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XML to C++

Generate C++ classes/structs automatically from an XML document.

100% Client-Side Local Execution
Class Name:
Input Data
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Transformed Result
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What is XML to C++?

This free online tool turns a sample XML document into a ready-to-use struct (paired with nlohmann::json) for C++, so you can use this xml to c++ class generator tool to go straight from raw XML to a typed struct without writing the boilerplate yourself. It is built around C++'s actual idiom — a C++ struct — rather than a generic one-size-fits-all class, and pairs naturally with tinyxml2 for real-world (de)serialization. Use it for convert xml to c++ struct or any time you need tinyxml to struct generator, whether you're scaffolding a new C++ 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 C++ type for each value — mapping basic XML types to `std::string`, `int`/`double`, and `bool` as appropriate. For nested data, it maps nested XML elements to nested structs and repeated elements to `std::vector<T>`, generating simple tinyxml2-style parsing code since C++ has no single standard XML-to-struct binding convention. On the naming side, it keeps the original JSON key names, and generates matching `NLOHMANN_DEFINE_TYPE_INTRUSIVE` (or `to_json`/`from_json`) macro calls so `nlohmann::json` can serialize the struct without any manual field mapping. For optional data, fields observed as `null`, or missing from some sample objects, are typed with `std::optional<T>` so the struct can represent an absent value without a sentinel or extra boolean flag.

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 C++ 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 C++ (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 struct will be).
  2. Paste it into the input panel of the XML to C++ 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 C++ struct to use a specific class or type name instead of the default.
  4. Click "Generate" to produce the C++ 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 `.hpp` file.
  6. Paste the result into your C++ 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 C++?
A: The tool maps nested XML elements to nested structs and repeated elements to `std::vector<T>`, generating simple tinyxml2-style parsing code since C++ has no single standard XML-to-struct binding convention That keeps the generated code organized the way a C++ developer would structure it by hand, instead of flattening everything into one giant struct.
Q: What happens to fields that are nullable or missing from some records?
A: For optional data, fields observed as `null`, or missing from some sample objects, are typed with `std::optional<T>` so the struct can represent an absent value without a sentinel or extra boolean flag. 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 struct by hand?
A: For anything beyond a trivial, flat payload, yes — writing the `to_json`/`from_json` free functions by hand for a nested nlohmann::json struct is boilerplate-heavy, and it's easy to forget a field when the struct has a dozen members. 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 C++ 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 xml to cpp class online?
A: Yes — the tool is commonly used as a xml to cpp class online, 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 C++ naming conventions?
A: Yes. It keeps the original JSON key names, and generates matching `NLOHMANN_DEFINE_TYPE_INTRUSIVE` (or `to_json`/`from_json`) macro calls so `nlohmann::json` can serialize the struct without any manual field mapping, so the generated code reads naturally in C++ while still deserializing the original XML correctly.
Q: What C++ library does the generated code work with?
A: The output is written to pair cleanly with tinyxml2, the most common choice for C++ 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 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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