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Pydantic

Data validation using Python type hints observed · 2026-10-11

github.com/pydantic/pydantic · homepage · Python · MIT (permissive) observed · 2026-10-11

Health v2 · maintenance only

99/100

  • Activity 99
  • Release rhythm 98
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.

  • gap_med: 11
  • age_days: 3457
  • days_rel: 12
  • days_push: 10
  • n_releases_24m: 36

Full methodology

Adoption not part of the score

28972 stars · 3025 forks observed · 2026-10-11

What it is AI-extracted, prompt v1, taxonomy v1, 2026-10-05, confidence 0.99

Pydantic is a Python library that validates and parses data using type hints. It provides fast, extensible schema validation and JSON‑Schema generation.

Use cases

  • validate incoming JSON payloads in a FastAPI endpoint
  • parse configuration files into typed Python objects
  • convert untrusted user input into safe data models
  • generate JSON Schema from Python classes for API documentation
  • enforce strict data contracts in data pipelines

When to choose

  • you need type‑hint‑driven validation with minimal boilerplate
  • performance matters and you want Rust‑backed validation
  • you want automatic JSON Schema generation
  • your project already uses Python 3.9+ and type hints

When to avoid

  • you require validation in languages other than Python
  • you need a full ORM or database driver rather than just validation
  • your environment cannot install compiled extensions (pydantic‑core)

Facets

library · maturity active

serialization parser form-handling json data-science web-development python type-hints fast rust-backed pydantic-core ai-stack data-engineering

10 sources

Member repositories

RepositoryRoleHealth v2
pydantic/pydanticmain99
pydantic/pydantic-corebackend10

For agents

markdown · JSON · MCP: product_card(name="pydantic/pydantic")

Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem