airflow-hitl

作者: astronomer

使用可延遲運算子,在 Airflow DAG 中實現人工審批關卡、表單輸入與分支流程。包含四種運算子類型:ApprovalOperator 用於核准/拒絕決策、HITLOperator 用於多選項表單選擇、HITLBranchOperator 用於人工驅動的任務路由,以及 HITLEntryOperator 用於表單資料收集。所有運算子皆為可延遲,在等待人工回應時釋放工作槽位,可透過 Airflow UI 的「必要操作」標籤或 REST API 進行回應。支援選用功能,包括自訂...

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

Airflow Human-in-the-Loop Operators

Pause a DAG until a human responds via the Airflow UI or REST API. HITL operators are deferrable — they release their worker slot while waiting.

Requires Airflow 3.1+ (af config version).

UI location: Browse → Required Actions. Respond from the task instance page's Required Actions tab.

Cross-references: migrating-ai-sdk-to-common-ai for AI/LLM task decorators; airflow for registry and API discovery commands used below.


Step 1 — Pick the capability you need

CapabilityClass (verify in Step 2)
Approve or reject; downstream skips on rejectApprovalOperator
Present N options and return which were chosenHITLOperator
Branch to one or more downstream tasks based on a choiceHITLBranchOperator
Collect a form (no approve/select step)HITLEntryOperator
Use the HITL trigger directly (advanced / custom operators)HITLTrigger

This is the only place class names are hardcoded. The provider adds, renames, and removes params across releases — do not copy parameter lists from memory. Fetch the current signature before writing code.


Step 2 — Discover the current signatures from the Airflow Registry

Before writing HITL code, run these to see the live roster and constructor params (see the airflow skill for the full af registry reference):

# Every HITL-related module in the standard provider
af registry modules standard \
  | jq '.modules[] | select(.import_path | test("\\.hitl\\.")) | {name, type, import_path, short_description, docs_url}'

# Constructor signatures: name, type, default, required, description
af registry parameters standard \
  | jq '.classes | to_entries[] | select(.key | test("\\.hitl\\.")) | {fqn: .key, parameters: .value.parameters}'

# Pin to the exact installed provider version
af config providers \
  | jq '.providers[] | select(.package_name == "apache-airflow-providers-standard") | .version'
# then: af registry parameters standard --version <VERSION>

If the registry shows a param that this skill does not mention, prefer the registry. If the registry shows a class that is not in Step 1, treat it as additive — the decision table above may be stale.


Step 3 — Canonical example (approval gate)

Starting point for any HITL task. Adapt by swapping the class name and params per Step 2.

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",              # Auto-selected 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()

For the other classes in Step 1, the shape is the same (task_id, subject, plus class-specific params). Verify each constructor through Step 2 — for example, HITLBranchOperator requires every option either to match a downstream task id directly or to be resolved via a mapping param surfaced in the registry.


Step 4 — Behavior contracts (stable across versions)

Timeout

  • With defaults set: task succeeds on timeout, default option(s) selected.
  • Without defaults: task fails on timeout.

Markdown + Jinja in body

body supports Markdown and is Jinja-templatable. Render XCom context directly:

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

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

Callbacks

All HITL operators accept the standard Airflow callback kwargs (on_success_callback, on_failure_callback, etc.).

Notifiers

HITL operators accept a notifiers list. Inside a notifier's notify(context) method, build a link to the pending task with HITLOperator.generate_link_to_ui_from_context(context, base_url=...).

Restricting who can respond

The parameter name and accepted identifier format depend on the active auth manager. Do not hardcode — check which one is active and which kwarg the current provider exposes:

af config show | jq '.auth_manager // .core.auth_manager'

Then look up the current kwarg in Step 2 (at the time of writing it is assigned_users, accepting identifiers in whatever format the active auth manager uses — Astro uses the Astro user ID, FabAuthManager uses email, SimpleAuthManager uses username).


