---
id: apache-airflow-task-sdk
version: "1.3.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# apache-airflow-task-sdk — Python Task SDK for Apache Airflow DAG Authors

License: permissive · Maintenance: active · Downloads: 7.7M/mo

## What it is and what it does

Apache Airflow Task SDK is a Python library that provides the interfaces and execution logic needed to author and run tasks within Apache Airflow DAGs. It sits between DAG authors and the Airflow core runtime, offering a structured way to define task behavior and integrate with Airflow's orchestration engine.

The package brings together a substantial dependency graph including apache-airflow-core, pydantic for validation, structlog for logging, httpx for HTTP operations, and utilities like tenacity for retries and pendulum for date handling. It supports modern Python versions (3.10 through 3.14) and is actively maintained by the Apache Foundation, with recent releases and ongoing commits. The library is positioned for developers and system administrators building data pipelines and workflow automation on top of Airflow.

Use it for:

- Write custom task logic in Python for Airflow DAGs without directly modifying core Airflow code.
- Develop and test task interfaces locally before deploying to a production Airflow cluster.
- Build reusable task components and patterns for data pipeline orchestration.
- Integrate Python business logic with Airflow's scheduling, monitoring, and retry mechanisms.
- Automate data workflows, ETL pipelines, and scheduled jobs using Airflow's DAG framework.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides Python interfaces and task execution logic for authoring DAGs and running tasks in Apache Airflow workflows.

Yes, if you are building or maintaining Apache Airflow DAGs in Python. The SDK is actively maintained, carries no known vulnerabilities, has low install friction, and is backed by the Apache Foundation. The substantial dependency footprint is expected for an orchestration framework integration layer. Not relevant if you are not using Airflow.

## Install

pip install apache-airflow-task-sdk
uv add apache-airflow-task-sdk
poetry add apache-airflow-task-sdk

## Installing apache-airflow-task-sdk

Before you install: Low install friction with a pure-wheel distribution. Active maintenance with a release 2 days old and a recent commit history. Depends on apache-airflow-core and multiple runtime packages including structured logging, validation, and async utilities.

License in practice: Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—standard for Apache Foundation projects.

Quickstart:

pip install apache-airflow-task-sdk

from apache_airflow_task_sdk import ...
# Import and use task interfaces for DAG authoring

Requires Python 3.10–3.14 (not 3.15); apache-airflow-core must be installed as a runtime dependency.

Verify before relying:

- Specific task interface classes and their usage patterns are not detailed in the excerpt.
- Whether the SDK is intended for local task development, remote execution, or both.
- Integration points with existing Airflow deployments and version compatibility constraints.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 7.7M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

airflow task sdk, airflow dag authoring, python airflow tasks, airflow workflow execution, airflow task interfaces, airflow orchestration sdk, airflow pipeline development, airflow-integration, workflow-orchestration, data-pipelines

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