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Automatically convert JSON data into Dart data models for Flutter and Dart development. Supports null safety and nested structures.
Overview
Understand what the tool solves, how it works, and the boundaries of its data.
Paste valid JSON to generate Dart declarations inferred from its shape. JSON objects become fields, arrays suggest collection types, and nested objects can become additional model types. The sample is evidence about possible values, not a complete API contract: a field that appears non-null in one response may be absent or null in another.
The output is generated from one sample. You can set the root type name and adjust available generation options, including Dart options such as null safety, Freezed, Hive, JSON annotations, final properties, and copyWith. The default settings are language-specific. Dart’s JSON guidance describes serialization and deserialization as converting between JSON data and Dart objects; the generated code still needs to fit the packages and conventions in your project.
Guide
Follow the workflow and verify inputs and outputs with practical examples.
Use a valid object or array that includes realistic examples of optional, nullable, nested, and repeated fields. The tool reports an error when the JSON cannot be parsed.
Set the type name that represents the top-level structure. Keep it consistent with your application’s naming conventions.
Open the advanced settings to review shared inference settings and the available Dart-specific choices. Enable annotations or Freezed only if your project has the matching dependencies and build setup.
Review inferred property types and nested classes against the real API schema. Then copy or download the code and compile it within your project, adding any imports and package configuration it requires.
A sample with a string name, integer identifier, Boolean flag, and nested address can suggest corresponding Dart fields and a nested type. If a second API response allows the identifier to be missing or changes its representation, include that case in your sample or revise the generated type by hand.
Use cases
See how the tool fits into real work and everyday tasks.
Use a representative response to create an initial model, then compare every inferred field with the API documentation before relying on it.
Generate a readable first pass from a nested response to see its object and list structure. Treat the output as a review aid when mapping unfamiliar payloads.
Select generation features that align with packages already configured in the app. A setting that emits annotations or framework-specific code does not add those dependencies to the project.
Q&A
Find concise answers to common questions and confusing cases.
No. It reflects the values and shape in the sample. Check optional fields, nulls, alternate types, and future response variants against the API contract.
The generator exposes Dart-specific settings, including null safety, and applies its language defaults. Review the selected setting and the resulting types rather than assuming every generated field should be nullable.
A sample with no representative elements provides little evidence about the element type. Add realistic array items and regenerate, or specify the intended type yourself.
Only when the project has the corresponding packages and code-generation setup. Generated source does not install dependencies or configure a Dart project.
Notes
Review scope, result limitations, and important precautions before use.
Generated declarations are a scaffold. Validate them against the API schema and test them with real responses, especially when fields are optional, nullable, polymorphic, or inconsistent across records.
Related
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