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Use Aontu as a separate validation step: express the schema you need in Aontu’s model format, then run aontu vet against the data. To validate a single record against a named type within a larger schema, select that schema subtree with --at. The documented workflow does not establish automatic import from JSON Schema, Zod, or Pydantic, or guarantee that different validators will behave identically.
Choose the data boundary before validating
First decide what Aontu will receive: raw JSON text, a value already parsed by your application, or one record extracted from a larger document. That distinction matters when you compare validators. Pydantic documents different strict-mode behavior for raw JSON input and parsed Python values, so test the same input route your production system uses.
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Also decide whether the unit of validation is a complete document or an individual record. Aontu can validate against a named schema subtree, which is useful when the input is a bare record rather than the enclosing document.
Model the schema in Aontu
Represent the constraints you need in Aontu’s model format. Treat this as an explicit modeling or handoff step after using JSON Schema, Zod, or Pydantic; the available Aontu documentation describes validation and exporting an Aontu model to JSON Schema, but does not establish automatic import from those three tools.
Do not assume that matching schema shapes means matching runtime behavior. Validators can differ in how they handle input types, coercion, domain-specific constraints, and schema features. Confirm the behavior that matters using representative inputs instead of treating a generated schema as proof of equivalence.
Validate the whole document or select one named type
Validate against the schema as a whole
Run aontu vet <schema-file> <data-file>. For example, if your files are named domain.aontu and data.json, the command is aontu vet domain.aontu data.json. Aontu reports a validity verdict and findings; inspect the findings to locate constraint failures.
Validate a bare record against a schema subtree
Use --at to select the relevant node. A documented example is:
aontu vet --at '$.schema.Customer' domain.aontu data/customer-record.json
Here, the selected path targets the Customer schema node, while the data file contains the customer record to check. Use the path that corresponds to the type in your own schema.
Read the result and test both outcomes
A valid verdict means the supplied data passed the selected Aontu validation. If the data is invalid, Aontu can report findings at data paths, helping identify which value violated a constraint. Check both sides of the boundary: include examples expected to pass and examples expected to fail, and verify that the reported paths and constraints match your intended rules.
One Aontu data-model example uses a specially marked decimal form in a file with a .json name, even though a strict JSON parser rejects that form; the same example also demonstrates ordinary strict JSON input. This is an example-specific parser behavior, not evidence that Aontu accepts arbitrary non-standard JSON. Keep inputs in the format your application actually produces.
Export to JSON Schema when another tool needs it
Aontu documents exporting its model to JSON Schema through jsonschema. The project’s data-model example shows an exported pattern and a const marker. That establishes an export path, not lossless coverage of every Aontu construct or identical behavior in every JSON Schema validator.
If another system will consume the export, compare representative valid and invalid cases in both systems. Verify the particular constraints and input forms your application relies on rather than assuming that schema generation alone preserves runtime semantics.
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| Question | What is established | What to verify in your setup |
|---|---|---|
| What input is being checked? | Pydantic documents JSON Schema generation targeting Draft 2020-12 and OpenAPI 3.1.0, and distinguishes validation-input schemas from serialization-output schemas. Its strict behavior can also differ for raw JSON and parsed Python values. | Whether the Aontu and other-tool checks receive the same representation and enforce the same constraints on it. |
| Can one named type be targeted? | Aontu’s vet --at example selects a named schema subtree for a record. |
The corresponding way to target a type in your other validator; the available evidence does not establish a Zod API or a universal cross-tool equivalent. |
| Can a schema be transferred? | Aontu documents export from its model to JSON Schema. | Whether each construct you use survives conversion and behaves equivalently. Automatic import from JSON Schema, Zod, or Pydantic is not established. |
JSON Schema is a specification for defining and validating JSON data. The available documentation does not establish the exact current Zod APIs, version-specific behavior, or Aontu’s keyword-by-keyword conformance with JSON Schema. Avoid making a broader compatibility claim without checking the specific features and versions in your stack.
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