Pytastic

No Magic. Just Python.

Pytastic validates JSON-shaped data against schemas you write as ordinary TypedDict type hints. If you know TypedDict and Annotated, you already know how to use it. Zero dependencies, and your validated data stays a plain dict.

Full documentation: rayattack.github.io/pytastic

from typing import TypedDict, Annotated, Literal
from pytastic import Pytastic, ValidationError

vx = Pytastic()

class User(TypedDict):
    username: Annotated[str, "min_len=3; regex=^[a-z_]+$"]
    age: Annotated[int, "min=18"]
    role: Literal["admin", "user"]

user = vx.validate(User, {"username": "tersoo", "age": 25, "role": "admin"})

try:
    vx.validate(User, {"username": "x", "age": 25, "role": "admin"})
except ValidationError as e:
    print(e.errors)   # [{'path': '.username', 'message': 'Min length 3'}]

Constraints are semicolon-separated key=value pairs inside the Annotated metadata string. See the constraint reference.

Why?

Zero dependencies. Pure standard library, so it installs anywhere a compiled wheel is awkward. orjson is an optional accelerator.
Your data stays a dict. Validation returns the dict you passed in, typed as your own TypedDict — no model objects, no .model_dump() at every boundary.
No learning curve. Standard typing constructs your editor already understands.
Fast where it counts. See below.

Performance

Same six-field schema, same constraints, Python 3.12, best of 5 runs. Reproduce with python benchmark.py:

Comparison	Result
vs Pydantic `BaseModel(**kwargs)`	Pytastic ~1.5x faster
vs Pydantic `model_validate(dict)`	Pytastic ~1.4x faster
vs Pydantic `TypeAdapter(TypedDict)`	Pydantic ~1.3x faster
JSON bytes → validated	Pydantic ~2x faster (fused parse in Rust)
Memory, 10,000 records	Pytastic allocates nothing; Pydantic ~4.7 MiB

To be straight about it: Pytastic beats Pydantic's BaseModel paths and loses to TypeAdapter and to Pydantic's fused JSON parsing, which is compiled Rust. If raw throughput on large JSON payloads is your only concern, use msgspec. Pytastic's case is being fast and dependency-free and allocation-free while your data stays a dict.

Installation

pip install pytastic

With optional orjson acceleration:

pip install pytastic[fast]

Requires Python 3.9 or newer.

Two ways to call it

Typed — no registration, best for editor autocompletion:

user = vx.validate(User, data)

Dynamic — register once, then call the schema by name. Skips option handling, so it is marginally faster in a hot loop, but accepts no options:

vx.register(User)
user = vx.User(data)

Registering at startup also compiles the schema immediately, so a malformed schema fails at boot rather than on the first request.

JSON Schema export

print(vx.schema(User))
# {"type": "object", "properties": {"username": {"type": "string", "minLength": 3, ...}}, ...}

Returns a JSON string (Draft 2020-12). Use json.loads() if you need a dict.

Beyond the basics

Pytastic also supports partial/PATCH validation, unknown-field stripping, input and output field mapping, defaults, computed fields, pre- and post-validation hooks, dotted attribute access and conditional constraints. See Advanced Usage.

Known limitations

Worth knowing before you adopt:

One error per call. Validation stops at the first failure rather than collecting every problem, unlike Pydantic.
No rich types. datetime, UUID and Decimal are not validated natively; use a str field with format= plus a getter= hook to hydrate.
No recursive schemas. A self-referential TypedDict raises SchemaDefinitionError.
Validation mutates its input unless you pass copy=True.

License

MIT

Metadata

Release files for pytastic 0.6.0

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Uploaded via	`poetry/2.1.3 CPython/3.12.3 Linux/7.0.0-28-generic`

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