TypeScript-first schema declaration and validation library with static type inference
Zod is a TypeScript-first schema declaration and validation library. It allows developers to create complex schemas for data validation with a simple and intuitive API. Zod schemas are composable and can be used to validate data at the edge of your application, ensuring that you're working with well-structured and type-safe data.
Basic Type Validation
Validates that the input is a string.
{"const schema = zod.string(); try { schema.parse('hello world'); } catch (e) { console.error(e); }"}Object Schema Validation
Validates that the input is an object with specific properties of certain types.
{"const userSchema = zod.object({ name: zod.string(), age: zod.number(), email: zod.string().email() }); try { userSchema.parse({ name: 'John', age: 30, email: 'john@example.com' }); } catch (e) { console.error(e); }"}Array Validation
Validates that the input is an array of strings.
{"const stringArraySchema = zod.array(zod.string()); try { stringArraySchema.parse(['apple', 'banana']); } catch (e) { console.error(e); }"}Complex Nested Validation
Validates nested objects with various property types.
{"const nestedSchema = zod.object({ user: zod.object({ name: zod.string(), contact: zod.object({ email: zod.string().email(), phone: zod.string() }) }) }); try { nestedSchema.parse({ user: { name: 'Jane', contact: { email: 'jane@example.com', phone: '123-456-7890' } } }); } catch (e) { console.error(e); }"}Custom Validation
Validates that a number is positive using custom validation logic.
{"const positiveNumber = zod.number().positive(); try { positiveNumber.parse(42); } catch (e) { console.error(e); }"}Error Formatting
Formats validation errors for easier debugging and display.
{"const schema = zod.string(); try { schema.parse(42); } catch (e) { console.error(e.format()); }"}Joi is a powerful schema description language and data validator for JavaScript. It offers a similar API to Zod but has been around longer and is often considered more mature. Joi provides a wide range of built-in validators and is highly extensible.
Yup is a JavaScript schema builder for value parsing and validation. It defines a schema with an expressive API and can be used with or without TypeScript. Yup is often used in the context of form validation, especially with libraries like Formik.
Ajv is a JSON Schema Validator. It validates data against JSON Schema (draft 06/07/2019) and is known for its performance. Unlike Zod, which is TypeScript-first, Ajv focuses on JSON Schema validation and is often used for validating data structures in RESTful APIs.
Class-validator allows for validation of class instances based on decorators. It is tightly coupled with TypeScript and uses decorators to define validation rules, which can be more familiar to developers used to working with TypeScript classes and decorators.
✨ https://zod.dev ✨
TypeScript-first schema validation with static type inference
Zod 3.23 is out! View the release notes.
These docs have been translated into Chinese.
Zod is a TypeScript-first schema declaration and validation library. I'm using the term "schema" to broadly refer to any data type, from a simple string to a complex nested object.
Zod is designed to be as developer-friendly as possible. The goal is to eliminate duplicative type declarations. With Zod, you declare a validator once and Zod will automatically infer the static TypeScript type. It's easy to compose simpler types into complex data structures.
Some other great aspects:
.optional()) return a new instanceSponsorship at any level is appreciated and encouraged. For individual developers, consider the Cup of Coffee tier. If you built a paid product using Zod, consider one of the podium tiers.
Email me to discuss sponsoring Zod at this level.
Cerbos |
Scalar |
Speakeasy |
Deletype |
Trigger.dev |
Transloadit |
Infisical |
Whop |
CryptoJobsList |
Plain. |
Inngest |
Storyblok |
Mux |
@emreb
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| Brandon Bayer | Jiří Brabec | Alex Johansson | Fungible Systems |
| Adaptable | Avana Wallet | Jason Lengstorf | Global Illumination, Inc. |
| MasterBorn | Ryan Palmer | Michael Sweeney | Nextbase |
| Remotion | Connor Sinnott | Mohammad-Ali A'râbi | Supatool |
| Social Crow |
There are a growing number of tools that are built atop or support Zod natively! If you've built a tool or library on top of Zod, tell me about it on Twitter or start a Discussion. I'll add it below and tweet it out.
