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Access the Apache Airflow context

The Airflow context is a dictionary containing information about a running DAG and its Airflow environment that can be accessed from a task. One of the most common values to retrieve from the Airflow context is the ti / task_instance keyword, which allows you to access attributes and methods of the taskinstance object.

Other common reasons to access the Airflow context are:

  • You want to use DAG-level parameters in your Airflow tasks.
  • You want to use the DAG run's logical date in an Airflow task, for example as part of a file name.
  • You want to explicitly push and pull values to XCom with a custom key.
  • You want to make an action in your task conditional on the setting of a specific Airflow configuration.

Use this document to learn about the data stored in the Airflow context and how to access it.

Assumed knowledge

To get the most out of this guide, you should have an understanding of:

Access the Airflow context

The Airflow context is available in all Airflow tasks. You can access information from the context using the following methods:

You cannot access the Airflow context dictionary outside of an Airflow task.

Retrieve the Airflow context using the @task decorator or PythonOperator

To access the Airflow context in a @task decorated task or PythonOperator task, you need to add a **context argument to your task function. This will make the context available as a dictionary in your task.

The following code snippets show how to print out the full context dictionary from a task:

from pprint import pprint

def print_context(**context)

Retrieve the Airflow context using Jinja templating

Many elements of the Airflow context can be accessed by using Jinja templating. You can get the list of all parameters that allow templates for any operator by printing out its .template_fields attribute.

For example, you can access a DAG run's logical date in the format YYYY-MM-DD by using the template {{ ds }} in the bash_command parameter of the BashOperator.

print_logical_date = BashOperator(
bash_command="echo {{ ds }}",

It is also common to use Jinja templating to access XCom values in the parameter of a traditional task. In the code snippet below, the first task return_greeting will push the string "Hello" to XCom, and the second task greet_friend will use a Jinja template to pull that value from the ti (task instance) object of the Airflow context and print Hello friend! :) into the logs.

def return_greeting():
return "Hello"

greet_friend = BashOperator(
bash_command="echo '{{ ti.xcom_pull(task_ids='return_greeting') }} friend! :)'",

return_greeting() >> greet_friend

Find an up to date list of all available templates in the Airflow documentation. Learn more about using XComs to pass data between Airflow tasks in Pass data between tasks.

Retrieve the Airflow context using custom operators

In a traditional operator, the Airflow context is always passed to the .execute method using the context keyword argument. If you write a custom operator, you have to include a context kwarg in the execute method as shown in the following custom operator example.

class PrintDAGIDOperator(BaseOperator):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)

def execute(self, context):

Common Airflow context values

This section gives an overview of the most commonly used keys in the Airflow context dictionary. To see an up-to-date list of all keys and their types, view the Airflow source code.

ti / task_instance

The ti or task_instance key contains the TaskInstance object. The most commonly used attributes are .xcom_pull and .xcom_push, which allow you to push and pull XComs.

The following DAG shows an example of using context["ti"].xcom_push(...) and context["ti"].xcom_pull(...) to explicitly pass data between tasks.

from pendulum import datetime
from airflow.decorators import dag, task

start_date=datetime(2023, 6, 1),
def context_and_xcom():
def upstream_task(**context):
context["ti"].xcom_push(key="my_explicitly_pushed_xcom", value=23)
return 19

def downstream_task(passed_num, **context):
returned_num = context["ti"].xcom_pull(
task_ids="upstream_task", key="return_value"
explicit_num = context["ti"].xcom_pull(
task_ids="upstream_task", key="my_explicitly_pushed_xcom"

print("Returned Num: ", returned_num)
print("Passed Num: ", passed_num)
print("Explicit Num: ", explicit_num)



The downstream_task will print the following information to the logs:

[2023-06-16, 13:14:11 UTC] {} INFO - Returned Num:  19
[2023-06-16, 13:14:11 UTC] {} INFO - Passed Num: 19
[2023-06-16, 13:14:11 UTC] {} INFO - Explicit Num: 23

Scheduling keys

One of the most common reasons to access the Airflow context in your tasks is to retrieve information about the scheduling of their DAG. A common pattern is to use the timestamp of the logical date in names of files written from a DAG to create a unique file for each DAG run.

The task below creates a new text file in the include folder for each DAG run with the timestamp in the filename in the format YYYY-MM-DDTHH:MM:SS+00:00. Refer to Templates reference for an up to date list of time related keys in the context, and Jinja templating for more information on how to pass these values to templateable parameters of traditional operators.

def write_file_with_ts(**context):
ts = context["ts"]
with open(f"include/{ts}_hello.txt", "a") as f:
f.write("Hello, World!")


The conf key contains an AirflowConfigParser object that contains information about your Airflow configuration. To see your full Airflow configuration in dictionary format, use the following code:

from pprint import pprint

def print_config(**context)

The Airflow configuration includes information about the settings for your Airflow environment. By accessing these settings programmatically, you can make your pipeline conditional on the setting of a specific Airflow configuration. For example, you can have a task check which executor your environment is using by accessing the executor configuration setting. The task can then branch depending on the result:

def branch_based_on_executor(**context):
executor = context["conf"].get(section="core", key="EXECUTOR")
if executor == "LocalExecutor":
return "t1"
return "t2"


The dag key contains the DAG object. There are many attributes and methods available for the DAG object. One example is the .get_run_dates method from which you can fetch a list of all timestamps on which the DAG will run within a certain time period. This is especially useful for DAGs with complex schedules.

def get_dagrun_dates(**context):
run_dates = context["dag"].get_run_dates(
start_date=datetime(2023, 10, 1), end_date=datetime(2023, 10, 10)

You can find a list of all attributes and methods of the DAG object in the Airflow documentation of the DAG object.


The dag_run key contains the DAG run object. Two commonly used elements of the DAG run object are the .active_runs_of_dags method, which gives you the number of currently active runs of a specific DAG, and the .external_trigger attribute, which returns True if a DAG run was triggered outside of its normal schedule.

def print_dagrun_info(**context):

You can find a list of all attributes and methods of the DAG run object in the Airflow source code.


The params key contains a dictionary of all DAG- and task-level params that were passed to a specific task instance. Individual params can be accessed using their respective key.

def print_param(**context):

Learn more about params in the Airflow params guide.


The var key contains all Airflow variables of your Airflow instance. Airflow variables are key-value pairs that are commonly used to store instance-level information that rarely changes.

def get_var_from_context(**context):

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