Step 5 — Responding from external integrations

For Slack bots, custom apps, or scripts. Discover the live endpoint rather than hardcoding a path:

af api ls --filter hitl           # live endpoint list
af api spec \
  | jq '.paths | to_entries[] | select(.key | test("hitl"))'   # request/response schemas

The PATCH-to-respond pattern is stable; the exact path is discovered. Typical shape:

import os, requests

HOST = os.environ["AIRFLOW_HOST"]
TOKEN = os.environ["AIRFLOW_API_TOKEN"]
HEADERS = {"Authorization": f"Bearer {TOKEN}"}

# List pending — use the path from `af api ls --filter hitl`
requests.get(f"{HOST}/<path>", headers=HEADERS, params={"state": "pending"})

# Respond — same discovered path family, PATCH
requests.patch(
    f"{HOST}/<path>/{dag_id}/{run_id}/{task_id}",
    headers=HEADERS,
    json={"chosen_options": ["Approve"], "params_input": {"comments": "ok"}},
)

Step 6 — Safety checks

  • Airflow version ≥ 3.1 (af config version).
  • Constructor kwargs match the current registry output from Step 2 — no respondents-vs-assigned_users style drift.
  • For branching: every option resolves to a downstream task id (directly or via the mapping kwarg from Step 2).
  • Every value in defaults is also in options.
  • execution_timeout set; defaults configured if timeout should succeed rather than fail.
  • API token configured if external responders are part of the flow.

References

The upstream docs URL is surfaced per-module by the registry — do not hardcode:

af registry modules standard \
  | jq '.modules[] | select(.import_path | test("\\.hitl\\.")) | {name, docs_url}'

Related skills

  • airflow — af registry, af api, af config command reference.
  • migrating-ai-sdk-to-common-ai — AI/LLM task decorators and GenAI patterns (common-ai provider).
  • authoring-dags — general DAG writing best practices.
  • testing-dags — iterative test → debug → fix cycles.

來自 astronomer 的更多技能

airflow-state-store
astronomer
Persists task and asset state across retries and DAG runs using Airflow 3.3's AIP-103 key/value stores (`task_state_store`, `asset_state_store`) and the…
creating-openlineage-extractors
astronomer
為不支援的Airflow運算子及複雜血緣場景設計的自訂OpenLineage提取器。提供兩種方法:直接在你擁有的運算子中加入OpenLineage方法(建議做法),或為無法修改的第三方運算子建立自訂提取器。提取器在三個時間點攔截運算子執行:執行前取得靜態血緣、成功後取得執行階段決定的輸出、以及選擇性地在失敗後取得部分血緣。可透過airflow.cfg或環境變數註冊提取器...
debugging-dags
astronomer
針對失敗的 Airflow DAG 進行系統性根本原因分析與修復,並提供結構化的調查流程。引導完成四個階段的診斷步驟:識別失敗、提取錯誤細節、收集背景資訊,以及提供可行的修復步驟。將失敗分為四種類型(資料、程式碼、基礎設施、相依性),以聚焦調查並建議適當的修正方式。提供可直接使用的 CLI 指令,用於日誌擷取、執行比較、任務清除與 DAG...
delegating-to-otto
astronomer
驅動 Astronomer 的 Otto 代理
deploying-airflow
astronomer
部署 Airflow DAG 和專案。當使用者想要部署程式碼、推送 DAG、設定 CI/CD、部署到生產環境,或詢問部署策略時使用…
deploying-go-sdk-bundles
astronomer
建置、打包並部署已編譯的 Airflow Go SDK 套件,以便 ExecutableCoordinator 能執行它們。當使用者想要編譯 Go 任務套件、要求…時使用。
testing-dags
astronomer
針對Airflow DAG的反覆測試-除錯-修復循環,提供全面的失敗診斷。從af runs trigger-wait <dag_id>開始執行DAG並等待完成,無需預先檢查。失敗時,使用af runs diagnose獲取完整的失敗摘要,並透過af tasks logs檢查特定任務的錯誤細節。支援自訂配置、超時設定與重試機制;能處理成功、失敗及超時情境,並提供清晰的回應解讀。快速驗證功能亦已就緒...
tracing-downstream-lineage
astronomer
追蹤下游資料血緣,在修改資料表或DAG前評估變更影響。透過原始碼搜尋、檢視相依性及BI工具連線,識別目標資料表或DAG的直接消費者。建立完整的相依性樹狀圖,繪製從資料表到儀表板再到機器學習模型的所有下游影響。依關鍵性(關鍵、高、中、低)分類相依性,以優先處理利害關係人溝通與測試。產出包含風險評估、受影響範圍的影響報告。