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A task is a function that represents a discrete unit of work in a Prefect workflow. Tasks are not required — you may define Prefect workflows that consist only of flows, using regular Python statements and functions. Tasks enable you to encapsulate elements of your workflow logic in observable units that can be reused across flows and subflows.

Tasks overview

Tasks are functions: they can take inputs, perform work, and return an output. A Prefect task can do almost anything a Python function can do.

Tasks are special because they receive metadata about upstream dependencies and the state of those dependencies before they run, even if they don't receive any explicit data inputs from them. This gives you the opportunity to, for example, have a task wait on the completion of another task before executing.

Tasks also take advantage of automatic Prefect logging to capture details about task runs such as runtime, tags, and final state.

You can define your tasks within the same file as your flow definition, or you can define tasks within modules and import them for use in your flow definitions. All tasks must be called from within a flow. Tasks may not be called from other tasks.

Calling a task from a flow

Use the @task decorator to designate a function as a task. Calling the task from within a flow function creates a new task run:

from prefect import flow, task

def my_task():
    print("Hello, I'm a task")

def my_flow():

Tasks are uniquely identified by a task key, which is a hash composed of the task name, the fully-qualified name of the function, and any tags. If the task does not have a name specified, the name is derived from the task function.

How big should a task be?

Prefect encourages "small tasks" — each one should represent a single logical step of your workflow. This allows Prefect to better contain task failures.

To be clear, there's nothing stopping you from putting all of your code in a single task — Prefect will happily run it! However, if any line of code fails, the entire task will fail and must be retried from the beginning. This can be avoided by splitting the code into multiple dependent tasks.

Calling a task's function from another task

Prefect does not allow triggering task runs from other tasks. If you want to call your task's function directly, you can use task.fn().

from prefect import flow, task

def my_first_task(msg):
    print(f"Hello, {msg}")

def my_second_task(msg):

def my_flow():

Note that in the example above you are only calling the task's function without actually generating a task run. Prefect won't track task execution in your Prefect backend if you call the task function this way. You also won't be able to use features such as retries with this function call.

Task arguments

Tasks allow for customization through optional arguments:

Argument Description
name An optional name for the task. If not provided, the name will be inferred from the function name.
description An optional string description for the task. If not provided, the description will be pulled from the docstring for the decorated function.
tags An optional set of tags to be associated with runs of this task. These tags are combined with any tags defined by a prefect.tags context at task runtime.
cache_key_fn An optional callable that, given the task run context and call parameters, generates a string key. If the key matches a previous completed state, that state result will be restored instead of running the task again.
cache_expiration An optional amount of time indicating how long cached states for this task should be restorable; if not provided, cached states will never expire.
retries An optional number of times to retry on task run failure.
retry_delay_seconds An optional number of seconds to wait before retrying the task after failure. This is only applicable if retries is nonzero.
log_prints An optional boolean indicating whether to log print statements.
persist_result An optional boolean indicating whether to persist the result of the task run to storage.

See all possible parameters in the Python SDK API docs.

For example, you can provide a name value for the task. Here we've used the optional description argument as well.

      description="This task says hello.")
def my_task():
    print("Hello, I'm a task")

You can distinguish runs of this task by providing a task_run_name; this setting accepts a string that can optionally contain templated references to the keyword arguments of your task. The name will be formatted using Python's standard string formatting syntax as can be seen here:

import datetime
from prefect import flow, task

@task(name="My Example Task", 
      description="An example task for a tutorial.",
def my_task(name, date):

def my_flow():
    # creates a run with a name like "hello-marvin-on-Thursday"
    my_task(name="marvin", date=datetime.datetime.utcnow())

Additionally this setting also accepts a function that returns a string to be used for the task run name:

import datetime
from prefect import flow, task

def generate_task_name():
    date = datetime.datetime.utcnow()
    return f"{date:%A}-is-a-lovely-day"

