< Marvin> I want to use a pydantic base model to constrain m Prefect Community #ask-marvin

<@ULVA73B9P> I want to use a pydantic base model t...

Theom

10/29/2025, 10:47 AM

@Marvin I want to use a pydantic base model to constrain my flow inputs. However, when writing the following:

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class MyParamsModel(BaseModel)
   foo: str
   bar: int

@flow
def my_flow(params: MyParamsModel):
   ...

Then, when running the flow with

my_flow(foo="foo", bar=42)

, then I receive an error saying the input is missing a required argument "params". Can you confirm the only way to do this is to restructure my input and feed the flow the following:

{"params": {"foo": "foo", "bar": 42}}

Marvin

10/29/2025, 10:47 AM

✅ thought for 184.8 seconds

Marvin

10/29/2025, 10:50 AM

Short answer: you’re seeing normal Python behavior. With this signature:

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from pydantic import BaseModel
from prefect import flow

class MyParamsModel(BaseModel):
    foo: str
    bar: int

@flow
def my_flow(params: MyParamsModel):
    ...

Python expects a single argument named

params

. Prefect does not auto-unpack Pydantic model fields into top-level kwargs. That’s why

my_flow(foo="foo", bar=42)

errors. Your options: - Keep the single model parameter - Calling in Python:

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my_flow(params=MyParamsModel(foo="foo", bar=42))
    # or let Prefect/Pydantic construct it from a dict
    my_flow(params={"foo": "foo", "bar": 42})

- From a deployment/UI/API: pass a nested parameters dict:

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{
  "params": {
    "foo": "foo",
    "bar": 42
  }
}

- Expose individual parameters if you want top-level kwargs

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@flow
  def my_flow(foo: str, bar: int):
      params = MyParamsModel(foo=foo, bar=bar)
      ...

So, yes: with your current signature, you must provide a single

params

argument (either a

MyParamsModel

instance or a dict). If you prefer

my_flow(foo=..., bar=...)

, change the flow signature to take those fields directly. Refs: - Flows in Prefect 3 - Deployment parameters via prefect.yaml

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