Workflow automation · API / SDK

Apache Airflow REST API

Apache Software Foundation

Access the public interface of a data-workflow scheduler.

Official documentation Download development spec

APACHE AIRFLOW / DEVELOPER FIELD GUIDE

Choose an interface. Define a verifiable result.

Company or project context, ten technical entry points and a practical analysis of every reference.

2026-10-08 · Integration not tested

Company / project overview

Original summary of official company or project statements; use the source for the full original page.

Apache Airflow · About
Project	Apache Airflow is a community-developed platform for authoring, scheduling and monitoring workflows.
Programming model	Python Dag definitions describe work dependencies, while executors and providers connect execution infrastructure.
API boundary	This guide follows current Airflow 3 documentation; verify API version and authentication before adapting older Airflow 2 integrations.

Complete official About page ↗

About reference — TA analysis
Audience	Buyers, partners and product owners shortlisting an AI provider.
Our assessment	Use positioning and company history for initial fit. Obtain project-specific deployment, support and commercial terms separately.

Official sources reviewed. No provider API or inference was run; results are illustrative internal contracts.

Top 10 technical entry points

Ten documented technical entry points selected for implementation decisions, not ten independent SDKs or an official ranking.

Choose by the work you need to complete
Interface	Purpose and audience
01 · Public API authenticationAPI / SDK / developer guide	Authenticate calls to the current public API.API developers
02 · DagsAPI / SDK / developer guide	Define tasks, dependencies and schedules.Workflow authors
03 · TasksAPI / SDK / developer guide	Execute and track discrete work units.Task developers
04 · Dag runs and data intervalsAPI / SDK / developer guide	Track runs for specific data intervals.Scheduling developers
05 · SchedulerAPI / SDK / developer guide	Schedule eligible task instances.Platform operators
06 · ExecutorAPI / SDK / developer guide	Choose how tasks execute on available resources.Infrastructure teams
07 · PoolsAPI / SDK / developer guide	Limit task concurrency against constrained systems.Resource owners
08 · XComsAPI / SDK / developer guide	Pass task-level data references or small values.Pipeline developers
09 · Connections and hooksAPI / SDK / developer guide	Configure access to external systems.External integration teams
10 · Best practicesAPI / SDK / developer guide	Design reproducible and retry-safe tasks.Production maintainers

Reference analysis

Capabilities summarize official documentation. Constraints and proposed tests are our engineering assessment.

API / SDK / developer guide

Public API authentication

Public API authentication · TA
Target audience	API developers
Documented capability	Authenticate calls to the current public API.
Input → output	Valid bearer token and request → authorized response.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Airflow 3 API and configured auth manager differ from older deployments.
Our suggested validation	Check deployed API schema, token expiry and denied access.

Official reference · Public API authentication ↗

API / SDK / developer guide

Dags

Dags · TA
Target audience	Workflow authors
Documented capability	Define tasks, dependencies and schedules.
Input → output	Python Dag definition → discoverable workflow.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Parsing and import failures prevent discovery.
Our suggested validation	Validate Dag discovery and expected dependency graph.

Official reference · Dags ↗

API / SDK / developer guide

Tasks

Tasks · TA
Target audience	Task developers
Documented capability	Execute and track discrete work units.
Input → output	Task input and dependency state → task state/result.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Skipped and upstream-failed states differ from successful work.
Our suggested validation	Test retries, timeouts and dependency outcomes.

Official reference · Tasks ↗

API / SDK / developer guide

Dag runs and data intervals

Dag runs and data intervals · TA
Target audience	Scheduling developers
Documented capability	Track runs for specific data intervals.
Input → output	Trigger or schedule → Dag run.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Logical date is not actual execution time; leaf rules affect run success.
Our suggested validation	Verify required tasks and output interval, not only top-level success.

Official reference · Dag runs and data intervals ↗

API / SDK / developer guide

Scheduler

Scheduler · TA
Target audience	Platform operators
Documented capability	Schedule eligible task instances.
Input → output	Dag/run state → scheduled tasks.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Scheduler health alone does not establish executor capacity.
Our suggested validation	Track scheduling delay and stalled runs.

Official reference · Scheduler ↗

API / SDK / developer guide

Executor

Executor · TA
Target audience	Infrastructure teams
Documented capability	Choose how tasks execute on available resources.
Input → output	Queued task → execution backend.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Executor and provider dependencies are deployment-specific.
Our suggested validation	Test capacity, dependency availability and worker loss.

Official reference · Executor ↗

API / SDK / developer guide

Pools

Pools · TA
Target audience	Resource owners
Documented capability	Limit task concurrency against constrained systems.
Input → output	Pool slots and queued tasks → allowed concurrency.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Slot exhaustion should queue work rather than imply failure.
Our suggested validation	Verify slots under burst load and task completion.