@task(name="My Example Task",
      description="An example task for a tutorial.",
def my_task(name):

def my_flow():
    # creates a run with a name like "Thursday-is-a-lovely-day"

If you need access to information about the task, use the prefect.runtime module. For example:

from prefect import flow
from prefect.runtime import flow_run, task_run

def generate_task_name():
    flow_name = flow_run.flow_name
    task_name = task_run.task_name

    parameters = task_run.parameters
    name = parameters["name"]
    limit = parameters["limit"]

    return f"{flow_name}-{task_name}-with-{name}-and-{limit}"

      description="An example task for a tutorial.",
def my_task(name: str, limit: int = 100):

def my_flow(name: str):
    # creates a run with a name like "my-flow-my-example-task-with-marvin-and-100"


Tags are optional string labels that enable you to identify and group tasks other than by name or flow. Tags are useful for:

Tags may be specified as a keyword argument on the task decorator.

@task(name="hello-task", tags=["test"])
def my_task():
    print("Hello, I'm a task")

You can also provide tags as an argument with a tags context manager, specifying tags when the task is called rather than in its definition.

from prefect import flow, task
from prefect import tags

def my_task():
    print("Hello, I'm a task")

def my_flow():
    with tags("test"):


Prefect can automatically retry tasks on failure. In Prefect, a task fails if its Python function raises an exception.

To enable retries, pass retries and retry_delay_seconds parameters to your task. If the task fails, Prefect will retry it up to retries times, waiting retry_delay_seconds seconds between each attempt. If the task fails on the final retry, Prefect marks the task as crashed if the task raised an exception or failed if it returned a string.

Retries don't create new task runs

A new task run is not created when a task is retried. A new state is added to the state history of the original task run.

A real-world example: making an API request

Consider the real-world problem of making an API request. In this example, we'll use the httpx library to make an HTTP request.

import httpx

from prefect import flow, task

@task(retries=2, retry_delay_seconds=5)
def get_data_task(
    url: str = ""
) -> dict:
    response = httpx.get(url)

    # If the response status code is anything but a 2xx, httpx will raise
    # an exception. This task doesn't handle the exception, so Prefect will
    # catch the exception and will consider the task run failed.

    return response.json()

def get_data_flow():

In this task, if the HTTP request to the brittle API receives any status code other than a 2xx (200, 201, etc.), Prefect will retry the task a maximum of two times, waiting five seconds in between retries.

Custom retry behavior

The retry_delay_seconds option accepts a list of delays for more custom retry behavior. The following task will wait for successively increasing intervals of 1, 10, and 100 seconds, respectively, before the next attempt starts:

from prefect import task

@task(retries=3, retry_delay_seconds=[1, 10, 100])
def some_task_with_manual_backoff_retries():

Additionally, you can pass a callable that accepts the number of retries as an argument and returns a list. Prefect includes an exponential_backoff utility that will automatically generate a list of retry delays that correspond to an exponential backoff retry strategy. The following flow will wait for 10, 20, then 40 seconds before each retry.

from prefect import task
from prefect.tasks import exponential_backoff

@task(retries=3, retry_delay_seconds=exponential_backoff(backoff_factor=10))
def some_task_with_exponential_backoff_retries():

Advanced topic: adding "jitter"

While using exponential backoff, you may also want to add jitter to the delay times. Jitter is a random amount of time added to retry periods that helps prevent "thundering herd" scenarios, which is when many tasks all retry at the exact same time, potentially overwhelming systems.

