name: migrating-airflow-2-to-3
description: Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request.
hooks:
PostToolUse:
- matcher: "Edit"
hooks:
- type: command
command: "echo 'Consider running: ruff check --preview --select AIR .'"

Airflow 2 to 3 Migration

This skill helps migrate Airflow 2.x DAG code to Airflow 3.x, focusing on code changes (imports, operators, hooks, context, API usage).

Important: Before migrating to Airflow 3, strongly recommend upgrading to Airflow 2.11 first, then to at least Airflow 3.0.11 (ideally directly to 3.1). Other upgrade paths would make rollbacks impossible. See: https://www.astronomer.io/docs/astro/airflow3/upgrade-af3#upgrade-your-airflow-2-deployment-to-airflow-3. Additionally, early 3.0 versions have many bugs - 3.1 provides a much better experience.

Migration at a Glance

1. Run Ruff's Airflow migration rules to auto-fix detectable issues (AIR30/AIR301/AIR302/AIR31/AIR311/AIR312).
2. ruff check --preview --select AIR --fix --unsafe-fixes .
3. Scan for remaining issues using the manual search checklist in reference/migration-checklist.md.
4. Focus on: direct metadata DB access, legacy imports, scheduling/context keys, XCom pickling, datasets-to-assets, REST API/auth, plugins, and file paths.
5. Plan changes per file and issue type:
6. Fix imports - update operators/hooks/providers - refactor metadata access to using the Airflow client instead of direct access - fix use of outdated context variables - fix scheduling logic.
7. Implement changes incrementally, re-running Ruff and code searches after each major change.
8. Explain changes to the user and caution them to test any updated logic such as refactored metadata, scheduling logic and use of the Airflow context.

Architecture & Metadata DB Access

Airflow 3 changes how components talk to the metadata database:

• Workers no longer connect directly to the metadata DB.
• Task code runs via the Task Execution API exposed by the API server.
• The DAG processor runs as an independent process separate from the scheduler.
• The Triggerer uses the task execution mechanism via an in-process API server.

Key code impact: Task code can still import ORM sessions/models, but any attempt to use them to talk to the metadata DB will fail with:

RuntimeError: Direct database access via the ORM is not allowed in Airflow 3.x

Patterns to search for

When scanning DAGs, custom operators, and @task functions, look for:

• Session helpers: provide_session, create_session, @provide_session
• Sessions from settings: from airflow.settings import Session
• Engine access: from airflow.settings import engine
• ORM usage with models: session.query(DagModel)..., session.query(DagRun)...

Replacement: Airflow Python client

Preferred for rich metadata access patterns. Add to requirements.txt:

apache-airflow-client==<your-airflow-runtime-version>

Example usage:

import os
from airflow.sdk import BaseOperator
import airflow_client.client
from airflow_client.client.api.dag_api import DAGApi

_HOST = os.getenv("AIRFLOW__API__BASE_URL", "https://<your-org>.astronomer.run/<deployment>/")
_TOKEN = os.getenv("DEPLOYMENT_API_TOKEN")

class ListDagsOperator(BaseOperator):
    def execute(self, context):
        config = airflow_client.client.Configuration(host=_HOST, access_token=_TOKEN)
        with airflow_client.client.ApiClient(config) as api_client:
            dag_api = DAGApi(api_client)
            dags = dag_api.get_dags(limit=10)
            self.log.info("Found %d DAGs", len(dags.dags))

Replacement: Direct REST API calls

For simple cases, call the REST API directly using requests:

from airflow.sdk import task
import os
import requests

_HOST = os.getenv("AIRFLOW__API__BASE_URL", "https://<your-org>.astronomer.run/<deployment>/")
_TOKEN = os.getenv("DEPLOYMENT_API_TOKEN")

@task
def list_dags_via_api() -> None:
    response = requests.get(
        f"{_HOST}/api/v2/dags",
        headers={"Accept": "application/json", "Authorization": f"Bearer {_TOKEN}"},
        params={"limit": 10}
    )
    response.raise_for_status()
    print(response.json())

Ruff Airflow Migration Rules

Use Ruff's Airflow rules to detect and fix many breaking changes automatically.

• AIR30 / AIR301 / AIR302: Removed code and imports in Airflow 3 - must be fixed.
• AIR31 / AIR311 / AIR312: Deprecated code and imports - still work but will be removed in future versions; should be fixed.

Commands to run (via uv) against the project root:

# Auto-fix all detectable Airflow issues (safe + unsafe)
ruff check --preview --select AIR --fix --unsafe-fixes .

# Check remaining Airflow issues without fixing
ruff check --preview --select AIR .

Reference Files

For detailed code examples and migration patterns, see:

• reference/migration-patterns.md - Detailed code examples for:
• Removed modules and import reorganizations
• Task SDK and Param usage
• SubDAGs, SLAs, and removed features
• Scheduling and context changes
• XCom pickling removal
• Datasets to Assets migration

• DAG bundles and file paths

• reference/migration-checklist.md - Manual search checklist with:

• Search patterns for each issue type
• Recommended fixes

Quick Reference Tables

Key Import Changes

Context Key Changes

Default Behavior Changes

Resources

• Astronomer Airflow 3 Upgrade Guide
• Airflow 3 Release Notes
• Ruff Airflow Rules

Related Skills

• testing-dags: For testing DAGs after migration
• debugging-dags: For troubleshooting migration issues