Official reference · Pools ↗

API / SDK / developer guide

XComs

XComs · TA
Target audience	Pipeline developers
Documented capability	Pass task-level data references or small values.
Input → output	Task-produced value → downstream value.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Large payload storage requires an appropriate backend strategy.
Our suggested validation	Check serialization and missing upstream values.

Official reference · XComs ↗

API / SDK / developer guide

Connections and hooks

Connections and hooks · TA
Target audience	External integration teams
Documented capability	Configure access to external systems.
Input → output	Connection reference → provider-specific client access.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Provider packages and credentials must match target services.
Our suggested validation	Test connection availability without logging secrets.

Official reference · Connections and hooks ↗

API / SDK / developer guide

Best practices

Best practices · TA
Target audience	Production maintainers
Documented capability	Design reproducible and retry-safe tasks.
Input → output	Dag/task design → maintainable execution behavior.
Execution environment	Configured Airflow 3 deployment with scheduler, executor and relevant providers
Our decision constraint	Retries can duplicate writes unless tasks are designed for them.
Our suggested validation	Test idempotency and historical interval reruns.

Official reference · Best practices ↗

Official documentation

Official documentation · Supporting reference analysis
TA	Developers and technical product owners
Our validation advice	Pin the release and follow the target-specific support requirements before implementation.

Official reference · Official documentation ↗

04 / INPUT · EXPECTED OUTPUT · ACCEPTANCE

Expected results before implementation

Illustrative batch-data workflow contract, not the raw Airflow REST response. No Dag was triggered or task executed.

Illustrative contract · no API call or model execution · not measured provider output

Verified batch completion

Expected behavior — illustrative, not executed
Input	Valid Dag trigger for a defined input interval.
Expected result	ok after required tasks and destination records are checked.
Acceptance	Do not rely solely on leaf-derived Dag success.

Empty interval

Expected behavior — illustrative, not executed
Input	Authorized source query finds no records for the interval.
Expected result	Successful no-op with zero records and interval identity.
Acceptance	Distinguish legitimate zero from source authentication failure.

Pool capacity exhausted

Expected behavior — illustrative, not executed
Input	Runnable task waits for configured pool slots.
Expected result	queued/pending without claiming processed data.
Acceptance	Check pool availability and scheduling delay.

Upstream task failure

Expected behavior — illustrative, not executed
Input	Required extraction task fails after retries.
Expected result	failed or upstream_failed at affected tasks; no business-success claim.
Acceptance	Reconcile partial writes and rerun the same interval idempotently.

Map the expected result to your application

Map your actual runtime response into this proposed application contract. All values are invented examples; no credentials or live execution are included.

Expected output JSON — illustrative, not measured · AirflowAPI-expected-results.json

{
  "schema_version": "1.0.0",
  "example": true,
  "provider": "Apache Airflow",
  "status": "ok",
  "input_id": "sample-001",
  "data": {
    "dag_id": "example-daily-import",
    "dag_run_id": "example-run",
    "dag_state": "success",
    "required_tasks_verified": true,
    "records_written": 12
  },
  "error": null,
  "verification": "illustrative-only;not-provider-response"
}

Download specification, example and acceptance plan

Engineering handoff — our proposed contract and gates
State semantics	Valid empty results differ from errors. Pending work must not appear successful before completion.
Evidence	Record service and SDK versions, request identity, configuration and response mapping. No execution or performance was measured here.
Acceptance proposal	Agree numerical tolerances and workload targets before testing on representative authorized samples.

Development assessment

Industrial data refresh pipeline

Development concept · integration not tested

Provider capabilities above are based on official documentation or repositories. The proposed product, inputs, deliverable and acceptance criteria below are our development assessment.

Integration pilot

Validate runtime, schemas or hardware together before estimating a deployable product.

Proposed inputs: A sample event, authorized systems and one defined workflow transition.
Proposed deliverable: A visible job state and audit trail for a single workflow.
Acceptance criterion: A retried event must not create duplicate work; an unavailable upstream must remain visibly pending or failed.
Dependencies: Runtime access, workflow permissions and a reviewed notification or write destination.

Development sequence

Confirm access to Apache Airflow REST API, license and the exact supported version.
Prepare the sample above and implement one documented operation for “Industrial data refresh pipeline”.
Normalize the result with source, time and explicit error state; keep the provider response for review.
Run the acceptance criterion before estimating rollout effort or committing a customer deliverable.

How to validate demand

Record product views, documentation clicks, specification downloads and contextual hub clicks. These are event counts, not unique people or completed integrations.

This feasibility assessment uses implementation conditions. No traffic-based rank or delivery-time promise is assigned.

Apache Airflow REST API

Choose an interface. Define a verifiable result.

Company / project overview

Top 10 technical entry points

Reference analysis

Official documentation

Expected results before implementation

Verified batch completion

Empty interval

Pool capacity exhausted

Upstream task failure

Map the expected result to your application

Industrial data refresh pipeline

Development sequence

How to validate demand

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