tRPC: Build end-to-end typesafe APIs without GraphQL.@anatine/zod-nestjs: Helper methods for using Zod in a NestJS project.zod-endpoints: Contract-first strictly typed endpoints with Zod. OpenAPI compatible.zhttp: An OpenAPI compatible, strictly typed http library with Zod input and response validation.domain-functions: Decouple your business logic from your framework using composable functions. With first-class type inference from end to end powered by Zod schemas.@zodios/core: A typescript API client with runtime and compile time validation backed by axios and zod.express-zod-api: Build Express-based APIs with I/O schema validation and custom middlewares.tapiduck: End-to-end typesafe JSON APIs with Zod and Express; a bit like tRPC, but simpler.koa-zod-router: Create typesafe routes in Koa with I/O validation using Zod.react-hook-form: A first-party Zod resolver for React Hook Form.zod-validation-error: Generate user-friendly error messages from ZodErrors.zod-formik-adapter: A community-maintained Formik adapter for Zod.react-zorm: Standalone <form> generation and validation for React using Zod.zodix: Zod utilities for FormData and URLSearchParams in Remix loaders and actions.conform: A typesafe form validation library for progressive enhancement of HTML forms. Works with Remix and Next.js.remix-params-helper: Simplify integration of Zod with standard URLSearchParams and FormData for Remix apps.formik-validator-zod: Formik-compliant validator library that simplifies using Zod with Formik.zod-i18n-map: Useful for translating Zod error messages.@modular-forms/solid: Modular form library for SolidJS that supports Zod for validation.houseform: A React form library that uses Zod for validation.sveltekit-superforms: Supercharged form library for SvelteKit with Zod validation.mobx-zod-form: Data-first form builder based on MobX & Zod.@vee-validate/zod: Form library for Vue.js with Zod schema validation.zod-to-ts: Generate TypeScript definitions from Zod schemas.zod-to-json-schema: Convert your Zod schemas into JSON Schemas.@anatine/zod-openapi: Converts a Zod schema to an OpenAPI v3.x SchemaObject.zod-fast-check: Generate fast-check arbitraries from Zod schemas.zod-dto: Generate Nest.js DTOs from a Zod schema.fastify-type-provider-zod: Create Fastify type providers from Zod schemas.zod-to-openapi: Generate full OpenAPI (Swagger) docs from Zod, including schemas, endpoints & parameters.nestjs-graphql-zod: Generates NestJS GraphQL model classes from Zod schemas. Provides GraphQL method decorators working with Zod schemas.zod-openapi: Create full OpenAPI v3.x documentation from Zod schemas.fastify-zod-openapi: Fastify type provider, validation, serialization and @fastify/swagger support for Zod schemas.typeschema: Universal adapter for schema validation.ts-to-zod: Convert TypeScript definitions into Zod schemas.@runtyping/zod: Generate Zod from static types & JSON schema.json-schema-to-zod: Convert your JSON Schemas into Zod schemas. Live demo.json-to-zod: Convert JSON objects into Zod schemas. Live demo.graphql-codegen-typescript-validation-schema: GraphQL Code Generator plugin to generate form validation schema from your GraphQL schema.zod-prisma: Generate Zod schemas from your Prisma schema.Supervillain: Generate Zod schemas from your Go structs.prisma-zod-generator: Emit Zod schemas from your Prisma schema.prisma-trpc-generator: Emit fully implemented tRPC routers and their validation schemas using Zod.zod-prisma-types Create Zod types from your Prisma models.quicktype: Convert JSON objects and JSON schemas into Zod schemas.@sanity-typed/zod: Generate Zod Schemas from Sanity Schemas.java-to-zod: Convert POJOs to Zod schemasOrval: Generate Zod schemas from OpenAPI schemas@anatine/zod-mock: Generate mock data from a Zod schema. Powered by faker.js.zod-mocking: Generate mock data from your Zod schemas.zod-fixture: Use your zod schemas to automate the generation of non-relevant test fixtures in a deterministic way.zocker: Generate plausible mock-data from your schemas.zodock Generate mock data based on Zod schemas.freerstore: Firestore cost optimizer.slonik: Node.js Postgres client with strong Zod integration.soly: Create CLI applications with zod.pastel: Create CLI applications with react, zod, and ink.zod-xlsx: A xlsx based resource validator using Zod schemas.znv: Type-safe environment parsing and validation for Node.js with Zod schemas.zod-config: Load configurations across multiple sources with flexible adapters, ensuring type safety with Zod.zod_utilz: Framework agnostic utilities for Zod.zod-playground: A tool for learning and testing Zod schema validation functionalities. Link.zod-sandbox: Controlled environment for testing zod schemas. Live demo.zod-dev: Conditionally disables Zod runtime parsing in production.zod-accelerator: Accelerates Zod's throughput up to ~100x.TypeScript 4.5+!
