pydantic
Fast data validation and settings management library using Python type annotations — defines data models as classes, validates on instantiation, serializes to/from JSON. pydantic v2 features: BaseModel with field type hints, Field() for constraints/defaults/aliases, model.model_validate() for dict input, model.model_dump() for serialization, model_json_schema() for JSON Schema, @field_validator and @model_validator decorators, discriminated unions, generics, computed_field, ConfigDict for model config, BaseSettings for env var loading, Annotated types, strict mode, and Rust-backed core for 5-50x v1 speedup.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Validation library. model_validate() validates all input — use for all external data. Coercion may mask type errors in lax mode — use strict=True for security-sensitive validation. SecretStr type hides secrets from repr() and model_dump(). Annotated constraints (ge/le/max_length) enforce data bounds.
⚡ Reliability
Best When
FastAPI applications, data pipeline validation, and any Python code needing type-safe data models — pydantic v2 is the standard for Python data validation with the best performance and ecosystem integration.
Avoid When
Dynamic schema construction at runtime (use marshmallow), non-Python environments, or when stdlib dataclasses with manual validation suffice.
Use Cases
- • Agent data model validation — from pydantic import BaseModel, Field; class Config(BaseModel): api_key: str; timeout: int = Field(default=30, ge=1, le=300); urls: list[str]; config = Config.model_validate(raw_dict) — instantiation validates; agent validates configuration or API response data; ValidationError raised with field-level errors if invalid; type coercion applied automatically
- • Agent API response parsing — class Response(BaseModel): items: list[Item]; total: int; next_page: str | None = None; resp = Response.model_validate_json(response_body) — JSON parsing; agent parses API JSON responses directly to typed Python objects; model_validate_json() is faster than json.loads + model_validate
- • Agent settings from env — from pydantic_settings import BaseSettings; class Settings(BaseSettings): api_key: str; debug: bool = False; class Config: env_prefix = 'APP_'; settings = Settings() — env vars; agent loads configuration from environment variables with type coercion and validation; API_KEY env var → api_key field
- • Agent schema generation — class RequestModel(BaseModel): query: str; limit: int = 10; schema = RequestModel.model_json_schema() — JSON Schema; agent generates JSON Schema for API documentation or function calling schemas for LLM tool use; fully compatible with OpenAI function calling format
- • Agent nested validation — class Order(BaseModel): id: int; customer: Customer; items: list[OrderItem]; total: Decimal; order = Order.model_validate(order_dict) — nested models; agent validates complex nested data structures; nested model validation is recursive; ValidationError shows exact path to invalid field
Not For
- • Dynamic schemas — pydantic models are class-based; for runtime schema construction use marshmallow or jsonschema
- • Non-Python environments — pydantic is Python only; for polyglot validation use JSON Schema directly
- • Extremely high-frequency validation — even pydantic v2 has overhead; for microsecond-level validation of hot paths use manual TypedDict or dataclass
Interface
Authentication
No auth — validation library.
Pricing
pydantic is MIT licensed. Free for all use. pydantic v2 core is Rust (pydantic-core).
Agent Metadata
Known Gotchas
- ⚠ pydantic v2 breaking changes from v1 — v2 renamed .dict() to .model_dump(), .json() to .model_dump_json(), .parse_obj() to .model_validate(), .schema() to .model_json_schema(); agent code migrating from v1: replace all deprecated methods; pydantic v1 compatibility shim exists but deprecated; mixing v1 and v2 models causes subtle bugs
- ⚠ Type coercion is on by default (lax mode) — pydantic v2 coerces '42' to int 42, 1 to True; strict mode disables coercion: model_validate(data, strict=True) or ConfigDict(strict=True); agent code expecting strict types: enable strict mode to prevent silent coercions; field-level strict: Field(strict=True)
- ⚠ ValidationError.errors() not .messages — unlike marshmallow, pydantic raises ValidationError; access errors via e.errors() which returns list of dicts; e.errors()[0]['loc'] is tuple path (('field', 'nested_field')); e.errors()[0]['msg'] is human message; agent code: serialize for API response: json.dumps(e.errors())
- ⚠ model_dump() excludes None by default — model.model_dump(exclude_none=True) drops None fields; model.model_dump() includes None as null; agent code building API payloads: use exclude_none=True to omit optional unset fields; exclude_unset=True excludes fields not explicitly set (different from None)
- ⚠ @field_validator runs after type coercion — validator receives already-coerced value (int not str); @field_validator('field', mode='before') runs before coercion; agent code: use mode='before' for validators that need raw input; use mode='after' (default) for validators on already-typed values; @model_validator runs after all fields validated
- ⚠ Nested models require explicit type annotation — class Parent(BaseModel): child: Child — Child must be defined before Parent or use string annotation 'Child'; forward references need model_rebuild(): Parent.model_rebuild() after all models defined; agent code with circular references: use Optional['ModelName'] string annotations and call model_rebuild()
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Scores are editorial opinions as of 2026-03-06.