Airflow

Airflow Operations

Use af commands to query, manage, and troubleshoot Airflow workflows.

Astro CLI

The Astro CLI is the recommended way to run Airflow locally and deploy to production. It provides a containerized Airflow environment that works out of the box:

# Initialize a new projectastro dev init
# Start local Airflow (webserver at http://localhost:8080)astro dev start
# Parse DAGs to catch errors quickly (no need to start Airflow)astro dev parse
# Run pytest against your DAGsastro dev pytest
# Deploy to productionastro deploy            # Full deploy (image + DAGs)astro deploy --dags     # DAG-only deploy (fast, no image build)

For more details:

• New project? See the setting-up-astro-project skill
• Local environment? See the managing-astro-local-env skill
• Deploying? See the deploying-airflow skill

Running the CLI

These commands assume af is on PATH. Run via astro otto to get it automatically, or install standalone with uv tool install astro-airflow-mcp.

Instance Configuration

Manage multiple Airflow instances with persistent configuration:

# Add a new instanceaf instance add prod --url https://airflow.example.com --token "$API_TOKEN"af instance add staging --url https://staging.example.com --username admin --password admin
# List and switch instancesaf instance list      # Shows all instances in a tableaf instance use prod  # Switch to prod instanceaf instance current   # Show current instanceaf instance delete old-instance
# Auto-discover instances (use --dry-run to preview first)af instance discover --dry-run        # Preview all discoverable instancesaf instance discover                  # Discover from all backends (astro, local)af instance discover astro            # Discover Astro deployments onlyaf instance discover astro --all-workspaces  # Include all accessible workspacesaf instance discover local            # Scan common local Airflow portsaf instance discover local --scan     # Deep scan all ports 1024-65535
# IMPORTANT: Always run with --dry-run first and ask for user consent before# running discover without it. The non-dry-run mode creates API tokens in# Astro Cloud, which is a sensitive action that requires explicit approval.
# Show where an instance came from (file path + scope)af instance show prod
# Override instance for a single command via env varsAIRFLOW_API_URL=https://staging.example.com AIRFLOW_AUTH_TOKEN=$STG af dags list
# Or switch persistentlyaf instance use staging

Config layout (mirrors git config system/global/local):

<root> is found by walking up from cwd looking for .astro/. Default write routing inside a project: add/discover → project-shared, use → project-local. Override with --global / --project / --local. Set AF_CONFIG=<path> to bypass layering and use a single file.

Migrate from the legacy ~/.af/config.yaml with af migrate (idempotent; renames the old file to .bak).

Tokens in config can reference environment variables using ${VAR} syntax:

instances:- name: prod  url: https://airflow.example.com  auth:    token: ${AIRFLOW_API_TOKEN}

Or use environment variables directly (no config file needed):

export AIRFLOW_API_URL=http://localhost:8080export AIRFLOW_AUTH_TOKEN=your-token-here# Or username/password:export AIRFLOW_USERNAME=adminexport AIRFLOW_PASSWORD=admin

Or CLI flags: af --airflow-url http://localhost:8080 --token "$TOKEN" <command>

Quick Reference

User Intent Patterns

Getting Started

• "How do I run Airflow locally?" / "Set up Airflow" -> use the managing-astro-local-env skill (uses Astro CLI)
• "Create a new Airflow project" / "Initialize project" -> use the setting-up-astro-project skill (uses Astro CLI)
• "How do I install Airflow?" / "Get started with Airflow" -> use the setting-up-astro-project skill

DAG Operations

• "What DAGs exist?" / "List all DAGs" -> af dags list
• "Tell me about DAG X" / "What is DAG Y?" -> af dags explore <dag_id>
• "What's the schedule for DAG X?" -> af dags get <dag_id>
• "Show me the code for DAG X" -> af dags source <dag_id>
• "Stop DAG X" / "Pause this workflow" -> af dags pause <dag_id>
• "Resume DAG X" -> af dags unpause <dag_id>
• "Are there any DAG errors?" -> af dags errors
• "Create a new DAG" / "Write a pipeline" -> use the authoring-dags skill

Run Operations

• "What runs have executed?" -> af runs list
• "Run DAG X" / "Trigger the pipeline" -> af runs trigger <dag_id>
• "Run DAG X and wait" -> af runs trigger-wait <dag_id>
• "Why did this run fail?" -> af runs diagnose <dag_id> <run_id>
• "Delete this run" / "Remove stuck run" -> af runs delete <dag_id> <run_id>
• "Clear this run" / "Retry this run" / "Re-run this" -> af runs clear <dag_id> <run_id>
• "Test this DAG and fix if it fails" -> use the testing-dags skill

Task Operations

• "What tasks are in DAG X?" -> af tasks list <dag_id>
• "Get task logs" / "Why did task fail?" -> af tasks logs <dag_id> <run_id> <task_id>
• "Full root cause analysis" / "Diagnose and fix" -> use the debugging-dags skill

Data Operations

• "Is the data fresh?" / "When was this table last updated?" -> use the checking-freshness skill
• "Where does this data come from?" -> use the tracing-upstream-lineage skill
• "What depends on this table?" / "What breaks if I change this?" -> use the tracing-downstream-lineage skill

