zodify 0.3.0

Zod-inspired dict validation for Python. Zero deps. One file.

zodify

Zod-inspired dict validation for Python. Zero deps. One file.

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Note: zodify is in alpha. The API is minimal and may change. Feedback and contributions are welcome!

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Quick Start

from zodify import validate

config = validate(
    {"port": int, "debug": bool},
    {"port": 8080, "debug": True},
)

That's it. Plain dicts in, validated dicts out. No classes, no DSL, no dependencies.

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Why zodify?

Most validation libraries ask you to learn a new DSL or model system. zodify doesn't.

	zodify	zon	zodic	Pydantic
Philosophy	Minimalist	Full Zod port	Full Zod port	Full ORM
API style	Plain dicts	Chained builders	Chained builders	Classes
Dependencies	0	2	0	Many
Code size	~250 LOC	1,000s+ LOC	1,000s+ LOC	Large
Learning curve	Zero	Must learn DSL	Must learn DSL	Must learn DSL
Env var support	Built-in	No	No	Partial

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Install

pip install zodify

Requires Python 3.10+

To install from source:

git clone https://github.com/junyoung2015/zodify.git && pip install ./zodify

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Usage

validate() — Dict Schema Validation

Define a schema as a plain dict of key: type pairs, then validate any dict against it.

from zodify import validate

schema = {"port": int, "debug": bool, "name": str}

# Exact type match
result = validate(schema, {"port": 8080, "debug": True, "name": "myapp"})
# → {"port": 8080, "debug": True, "name": "myapp"}

# Coerce strings — great for env vars and config files
raw = {"port": "8080", "debug": "true", "name": "myapp"}
result = validate(schema, raw, coerce=True)
# → {"port": 8080, "debug": True, "name": "myapp"}

# All errors are collected at once
validate({"a": int, "b": str}, {"a": "x", "b": 42})
# ValueError: a: expected int, got str
#             b: expected str, got int

Parameters:

Param	Type	Default	Description
schema	dict	—	Mapping of keys to expected types (str, int, ...)
data	dict	—	The dict to validate
coerce	bool	False	Cast string values to the target type when possible
max_depth	int	32	Maximum nesting depth to prevent stack overflow
unknown_keys	str	"reject"	How to handle extra keys: "reject" or "strip"

Behavior:

• Extra keys in data are rejected by default (unknown_keys="reject").
• Use unknown_keys="strip" to silently drop extra keys and return only schema-declared keys.
• Missing keys raise ValueError.
• When coerce=True, only str inputs are coerced to int, float, or bool (non-string mismatches still error). For str targets, any value is accepted via Python's str() builtin.
• Bool coercion accepts: true/false, 1/0, yes/no (case-insensitive).

# Default: reject unknown keys
validate({"name": str}, {"name": "kai", "age": 25})
# ValueError: age: unknown key

# Opt-in: strip unknown keys
validate(
    {"name": str},
    {"name": "kai", "age": 25},
    unknown_keys="strip",
)
# -> {"name": "kai"}

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Union Types

Use Python's str | int syntax to accept multiple types for a single key.

schema = {"value": str | int}

validate(schema, {"value": "hello"})  # → {"value": "hello"}
validate(schema, {"value": 42})       # → {"value": 42}

validate(schema, {"value": 3.14})
# ValueError: value: expected str | int, got float

Types are checked left-to-right. With coerce=True, type order controls coercion priority:

# str first → "42" stays as string (str coercion matches first)
validate({"value": str | int}, {"value": "42"}, coerce=True)
# → {"value": "42"}

# int first → "42" coerced to int (int coercion matches first)
validate({"value": int | str}, {"value": "42"}, coerce=True)
# → {"value": 42}

Union types compose with lists, nested dicts, and Optional:

validate({"items": [int | str]}, {"items": ["42"]}, coerce=True)
# → {"items": [42]}

validate({"config": {"v": int | str}}, {"config": {"v": "42"}}, coerce=True)
# → {"config": {"v": 42}}

Note: When str is a union member and coerce=True, str acts as a catch-all fallback — any value that fails earlier union members will coerce via str() (e.g., int | str with True produces "True"). Place str last in unions to use it as a deliberate fallback, or first to prefer string preservation.

Requires Python 3.10+ (for X | Y union syntax).

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Nested Dict Validation

Your schema can contain nested dicts — validation recurses automatically.

schema = {"db": {"host": str, "port": int}}

validate(schema, {"db": {"host": "localhost", "port": 5432}})
# → {"db": {"host": "localhost", "port": 5432}}

validate(schema, {"db": {"host": "localhost", "port": "bad"}})
# ValueError: db.port: expected int, got str

Errors use dot-notation paths: db.host, a.b.c, etc.

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Optional Keys

Use Optional to mark keys that can be missing. Provide a default, or omit it to exclude the key from results.

from zodify import validate, Optional

schema = {
    "host": str,
    "port": Optional(int, 8080),     # default 8080
    "debug": Optional(bool),          # absent if missing
}

validate(schema, {"host": "localhost"})
# → {"host": "localhost", "port": 8080}

Note: Optional shadows typing.Optional. If you use both in the same file, alias it: from zodify import Optional as Opt or use zodify.Optional(...).

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List Element Validation

Use a single-element list as the schema value to validate every element in the list.

validate({"tags": [str]}, {"tags": ["python", "config"]})
# → {"tags": ["python", "config"]}

validate({"tags": [str]}, {"tags": ["ok", 42]})
# ValueError: tags[1]: expected str, got int

List of dicts works too:

validate(
    {"users": [{"name": str, "age": int}]},
    {"users": [{"name": "Alice", "age": 30}]},
)

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Combined Example

All features compose naturally:

from zodify import validate, Optional

schema = {
    "db": {"host": str, "port": Optional(int, 5432)},
    "tags": [str],
    "debug": Optional(bool, False),
}

validate(schema, {
    "db": {"host": "localhost"},
    "tags": ["prod"],
})
# → {"db": {"host": "localhost", "port": 5432},
#    "tags": ["prod"], "debug": False}

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env() — Typed Environment Variables

Read and type-cast environment variables with a single call.

from zodify import env

port   = env("PORT", int, default=3000)
debug  = env("DEBUG", bool, default=False)
secret = env("SECRET_KEY", str)  # raises ValueError if missing

Parameters:

Param	Type	Default	Description
name	str	—	Environment variable name
cast	type	—	Target type (str, int, float, bool)
default	any	(none)	Fallback if the var is unset. Not type-checked — ensure your default matches the cast type.

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Release Process

Release automation is tag-driven:

• Pushing a tag that matches v* triggers .github/workflows/publish.yml.
• The workflow runs tests, builds distributions, publishes to PyPI, and creates a GitHub Release.
• GitHub Release notes are sourced from the matching section in CHANGELOG.md (for example, ## [v0.1.0]).

Run local preflight before tagging:

./scripts/release_preflight.sh

If preflight passes, push the version tag:

git tag v0.3.0
git push origin v0.3.0

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Roadmap

zodify is in alpha (v0.3.0). The API surface is small and may evolve. See CHANGELOG.md for version-by-version details.

Shipped:

• Nested schema validation with dot-path errors
• Optional keys with defaults
• List element validation (including list-of-dicts)
• Custom validator functions
• unknown_keys parameter ("reject" / "strip")
• max_depth recursion depth limit
• Performance benchmark infrastructure
• PEP 561 py.typed marker & inline type annotations
• @overload signatures for env() (IDE type inference)
• Google-style docstrings on all public API symbols
• mypy (strict) & pyright CI gates
• Union type schemas (str | int syntax) with left-to-right coercion priority

Near-term:

• Chained builder API

Exploring:

• .env file loading
• JSON Schema export
• Framework integrations (FastAPI, Django)
• Async validation support

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License

MIT — 2026 Jun Young Sohn