The retry_jitter_factor option can be used to add variance to the base delay. For example, a retry delay of 10 seconds with a retry_jitter_factor of 0.5 will be allowed to delay up to 15 seconds. Large values of retry_jitter_factor provide more protection against "thundering herds," while keeping the average retry delay time constant. For example, the following task adds jitter to its exponential backoff so the retry delays will vary up to a maximum delay time of 20, 40, and 80 seconds respectively.

from prefect import task
from prefect.tasks import exponential_backoff

def some_task_with_exponential_backoff_retries():

Configuring retry behavior globally with settings

You can also set retries and retry delays by using the following global settings. These settings will not override the retries or retry_delay_seconds that are set in the flow or task decorator.

prefect config set PREFECT_FLOW_DEFAULT_RETRY_DELAY_SECONDS = [1, 10, 100]
prefect config set PREFECT_TASK_DEFAULT_RETRY_DELAY_SECONDS = [1, 10, 100]


Caching refers to the ability of a task run to reflect a finished state without actually running the code that defines the task. This allows you to efficiently reuse results of tasks that may be expensive to run with every flow run, or reuse cached results if the inputs to a task have not changed.

To determine whether a task run should retrieve a cached state, we use "cache keys". A cache key is a string value that indicates if one run should be considered identical to another. When a task run with a cache key finishes, we attach that cache key to the state. When each task run starts, Prefect checks for states with a matching cache key. If a state with an identical key is found, Prefect will use the cached state instead of running the task again.

To enable caching, specify a cache_key_fn — a function that returns a cache key — on your task. You may optionally provide a cache_expiration timedelta indicating when the cache expires. If you do not specify a cache_expiration, the cache key does not expire.

You can define a task that is cached based on its inputs by using the Prefect task_input_hash. This is a task cache key implementation that hashes all inputs to the task using a JSON or cloudpickle serializer. If the task inputs do not change, the cached results are used rather than running the task until the cache expires.

Note that, if any arguments are not JSON serializable, the pickle serializer is used as a fallback. If cloudpickle fails, task_input_hash returns a null key indicating that a cache key could not be generated for the given inputs.

In this example, until the cache_expiration time ends, as long as the input to hello_task() remains the same when it is called, the cached return value is returned. In this situation the task is not rerun. However, if the input argument value changes, hello_task() runs using the new input.

from datetime import timedelta
from prefect import flow, task
from prefect.tasks import task_input_hash

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
def hello_task(name_input):
    # Doing some work
    print("Saying hello")
    return "hello " + name_input

def hello_flow(name_input):

Alternatively, you can provide your own function or other callable that returns a string cache key. A generic cache_key_fn is a function that accepts two positional arguments:

  • The first argument corresponds to the TaskRunContext, which stores task run metadata in the attributes task_run_id, flow_run_id, and task.
  • The second argument corresponds to a dictionary of input values to the task. For example, if your task is defined with signature fn(x, y, z) then the dictionary will have keys "x", "y", and "z" with corresponding values that can be used to compute your cache key.

Note that the cache_key_fn is not defined as a @task.

Task cache keys

By default, a task cache key is limited to 2000 characters, specified by the PREFECT_API_TASK_CACHE_KEY_MAX_LENGTH setting.

from prefect import task, flow

def static_cache_key(context, parameters):
    # return a constant
    return "static cache key"

def cached_task():
    print('running an expensive operation')
    return 42

def test_caching():

In this case, there's no expiration for the cache key, and no logic to change the cache key, so cached_task() only runs once.

>>> test_caching()
running an expensive operation
>>> test_caching()
>>> test_caching()

When each task run requested to enter a Running state, it provided its cache key computed from the cache_key_fn. The Prefect backend identified that there was a COMPLETED state associated with this key and instructed the run to immediately enter the same COMPLETED state, including the same return values.

A real-world example might include the flow run ID from the context in the cache key so only repeated calls in the same flow run are cached.

def cache_within_flow_run(context, parameters):
    return f"{context.task_run.flow_run_id}-{task_input_hash(context, parameters)}"

def cached_task():
    print('running an expensive operation')
    return 42

Task results, retries, and caching

Task results are cached in memory during a flow run and persisted to the location specified by the PREFECT_LOCAL_STORAGE_PATH setting. As a result, task caching between flow runs is currently limited to flow runs with access to that local storage path.

