Prefect: Python-Native Orchestration for AI Pipelines
What it is: Prefect is a Python-native workflow orchestrator — you decorate ordinary Python functions as @flow and @task, and Prefect handles scheduling, retries, caching, and a UI for observing runs, without a separate DAG-definition language. Because a "workflow" is just annotated Python, it's a natural place to wrap agent and LLM calls alongside the rest of a data pipeline: an ingestion task, an LLM classification task, and a load-to-warehouse task can all live in the same flow.
Quick answer: If your team already writes Python for its data pipelines and wants scheduling, retries, and a run history without adopting a heavyweight DAG framework, Prefect is a lower-ceremony fit than Airflow — including for pipelines that now include one or more LLM calls as regular tasks.
Why it matters for data work
Most AI-in-the-pipeline work isn't a standalone agent — it's one or two LLM calls stitched into an otherwise ordinary ETL job: classify a batch of records, extract structured fields from documents, then load the results. Prefect lets you write that as plain Python functions with decorators rather than reaching for a separate agent framework's orchestration layer, and you get retries, caching, and a run history for the LLM steps the same way you'd get them for a database load.
Install & configure
Install from PyPI:
pip install -U prefectDecorate a function with @flow for the overall pipeline and @task for individual steps; calling the flow function runs it immediately, with each task's inputs, outputs, retries, and timing tracked automatically. Run prefect server start for a local UI to inspect runs, or point PREFECT_API_URL at Prefect Cloud for a hosted version. Deployments add scheduling (cron or event-triggered) on top of the same flow code.
Prefect or Airflow — which for AI/data pipelines?
Airflow is the more established choice for large, complex batch DAGs with a huge ecosystem of pre-built operators — it's the safer default if your organization already runs Airflow and the new pipeline is one more DAG among hundreds. Prefect tends to win when the team writes plain Python first and wants orchestration to wrap that code rather than define workflows in a separate DAG-authoring paradigm; dynamic, runtime-determined task graphs (common when the number of items to process — files, API pages, LLM batches — isn't known until the flow runs) are also more natural in Prefect than in Airflow's more static DAG model. Neither is a wrong choice; the deciding factor is usually which paradigm your team already writes and whether you're greenfield or joining an existing Airflow estate.
Limits and gotchas
Prefect's flexibility (any Python function can be a task) cuts both ways — it's easy to write flows that are hard to reason about because the dependency graph isn't explicit the way a declared Airflow DAG is; keeping task boundaries meaningful is a discipline you have to maintain yourself. Retries and caching are opt-in per task, so an LLM call without a configured retry policy will fail the whole flow run on a single transient API error unless you set one. And self-hosting the full Prefect server (versus just running flows locally) is its own piece of infrastructure to operate, same as any orchestrator.
Troubleshooting
Most issues trace back to task boundaries or missing retry configuration rather than Prefect itself.
- A flaky LLM call fails the whole flow: add a
retriesargument to that task's decorator rather than relying on flow-level error handling. - Flow runs don't show up in the UI: confirm
PREFECT_API_URLpoints at the server or Prefect Cloud workspace you're viewing — a flow run against a different API URL won't appear. - Scheduled deployment never triggers: check that a worker is running and polling the work pool the deployment is assigned to; a deployment without an active worker just queues silently.
- Task results seem to reuse stale data: caching is enabled per task via a cache key/policy — clear or adjust it if you're seeing unexpectedly cached results from a previous run.
Author & links
Author: Prefect (PrefectHQ)
Repo: github.com/PrefectHQ/prefect
License: Apache-2.0
Related skills
For agent runs that need to survive process crashes over hours or days, see Temporal. For code-first flows with an auto-generated UI and webhook triggers, see Windmill.
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