Generate TypeScript interfaces and types from JSON payloads
Convert JSON payloads and API responses into strongly-typed TypeScript interfaces with recursive sub-type generation.
🛡️ Your rows never leave your browser.
Why Onfyt: Processes json to typescript interface entirely in-browser with dedicated Web Workers so your rows never leave your machine.
Interactive Sample Data
| user_id | first_name | last_name | role | is_active |
|---|---|---|---|---|
| usr_99 | Amina | Kassim | admin | true |
| usr_100 | David | Mwangi | editor | true |
| usr_101 | Faith | Chebet | viewer | false |
| usr_102 | Samuel | Otieno | admin | true |
How this tool works
Developing robust web applications, microservices, and mobile frontends using TypeScript requires precise type declarations for every incoming API response and configuration payload. Writing these interfaces by hand for complex, deeply nested JSON responses is tedious, error-prone, and difficult to maintain as backend schemas evolve.
This tool recursively analyzes your JSON payload to construct modular, clean TypeScript type definitions. Unlike basic single-level generators that output massive inline object literals, our engine identifies nested child structures and extracts them into dedicated, reusable sub-interfaces with PascalCase names. For example, a user entity containing an address sub-object will generate both a User interface and an Address interface.
When processing arrays of multiple objects, the generator aggregates keys across every record in the collection. If certain properties appear in some objects but are omitted in others, the corresponding property is marked as optional (?) or given a union with undefined/null. Primitive types, arrays of primitives (string[], number[]), and polymorphic arrays are typed accurately.
Everything runs locally inside your browser via Web Workers. Your private API tokens, user profiles, and proprietary data structures never leave your device, ensuring strict confidentiality and instantaneous interface generation.
From a software engineering perspective, generating maintainable TypeScript interfaces from nested JSON payloads requires recursive schema decomposition. Rather than outputting massive, unreadable inline type declarations, our generator extracts nested child objects into independent, reusable sub-interfaces with PascalCase identifiers (e.g., Address, UserProfile). Furthermore, when analyzing arrays containing polymorphic objects, the engine detects omitted properties across records and marks them with optional property flags (?), ensuring full type safety in downstream application code.
From a security and compliance perspective, frontend and full-stack developers can generate type definitions for proprietary REST APIs and sensitive database models without exposing schema definitions to third-party web utilities. Traditional online type converters transmit JSON payloads to remote web servers, creating intellectual property and data leakage risks. DataFix runs locally in your browser via Web Workers, ensuring absolute confidentiality and instantaneous generation.
From an enterprise code quality standpoint, having recursive sub-interface extraction ensures that your codebase adheres to the DRY (Don't Repeat Yourself) principle. When multiple endpoints share identical sub-objects (such as author or timestamp metadata), modular interfaces allow developers to export and import shared types cleanly across microservice packages. Our engine also supports adding the readonly modifier to all generated properties, encouraging immutable state management patterns within modern Redux, Zustand, and React query workflows.
From a security and compliance perspective, frontend and full-stack developers can generate type definitions for proprietary REST APIs and sensitive database models without exposing schema definitions to third-party web utilities. Traditional online type converters transmit JSON payloads to remote web servers, creating intellectual property and data leakage risks. DataFix runs locally in your browser via Web Workers, ensuring absolute confidentiality and instantaneous generation.
Things that usually go wrong
- Empty Arrays in JSON: If an array is empty ([]), the type cannot be inferred and defaults to any[]. Provide sample items for accurate typing.
- Heterogeneous Array Types: Arrays with mixed numbers and strings produce union types like (string | number)[].
- Circular References: DataFix uses tree traversal that safely detects and prevents infinite recursion in cyclical JSON structures.
Edge cases we tested
🧪 Empty cells or missing values
Safely handled without schema corruption.
🧪 Leading or trailing cell whitespace
Sanitized prior to processing.
🧪 Large files with 25,000+ rows
Streamed via client-side Web Worker without tab freezing.
Frequently Asked Questions
Does it create separate interfaces for nested objects?
Yes! Nested objects are extracted into clean, reusable sub-interfaces with PascalCase names.
How does it handle optional properties?
When you paste an array of objects, any property not present in all objects is marked with a question mark (?).
Can I generate type aliases instead of interfaces?
Yes. You can switch between "interface" and "type" declarations.
Is any data uploaded?
No. The generation occurs entirely within your local browser memory.
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