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XML to Swift

Generate Swift classes/structs automatically from an XML document.

100% Client-Side Local Execution
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Input Data
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Transformed Result
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What is XML to Swift?

This free online tool turns a sample XML document into a ready-to-use struct conforming to Codable for Swift, so you can use this xml to swift struct generator tool to go straight from raw XML to a typed Codable struct without writing the boilerplate yourself. It is built around Swift's actual idiom — a Codable struct — rather than a generic one-size-fits-all class, and pairs naturally with XMLParser for real-world (de)serialization. Use it for xml to swift codable or any time you need xml parser swift generator, whether you're scaffolding a new Swift project or mapping an existing API response.

Technical Execution

Paste or upload a sample XML document and the tool walks its structure field by field, inferring a concrete Swift type for each value — mapping basic XML types to `String`, `Int`, `Bool`, and `Double` as appropriate. For nested data, it maps nested XML elements to nested Codable structs and repeated elements to `[T]` arrays, since Swift's Codable doesn't parse XML natively, exposing attributes as separate stored properties. On the naming side, it converts snake_case keys into Swift's camelCase property convention and generates a matching `CodingKeys` enum so `Codable` still decodes the original JSON key correctly. For optional data, fields that appear as `null`, or are missing from one or more sample objects, are typed as optionals (`String?`, `Int?`) so `Codable`'s synthesized decoder can tolerate their absence.

Zero Data Storage Guarantee

Everything runs client-side in your browser: the sample XML 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 Codable struct. Closing or refreshing the tab clears everything, since no copy of your data ever left your machine in the first place.

How to Use XML to Swift (Step-by-Step Guide)

  1. Copy a representative sample of your XML (the more fields and nested objects it includes, the more complete the generated Codable struct will be).
  2. Paste it into the input panel of the XML to Swift tool, or click "Upload" to load it from a xml file.
  3. Review the auto-detected root name, and rename it if you want the generated Codable struct to use a specific class or type name instead of the default.
  4. Click "Generate" to produce the Swift Codable struct, 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 `.swift` file.
  6. Paste the result into your Swift 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 XML to Swift?
A: The tool maps nested XML elements to nested Codable structs and repeated elements to `[T]` arrays, since Swift's Codable doesn't parse XML natively, exposing attributes as separate stored properties That keeps the generated code organized the way a Swift developer would structure it by hand, instead of flattening everything into one giant Codable struct.
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 one or more sample objects, are typed as optionals (`String?`, `Int?`) so `Codable`'s synthesized decoder can tolerate their absence. 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 Codable struct by hand?
A: For anything beyond a trivial, flat payload, yes — writing a Codable struct by hand for a nested response means getting the `CodingKeys` enum exactly right for every renamed property, and that's easy to get out of sync as the API evolves. 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 XML 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 XML 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 Codable struct. 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 xml to swift model?
A: Yes — the tool is commonly used as a convert xml to swift model, 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 Swift naming conventions?
A: Yes. It converts snake_case keys into Swift's camelCase property convention and generates a matching `CodingKeys` enum so `Codable` still decodes the original JSON key correctly, so the generated code reads naturally in Swift while still deserializing the original XML correctly.
Q: What Swift library does the generated code work with?
A: The output is written to pair cleanly with XMLParser, the most common choice for Swift developers handling XML, so you can drop it into an existing project without changing your serialization setup.
Q: Does this tool work for deeply nested or large XML samples?
A: Yes. The tool recurses through every level of nesting it finds and generates a complete set of linked Codable struct 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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