Short answer: Yes—with caveats. - If your task uses yield (a “generator task”), you can iterate over it inside a flow to stream values as they’re produced. Yielded items are not persisted by default; only the task’s final return value (if any) is stored. Generator tasks also do not use caching. - If your task returns a generator object (e.g., returns (i for i in ...)), Prefect will automatically materialize it into a list for result handling. That defeats streaming and can use a lot of memory. - For other iterator types, Prefect will try to serialize them; if they aren’t picklable/JSON-serializable, you’ll get a serialization error. Prefer concrete data structures if you need persistence. Recommended patterns - Stream large data: make the task a generator (use yield) and iterate in the flow. - Parallelize processing: use task mapping over a concrete iterable for concurrency. - Persist full results: explicitly return a list (or another serializable collection). Examples - Generator task (streaming)

from prefect import task, flow

@task
def read_lines(path: str):
    with open(path) as f:
        for line in f:
            yield line.rstrip("\n")

@flow
def pipeline():
    for line in read_lines("huge.txt"):
        # process each line without loading all into memory
        print(line)

- Parallel processing with mapping

from prefect import task, flow

@task
def process_item(x: int) -> int:
    return x * 2

@flow
def pipeline():
    items = range(1_000_000)  # concrete iterable
    futures = process_item.map(items)  # concurrent execution
    results = [f.result() for f in futures]

- Avoid returning a raw generator (it becomes a list anyway)

@task
def bad():
    return (i * 2 for i in range(1000))  # will be auto-materialized to a list

Docs and references - Write and run workflows (tasks, generators): https://docs.prefect.io/v3/how-to-guides/workflows/write-and-run#create-tasks-and-child-workflows - Running work concurrently (map): https://docs.prefect.io/v3/how-to-guides/workflows/run-work-concurrently#using-map - Result persistence and serialization: https://docs.prefect.io/v3/advanced/results - Task engine generator internals (API reference): - https://docs.prefect.io/api-ref/python/prefect-task_engine/#prefect.task_engine.run_generator_task_sync - https://docs.prefect.io/api-ref/python/prefect-task_engine/#prefect.task_engine.run_generator_task_async If you can share your specific use case (streaming vs. persisting, size of data, need for concurrency), I can suggest the best pattern and a concrete snippet.