Apache Airflow Ctl 0.1.5 Release: CLI Parameter Refactoring and Bulk Operations

Apache Airflow Ctl version 0.1.5 was released on June 3, 2026, bringing a significant change to how operators interact with the airflowctl command line interface. The most notable update is the refactoring of CLI parameters to use positional arguments for required primitives, a move aimed at aligning the tool with common terminal patterns and streamlining command execution for SREs and data engineers.

The full release notes and downloads are on the GitHub release page.

A major theme in the 0.1.5 release is the change in how airflowctl handles command line arguments. Following a lazy consensus on the Airflow development mailing list, required primitive parameters of auto generated commands are now exposed as positional arguments. Previously, these were often passed using the --flag style options.

This change specifically affects required primitives, while optional parameters continue to use the flag form. This distinction reduces the typing overhead for common operations while maintaining the explicitness required for optional configurations. For example, when targeting a specific DAG or task instance, the identifier is now passed directly as a positional argument. This shift reflects a maturing CLI design that prioritizes speed and idiomatic shell usage. The discussion leading to this decision can be found in the mailing list thread m1qvcvow3l17ytv40vhslh40wn3rntrm.

The project also aligned the capitalization of “Dag” within the airflowctl context, moving from “DAG” to “Dag” in several internal and user facing strings. This is visible in PR 66112 and contributes to a more consistent naming convention across the CLI tool.

Operating Airflow at scale often requires managing hundreds or thousands of Dag runs simultaneously. The 0.1.5 release addresses this by adding support for bulk operations. Users can now perform bulk deletes on Dag runs, significantly reducing the manual effort required to clean up metadata for large scale backfills or failed experimental runs.

Furthermore, the CLI now supports clearing, marking success or failure, and deleting multiple task instances in a single operation. This is a critical improvement for platform engineers who need to manage state across large task groups or complex dependency trees. The implementation of task group instance patching in the API also enables more granular control over task execution state via the CLI.

A new command, dags next-execution, has been added to provide quick visibility into the scheduled timing of future Dag runs. This is particularly useful for debugging scheduling logic or verifying the impact of schedule changes without accessing the web UI.

For installations with massive amounts of metadata, traditional offset based pagination often leads to performance bottlenecks in the database. Airflowctl 0.1.5 introduces cursor based pagination for the get_dag_runs and get_task_instances endpoints. This move toward cursor based navigation allows for more efficient traversal of large datasets by avoiding the overhead associated with large offsets in SQL queries.

The release also incorporates work from AIP 103, adding Core API endpoints for task state and asset state. These additions provide better observability and programmatic control over the internal state of the Airflow environment. Other notable API changes include:

  • The addition of the is_backfillable property to DAG API responses.
  • Implementation of the rerun_with_latest_version configuration hierarchy for clear and rerun behavior.
  • Support for remote version checks without authentication, facilitating easier version monitoring across distributed environments.

The 0.1.5 release includes several important stability fixes. One critical fix addresses a missing pyyaml runtime dependency that caused issues in previous versions. The dagrun list command was also fixed to prevent crashes when the --state flag is omitted.

Data integrity in connection management was improved by fixing schema handling during connections import. For operators running backfills, a fix was implemented to ensure that backfill parameters correctly override existing DAG run configurations.

Static analysis and code quality were also addressed, with Ruff issues resolved in the client-py component. The project also moved non provider mypy checks to run as regular pre commit static checks, optimizing the CI pipeline.

The airflowctl 0.1.5 release is available through the standard Apache Airflow distribution channels.

Operators are encouraged to review the updated documentation for the new positional argument patterns and bulk operation syntax.