You must enable strict mode in your tsconfig.json. This is a best practice for all TypeScript projects.
// tsconfig.json
{
// ...
"compilerOptions": {
// ...
"strict": true
}
}
npm (Node/Bun)npm install zod # npm
yarn add zod # yarn
bun add zod # bun
pnpm add zod # pnpm
Zod also publishes a canary version on every commit. To install the canary:
npm install zod@canary # npm
yarn add zod@canary # yarn
bun add zod@canary # bun
pnpm add zod@canary # pnpm
deno.land/x (Deno)Unlike Node, Deno relies on direct URL imports instead of a package manager like NPM. Zod is available on deno.land/x. The latest version can be imported like so:
import { z } from "https://deno.land/x/zod/mod.ts";
You can also specify a particular version:
import { z } from "https://deno.land/x/zod@v3.16.1/mod.ts";
The rest of this README assumes you are using npm and importing directly from the
"zod"package.
Creating a simple string schema
import { z } from "zod";
// creating a schema for strings
const mySchema = z.string();
// parsing
mySchema.parse("tuna"); // => "tuna"
mySchema.parse(12); // => throws ZodError
// "safe" parsing (doesn't throw error if validation fails)
mySchema.safeParse("tuna"); // => { success: true; data: "tuna" }
mySchema.safeParse(12); // => { success: false; error: ZodError }
Creating an object schema
import { z } from "zod";
const User = z.object({
username: z.string(),
});
User.parse({ username: "Ludwig" });
// extract the inferred type
type User = z.infer<typeof User>;
// { username: string }
import { z } from "zod";
// primitive values
z.string();
z.number();
z.bigint();
z.boolean();
z.date();
z.symbol();
// empty types
z.undefined();
z.null();
z.void(); // accepts undefined
// catch-all types
// allows any value
z.any();
z.unknown();
// never type
// allows no values
z.never();
Zod now provides a more convenient way to coerce primitive values.
const schema = z.coerce.string();
schema.parse("tuna"); // => "tuna"
schema.parse(12); // => "12"
During the parsing step, the input is passed through the String() function, which is a JavaScript built-in for coercing data into strings.
schema.parse(12); // => "12"
schema.parse(true); // => "true"
schema.parse(undefined); // => "undefined"
schema.parse(null); // => "null"
The returned schema is a normal ZodString instance so you can use all string methods.
z.coerce.string().email().min(5);
How coercion works
All primitive types support coercion. Zod coerces all inputs using the built-in constructors: String(input), Number(input), new Date(input), etc.
z.coerce.string(); // String(input)
z.coerce.number(); // Number(input)
z.coerce.boolean(); // Boolean(input)
z.coerce.bigint(); // BigInt(input)
z.coerce.date(); // new Date(input)
Note — Boolean coercion with z.coerce.boolean() may not work how you expect. Any truthy value is coerced to true, and any falsy value is coerced to false.
const schema = z.coerce.boolean(); // Boolean(input)
schema.parse("tuna"); // => true
schema.parse("true"); // => true
schema.parse("false"); // => true
schema.parse(1); // => true
schema.parse([]); // => true
schema.parse(0); // => false
schema.parse(""); // => false
schema.parse(undefined); // => false
schema.parse(null); // => false
For more control over coercion logic, consider using z.preprocess or z.pipe().
Literal schemas represent a literal type, like "hello world" or 5.
const tuna = z.literal("tuna");
const twelve = z.literal(12);
const twobig = z.literal(2n); // bigint literal
const tru = z.literal(true);
const terrificSymbol = Symbol("terrific");
const terrific = z.literal(terrificSymbol);
// retrieve literal value
tuna.value; // "tuna"
Currently there is no support for Date literals in Zod. If you have a use case for this feature, please file an issue.
Zod includes a handful of string-specific validations.
