pydantic-v2-strict

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Pydantic V2 data modeling with strict mode enforcement for type safety. PROACTIVELY activate for: (1) Creating data models or schemas, (2) Validating API request/response types, (3) Building configuration classes, (4) Defining data contracts, (5) Implementing DTOs. Triggers: "pydantic", "BaseModel", "ConfigDict", "Field", "validator", "model_dump", "strict mode", "data model"

Agentient By Agentient schedule Updated 2/1/2026
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name: pydantic-v2-strict version: "1.0" description: > Pydantic V2 data modeling with strict mode enforcement for type safety. PROACTIVELY activate for: (1) Creating data models or schemas, (2) Validating API request/response types, (3) Building configuration classes, (4) Defining data contracts, (5) Implementing DTOs. Triggers: "pydantic", "BaseModel", "ConfigDict", "Field", "validator", "model_dump", "strict mode", "data model" core-integration: techniques: primary: ["completeness_verification"] secondary: ["structured_decomposition"] contracts: input: "none" output: "none" patterns: "none"

rubrics: "none"

Pydantic V2 Strict Mode Data Modeling

Core Principles

Pydantic V2 is a complete rewrite with a Rust core for maximum performance. All models in the Vibekit ecosystem MUST use strict mode to ensure type safety and eliminate implicit type coercion.

BaseModel with ConfigDict (Required Pattern)

The modern way to configure Pydantic models uses ConfigDict:

from pydantic import BaseModel, ConfigDict, Field
from typing import Annotated

# ✅ REQUIRED: Use ConfigDict for all model configuration
class User(BaseModel):
    model_config = ConfigDict(
        strict=True,              # MANDATORY: No type coercion
        frozen=True,              # Immutable after creation
        extra='forbid',           # Reject unknown fields
        validate_assignment=True  # Validate on field updates
    )

    id: int
    email: str
    username: str
    is_active: bool = True

# ✅ REQUIRED: Use Field() for constraints and metadata
class Product(BaseModel):
    model_config = ConfigDict(strict=True, frozen=True)

    id: int = Field(gt=0, description="Product ID must be positive")
    name: str = Field(min_length=1, max_length=100)
    price: float = Field(gt=0.0, description="Price must be positive")
    tags: list[str] = Field(default_factory=list)

# ❌ FORBIDDEN: Legacy V1 Config class
class LegacyUser(BaseModel):
    class Config:  # DO NOT USE THIS
        frozen = True
        extra = 'forbid'

Strict Mode: No Implicit Coercion

Strict mode is mandatory for all Vibekit Python code. It prevents silent bugs from type coercion:

from pydantic import BaseModel, ConfigDict, ValidationError

class StrictModel(BaseModel):
    model_config = ConfigDict(strict=True)

    age: int
    score: float
    is_active: bool

# ✅ With strict=True, only exact types are accepted
try:
    StrictModel(age=25, score=95.5, is_active=True)  # ✅ Works
    StrictModel(age="25", score=95.5, is_active=True)  # ❌ Raises ValidationError
except ValidationError as e:
    print(e)
    # Input should be a valid integer, got str

# Without strict mode (DO NOT USE):
class LooseModel(BaseModel):
    # NO model_config - defaults to strict=False
    age: int

# This silently converts "25" to 25 - DANGEROUS!
m = LooseModel(age="25")  # Works but shouldn't
print(m.age)  # 25 (int)

Nested Models and Composition

Build complex data structures by nesting models:

from pydantic import BaseModel, ConfigDict, Field

class Address(BaseModel):
    model_config = ConfigDict(strict=True, frozen=True)

    street: str
    city: str
    country: str
    postal_code: str

class Company(BaseModel):
    model_config = ConfigDict(strict=True, frozen=True)

    name: str
    headquarters: Address

class Employee(BaseModel):
    model_config = ConfigDict(strict=True, frozen=True)

    id: int
    name: str
    email: str
    company: Company
    home_address: Address | None = None

# Usage
employee = Employee(
    id=1,
    name="Alice",
    email="alice@example.com",
    company=Company(
        name="TechCorp",
        headquarters=Address(
            street="123 Tech St",
            city="San Francisco",
            country="USA",
            postal_code="94105"
        )
    )
)

Field Aliasing for External Data

Map model fields to different keys in JSON/external data:

from pydantic import BaseModel, ConfigDict, Field

class APIResponse(BaseModel):
    model_config = ConfigDict(strict=True)

    # Map camelCase API to snake_case Python
    user_id: int = Field(validation_alias='userId', serialization_alias='userId')
    first_name: str = Field(validation_alias='firstName', serialization_alias='firstName')
    last_name: str = Field(validation_alias='lastName', serialization_alias='lastName')
    created_at: str = Field(validation_alias='createdAt', serialization_alias='createdAt')

# Input from API (camelCase)
api_data = {
    'userId': 123,
    'firstName': 'John',
    'lastName': 'Doe',
    'createdAt': '2025-01-01T00:00:00Z'
}

response = APIResponse.model_validate(api_data)
print(response.user_id)  # 123 (Python snake_case)

# Serialize back to camelCase
output = response.model_dump(by_alias=True)
# {'userId': 123, 'firstName': 'John', ...}

Validators (V2 Syntax)

