JSON to TypeScript & Schema Generator — Free Online Tool

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How to JSON to TypeScript & Schema Generator Online

1. 1

   Paste your JSON or YAML data into the input editor, or click one of the pre-loaded example templates.

2. 2

   Select your desired output specification: TypeScript Interface, TypeScript Type Alias, Zod Schema (v3), Valibot Schema (v1), or JSON Schema (Draft 2020-12 / Draft-07).

3. 3

   Configure code generation parameters: customize the root entity name, choose between extracted modular types or inlined structures, and toggle export statements.

4. 4

   Tune type inference options: enable automatic detection for ISO 8601 date strings, string literal enum unions, readonly/immutable fields, and optional null handling.

5. 5

   Trigger generation via the Generate button or keyboard shortcut (⌘↵ / Ctrl+Enter) to view real-time compiled schemas.

6. 6

   Copy the generated code directly to your clipboard with ⌘⇧C, or share a compressed, encrypted URL parameter with your engineering team.

JSON to TypeScript & Schema Generator Features

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  Multi-Format Type & Schema Compilation: Generates TypeScript interfaces, type aliases, Zod runtime schemas, Valibot schemas, and JSON Schema specifications from a single payload.

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  Dual JSON & YAML Ingestion: Automatically parses raw JSON payloads or YAML documents with comprehensive syntax validation and error reporting.

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  Bidirectional JSON Schema Parsing: Accepts existing JSON Schema Draft-07 and Draft 2020-12 inputs to convert them directly into TypeScript or runtime Zod/Valibot schemas.

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  Smart Polymorphic Array & Union Inference: Merges heterogeneously shaped objects across array collections into unified, type-safe union types.

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  String Literal Union Detection: Scans string arrays and repeated string values to infer precise union literals (e.g., "admin" | "editor" | "viewer") instead of generic string arrays.

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  ISO 8601 Date String Recognition: Detects UTC timestamps and formatted date-time strings to generate z.string().datetime() or Date type annotations.

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  Configurable Modular vs Inline Architecture: Choose whether nested child objects are extracted into PascalCase helper types or declared inline.

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  Intelligent Nullability & Optionality Normalization: Automatically infers optional (?) and nullable (| null) flags for missing or null-bearing properties.

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  Immutable Readonly Modifiers: Option to prefix TypeScript properties with readonly and append .readonly() wrappers to Zod and Valibot validation chains.

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  Zero-Dependency Copy-Ready Imports: Emits complete ES import statements (e.g., import { z } from "zod") for immediate integration into your codebase.

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  Real-Time Compilation & Complexity Statistics: Calculates active type count, total property count, and structural nesting depth instantly.

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  Stateless URL Sharing: Compresses sample payloads and compiler settings into URL hash fragments for seamless team collaboration without cloud storage.

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  100% Client-Side Privacy: All parsing, tree traversal, and code generation execute entirely within browser memory — zero data is transmitted over the wire.

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  Developer Productivity Hotkeys: Quick-switch targets using ⌘⇧T (TypeScript Interface), ⌘⇧Z (Zod Schema), ⌘↵ (Generate), and ⌘⇧C (Copy).

Supported Formats & Dialects

The JSON to TypeScript & Schema Generator supports 6 syntax formats and dialects for accurate parsing and processing.

TypeScript Interfaces (export interface Root { ... })

Standard object contract definitions with extendable declarations, optional modifiers (?:), nested type references, and readonly property flags suitable for domain modeling and API response types.

TypeScript Type Aliases (export type Root = { ... })

Immutable type definitions supporting union types, primitive aliases, tuple arrays, and mapped types for modern TypeScript applications and utility type transformations.

Zod v3 Runtime Validation Schemas (z.object({ ... }))

TypeScript-first runtime schema declarations featuring static type inference via z.infer<typeof Schema>, strict parsing, array validations, and composable union validators.

Valibot v1 Modular Validation Schemas (v.object({ ... }))

High-performance, tree-shakeable schema library providing up to 98% smaller bundle footprints compared to legacy validation libraries, with static types derived via v.InferOutput<typeof Schema>.

JSON Schema Draft 2020-12 ($schema: "https://json-schema.org/draft/2020-12/schema")

The latest IETF standard for JSON Schema, featuring $defs referencing, prefixItems for tuples, dynamic validation keywords, and modern tooling compatibility across OpenAPI 3.1 and LLM structured outputs.

JSON Schema Draft-07 ($schema: "http://json-schema.org/draft-07/schema#")

Widely adopted legacy standard with universal validator support across Python (jsonschema), Java (networknt), Go, and older OpenAPI 3.0 toolchains using definitions schemas.

Frequently Asked Questions

How does DevFlow infer TypeScript types from sample JSON payloads?