Refreshing the cache

Sometimes, you want a task to update the data associated with its cache key instead of using the cache. This is a cache "refresh".

The refresh_cache option can be used to enable this behavior for a specific task:

import random

def static_cache_key(context, parameters):
    # return a constant
    return "static cache key"

@task(cache_key_fn=static_cache_key, refresh_cache=True)
def caching_task():
    return random.random()

When this task runs, it will always update the cache key instead of using the cached value. This is particularly useful when you have a flow that is responsible for updating the cache.

If you want to refresh the cache for all tasks, you can use the PREFECT_TASKS_REFRESH_CACHE setting. Setting PREFECT_TASKS_REFRESH_CACHE=true will change the default behavior of all tasks to refresh. This is particularly useful if you want to rerun a flow without cached results.

If you have tasks that should not refresh when this setting is enabled, you may explicitly set refresh_cache to False. These tasks will never refresh the cache — if a cache key exists it will be read, not updated. Note that, if a cache key does not exist yet, these tasks can still write to the cache.

@task(cache_key_fn=static_cache_key, refresh_cache=False)
def caching_task():
    return random.random()


Task timeouts are used to prevent unintentional long-running tasks. When the duration of execution for a task exceeds the duration specified in the timeout, a timeout exception will be raised and the task will be marked as failed. In the UI, the task will be visibly designated as TimedOut. From the perspective of the flow, the timed-out task will be treated like any other failed task.

Timeout durations are specified using the timeout_seconds keyword argument.

from prefect import task, get_run_logger
import time

def show_timeouts():
    logger = get_run_logger()"I will execute")
    time.sleep(5)"I will not execute")

Task results

Depending on how you call tasks, they can return different types of results and optionally engage the use of a task runner.

Any task can return:

  • Data , such as int, str, dict, list, and so on —  this is the default behavior any time you call your_task().
  • PrefectFuture —  this is achieved by calling your_task.submit(). A PrefectFuture contains both data and State
  • Prefect State  — anytime you call your task or flow with the argument return_state=True, it will directly return a state you can use to build custom behavior based on a state change you care about, such as task or flow failing or retrying.

To run your task with a task runner, you must call the task with .submit().

See state returned values for examples.

Task runners are optional

If you just need the result from a task, you can simply call the task from your flow. For most workflows, the default behavior of calling a task directly and receiving a result is all you'll need.

Wait for

To create a dependency between two tasks that do not exchange data, but one needs to wait for the other to finish, use the special wait_for keyword argument:

def task_1():

def task_2():

def my_flow():
    x = task_1()

    # task 2 will wait for task_1 to complete
    y = task_2(wait_for=[x])


Prefect provides a .map() implementation that automatically creates a task run for each element of its input data. Mapped tasks represent the computations of many individual children tasks.

The simplest Prefect map takes a tasks and applies it to each element of its inputs.

from prefect import flow, task

def print_nums(nums):
    for n in nums:

def square_num(num):
    return num**2

def map_flow(nums):
    squared_nums = 


Prefect also supports unmapped arguments, allowing you to pass static values that don't get mapped over.

from prefect import flow, task

def add_together(x, y):
    return x + y

def sum_it(numbers, static_value):
    futures =, static_value)
    return futures

sum_it([1, 2, 3], 5)

If your static argument is an iterable, you'll need to wrap it with unmapped to tell Prefect that it should be treated as a static value.

from prefect import flow, task, unmapped

def sum_plus(x, static_iterable):
    return x + sum(static_iterable)

def sum_it(numbers, static_iterable):
    futures =, static_iterable)
    return futures

sum_it([4, 5, 6], unmapped([1, 2, 3]))

Async tasks

Prefect also supports asynchronous task and flow definitions by default. All of the standard rules of async apply:

import asyncio

from prefect import task, flow

async def print_values(values):
    for value in values:
        await asyncio.sleep(1) # yield
        print(value, end=" ")

async def async_flow():
    await print_values([1, 2])  # runs immediately
    coros = [print_values("abcd"), print_values("6789")]

    # asynchronously gather the tasks
    await asyncio.gather(*coros)

Note, if you are not using asyncio.gather, calling .submit() is required for asynchronous execution on the ConcurrentTaskRunner.