Pydantic V2 uses new decorator syntax for validators:

from pydantic import BaseModel, ConfigDict, field_validator, model_validator
from typing import Self

class UserRegistration(BaseModel):
    model_config = ConfigDict(strict=True)

    email: str
    password: str
    confirm_password: str
    age: int

    # ✅ REQUIRED: Use @field_validator for field-level validation
    @field_validator('email')
    @classmethod
    def validate_email(cls, v: str) -> str:
        if '@' not in v:
            raise ValueError('Invalid email address')
        return v.lower()  # Normalize to lowercase

    @field_validator('age')
    @classmethod
    def validate_age(cls, v: int) -> int:
        if v < 18:
            raise ValueError('Must be 18 or older')
        return v

    # ✅ REQUIRED: Use @model_validator for cross-field validation
    @model_validator(mode='after')
    def check_passwords_match(self) -> Self:
        if self.password != self.confirm_password:
            raise ValueError('Passwords do not match')
        return self

# ❌ FORBIDDEN: V1 validator syntax
class LegacyModel(BaseModel):
    email: str

    @validator('email')  # Old V1 decorator - DO NOT USE
    def validate_email(cls, v):
        pass

Serialization with model_dump()

Control how models are serialized:

from pydantic import BaseModel, ConfigDict, Field

class User(BaseModel):
    model_config = ConfigDict(strict=True)

    id: int
    email: str
    password_hash: str
    is_admin: bool = False
    metadata: dict[str, any] = Field(default_factory=dict)

user = User(
    id=1,
    email="user@example.com",
    password_hash="hashed_secret",
    is_admin=True
)

# ✅ REQUIRED: Use model_dump() (not dict())
user_dict = user.model_dump()
# {'id': 1, 'email': 'user@example.com', 'password_hash': 'hashed_secret', ...}

# Exclude sensitive fields
public_data = user.model_dump(exclude={'password_hash'})
# {'id': 1, 'email': 'user@example.com', 'is_admin': True, ...}

# Include only specific fields
minimal = user.model_dump(include={'id', 'email'})
# {'id': 1, 'email': 'user@example.com'}

# ✅ REQUIRED: Use model_dump_json() for JSON strings
json_string = user.model_dump_json(exclude={'password_hash'})

# ❌ FORBIDDEN: V1 methods
user.dict()  # DO NOT USE - deprecated
user.json()  # DO NOT USE - deprecated

Loading Data with model_validate()

Parse and validate external data:

from pydantic import BaseModel, ConfigDict, ValidationError

class Config(BaseModel):
    model_config = ConfigDict(strict=True)

    api_key: str
    timeout: int
    debug: bool

# ✅ REQUIRED: Use model_validate() for dicts
config_data = {"api_key": "secret", "timeout": 30, "debug": True}
config = Config.model_validate(config_data)

# ✅ REQUIRED: Use model_validate_json() for JSON strings
json_str = '{"api_key": "secret", "timeout": 30, "debug": true}'
config = Config.model_validate_json(json_str)

# Error handling
try:
    bad_data = {"api_key": "secret", "timeout": "not_an_int", "debug": True}
    Config.model_validate(bad_data)
except ValidationError as e:
    print(e.errors())
    # [{'type': 'int_type', 'loc': ('timeout',), 'msg': 'Input should be a valid integer', ...}]

# ❌ FORBIDDEN: V1 methods
Config.parse_obj(config_data)  # DO NOT USE
Config.parse_raw(json_str)     # DO NOT USE

Anti-Patterns to Avoid

Not Using strict=True

# BAD: Allows silent type coercion
class LooseModel(BaseModel):
    age: int

m = LooseModel(age="25")  # Silently converts string to int

# GOOD: Strict mode prevents this
class StrictModel(BaseModel):
    model_config = ConfigDict(strict=True)
    age: int

# Raises ValidationError on string input

Using V1 Config Class

# BAD: Legacy V1 syntax
class OldModel(BaseModel):
    class Config:
        frozen = True

# GOOD: V2 ConfigDict
class NewModel(BaseModel):
    model_config = ConfigDict(frozen=True)

Using V1 Validator Decorators

# BAD: V1 @validator
class OldValidation(BaseModel):
    email: str

    @validator('email')
    def check_email(cls, v):
        pass

# GOOD: V2 @field_validator
class NewValidation(BaseModel):
    email: str

    @field_validator('email')
    @classmethod
    def check_email(cls, v: str) -> str:
        pass

When to Use This Skill

Activate this skill when:

  • Creating new Pydantic models or schemas
  • Validating API request/response data
  • Building configuration management systems
  • Defining data contracts between services
  • Implementing data transfer objects (DTOs)
  • Migrating Pydantic V1 code to V2

Integration Points

This skill is a required dependency for:

  • agentient-rag-engine/data-ingestion-schemas - RAG data models
  • agentient-frontend-bff/api-contracts - API schemas
  • agentient-adk-agents/agent-tool-schemas - ADK tool definitions

Related Resources

For advanced patterns, see:

Install via CLI
npx skills add https://github.com/Agentient/vibekit --skill pydantic-v2-strict
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