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JSON to Crystal

Generate Crystal classes/structs automatically from a JSON object.

100% Client-Side Local Execution
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What is JSON to Crystal?

This free online tool turns a sample JSON payload into a ready-to-use class with JSON::Serializable for Crystal, so you can use this json to crystal struct tool to go straight from raw JSON to a typed class without writing the boilerplate yourself. It is built around Crystal's actual idiom — a Crystal class — rather than a generic one-size-fits-all class, and pairs naturally with the JSON::Serializable module for real-world (de)serialization. Use it for json to crystal class online or any time you need crystal lang json mapping generator, whether you're scaffolding a new Crystal project or mapping an existing API response.

Technical Execution

Paste or upload a sample JSON object or array and the tool walks its structure field by field, inferring a concrete Crystal type for each value — mapping basic JSON types to `String`, `Int32`, `Bool`, and `Float64` as appropriate. For nested data, it generates a separate `JSON::Serializable` class for each nested JSON object, and types arrays as `Array(T)` of the matching element type. On the naming side, it keeps snake_case keys as-is to match Crystal's idiomatic naming, and adds a `@[JSON::Field(key: "originalKey")]` annotation when a key doesn't already follow Crystal convention. For optional data, fields that appear as `null`, or are missing from some sample objects, are typed as a nilable union (`String?`, `Int32?`) so Crystal's static type checker allows them to hold `nil`.

Zero Data Storage Guarantee

Everything runs client-side in your browser: the sample JSON you paste in — which may contain real field names, endpoint shapes, or structure from a private API or project — is parsed and converted entirely on your device and is never uploaded to any server. Nothing you enter is logged, stored, or transmitted anywhere, so you can safely paste a real payload from an internal or unreleased API to generate an accurate Crystal class. Closing or refreshing the tab clears everything, since no copy of your data ever left your machine in the first place.

How to Use JSON to Crystal (Step-by-Step Guide)

  1. Copy a representative sample of your JSON (the more fields and nested objects it includes, the more complete the generated class will be).
  2. Paste it into the input panel of the JSON to Crystal tool, or click "Upload" to load it from a json file.
  3. Review the auto-detected root name, and rename it if you want the generated Crystal class to use a specific class or type name instead of the default.
  4. Click "Generate" to produce the Crystal class, which appears instantly in the output panel with syntax highlighting.
  5. Click "Copy" to copy the generated code to your clipboard, or "Download" to save it as a `.cr` file.
  6. Paste the result into your Crystal project and adjust field names or types if your data has edge cases the sample didn't cover.

Frequently Asked Questions (FAQ)

Q: How does this tool handle nested objects and arrays when converting JSON to Crystal?
A: The tool generates a separate `JSON::Serializable` class for each nested JSON object, and types arrays as `Array(T)` of the matching element type That keeps the generated code organized the way a Crystal developer would structure it by hand, instead of flattening everything into one giant class.
Q: What happens to fields that are nullable or missing from some records?
A: For optional data, fields that appear as `null`, or are missing from some sample objects, are typed as a nilable union (`String?`, `Int32?`) so Crystal's static type checker allows them to hold `nil`. If your sample only has one example object, fields that could be null in other records might not be detected as optional — paste a few varied samples (or an array of objects) for the most accurate result.
Q: Is this better than writing the class by hand?
A: For anything beyond a trivial, flat payload, yes — writing Crystal's `JSON::Serializable` classes by hand for nested JSON means working out every nilable union type yourself, since Crystal's compiler will reject a type declaration that's wrong at compile time. The generator produces the same result in seconds and gives you a correct starting point to refine, rather than a blank page.
Q: Is my JSON data safe to paste into this tool, especially if it's from a private API?
A: Yes. Everything runs client-side in your browser: the sample JSON you paste in — which may contain real field names, endpoint shapes, or structure from a private API or project — is parsed and converted entirely on your device and is never uploaded to any server. Nothing you enter is logged, stored, or transmitted anywhere, so you can safely paste a real payload from an internal or unreleased API to generate an accurate Crystal class. Closing or refreshing the tab clears everything, since no copy of your data ever left your machine in the first place.
Q: Can I use this as a convert json to crystal lang struct?
A: Yes — the tool is commonly used as a convert json to crystal lang struct, and works the same way whether you're converting a one-off response for a prototype or generating model code for a production project.
Q: Does the tool rename fields to match Crystal naming conventions?
A: Yes. It keeps snake_case keys as-is to match Crystal's idiomatic naming, and adds a `@[JSON::Field(key: "originalKey")]` annotation when a key doesn't already follow Crystal convention, so the generated code reads naturally in Crystal while still deserializing the original JSON correctly.
Q: What Crystal library does the generated code work with?
A: The output is written to pair cleanly with the JSON::Serializable module, the most common choice for Crystal developers handling JSON, so you can drop it into an existing project without changing your serialization setup.
Q: Does this tool work for deeply nested or large JSON samples?
A: Yes. The tool recurses through every level of nesting it finds and generates a complete set of linked class definitions, not just the top level — though for very large samples, trimming it down to one representative record per array is usually enough to get an accurate result.
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