Data

JSON to Types Generator

Paste a JSON sample and get matching type definitions in the language you work in, from TypeScript interfaces and Go structs to Rust, Swift, Kotlin, Python, C#, and more. The tool reads the shape of your data, infers each field type, handles nested objects, arrays, and optional keys, and writes out clean declarations you can drop straight into a codebase. Editing happens in a real code editor on both sides, so large payloads stay readable and the generated code is syntax highlighted. Everything runs in your browser, so confidential API responses never leave your machine.

  • Turn a JSON sample into ready-to-use types for more than twenty languages
  • Infers nested objects, arrays, optional fields, and union types from the shape of the data
  • Pick the target language and root type name, then copy the result in one click
  • Both panes use a real code editor with syntax highlighting for large inputs and outputs
  • Runs entirely in the browser, so private API responses are never uploaded
tools/JSON to Types Generator
8%
Loading the type engine
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The code generation engine loads once, then stays ready for instant conversions.
Quick actions

Overview

Writing types by hand to match an API response is slow and error prone. This reads the JSON and produces the declarations for you, so the type layer stays faithful to the data it describes.

  1. 01

    Many target languages

    Generate TypeScript, Go, Rust, Swift, Kotlin, Java, C#, Python, Dart, Ruby, PHP, and more from one JSON sample.

  2. 02

    Structure inference

    Nested objects become nested types, arrays infer their element type, and fields that are sometimes missing become optional.

  3. 03

    Sensible type detection

    Numbers, booleans, strings, null, and mixed values are mapped to the closest type, with unions where a field holds more than one shape.

  4. 04

    A real editor on both sides

    Input and output use a code editor with syntax highlighting, folding, and smooth scrolling, so large responses stay readable.

  5. 05

    Clean, types-only output

    Keep the result to plain declarations, or include the helper code the engine can also emit, depending on what your project needs.

  6. 06

    Local and private

    The engine runs in your browser after loading once, so JSON from internal or unreleased APIs never leaves your machine.

How to use

Go from a raw API response to typed declarations your editor understands in a few seconds.

  1. 01

    Paste a representative JSON sample into the input editor, or load the example to see how it works.

  2. 02

    Choose the target language, such as TypeScript, Go, or Rust.

  3. 03

    Set the root type name so the top-level type reads naturally in your code.

  4. 04

    Read the generated types in the output editor and adjust the JSON if a field should be optional.

  5. 05

    Copy the result and paste it into your project.

Details

A type generator is only useful if the output is correct and ready to paste, so the tool focuses on faithful inference and clean declarations across languages.

  • Supports more than twenty target languages from a single JSON sample
  • Infers nested object types, array element types, and optional fields automatically
  • Detects unions when a field holds values of more than one type across the sample
  • Lets you name the root type so the top-level declaration fits your code
  • Offers a types-only mode for clean declarations without extra helper code
  • Highlights both the JSON input and the generated output for the chosen language
  • Handles large payloads in a code editor that scrolls and folds instead of overflowing
  • Reports invalid JSON clearly so you can fix the input before generating
  • Loads the generation engine once with a visible progress indicator, then stays instant
  • Runs fully in the browser, so confidential responses are never uploaded

Use cases

Most real work starts from a JSON response rather than a schema. Generating types from that response keeps your code aligned with the data and saves a lot of careful transcription.

  1. Typing an API response

    Turn a sample response into an interface or struct so calls to that endpoint are checked by the compiler.

  2. Cross-language clients

    Generate matching models for a mobile app and a backend from the same JSON, keeping Swift, Kotlin, and Go in sync.

  3. Onboarding to a codebase

    Paste an unfamiliar payload to quickly understand its shape, names, and nesting before writing any code against it.

  4. Config and fixture types

    Create types for configuration files or test fixtures so changes to their structure surface as type errors.

  5. Prototyping data models

    Sketch a JSON shape by hand and generate a first version of the model classes to start building from.

  6. Migrating untyped code

    Add a type layer to a JavaScript or Python project by generating declarations from the responses it already handles.

See also

If you need a different shape of the same data rather than its types, the JSON Converter converts JSON to YAML, CSV, and more, and the JSON Formatter cleans up and validates the JSON before you generate from it.

Languages and what it generates

One JSON sample maps to idiomatic types in each target language. Pick the one your project uses and the output follows that language’s conventions for naming, optionality, and serialization.

  1. TypeScript

    Generates interfaces and type aliases with optional properties and union types, the default choice for typing fetch and API responses in web and Node projects.

