Aontu models system definitions as constraints: documents can be combined through unification, which yields the most specific value that satisfies both or an error identifying a contradiction. Its JSON-superset syntax accepts ordinary JSON, while schema-like constraints describe which values are allowed. The key distinction for developers is that checking a schema, generating a concrete value, exporting JSON Schema, and comparing schema versions are related but separate operations.
What is Aontu, and how do its schemas work?
Aontu is an open-source language for defining system models, including entities, fields, types, and relations. The project describes its unification approach as inspired by CUE. Because Aontu is a superset of JSON, ordinary JSON documents are valid input; additional constraints let a model specify which values are admitted.
Unification combines documents by finding the most specific value that satisfies both. If their constraints conflict, unification fails and reports the contradiction and where it occurs. That same model underlies checking definitions, explaining conflicts, and reasoning about changes. The project overview describes these concepts and the command-line and MCP operations at Aontu’s project page.
What does “inference” mean in Aontu?
Inference should not be treated as one operation that automatically validates and materializes every schema. The documented Go API distinguishes parsing, unification, checking, and generation. Parsing produces an abstract syntax tree; unification combines constraints and can fail on conflicts; generation returns native values and requires the result to be fully concrete.
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By contrast, the Go API’s Check operation can accept a valid, non-concrete schema such as a:string. That schema describes a constraint, but it does not specify a concrete string value for generation. A successful check therefore does not mean generation can produce a fully specified output without more information.
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Can Aontu export a schema to JSON Schema?
Yes. The Go API provides JSONSchema(src, at); the at argument selects the subtree to export. The result’s SchemaReport includes the generated schema and a Lossy list.
That list matters: JSON Schema cannot express every Aontu construct. Each reported loss identifies the construct and path, explains why it could not be represented, and describes what was emitted instead. Inspect the report before relying on an export as a complete equivalent of the original Aontu definition.
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How should you read Aontu errors?
Aontu exposes errors both as human-readable explanations and as structured information. The project describes a conflict as an error that identifies the incompatible constraints and their location. In the documented Go API, an AontuError includes a message and error code; parse failures also carry row and column information. A Problem has a reason code, a registered class, and a readable message.
The listed problem classes are conflict, incomplete, reference, parse, budget, and internal. The API documentation says error codes are intended to stay in cross-implementation parity and are registered in a shared code registry; the message text is not the parity contract. Its documented location-aware full message can include an [aontu/<code>] marker, an attempt or path headline, hints, and source frames. Treat those as described API behavior rather than expecting every error to use identical wording or every implementation to expose the same presentation.
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How can teams assess schema evolution?
The project overview lists breaking as a schema-evolution command. A related compatibility question is whether one schema subsumes another. The use-case reference demonstrates aontu subsume reporting.aontu domain.aontu: a “subsumes” result means every document admitted by the domain schema is also admitted by the reporting view.
This gives teams a useful way to compare the sets of inputs accepted by two definitions. When assessing a proposed change, consider whether the accepted set has become narrower or broader, whether the subsumption relation still holds in the direction your consumers need, and whether the breaking-change command reports a break. The cited documentation establishes these as comparison tools, but does not define a universal migration policy, every breaking-change rule, or a rollout sequence. Confirm exact behavior against the documentation for the version you use.
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The package reference also lists diff and schema-related reports. Those can inform a comparison, but the available descriptions do not establish a complete migration decision table. A compatibility result is evidence for a change review, not by itself a deployment or data-migration plan.
Which Aontu implementation should you use?
The project page identifies TypeScript and Go implementations and says they are held to a shared test suite; the Go package reference documents a native Go API. Choose based on the language and integration surface your project needs. The cited materials do not provide a comparative performance benchmark or an exhaustive feature matrix, so they do not support a claim that one implementation is faster or more capable.
Quick Recap
Which distinctions matter most?
- Constraint versus value: a schema can be valid while still being non-concrete.
- Check versus generate: checking reports issues; generating requires enough information to produce a concrete native value.
- Aontu versus JSON Schema: export is available in the Go API, but the loss report identifies constructs that do not carry over.
- Error text versus error code: use structured codes and classes for machine handling; use messages and source locations to understand a specific failure.
- Subsumption versus migration: subsumption compares accepted inputs; it does not prescribe a complete migration or rollout plan.
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