// validations
z.string().max(5);
z.string().min(5);
z.string().length(5);
z.string().email();
z.string().url();
z.string().emoji();
z.string().uuid();
z.string().nanoid();
z.string().cuid();
z.string().cuid2();
z.string().ulid();
z.string().regex(regex);
z.string().includes(string);
z.string().startsWith(string);
z.string().endsWith(string);
z.string().datetime(); // ISO 8601; by default only `Z` timezone allowed
z.string().ip(); // defaults to allow both IPv4 and IPv6
// transforms
z.string().trim(); // trim whitespace
z.string().toLowerCase(); // toLowerCase
z.string().toUpperCase(); // toUpperCase
// added in Zod 3.23
z.string().date(); // ISO date format (YYYY-MM-DD)
z.string().time(); // ISO time format (HH:mm:ss[.SSSSSS])
z.string().duration(); // ISO 8601 duration
z.string().base64();
Check out validator.js for a bunch of other useful string validation functions that can be used in conjunction with Refinements.
You can customize some common error messages when creating a string schema.
const name = z.string({
required_error: "Name is required",
invalid_type_error: "Name must be a string",
});
When using validation methods, you can pass in an additional argument to provide a custom error message.
z.string().min(5, { message: "Must be 5 or more characters long" });
z.string().max(5, { message: "Must be 5 or fewer characters long" });
z.string().length(5, { message: "Must be exactly 5 characters long" });
z.string().email({ message: "Invalid email address" });
z.string().url({ message: "Invalid url" });
z.string().emoji({ message: "Contains non-emoji characters" });
z.string().uuid({ message: "Invalid UUID" });
z.string().includes("tuna", { message: "Must include tuna" });
z.string().startsWith("https://", { message: "Must provide secure URL" });
z.string().endsWith(".com", { message: "Only .com domains allowed" });
z.string().datetime({ message: "Invalid datetime string! Must be UTC." });
z.string().date({ message: "Invalid date string!" });
z.string().time({ message: "Invalid time string!" });
z.string().ip({ message: "Invalid IP address" });
As you may have noticed, Zod string includes a few date/time related validations. These validations are regular expression based, so they are not as strict as a full date/time library. However, they are very convenient for validating user input.
The z.string().datetime() method enforces ISO 8601; default is no timezone offsets and arbitrary sub-second decimal precision.
const datetime = z.string().datetime();
datetime.parse("2020-01-01T00:00:00Z"); // pass
datetime.parse("2020-01-01T00:00:00.123Z"); // pass
datetime.parse("2020-01-01T00:00:00.123456Z"); // pass (arbitrary precision)
datetime.parse("2020-01-01T00:00:00+02:00"); // fail (no offsets allowed)
Timezone offsets can be allowed by setting the offset option to true.
const datetime = z.string().datetime({ offset: true });
datetime.parse("2020-01-01T00:00:00+02:00"); // pass
datetime.parse("2020-01-01T00:00:00.123+02:00"); // pass (millis optional)
datetime.parse("2020-01-01T00:00:00.123+0200"); // pass (millis optional)
datetime.parse("2020-01-01T00:00:00.123+02"); // pass (only offset hours)
datetime.parse("2020-01-01T00:00:00Z"); // pass (Z still supported)
You can additionally constrain the allowable precision. By default, arbitrary sub-second precision is supported (but optional).
const datetime = z.string().datetime({ precision: 3 });
datetime.parse("2020-01-01T00:00:00.123Z"); // pass
datetime.parse("2020-01-01T00:00:00Z"); // fail
datetime.parse("2020-01-01T00:00:00.123456Z"); // fail
Added in Zod 3.23
The z.string().date() method validates strings in the format YYYY-MM-DD.
const date = z.string().date();
date.parse("2020-01-01"); // pass
date.parse("2020-1-1"); // fail
date.parse("2020-01-32"); // fail
Added in Zod 3.23
The z.string().time() method validates strings in the format HH:MM:SS[.s+]. The second can include arbitrary decimal precision. It does not allow timezone offsets of any kind.
const time = z.string().time();
time.parse("00:00:00"); // pass
time.parse("09:52:31"); // pass
time.parse("23:59:59.9999999"); // pass (arbitrary precision)
time.parse("00:00:00.123Z"); // fail (no `Z` allowed)
time.parse("00:00:00.123+02:00"); // fail (no offsets allowed)
You can set the precision option to constrain the allowable decimal precision.
