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Fix Pydantic v2 class Config deprecation warnings

Convert safe class Config blocks into model_config with ConfigDict.

Start here

The safe first move

Use ConfigDict and model_config for supported keys like extra, from_attributes, populate_by_name, and json_schema_extra.

Stop before automation when: Config.fields and other removed keys. Dynamic Config attributes.

Target shape

from pydantic import ConfigDict

class User(BaseModel):
    model_config = ConfigDict(
        from_attributes=True,
        populate_by_name=True,
    )

Repo fit check

Want to know if your repo has this pattern?

Run the matching scanner locally first. Read the free findings and manual alternatives; consider paid cleanup only if supported patterns repeat enough to justify the price.

  • Local scan; no repository upload.
  • Supported findings are separated from manual-review findings.
  • Buy only when the repeated pattern is worth automating.
Run the Pydantic scan first View example report See when to buy 
Example scan summary
supported_findings: 38
manual_review_findings: 6
files_uploaded: 0
confidence: reviewable

Why this page exists

Fix Pydantic v2 class Config deprecation warnings

A nested class Config can become model_config when every key is in the supported rename subset.

Direct answer

What changes

Use ConfigDict and model_config for supported keys like extra, from_attributes, populate_by_name, and json_schema_extra.

Example

Before and after

Before

class User(BaseModel):
    class Config:
        orm_mode = True
        allow_population_by_field_name = True

After

from pydantic import ConfigDict

class User(BaseModel):
    model_config = ConfigDict(
        from_attributes=True,
        populate_by_name=True,
    )

Decision path

If this repeats across the repo, open the evaluation pages next.

The cleanup pack is strongest when the repo still uses direct pydantic imports and a clean validator/settings/config subset.

  • The repo has direct pydantic or pydantic.v1 imports, BaseSettings usage, safe Config blocks, or simple validators.
  • The migration pain is repetitive v1-to-v2 cleanup, not a custom semantic rewrite.
  • If the scan output is ambiguous, review its findings and upstream guidance. Do not buy expecting unsupported cases to be resolved.

Typical symptoms

  • Warnings mention class-based Config being deprecated.
  • Models use old config keys that were renamed.
  • Some Config blocks contain removed keys.

What the product covers

  • Safe Config to model_config conversion.
  • Supported key renames.
  • Manual-review findings for removed Config keys.

Manual-review boundary

  • Config.fields and other removed keys.
  • Dynamic Config attributes.
  • Config behavior coupled to custom validators.

Keep the migration narrow

Use the exact-problem guides as a triage layer, then decide whether the repo is inside the supported subset.

FAQ

Fast answers before you decide

What is the safest fix for PydanticDeprecatedSince20 class Config model_config?

Use ConfigDict and model_config for supported keys like extra, from_attributes, populate_by_name, and json_schema_extra.

Can PydanticDeprecatedSince20 class Config model_config be automated?

Automate only the supported subset: Safe Config to model_config conversion. Supported key renames. Manual-review findings for removed Config keys. Keep these cases in manual review: Config.fields and other removed keys. Dynamic Config attributes. Config behavior coupled to custom validators.

When should I buy Pydantic v1 to v2 Migration Cleanup Pack?

Use Pydantic v1 to v2 Migration Cleanup Pack only when this pattern repeats across enough files that manual cleanup is still costly. Use the free report and upstream guide to evaluate fit. Buy only if repeated supported edits justify the price; no paid assessment is required.

Purchase fit

Choose the lowest-risk next step.

One matching page is not enough by itself. Run the local scan, then buy only when the same supported pattern appears often enough to matter.

Buy if

  • The repo has direct pydantic or pydantic.v1 imports, BaseSettings usage, safe Config blocks, or simple validators.
  • The migration pain is repetitive v1-to-v2 cleanup, not a custom semantic rewrite.
  • The team accepts manual-review findings for alias-heavy imports and signature-heavy validators.

What the workflow includes

  • Installable local CLI workflow, not a hosted black box.
  • Supported validator, settings, import, and config rewrites.
  • Demo report, public proof, coverage notes, rollback checklist, and buyer terms.

Before checkout

  • Stripe Checkout handles secure payment and receipts.
  • Download your purchased ZIP after payment.
  • Runs locally; no hosted API, repo upload, or production credentials needed.
  • The free report includes the evidence needed to evaluate fit. Paid summary and template add-ons are optional.
  • 14-day refund review for published-scope or delivery mismatches.
Run the Pydantic scan first

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