DevFlow parses your JSON or YAML input into an Abstract Syntax Tree (AST) called a TypeTree. It recursively analyzes each property, determining whether it represents a primitive (string, number, boolean, null), an array, or a nested object. When encountering arrays containing objects, it merges the properties across every element to produce an accurate unified type, ensuring optional properties and unions are captured even if some sample elements omit them.

What is the difference between generating a TypeScript Interface versus a Type Alias?

TypeScript Interfaces (interface User { ... }) support declaration merging and are traditionally preferred for defining object shapes and public API contracts in object-oriented patterns. Type Aliases (type User = { ... }) are more versatile, capable of directly expressing primitive unions, intersections, mapped types, and tuple types. For standard JSON objects, both behave identically in terms of type checking, but type aliases are necessary when the root payload is an array or primitive union.

How does the generator handle polymorphic arrays and heterogeneous JSON values?

When an array contains multiple different data types (e.g. [1, "test", {"active": true}]), the generator analyzes each element and compiles a union type. In TypeScript output, this produces Array<number | string | ItemType>. In Zod output, it emits z.array(z.union([z.number(), z.string(), ItemSchema])). In Valibot, it produces v.array(v.union([v.number(), v.string(), ItemSchema])).

How does runtime schema validation with Zod (z.infer) compare to compile-time TypeScript interfaces?

TypeScript interfaces only exist during development and compilation; they are completely erased from JavaScript output at build time. If an external API returns unexpected or corrupted data at runtime, TypeScript interfaces cannot prevent runtime crashes. Zod schemas validate data at runtime as it enters your application boundary (e.g., in Next.js Server Actions, Route Handlers, or fetch responses) while automatically providing compile-time types via z.infer<typeof Schema>.

Why choose Valibot over Zod for schema validation in modern frontend applications?

Valibot is engineered around a modular, functional architecture where validators and transformations are imported as standalone functions rather than chained methods on a monolithic object. This enables modern bundlers (like Vite, Webpack, and Turbopack) to tree-shake unused validation rules, often reducing the validation library bundle size from ~12KB (Zod) down to under 1KB (Valibot).

How does the tool handle missing properties, null values, and optional fields?

When the "Nulls as Optional" setting is enabled, any property in your sample JSON that contains a null value is inferred as optional and nullable (? in TypeScript, z.nullable().optional() in Zod, and v.nullish() in Valibot). When analyzing an array of objects where a key appears in some objects but is absent in others, the compiler automatically flags that key as optional in the merged schema.

Can this tool convert existing JSON Schema specifications back into TypeScript or Zod?

Yes. If you paste a JSON Schema specification (containing properties, $schema, type: "object", or required arrays), DevFlow automatically detects the JSON Schema schema definition and converts its property constraints, required flags, enum values, and nested subschemas into clean TypeScript interfaces, Zod validators, or Valibot definitions.

How does string literal union inference work for enumerated API values?

When the "String Literal Unions" option is checked, the generator inspects string arrays (e.g., ["active", "pending", "archived"]). Instead of outputting a generic string[] type, it compiles an exact union of allowed string literals ("active" | "pending" | "archived" in TypeScript, or z.union([z.literal("active"), z.literal("pending"), z.literal("archived")]) in Zod).

How does ISO 8601 date string detection work in generated schemas?

When the "Detect Date Strings" option is activated, strings matching standard ISO 8601 patterns (e.g., "2026-09-06T12:00:00Z" or "2026-09-06") are recognized as timestamps. In TypeScript, they can be emitted as string or Date comments, and in Zod, they are emitted as z.string().datetime() to enforce valid timestamp formats at runtime.

How are special character keys, hyphens, and reserved keywords handled?

If a JSON key contains hyphens, spaces, dots, or JavaScript reserved words (such as "content-type", "user.name", or "class"), the generator automatically escapes the key with quotes in TypeScript interfaces (e.g., "content-type": string) and wraps the property in string literal keys in Zod/Valibot schemas (e.g., "content-type": z.string()), preventing syntax errors.

Does this tool support YAML input in addition to standard JSON?

Yes. The input parser automatically detects YAML syntax if JSON parsing fails. You can paste Kubernetes manifests, GitHub Actions configurations, or Docker Compose files in YAML format, and the tool will parse the data structure and generate matching TypeScript, Zod, or JSON Schema code.

Are my JSON payloads or API responses sent to any remote server or stored in the cloud?

No. All data processing, Abstract Syntax Tree generation, schema inference, and code formatting run 100% locally in your web browser using JavaScript. No payload data, schema structures, or metadata are ever transmitted to or stored on DevFlow servers, ensuring full compliance with enterprise privacy and data security policies.

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