Pydantic
Data validation and settings management library for Python using type annotations. Define models as Python classes with type hints — Pydantic validates, coerces, and serializes data automatically. Core dependency of FastAPI, LangChain, and most modern Python AI frameworks. v2 is a complete Rust-backed rewrite with 5-50x performance improvements over v1.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
MIT licensed. Input validation via Pydantic significantly reduces injection risks. SecretStr type hides sensitive values from repr/logs. BaseSettings SecretStr prevents accidental logging of secrets.
⚡ Reliability
Best When
You're building Python applications (especially FastAPI, LangChain, or AI tooling) that need runtime data validation with TypeScript-like type safety from Python annotations.
Avoid When
You only need simple data containers without validation — Python dataclasses or attrs are simpler without Pydantic's overhead.
Use Cases
- • Define typed, validated data models for agent API request/response bodies with automatic type coercion
- • Parse and validate LLM tool call parameters with schema generation compatible with OpenAI function calling format
- • Build validated settings/configuration classes from environment variables with BaseSettings
- • Serialize agent domain objects to/from JSON with custom field validators and serializers
- • Create typed agent tool schemas where model.model_json_schema() generates OpenAPI-compatible JSON Schema
Not For
- • Non-Python environments — Pydantic is Python-only
- • Async validation pipelines — Pydantic validators are synchronous; add async validation separately
- • Complex cross-field validation requiring database lookups — Pydantic model validators work in-memory only
Interface
Authentication
Local library — no authentication required. MIT licensed.
Pricing
MIT licensed. Zero cost.
Agent Metadata
Known Gotchas
- ⚠ Pydantic v2 is NOT backward compatible with v1 — .dict() is deprecated (use .model_dump()), .parse_obj() is .model_validate(), class Config is model_config; check migration guide
- ⚠ Field validation order matters: @field_validator runs before @model_validator; use mode='before' for pre-parsing transformation vs default 'after' for post-parsing validation
- ⚠ Optional[str] in Pydantic v2 means str | None but NOT optional — field is STILL required unless you set default=None: field: Optional[str] = None
- ⚠ JSON Schema generation: model.model_json_schema() returns OpenAPI-compatible schema — directly usable for OpenAI function calling or FastAPI route definitions
- ⚠ Circular model references require model_rebuild(): class A(BaseModel): b: 'B'; class B(BaseModel): a: Optional[A]; A.model_rebuild() — needed for forward references
- ⚠ BaseSettings reads from environment variables — field names case-insensitive by default: DATABASE_URL env var populates database_url field in Settings class
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for Pydantic.
Scores are editorial opinions as of 2026-03-06.