Agent skill

airflow-hitl

Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).

Stars 295
Forks 34

Install this agent skill to your Project

npx add-skill https://github.com/astronomer/agents/tree/main/skills/airflow-hitl

SKILL.md

Airflow Human-in-the-Loop Operators

Implement human approval gates, form inputs, and human-driven branching in Airflow DAGs using the HITL operators. These deferrable operators pause workflow execution until a human responds via the Airflow UI or REST API.

Implementation Checklist

Execute steps in order. Prefer deferrable HITL operators over custom sensors/polling loops.

CRITICAL: Requires Airflow 3.1+. NOT available in Airflow 2.x.

Deferrable: All HITL operators are deferrable—they release their worker slot while waiting for human input.

UI Location: View pending actions at Browse → Required Actions in Airflow UI. Respond via the task instance page's Required Actions tab or the REST API.

Cross-reference: For AI/LLM calls, see the airflow-ai skill.


Step 1: Choose operator

Operator Human action Outcome
ApprovalOperator Approve or Reject Reject causes downstream tasks to be skipped (approval task itself succeeds)
HITLOperator Select option(s) + form Returns selections
HITLBranchOperator Select downstream task(s) Runs selected, skips others
HITLEntryOperator Submit form Returns form data

Step 2: Implement operator

ApprovalOperator

python
from airflow.providers.standard.operators.hitl import ApprovalOperator
from airflow.sdk import dag, task, chain, Param
from pendulum import datetime

@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def approval_example():
    @task
    def prepare():
        return "Review quarterly report"

    approval = ApprovalOperator(
        task_id="approve_report",
        subject="Report Approval",
        body="{{ ti.xcom_pull(task_ids='prepare') }}",
        defaults="Approve",  # Optional: auto on timeout
        params={"comments": Param("", type="string")},
    )

    @task
    def after_approval(result):
        print(f"Decision: {result['chosen_options']}")

    chain(prepare(), approval)
    after_approval(approval.output)

approval_example()

HITLOperator

Required parameters: subject and options.

python
from airflow.providers.standard.operators.hitl import HITLOperator
from airflow.sdk import dag, task, chain, Param
from datetime import timedelta
from pendulum import datetime

@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def hitl_example():
    hitl = HITLOperator(
        task_id="select_option",
        subject="Select Payment Method",
        body="Choose how to process payment",
        options=["ACH", "Wire", "Check"],  # REQUIRED
        defaults=["ACH"],
        multiple=False,
        execution_timeout=timedelta(hours=4),
        params={"amount": Param(1000, type="number")},
    )

    @task
    def process(result):
        print(f"Selected: {result['chosen_options']}")
        print(f"Amount: {result['params_input']['amount']}")

    process(hitl.output)

hitl_example()

HITLBranchOperator

IMPORTANT: Options can either:

  1. Directly match downstream task IDs - simpler approach
  2. Use options_mapping - for human-friendly labels that map to task IDs
python
from airflow.providers.standard.operators.hitl import HITLBranchOperator
from airflow.sdk import dag, task, chain
from pendulum import datetime

DEPTS = ["marketing", "engineering", "sales"]

@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def branch_example():
    branch = HITLBranchOperator(
        task_id="select_dept",
        subject="Select Departments",
        options=[f"Fund {d}" for d in DEPTS],
        options_mapping={f"Fund {d}": d for d in DEPTS},
        multiple=True,
    )

    for dept in DEPTS:
        @task(task_id=dept)
        def handle(dept_name: str = dept):
            # Bind the loop variable at definition time to avoid late-binding bugs
            print(f"Processing {dept_name}")
        chain(branch, handle())

branch_example()

HITLEntryOperator

python
from airflow.providers.standard.operators.hitl import HITLEntryOperator
from airflow.sdk import dag, task, chain, Param
from pendulum import datetime

@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def entry_example():
    entry = HITLEntryOperator(
        task_id="get_input",
        subject="Enter Details",
        body="Provide response",
        params={
            "response": Param("", type="string"),
            "priority": Param("p3", type="string"),
        },
    )

