finishes. This is useful when agents have uneven response times and you can act on early answers without waiting for the slowest call.
async def collect_early(prompts):
tasks = [asyncio.create_task(ask_agent(f"agent-{i}", p))
for i, p in enumerate(prompts)]
try:
for completed in asyncio.as_completed(tasks):
result = await completed
handle_result(result)
finally:
for task in tasks:
if not task.done():
task.cancel()
await asyncio.gather(*tasks, return_exceptions=True)The cleanup here is deliberate: ending iteration early does not itself cancel the remaining work. Cancel unfinished tasks and await them if the caller no longer needs their results. In Python 3.13 and later, the object returned by as_completed() supports both asynchronous iteration and ordinary iteration; the example’s ordinary iteration form also works on earlier versions. Consult the Python task reference for version-specific details.
6. Set a deadline and preserve cancellation
Use asyncio.timeout(seconds) to give a whole operation a deadline. If it expires, asyncio cancels the current task inside the context and raises TimeoutError outside it.
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async def run_with_deadline(prompts, seconds=30):
try:
async with asyncio.timeout(seconds):
return await asyncio.gather(
*(ask_agent(f"agent-{i}", p)
for i, p in enumerate(prompts))
)
except TimeoutError:
return NoneThis example makes timeout handling explicit, but returning None is only suitable if the caller treats it as a meaningful outcome; otherwise let the exception propagate or translate it into an application-specific error. The timeout context manager was added in Python 3.11. asyncio.wait_for() is a related alternative for a deadline on one awaitable; on timeout it cancels that awaitable.
Coroutines that need cleanup should use try/finally and generally allow CancelledError to propagate after cleanup. Suppressing cancellation can interfere with timeout and TaskGroup behavior.
async def call_with_cleanup():
resource = await acquire_resource()
try:
return await do_agent_work(resource)
finally:
await resource.close()Timeout and cancellation behavior are described in the Python task reference.
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7. Move blocking synchronous I/O off the event loop
An async function still blocks the event loop if it calls a synchronous function that waits on network, disk, or another I/O operation. Use asyncio.to_thread() to run that blocking call in a thread so other asyncio tasks can continue while it waits.
def legacy_agent_call(prompt):
return sync_client.complete(prompt)
async def ask_legacy_agent(prompt):
return await asyncio.to_thread(legacy_agent_call, prompt)to_thread() is primarily for I/O-bound functions that would otherwise block the event loop. Because of the GIL, it generally does not speed up pure-Python CPU-bound work, except where code releases the GIL or on Python implementations without one. This behavior is covered in the Python task reference.
Which pattern should you choose?
| Need | Pattern | Key behavior |
|---|---|---|
| Related tasks should share a lifetime and stop together on failure | TaskGroup |
Waits for children at context exit; cancels the remaining group tasks after a failure. |
| Known fan-out and results in input order | gather() |
Returns successful results in submission order; by default, a child error propagates while peers continue. |
| Cap simultaneous access to an external service | Semaphore |
Limits entry to a guarded section, not task creation or requests per time window. |
| Incremental or large workloads with controlled consumption | Queue and fixed workers |
Caps worker count; a bounded queue can apply producer backpressure. |
| Use results as soon as they finish | as_completed() |
Yields completion-oriented processing rather than input-order aggregation. |
| Put a maximum duration on an operation | timeout() |
Applies a deadline through cancellation; cleanup should preserve cancellation semantics. |
| A synchronous I/O dependency blocks async work | to_thread() |
Runs the blocking function in a thread, primarily to keep the event loop responsive for I/O-bound work. |
Patterns can be combined. For example, a TaskGroup can own a set of agent tasks while each task acquires a semaphore before calling a rate-limited service. A TaskGroup can also supervise queue workers. Keep the ownership boundary clear: know which scope is responsible for waiting for tasks and what should happen to them when that scope fails or times out.
Keep created tasks owned
If you create tasks directly with asyncio.create_task(), retain references to them while they run and arrange to await or cancel them. The event loop keeps only weak references, so unreferenced tasks can disappear before completion. For child work that belongs to a surrounding operation, TaskGroup usually provides the clearer ownership boundary. See the Python documentation on tasks.
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