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How to convert Config to model_config in Pydantic v2

Convert safe nested Config classes into model_config and refuse removed keys.

Start here

The safe first move

Move supported Config values into model_config and rename the keys that have a direct Pydantic v2 equivalent.

Stop before automation when: Removed config keys. Config logic that depends on project-specific behavior.

Target shape

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

How to convert Config to model_config in Pydantic v2

The safe case is a nested Config class with known key renames. The unsafe case is everything else.

Direct answer

What changes

Move supported Config values into model_config and rename the keys that have a direct Pydantic v2 equivalent.

Example

Before and after

Before

class Config:
    orm_mode = True
    allow_population_by_field_name = True

After

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

  • Safe config renames are mixed with removed keys.
  • Teams want one report that shows both supported and blocked files.
  • Reviewers need proof that the porter will not bluff through removed keys.

What the product covers

  • Supported Config to model_config conversion.
  • Known key renames in the safe subset.
  • Manual-review findings for removed or unsupported keys.

Manual-review boundary

  • Removed config keys.
  • Config logic that depends on project-specific behavior.
  • Alias-heavy imports in the same file.

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 Config to model_config pydantic v2?

Move supported Config values into model_config and rename the keys that have a direct Pydantic v2 equivalent.

Can Config to model_config pydantic v2 be automated?

Automate only the supported subset: Supported Config to model_config conversion. Known key renames in the safe subset. Manual-review findings for removed or unsupported keys. Keep these cases in manual review: Removed config keys. Config logic that depends on project-specific behavior. Alias-heavy imports in the same file.

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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