    @task
    def process(result):
        print(f"Response: {result['params_input']['response']}")

    process(entry.output)

entry_example()

Step 3: Optional features

Notifiers

python
from airflow.sdk import BaseNotifier, Context
from airflow.providers.standard.operators.hitl import HITLOperator

class MyNotifier(BaseNotifier):
    template_fields = ("message",)
    def __init__(self, message=""): self.message = message
    def notify(self, context: Context):
        if context["ti"].state == "running":
            url = HITLOperator.generate_link_to_ui_from_context(context, base_url="https://airflow.example.com")
            self.log.info(f"Action needed: {url}")

hitl = HITLOperator(..., notifiers=[MyNotifier("{{ task.subject }}")])

Restrict respondents

Format depends on your auth manager:

Auth Manager Format Example
SimpleAuthManager Username ["admin", "manager"]
FabAuthManager Email ["manager@example.com"]
Astro Astro ID ["cl1a2b3cd456789ef1gh2ijkl3"]

Astro Users: Find Astro ID at Organization → Access Management.

python
hitl = HITLOperator(..., respondents=["manager@example.com"])  # FabAuthManager

Timeout behavior

  • With defaults: Task succeeds, default option(s) selected
  • Without defaults: Task fails on timeout
python
hitl = HITLOperator(
    ...,
    options=["Option A", "Option B"],
    defaults=["Option A"],  # Auto-selected on timeout
    execution_timeout=timedelta(hours=4),
)

Markdown in body

The body parameter supports markdown formatting and is Jinja templatable:

python
hitl = HITLOperator(
    ...,
    body="""**Total Budget:** {{ ti.xcom_pull(task_ids='get_budget') }}

| Category | Amount |
|----------|--------|
| Marketing | $1M |
""",
)

Callbacks

All HITL operators support standard Airflow callbacks:

python
def on_hitl_failure(context):
    print(f"HITL task failed: {context['task_instance'].task_id}")

def on_hitl_success(context):
    print(f"HITL task succeeded with: {context['task_instance'].xcom_pull()}")

hitl = HITLOperator(
    task_id="approval_required",
    subject="Review needed",
    options=["Approve", "Reject"],
    on_failure_callback=on_hitl_failure,
    on_success_callback=on_hitl_success,
)

Step 4: API integration

For external responders (Slack, custom app):

python
import requests, os

HOST = os.getenv("AIRFLOW_HOST")
TOKEN = os.getenv("AIRFLOW_API_TOKEN")

# Get pending actions
r = requests.get(f"{HOST}/api/v2/hitlDetails/?state=pending",
                 headers={"Authorization": f"Bearer {TOKEN}"})

# Respond
requests.patch(
    f"{HOST}/api/v2/hitlDetails/{dag_id}/{run_id}/{task_id}",
    headers={"Authorization": f"Bearer {TOKEN}"},
    json={"chosen_options": ["ACH"], "params_input": {"amount": 1500}}
)

Step 5: Safety checks

Before finalizing, verify:

  • Airflow 3.1+ installed
  • For HITLBranchOperator: options map to downstream task IDs
  • defaults values are in options list
  • API token configured if using external responders

Reference


Related Skills

  • airflow-ai: For AI/LLM task decorators and GenAI patterns
  • authoring-dags: For general DAG writing best practices
  • testing-dags: For testing DAGs with debugging cycles

Expand your agent's capabilities with these related and highly-rated skills.

astronomer/agents

testing-dags

Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill is for iterative test-debug-fix cycles.

295 34
Explore
astronomer/agents

managing-astro-local-env

Manage local Airflow environment with Astro CLI. Use when the user wants to start, stop, or restart Airflow, view logs, troubleshoot containers, or fix environment issues. For project setup, see setting-up-astro-project.

295 34
Explore
astronomer/agents

analyzing-data

Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.

295 34
Explore
astronomer/agents

setting-up-astro-project

Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.

295 34
Explore
astronomer/agents

tracing-upstream-lineage

Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.

295 34
Explore
astronomer/agents

airflow-plugins

Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.

295 34
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results