About JSON to Code
JSON to Code infers a static type definition for a JSON sample, in the language of your choice — TypeScript interfaces, Go structs with `json:` tags, Python dataclasses, Rust serde structs, Swift Codables, Kotlin data classes, C# records, Java POJOs, Dart classes, PHP DTOs, or a Zod schema. Names, optionality, and nested types are inferred from the sample; per-language options control naming convention (camel/snake/Pascal), nullability, and tag generation.
Reach for it whenever you have a real JSON payload from an API or fixture and you want type-safe code that consumes it without hand-typing the shape. The reverse direction is supported too: paste an existing type definition and the tool generates a sample JSON document that conforms to it. JSON Schema Generator is the schema-language counterpart — same input, different output (a JSON Schema document instead of source code). Use this tool when the consumer is your own code; use Schema Generator when the consumer is another JSON-Schema-aware system.
Examples
{"id": 42, "name": "Ada", "tags": ["math", "programming"]}interface Root {
id: number;
name: string;
tags: string[];
}TypeScript output. Switch language for Go structs, Python dataclasses, Rust serde structs, Zod schemas, and others — same JSON, different idioms.
Frequently asked questions
How is this different from JSON Schema Generator?
Both infer a type from a JSON sample. JSON to Code emits source code in a programming language (TypeScript, Go, Rust, etc.); JSON Schema Generator emits a JSON Schema document. Use this one when the consumer is your code; use Schema Generator when the consumer is another JSON-Schema-aware tool (validators, OpenAPI, code generators in other languages).
What if a field can be null or missing?
Toggle the *make optional* option to mark fields as optional / nullable in the chosen language (`?` in TypeScript, pointer in Go, `Option<T>` in Rust, `Optional<T>` in Java). Best practice: feed the tool a sample that includes the optional fields, otherwise the inferred type will mark them as required.
Can I influence the names?
Yes. The tool auto-generates a root type name from the structure but exposes options to override it and to apply a naming convention (camelCase / snake_case / PascalCase) consistently across nested types.
Is the reverse direction lossy?
Going JSON → code is lossy in the sense that JSON's nominal types do not carry semantic constraints (an `email`-shaped string is just `string`). Going code → sample JSON produces one valid example; for many examples or schema-driven mocks, use JSON Mock Generator instead.