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❓ Frequently Asked Questions

📋 Schema Handling

🔀 oneOf/anyOf generates unexpected Union types

When using oneOf or anyOf, the generated models may not match your expectations. Use --union-mode to control how unions are generated:

# Smart union (Pydantic v2 only) - validates against types in order
datamodel-codegen --union-mode smart --output-model-type pydantic_v2.BaseModel ...

# Left-to-right validation
datamodel-codegen --union-mode left_to_right ...

See CLI Reference: --union-mode for details.

🔗 allOf doesn't merge properties as expected

Control how allOf schemas merge fields:

# Merge only constraints (minItems, maxItems, pattern, etc.) - default
datamodel-codegen --allof-merge-mode constraints ...

# Merge constraints + annotations (default, examples)
datamodel-codegen --allof-merge-mode all ...

# Don't merge any fields
datamodel-codegen --allof-merge-mode none ...

See CLI Reference: --allof-merge-mode for details.

📎 Related: #399

📁 How to generate from multiple schema files?

Use a directory as input, or use $ref to reference other files:

# Generate from directory containing multiple schemas
datamodel-codegen --input schemas/ --output models/

For schemas with cross-file $ref, ensure you have the HTTP extra for remote refs:

pip install 'datamodel-code-generator[http]'

📎 Related: #215

🔤 YAML bool keywords (YES, NO, true, false) in string enums

YAML 1.1 treats unquoted keywords like YES, NO, on, off, true, false as boolean values. This tool preserves them as strings to avoid unexpected conversions in schema contexts:

# Input YAML
enum:
  - YES
  - NO
  - NOT_APPLICABLE

# Generated code (strings preserved)
class MyEnum(Enum):
    YES = 'YES'
    NO = 'NO'
    NOT_APPLICABLE = 'NOT_APPLICABLE'

This matches the expected behavior when type: string is specified in your schema. If you need the previous behavior where YAML bool keywords were converted to Python booleans, please open an issue describing your use case.

📎 Related: #1653, #1766, #2338

🔍 Type Checking

⚠️ mypy complains about Field constraints

If mypy reports errors about conint, constr, or other constrained types, use --field-constraints or --use-annotated:

# Use Field(..., ge=0) instead of conint(ge=0)
datamodel-codegen --field-constraints ...

# Use Annotated[int, Field(ge=0)]
datamodel-codegen --use-annotated ...

See Field Constraints for more information.

🤔 Type checker doesn't understand generated types

Ensure you're using the correct target Python version:

datamodel-codegen --target-python-version 3.11 ...

This affects type syntax generation (e.g., list[str] vs List[str], X | Y vs Union[X, Y]).

🏷️ Field Naming

🚫 Property names conflict with Python reserved words

Properties like class, from, import are automatically renamed with a field_ prefix. Control this behavior:

# Custom prefix (default: "field")
datamodel-codegen --special-field-name-prefix my_prefix ...

# Remove special prefix entirely
datamodel-codegen --remove-special-field-name-prefix ...

🔣 Field names have special characters

JSON/YAML property names with spaces, dashes, or special characters are converted to valid Python identifiers. An alias is automatically generated to preserve the original name:

class Model(BaseModel):
    my_field: str = Field(..., alias='my-field')

To disable aliases:

datamodel-codegen --no-alias ...

See Field Aliases for custom alias mappings.

🐍 Want snake_case field names from camelCase

datamodel-codegen --snake-case-field ...

This generates snake_case field names with camelCase aliases:

class User(BaseModel):
    first_name: str = Field(..., alias='firstName')

🔄 Output Stability

⏰ Generated output changes on every run

The timestamp in the header changes on each run. Disable it for reproducible output:

datamodel-codegen --disable-timestamp ...

🌍 Output differs between environments

Ensure consistent formatting across environments:

# Explicitly set formatters
datamodel-codegen --formatters black isort ...

# Or disable formatting entirely for raw output
datamodel-codegen --formatters ...

Also ensure the same Python version and formatter configurations (pyproject.toml) are used.

🤖 CI fails because generated code is different

Use --check mode in CI to verify generated files are up-to-date:

datamodel-codegen --check --input schema.yaml --output models.py

This exits with code 1 if the output would differ, without modifying files.

⚡ Performance

🐢 Generation is slow for large schemas

For very large schemas with many models:

  1. Use --reuse-model to deduplicate identical models
  2. Consider splitting schemas into multiple files
  3. Use --disable-warnings to reduce output
datamodel-codegen --reuse-model --disable-warnings ...

See Model Reuse and Deduplication for details.

🔧 Output Model Types

🤷 Which output model type should I use?

  • Pydantic v2 (pydantic_v2.BaseModel): ✨ Recommended for new projects. Better performance and modern API.
  • Pydantic v1 (pydantic.BaseModel): 🔄 Default for backward compatibility with existing codebases.
  • dataclasses: Simple data containers without validation.
  • TypedDict: Type hints for dict structures.
  • msgspec: High-performance serialization.

See Output Model Types for a detailed comparison.

# For new projects
datamodel-codegen --output-model-type pydantic_v2.BaseModel ...

See Pydantic v1 vs v2 for migration details.

📎 Related: #803

💥 Generated code doesn't work with my Pydantic version

Ensure the output model type matches your installed Pydantic version:

# Check your Pydantic version
python -c "import pydantic; print(pydantic.VERSION)"

# Generate for Pydantic v2
datamodel-codegen --output-model-type pydantic_v2.BaseModel ...

# Generate for Pydantic v1
datamodel-codegen --output-model-type pydantic.BaseModel ...

🌐 Remote Schemas

📡 Cannot fetch schema from URL

Install the HTTP extra:

pip install 'datamodel-code-generator[http]'

For authenticated endpoints:

datamodel-codegen --url https://api.example.com/schema.yaml \
    --http-headers "Authorization: Bearer TOKEN" \
    --output model.py

🔒 SSL certificate errors

For development/testing with self-signed certificates:

datamodel-codegen --url https://... --http-ignore-tls --output model.py

⚠️ Security Notice

Only use --http-ignore-tls in trusted environments.

📘 OpenAPI Specific

📝 How to handle readOnly/writeOnly properties?

Use --read-only-write-only-model-type to generate separate Request/Response models:

# Generate Request/Response models only
datamodel-codegen --read-only-write-only-model-type request-response ...

# Generate Base + Request + Response models
datamodel-codegen --read-only-write-only-model-type all ...

📎 Related: #727

❓ Why are nullable fields not Optional?

Use --strict-nullable to treat nullable fields as truly optional:

datamodel-codegen --strict-nullable ...

📎 Related: #327

🔧 Advanced

📦 How to use TypeAlias instead of RootModel?

Use --use-type-alias (experimental) to generate type aliases instead of root models:

datamodel-codegen --use-type-alias --output-model-type pydantic_v2.BaseModel ...

See Root Models and Type Aliases for details.

📎 Related: #2505

📖 See Also