Apache Airflow

The industry-standard platform for programmatically authoring, scheduling, and monitoring workflows.

MCPCLI10 Skills

About Apache Airflow

The industry-standard platform for programmatically authoring, scheduling, and monitoring workflows. Explore how Apache Airflow integrates with the agentic data stack ecosystem and supports autonomous data operations.

Key Features

  • Python-native DAG authoring with dynamic pipeline generation and Jinja templating
  • Extensive operator library: BashOperator, PythonOperator, KubernetesPodOperator, and 1000+ providers
  • Rich web UI for monitoring DAG runs, task logs, Gantt charts, and dependency graphs
  • Flexible scheduling with cron expressions, timetables, data-aware scheduling, and event triggers
  • Built-in retry logic, SLA monitoring, alerting, and failure callbacks
  • Provider ecosystem with 80+ packages for AWS, GCP, Azure, Databricks, Snowflake, dbt, and more
  • TaskFlow API for writing DAGs as decorated Python functions with automatic XCom passing
  • Horizontal scalability with Celery, Kubernetes, or local executors

Agent Integration

CLI — airflow

$ pip install apache-airflow
CLI Documentation

External Links

Astronomer Airflow MCP Server

Official MCP server for Airflow — DAG management, task monitoring, log access via AI assistants

Astronomer Agent Skills

22 agentskills.io-spec skills for Airflow workflows: DAG authoring, debugging, testing, deployment, and more

Airflow Provider Packages

80+ official provider packages for cloud services, databases, and data tools

Astro CLI

Astronomer CLI for local Airflow development, testing, and deployment to Astro Cloud

Airflow REST API

Stable REST API for programmatic DAG triggering, monitoring, and management

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