Validation for Python dictionaries

Validate configuration and script inputs using ordinary Python types. Zero required runtime dependencies.

Python >=3.10 / released zodify 0.8.1 / Alpha

pip install zodify

from zodify import validate

schema = {"port": int, "debug": bool}
data = {"port": 8080, "debug": False}

print(validate(schema, data))

Output

{'port': 8080, 'debug': False}

Invalid types and extra keys raise an error by default.

When to use zodify

Use it when you have a Python dict and want a readable, reusable check. Start with ordinary types; add nested shapes, optional keys, or explicit conversions when you need them.

Try zodify when…	Another approach may fit when…
You want reusable checks for dictionaries.	A few direct Python checks already solve the task.
You validate configuration or script inputs.	Your framework already handles validation.
Zero required runtime dependencies matter.	You need broader typing, models, or serialization.

Validation errors

An invalid field raises an error with its path. Here, port needs an int, but the input contains a string. Pass an integer, or choose coerce=True when string conversion is intended.

from zodify import validate

try:
    validate({"port": int}, {"port": "8080"})
except ValueError as error:
    print(f"ValueError: {error}")

Output

ValueError: port: expected int, got str

Default behavior

Types are strict and extra keys are rejected. Optional keys and nullable values are separate choices. Defaults are trusted: they are inserted without validation or copying, so mutable defaults can be shared.

Plain dict schemas return dicts. Optional class declarations provide attribute access.

Try your first validation