Task run concurrency limits

There are situations in which you want to actively prevent too many tasks from running simultaneously. For example, if many tasks across multiple flows are designed to interact with a database that only allows 10 connections, you want to make sure that no more than 10 tasks that connect to this database are running at any given time.

Prefect has built-in functionality for achieving this: task concurrency limits.

Task concurrency limits use task tags. You can specify an optional concurrency limit as the maximum number of concurrent task runs in a Running state for tasks with a given tag. The specified concurrency limit applies to any task to which the tag is applied.

If a task has multiple tags, it will run only if all tags have available concurrency.

Tags without explicit limits are considered to have unlimited concurrency.

0 concurrency limit aborts task runs

Currently, if the concurrency limit is set to 0 for a tag, any attempt to run a task with that tag will be aborted instead of delayed.

Execution behavior

Task tag limits are checked whenever a task run attempts to enter a Running state.

If there are no concurrency slots available for any one of your task's tags, the transition to a Running state will be delayed and the client is instructed to try entering a Running state again in 30 seconds.

Concurrency limits in subflows

Using concurrency limits on task runs in subflows can cause deadlocks. As a best practice, configure your tags and concurrency limits to avoid setting limits on task runs in subflows.

Configuring concurrency limits

Flow run concurrency limits are set at a work pool and/or work queue level

While task run concurrency limits are configured via tags (as shown below), flow run concurrency limits are configured via work pools and/or work queues.

You can set concurrency limits on as few or as many tags as you wish. You can set limits through:

  • Prefect CLI
  • Prefect API by using PrefectClient Python client
  • Prefect server UI or Prefect Cloud


You can create, list, and remove concurrency limits by using Prefect CLI concurrency-limit commands.

$ prefect concurrency-limit [command] [arguments]
Command Description
create Create a concurrency limit by specifying a tag and limit.
delete Delete the concurrency limit set on the specified tag.
inspect View details about a concurrency limit set on the specified tag.
ls View all defined concurrency limits.

For example, to set a concurrency limit of 10 on the 'small_instance' tag:

$ prefect concurrency-limit create small_instance 10

To delete the concurrency limit on the 'small_instance' tag:

$ prefect concurrency-limit delete small_instance

To view details about the concurrency limit on the 'small_instance' tag:

$ prefect concurrency-limit inspect small_instance

Python client

To update your tag concurrency limits programmatically, use PrefectClient.orchestration.create_concurrency_limit.

create_concurrency_limit takes two arguments:

  • tag specifies the task tag on which you're setting a limit.
  • concurrency_limit specifies the maximum number of concurrent task runs for that tag.

For example, to set a concurrency limit of 10 on the 'small_instance' tag:

from prefect import get_client

async with get_client() as client:
    # set a concurrency limit of 10 on the 'small_instance' tag
    limit_id = await client.create_concurrency_limit(

To remove all concurrency limits on a tag, use PrefectClient.delete_concurrency_limit_by_tag, passing the tag:

async with get_client() as client:
    # remove a concurrency limit on the 'small_instance' tag
    await client.delete_concurrency_limit_by_tag(tag="small_instance")

If you wish to query for the currently set limit on a tag, use PrefectClient.read_concurrency_limit_by_tag, passing the tag:

To see all of your limits across all of your tags, use PrefectClient.read_concurrency_limits.

async with get_client() as client:
    # query the concurrency limit on the 'small_instance' tag
    limit = await client.read_concurrency_limit_by_tag(tag="small_instance")