Deployment Operations

• "Deploy my DAGs" / "Push to production" -> use the deploying-airflow skill
• "Set up CI/CD" / "Automate deploys" -> use the deploying-airflow skill
• "Deploy to Kubernetes" / "Set up Helm" -> use the deploying-airflow skill
• "astro deploy" / "DAG-only deploy" -> use the deploying-airflow skill

System Operations

• "What version of Airflow?" -> af config version
• "What connections exist?" -> af config connections
• "Are pools full?" -> af config pools
• "Is Airflow healthy?" -> af health

API Exploration

• "What API endpoints are available?" -> af api ls
• "Find variable endpoints" -> af api ls --filter variable
• "Access XCom values" / "Get XCom" -> af api xcom-entries -F dag_id=X -F task_id=Y
• "Get event logs" / "Audit trail" -> af api event-logs -F dag_id=X
• "Create connection via API" -> af api connections -X POST --body '{...}'
• "Create variable via API" -> af api variables -X POST -F key=name -f value=val

Registry Discovery

• "What operators does provider X have?" -> af registry modules <provider>
• "What are the constructor params for operator Y?" -> af registry parameters <provider>
• "What providers exist?" / "Is there a provider for Z?" -> af registry providers
• "What connection types does provider X expose?" -> af registry connections <provider>
• "Writing a DAG with a specific operator" -> use registry to verify current signature before copying examples

Common Workflows

Validate DAGs Before Deploying

If you're using the Astro CLI, you can validate DAGs without a running Airflow instance:

# Parse DAGs to catch import errors and syntax issuesastro dev parse
# Run unit testsastro dev pytest

Otherwise, validate against a running instance:

af dags errors     # Check for parse/import errorsaf dags warnings   # Check for deprecation warnings

Discover Operator Signatures Before Writing Code

The Airflow Registry at airflow.apache.org/registry is the authoritative source for provider classes and their current constructor signatures. Prefer it over memory or stale documentation when authoring DAGs — the registry reflects the live provider release.

# List all providers and pick the one you needaf registry providers | jq '.providers[] | {id, name, version}'
# List every operator / hook / sensor in a provider (e.g. standard, amazon, google)af registry modules standard \  | jq '.modules[] | {name, type, import_path, docs_url}'
# Get the current constructor signature for a specific classaf registry parameters standard \  | jq '.classes["airflow.providers.standard.operators.hitl.ApprovalOperator"].parameters'
# Filter modules by substring (useful when you know the concept but not the class)af registry modules standard \  | jq '.modules[] | select(.import_path | test("hitl"))'

Results are cached locally: 1 hour for the latest version, 30 days for pinned versions (which are immutable). Add --version X.Y.Z to any modules / parameters / connections call to target a specific release.

Investigate a Failed Run

# 1. List recent runs to find failureaf runs list --dag-id my_dag
# 2. Diagnose the specific runaf runs diagnose my_dag manual__2024-01-15T10:00:00+00:00
# 3. Get logs for failed task (from diagnose output)af tasks logs my_dag manual__2024-01-15T10:00:00+00:00 extract_data
# 4. After fixing, clear the run to retry all tasksaf runs clear my_dag manual__2024-01-15T10:00:00+00:00

Morning Health Check

# 1. Overall system healthaf health
# 2. Check for broken DAGsaf dags errors
# 3. Check pool utilizationaf config pools

Understand a DAG

# Get comprehensive overview (metadata + tasks + source)af dags explore my_dag

Check Why DAG Isn't Running

# Check if pausedaf dags get my_dag
# Check for import errorsaf dags errors
# Check recent runsaf runs list --dag-id my_dag

Trigger and Monitor

# Option 1: Trigger and wait (blocking)af runs trigger-wait my_dag --timeout 1800
# Option 2: Trigger and check lateraf runs trigger my_dagaf runs get my_dag <run_id>

Output Format

All commands output JSON (except instance commands which use human-readable tables):

af dags list# {#   "total_dags": 5,#   "returned_count": 5,#   "dags": [...]# }

Use jq for filtering:

# Find failed runsaf runs list | jq '.dag_runs[] | select(.state == "failed")'
# Get DAG IDs onlyaf dags list | jq '.dags[].dag_id'
# Find paused DAGsaf dags list | jq '[.dags[] | select(.is_paused == true)]'

Task Logs Options

# Get logs for specific retry attemptaf tasks logs my_dag run_id task_id --try 2
# Get logs for mapped task indexaf tasks logs my_dag run_id task_id --map-index 5

Direct API Access with af api

Use af api for endpoints not covered by high-level commands (XCom, event-logs, backfills, etc).

# Discover available endpointsaf api lsaf api ls --filter variable
# Basic usageaf api dagsaf api dags -F limit=10 -F only_active=trueaf api variables -X POST -F key=my_var -f value="my value"af api variables/old_var -X DELETE

Field syntax: -F key=value auto-converts types, -f key=value keeps as string.

Full reference: See api-reference.md [blocked] for all options, common endpoints (XCom, event-logs, backfills), and examples.

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