Migrate Flyte to ZenML

This skill translates Flyte workflows into idiomatic ZenML pipelines. It handles the full migration workflow: analyzing Flytekit code, classifying each concept, translating what maps cleanly, flagging what needs redesign, and producing a working ZenML project with a migration report.

How migration works at a high level

Flyte and ZenML are closer to each other than Flyte and older scheduler-first systems like Airflow. Both are Python-first orchestration frameworks that care about typed execution units, retries, scheduling, and containerized execution. So the migration is often more like “rewire the execution story” than “translate operator objects into functions.”

But there are still sharp edges. Flyte has a richer workflow transport type system (FlyteFile, StructuredDataset, FlyteSchema), a stronger registered execution surface via LaunchPlan, and special execution features like @dynamic, @eager, map_task(), conditional(), ContainerTask, and reference entities. ZenML can often reach the same business outcome, but not always with the same runtime semantics.

This means migration is not a decorator-swap exercise. Some patterns translate directly, some need approximation, and some require honest redesign.

The three mapping types

Every Flyte concept falls into one of these categories: