valgebra

A closed, irreducible Boolean algebra of schemas for Python. A schema denotes a set of Python values, and validating asks whether a value you already hold is a member — no copy, no coercion. union, intersection, complement, refinement, and fixpoints are the only primitives; they close into a lattice whose laws are property-tested, and every other pattern is derived from them by composition rather than bundled as a special combinator. The schema compiles to a Rust validator, so a check is cheap enough to run on every request.

📖 Documentation: https://ppigazzini.github.io/valgebra/

🤖 For AI assistants and coding agents: the documentation is also published as llms.txt (a curated manifest) and llms-full.txt (the full text, including the API reference) per the llmstxt.org convention.

Schemas are sets; the operators are or, and, not

The everyday case looks like any validator — a type annotation is a schema, and checking it asks whether a value belongs to the set the annotation denotes:

from valgebra import ValidationError, Validator

is_user = Validator({"name": str, "age": int})

assert is_user.is_valid({"name": "Ada", "age": 36})
assert not is_user.is_valid({"name": "Ada", "age": "unknown"})

# validate() raises a structured error pointing at the offending value
try:
    is_user.validate({"name": "Ada", "age": "unknown"})
except ValidationError as err:
    assert err.code == "int_type"
    assert err.path == ("age",)

What sets valgebra apart starts when you treat schemas as the sets they denote. Because membership is Boolean, union, intersection, and complement are exactly or, and, and not, and they compose any schema into a lattice:

from valgebra import Validator, complement, intersection, union

non_bool_int = intersection(int, complement(bool))  # an int that is not a bool
assert non_bool_int.is_valid(5)
assert not non_bool_int.is_valid(True)

# Schemas are first-class values you can compare as sets — soundly.
assert Validator(bool).is_subtype_of(int)  # subtyping is set inclusion
assert union(bool, int).is_equivalent(int)  # same set, different syntax
assert intersection(int, complement(int)).is_empty()  # provably no value

What makes it peculiar

• A real, closed Boolean algebra. Schemas compose with union, intersection, and complement into a lattice with anything as top and nothing as bottom. Every Boolean law — associativity, idempotence, absorption, distributivity, De Morgan, double negation — is property-tested against the membership relation, not asserted.
• Irreducible: only the generators ship. valgebra bundles no conditional, no at_least_one, no one_of. Those are derived by composition (see below). A named wrapper for a one-line composition would make a standard library, not a schema algebra.
• Schemas are comparable values. is_subtype_of (inclusion), is_equivalent (mutual inclusion), and is_empty (unsatisfiable) form a sound decision procedure: a True is always correct, and the procedure decides a wide fragment completely and stays conservative beyond it — never a wrong answer. Keep is_equivalent (semantic) distinct from == (the schema's normal form).
• A normal form by construction. A schema is built in the lattice normal form, so repr shows it and == compares it: union(int, int) is int and union(str, int) is the schema union(int, str) is.
• Any is the top, spelled. A validator asks one question — does this value belong — and to it Any admits every value, exactly as anything does. They are the same schema and obey the same laws; what you wrote is kept for repr, not for the algebra.
• Check, don't parse. validate/is_valid never copy or coerce; the proof is about the object you keep, not a reconstructed copy. ensure is the separate, explicit value-returning mode.
• Typing-first. Standard annotations are the primary notation, read through the typing spec's own introspection. Union has the operator typing already uses, |; intersection and complement stay spelled out because typing has no operator for them and valgebra invents none.

from typing import Any

from valgebra import Validator, anything, complement, union

# A schema is built in the lattice normal form, so `repr` shows it.
assert repr(complement(complement(int))) == "int"  # double negation
assert repr(union(int, int)) == "int"  # idempotence
assert repr(union(str, int)) == "int | str"  # commutativity

# `anything` is the lattice top, and `Any` is the same set under another name.
assert repr(complement(anything)) == "nothing"  # top obeys the laws
assert repr(Validator(Any)) == "Any"  # the spelling is kept

# Union has typing's `|`; intersection and complement stay spelled out.
assert (Validator(int) | str | None).is_equivalent(union(int, str, None))

Everything else is derived

Because the algebra is closed, the patterns other libraries ship as built-in combinators are one-line compositions here. "If it is an int, it must be non-negative" is a union of two intersections — no implies primitive exists, you derive it:

from typing import Annotated

import annotated_types as at

from valgebra import anything, complement, intersection, union


def implies(condition, then, otherwise=anything):
    return union(
        intersection(condition, then),
        intersection(complement(condition), otherwise),
    )


non_negative_if_int = implies(int, Annotated[int, at.Ge(0)])
assert non_negative_if_int.is_valid(5)
assert not non_negative_if_int.is_valid(-1)
assert non_negative_if_int.is_valid("not an int")  # not an int: admitted

The same handful of operators derives first-matching-case dispatch, key cardinality ("at least one of these keys", "exactly one", "not both"), length-bounded lists, and conditional records. The recipes — each runnable and explained — live in the Boolean algebra guide.

Recursive schemas and JSON

Recursive (recursive) schemas describe trees and JSON-like data, and JSON input is validated directly on the Rust path — parsed and checked in one pass, never materialized into an untyped object graph first:

from valgebra import Validator

assert Validator(list[int]).is_valid_json(b"[1, 2, 3]")  # parse + check in Rust
assert Validator(list[int]).load("[1, 2, 3]") == [1, 2, 3]  # and keep the value

load is the one to reach for when the document is data you go on to use: it returns the parsed value, so an untrusted payload is parsed once instead of once by json.loads and again by the check.

Two ways to use it

As a contract in your codebase

Reach for valgebra when you already hold a Python object — a parsed request body, a config dict, an LLM tool-call argument, a function input — and need to check it against a composable, inspectable contract on the hot path, cheaply enough to run on every request or every agent turn. Because the algebra is closed, a subsystem's contract is the intersection of its parts' contracts, an exclusion is a complement, and a migration is "old schema or new" — contracts refactor like code instead of decaying into opaque predicate functions.

Start at the tutorial; the algebra guide covers composition.

As a harness over a codebase that has none

A schema is an ordinary annotation, so valgebra reads the annotations a codebase already has and answers questions about them the interpreter cannot. Nothing is added to the code under study: the schemas live in the script asking the question, and valgebra stays a development dependency. This is the mode that suits an agent working on a codebase it did not write.

The sharpest question it can settle is whether a contract the code implies is one anything enforces. A parameter used as a divisor must not be zero; one whose attribute is read must not be None. The body states that by using the value that way, and declared ∧ ¬implied is exactly the set of values that pass the type check and break the function:

from valgebra import Validator, complement, intersection


def unenforced(declared: object, implied: object) -> list[object]:
    """Values the declaration admits and the body cannot survive."""
    breaking = intersection(Validator(declared), complement(Validator(implied)))
    return [p for p in (None, 0, "", []) if breaking.is_valid(p)]


# `def make_grid(columns: int)` whose body computes `idx // columns`:
# the annotation is correct, the code typechecks, and zero breaks it.
assert unenforced(int, complement(Validator(0))) == [0]

Others in the same shape — a branch its own annotation makes unreachable, a union arm another already covers, an annotation that admits nothing, an override that narrows its base, and which arms of a union a test run never reached:

from valgebra import Validator, intersection, union

# `if isinstance(key, bytes)` inside `def __setitem__(self, key: str, ...)`.
# The branch is dead, so the code and its annotation disagree about what arrives.
assert intersection(Validator(str), Validator(bytes)).is_empty()

# `bool | int` is `int`: bool is a subclass, so the arm adds nothing.
assert union(bool, int).is_equivalent(int)

# `except (OSError, TimeoutError)` names one class that contains the other.
assert Validator(TimeoutError).is_subtype_of(OSError)

Inspecting a codebase is the full set of recipes, with what each cannot see.

Neither mode requires the other. A codebase can adopt valgebra as a runtime contract, or never import it in its own source and still be studied with it.

How it compares

valgebra checks an object you already hold; it never coerces or constructs. That makes it different from the tools you might already use:

• pydantic parses untrusted input into typed models with coercion and defaults, and guarantees the type of what it returns. Use it for ingestion; use valgebra to check a value you already hold — including one pydantic built.
• msgspec is the fastest path for deserializing bytes into structs, and it checks on that path only. A Struct constructor validates nothing, and convert hands back a Struct you already hold without re-examining it — there is no membership call to ask instead. Use it to decode; use valgebra to check what you decoded.
• jsonschema validates against the JSON Schema standard; valgebra validates against Python types and a set-theoretic algebra instead.

The difference is not only speed

The benchmark below measures one operation: how long a passing check takes. Three differences do not appear in it, because each is a verdict rather than a duration. tests/test_pydantic_boundary.py runs all three libraries over the same values and asserts every claim here.

An object you already hold is re-examined. Handed a value that is already an instance of the target class, TypeAdapter.validate_python returns it without checking its fields, and msgspec.convert returns it under strict and from_attributes alike. pydantic's re-check is a model config (revalidate_instances), so reaching it requires pydantic to own the class declaration — for a dataclass declared elsewhere TypeAdapter raises PydanticUserError rather than ignoring the setting. msgspec has no such setting: its checking runs on the decode path, from untyped input. valgebra reads the schema off the class, and every call asks the same membership question.

A value stays checkable after it changes. A check that runs at construction answers about the value handed to the constructor. Membership is a call you repeat on the same object, with no rebuild:

from dataclasses import dataclass

from valgebra import Validator


@dataclass
class Config:
    lr: float
    steps: int


is_config = Validator(Config)  # derived from the dataclass, never redeclared
config = Config(lr=0.1, steps=10)
assert is_config.is_valid(config)

config.steps = "ten"  # something mutates it later
assert not is_config.is_valid(config)  # the same question, asked again

Schemas answer questions with no value involved. is_subtype_of, is_equivalent and is_empty take no value at all, so a contract no value satisfies is decided from the schemas: intersection(int, str) reports empty without a test case reaching it. Neither TypeAdapter nor msgspec.inspect exposes any of the three, and the suite asserts that by reading both surfaces, so the claim fails if either library grows one. complement is the other half: a set defined by what it excludes is a schema here, and a Python predicate — carried and run, but not reasoned about — elsewhere.

Where the boundary runs the other way: pydantic in strict mode rejects without coercing on the ingestion path, msgspec reaches a held value through convert(to_builtins(value), type=T) — a check that costs building two objects and discarding them — and valgebra deep-checks neither a pydantic BaseModel nor a msgspec Struct: each reaches the frontend as a bare class, denoting the set of its instances. Check either one's fields through a mapping view of them.

On a synthetic benchmark a passing check is faster than a strict pydantic TypeAdapter: roughly 3× on a 50-field record, 5× on a large list[int] and 6× on deep nesting, on CPython 3.12 and on 3.14 alike. The free-threaded build holds the first two and reads about 3× on deep nesting, where every element of a mutable container is read under that container's lock. All of them are far faster than pure-Python jsonschema. The comparison is not apples-to-apples and is gated against regression in CI; see the performance page for the method, the matrix, and the limits.

Install

valgebra ships prebuilt wheels to PyPI, so installing it needs no Rust toolchain (Python ≥ 3.10):

pip install valgebra
# or
uv add valgebra

To build from source instead — for development or an unsupported platform — requires uv and stable Rust (edition 2024, MSRV 1.88):

git clone https://github.com/ppigazzini/valgebra && cd valgebra
uv sync                 # create .venv and install dev dependencies
uv run maturin develop  # build the Rust extension into the venv

Why valgebra (in one screen)

• Schemas are sets; validation is membership. Subtyping is set inclusion and equivalence is mutual inclusion — sound, deciding a wide fragment and staying deliberately conservative beyond it (foundations, decidability, soundness argument).
• A closed, irreducible algebra. Five primitives generate everything; the laws are property-tested and a law-justified simplifier exploits them.
• Check, don't parse. validate/is_valid never copy or coerce; ensure is the explicit value-returning mode.
• One boundary crossing. Tree walks, key lookups, and bound checks run in Rust; a comparison against a Python object — a literal, a refinement predicate, or an instance or attribute check — is the documented step into Python, never a silent fallback.
• Immutable and thread-safe by design. Free-threaded (no-GIL) CPython 3.14 is supported with a dedicated cp314t wheel where the release image exposes that interpreter.

Project

• Versioning follows SemVer; changes are recorded in CHANGELOG.md. Releases are dispatch-driven and published to PyPI through trusted publishing — no tag push publishes.
• Contributing: CONTRIBUTING.md and AGENTS.md cover the build-health gate and the project's rules; ARCHITECTURE.md maps the components.
• Security: the load-bearing property is soundness of acceptance — an accepted value really belongs to the schema's set. Report issues privately per SECURITY.md. valgebra is pre-alpha and unaudited.

License

Licensed under either of Apache License, Version 2.0 or MIT license at your option. Contributions are dual-licensed as above unless you state otherwise.