  2. TypeScript with Zod

    Generates Zod schemas that validate data at runtime, with the static type inferred from each schema so parsing and types stay in sync.

  3. Go

    Generates structs with json struct tags and matching field types, ready for encoding/json marshaling and unmarshaling.

  4. Rust

    Generates structs with serde derive attributes so the types deserialize from and serialize to JSON with serde_json.

  5. Python

    Generates typed classes with annotations that work with type checkers and editors, a clean base for dataclasses or model objects.

  6. Java

    Generates classes with typed fields and accessors that map cleanly to JSON binding libraries such as Jackson or Gson.

  7. Kotlin

    Generates data classes with nullable types where fields are optional, suited to kotlinx.serialization or Moshi on Android and the server.

  8. Swift

    Generates Codable structs that decode and encode JSON with JSONDecoder and JSONEncoder in iOS and macOS apps.

  9. C#

    Generates classes with typed properties and attributes that work with System.Text.Json or Json.NET in .NET projects.

  10. C++ and Objective-C

    Generates C++ structs and Objective-C classes describing the shape of the data for native and cross-platform code.

  11. Dart

    Generates classes with typed fields for Flutter and Dart, a starting point for JSON serialization in mobile apps.

  12. Ruby, PHP, and Scala

    Generates Ruby and PHP classes and Scala case classes that mirror the JSON structure for backend and scripting work.

  13. Elm, Haskell, and Crystal

    Generates Elm and Haskell records and Crystal types with the field types inferred from the sample for functional and systems languages.

  14. Flow and JSON Schema

    Generates Flow type annotations for JavaScript projects and a JSON Schema document describing the structure for validation and documentation.

Best practices

Inference is only as good as the sample, so a few habits make the generated types more accurate and easier to maintain.

  • Use a complete sample where every optional field is present, so the engine does not mark real fields as missing.
  • Include more than one item from a list if its objects can vary, so optional and union fields are detected.
  • Give the root type a clear name that matches the endpoint or entity it represents.
  • Review fields typed as a union or as a loose type, since they often point to inconsistent data worth tidying.
  • Treat the output as a starting point and rename generated nested types to something meaningful.
  • Regenerate when the API changes rather than editing the types by hand and letting them drift.
  • Prefer types-only output for shared model files, and keep helper code separate if you need it.
  • Validate the JSON first if it comes from logs or copied text, since trailing commas or comments will fail to parse.

Limitations

The tool infers types from an example, so a few limits are worth keeping in mind.

  • It infers structure from the sample you provide, so fields missing from the sample cannot appear in the result.
  • It reads JSON, not JSON Schema or OpenAPI, so it does not enforce formats, ranges, or required-field rules.
  • Inferred names for nested types are generated and usually benefit from renaming to match your domain.
  • Numbers are typed by language convention and may not distinguish integers from floats the way your API does.
  • Dates arrive as strings in JSON, so date handling depends on the language and may need adjustment.
  • Very large samples generate large type files, which are best trimmed to the parts you actually use.

FAQ

Common questions about generating types from JSON and using the result in a real project.

Which languages can it generate?

More than twenty, including TypeScript, Go, Rust, Swift, Kotlin, Java, C#, C++, Dart, Python, Ruby, PHP, Scala, Objective-C, Elm, Haskell, JavaScript, Flow, and JSON Schema.

Is the JSON uploaded anywhere?

No. The generation engine loads in your browser the first time you open the tool, then runs locally, so your JSON is never sent to a server.

Why does it load for a moment on first use?

The code generation engine is a sizable library, so it loads once with a progress indicator. After that, conversions are instant and no further loading happens.

How does it decide which fields are optional?

A field is treated as optional when it is missing from some items in the sample. Provide a complete sample so fields you rely on are not marked optional by mistake.

What does the types-only option do?

It keeps the output to plain type declarations. Turning it off lets the engine also emit helper code, such as parsing and serialization functions, for languages that support it.

Can it handle arrays of mixed objects?

Yes. When array items differ, the engine infers a combined type or a union so the result still describes every item it saw in the sample.

Why are some fields typed loosely?

A loose or union type usually means the sample held more than one shape for that field. It often points to inconsistent data that is worth cleaning up at the source.

Does it validate the JSON?

It parses the JSON and reports an error if it is invalid, but it does not enforce schema rules like required fields, value ranges, or string formats.

Related tools

Generating types is one step in working with JSON. When you need the same data in another format or want to tidy it first, the rest of the data tools pick up from here.