Pydantic Compact definition of dependent defaults (Fix)

The Fix

pip install pydantic==1.10.19

Based on closed pydantic/pydantic issue #8603 · PR/commit linked

Jump to Verify Open PR/Commit

@@ -372,21 +372,12 @@ jobs:
 
   test-typing-extensions:
-    name: test typing-extensions ${{ matrix.typing-extensions-version }} on Python ${{ matrix.python-version }}
+    name: test typing-extensions (`main` branch) on Python ${{ matrix.python-version }}
     runs-on: ubuntu-latest

repro.py

from pydantic import BaseModel, Field, model_validator
from typing import Optional


# Works, but a lot of code
class MyModel(BaseModel):
    a: int
    b: Optional[int] = None

    @model_validator(mode="before")
    @classmethod
    def validate(cls, values):
        values.setdefault("b", 5 * values["a"])
        return values


# Works, but still a lot of code
class MyModel2(BaseModel):
    a: int
    b: Optional[int] = None

    def __init__(self, **kwargs):
        kwargs.setdefault("b", 5 * kwargs["a"])
        super().__init__(**kwargs)

print(MyModel(a=1))
print(MyModel2(a=2))

verify

Re-run the minimal reproduction on your broken version, then apply the fix and re-run.

fix.md

Option A — Upgrade to fixed release\npip install pydantic==1.10.19\nWhen NOT to use: This fix should not be used if backward compatibility with existing models is a concern.\n\n

Why This Fix Works in Production

Trigger: - [ ] [Compatibility between releases](https://docs.pydantic.dev/changelog/)
Mechanism: The library lacked support for default factories that accept validated data as arguments
Why the fix works: Introduces support for default factories that can take validated data as an argument, addressing the issue of dependent defaults in Pydantic models. (first fixed release: 1.10.19).

Why This Breaks in Prod

The library lacked support for default factories that accept validated data as arguments
Production symptom (often without a traceback): - [ ] [Compatibility between releases](https://docs.pydantic.dev/changelog/)

Proof / Evidence

GitHub issue: #8603
Fix PR: https://github.com/pydantic/pydantic/pull/10678
First fixed release: 1.10.19
Reproduced locally: No (not executed)
Last verified: 2026-02-09
Confidence: 0.85
Did this fix it?: Yes (upstream fix exists)
Own content ratio: 0.53

Discussion

High-signal excerpts from the issue thread (symptoms, repros, edge-cases).

Jump to Sources Open on GitHub

“Thanks for the comment @sydney-runkle! I think the DependentDefault is a good solution for now. I agree it makes sense to wait and see whether…”

@adonath · 2024-01-26 · source

“Improved version of https://github.com/pydantic/pydantic/issues/8603#issuecomment-2264988605 that also works with models that use validate_default=True by default, e.g. pydantic_settings.BaseSettings:”

@pmeier · 2024-08-02 · source

“Just for completeness, I found a more compact way of expressing this: However I think it is still not as elegant as the default factory…”

@adonath · 2024-01-22 · source

“One disadvantage of the solution provided in https://github.com/pydantic/pydantic/issues/8603#issuecomment-1904939403 is that the static annotation is lost, because it is set at runtime. Running mypy on gives…”

@pmeier · 2024-08-02 · source

Failure Signature (Search String)

- [ ] [Compatibility between releases](https://docs.pydantic.dev/changelog/)
- [X] [Data validation/parsing](https://docs.pydantic.dev/concepts/models/#basic-model-usage)

Copy-friendly signature

signature.txt

Failure Signature
-----------------
- [ ] [Compatibility between releases](https://docs.pydantic.dev/changelog/)
- [X] [Data validation/parsing](https://docs.pydantic.dev/concepts/models/#basic-model-usage)

Error Message

Signature-only (no traceback captured)

error.txt

Error Message
-------------
- [ ] [Compatibility between releases](https://docs.pydantic.dev/changelog/)
- [X] [Data validation/parsing](https://docs.pydantic.dev/concepts/models/#basic-model-usage)

Minimal Reproduction

repro.py

from pydantic import BaseModel, Field, model_validator
from typing import Optional


# Works, but a lot of code
class MyModel(BaseModel):
    a: int
    b: Optional[int] = None

    @model_validator(mode="before")
    @classmethod
    def validate(cls, values):
        values.setdefault("b", 5 * values["a"])
        return values


# Works, but still a lot of code
class MyModel2(BaseModel):
    a: int
    b: Optional[int] = None

    def __init__(self, **kwargs):
        kwargs.setdefault("b", 5 * kwargs["a"])
        super().__init__(**kwargs)

print(MyModel(a=1))
print(MyModel2(a=2))

What Broke

Users faced increased complexity and verbosity when defining dependent defaults in models.

Why It Broke

The library lacked support for default factories that accept validated data as arguments

Fix Options (Details)

Option A — Upgrade to fixed release Safe default (recommended)

pip install pydantic==1.10.19

When NOT to use: This fix should not be used if backward compatibility with existing models is a concern.

Use when you can deploy the upstream fix. It is usually lower-risk than long-lived workarounds.

Fix reference: https://github.com/pydantic/pydantic/pull/10678

First fixed release: 1.10.19

Last verified: 2026-02-09. Validate in your environment.

When NOT to Use This Fix

This fix should not be used if backward compatibility with existing models is a concern.

Verify Fix

verify

Re-run the minimal reproduction on your broken version, then apply the fix and re-run.

Did This Fix Work in Your Case?

Quick signal helps us prioritize which fixes to verify and improve.

Prevention

Add a CI check that diffs key outputs after upgrades (OpenAPI schema snapshots, JSON payload shapes, CLI output).
Upgrade behind a canary and run integration tests against the canary before 100% rollout.

Version Compatibility Table

Version	Status
1.10.19	Fixed

Related Issues

No related fixes found.

Cluster: pydantic:serialization Pydantic hub Pydantic best practices All hubs All clusters

Related clusters: Configuration error Data consistency OpenAPI schema

Sources

We don’t republish the full GitHub discussion text. Use the links above for context.