const time = z.string().time({ precision: 3 });
time.parse("00:00:00.123"); // pass
time.parse("00:00:00.123456"); // fail
time.parse("00:00:00"); // fail
The z.string().ip() method by default validate IPv4 and IPv6.
const ip = z.string().ip();
ip.parse("192.168.1.1"); // pass
ip.parse("84d5:51a0:9114:1855:4cfa:f2d7:1f12:7003"); // pass
ip.parse("84d5:51a0:9114:1855:4cfa:f2d7:1f12:192.168.1.1"); // pass
ip.parse("256.1.1.1"); // fail
ip.parse("84d5:51a0:9114:gggg:4cfa:f2d7:1f12:7003"); // fail
You can additionally set the IP version.
const ipv4 = z.string().ip({ version: "v4" });
ipv4.parse("84d5:51a0:9114:1855:4cfa:f2d7:1f12:7003"); // fail
const ipv6 = z.string().ip({ version: "v6" });
ipv6.parse("192.168.1.1"); // fail
You can customize certain error messages when creating a number schema.
const age = z.number({
required_error: "Age is required",
invalid_type_error: "Age must be a number",
});
Zod includes a handful of number-specific validations.
z.number().gt(5);
z.number().gte(5); // alias .min(5)
z.number().lt(5);
z.number().lte(5); // alias .max(5)
z.number().int(); // value must be an integer
z.number().positive(); // > 0
z.number().nonnegative(); // >= 0
z.number().negative(); // < 0
z.number().nonpositive(); // <= 0
z.number().multipleOf(5); // Evenly divisible by 5. Alias .step(5)
z.number().finite(); // value must be finite, not Infinity or -Infinity
z.number().safe(); // value must be between Number.MIN_SAFE_INTEGER and Number.MAX_SAFE_INTEGER
Optionally, you can pass in a second argument to provide a custom error message.
z.number().lte(5, { message: "this👏is👏too👏big" });
Zod includes a handful of bigint-specific validations.
z.bigint().gt(5n);
z.bigint().gte(5n); // alias `.min(5n)`
z.bigint().lt(5n);
z.bigint().lte(5n); // alias `.max(5n)`
z.bigint().positive(); // > 0n
z.bigint().nonnegative(); // >= 0n
z.bigint().negative(); // < 0n
z.bigint().nonpositive(); // <= 0n
z.bigint().multipleOf(5n); // Evenly divisible by 5n.
You can customize certain error messages when creating a nan schema.
const isNaN = z.nan({
required_error: "isNaN is required",
invalid_type_error: "isNaN must be 'not a number'",
});
You can customize certain error messages when creating a boolean schema.
const isActive = z.boolean({
required_error: "isActive is required",
invalid_type_error: "isActive must be a boolean",
});
Use z.date() to validate Date instances.
z.date().safeParse(new Date()); // success: true
z.date().safeParse("2022-01-12T00:00:00.000Z"); // success: false
You can customize certain error messages when creating a date schema.
const myDateSchema = z.date({
required_error: "Please select a date and time",
invalid_type_error: "That's not a date!",
});
Zod provides a handful of date-specific validations.
z.date().min(new Date("1900-01-01"), { message: "Too old" });
z.date().max(new Date(), { message: "Too young!" });
Coercion to Date
Since zod 3.20, use z.coerce.date() to pass the input through new Date(input).
const dateSchema = z.coerce.date();
type DateSchema = z.infer<typeof dateSchema>;
// type DateSchema = Date
/* valid dates */
console.log(dateSchema.safeParse("2023-01-10T00:00:00.000Z").success); // true
console.log(dateSchema.safeParse("2023-01-10").success); // true
console.log(dateSchema.safeParse("1/10/23").success); // true
console.log(dateSchema.safeParse(new Date("1/10/23")).success); // true
/* invalid dates */
console.log(dateSchema.safeParse("2023-13-10").success); // false
console.log(dateSchema.safeParse("0000-00-00").success); // false
For older zod versions, use z.preprocess like described in this thread.
const FishEnum = z.enum(["Salmon", "Tuna", "Trout"]);
type FishEnum = z.infer<typeof FishEnum>;
// 'Salmon' | 'Tuna' | 'Trout'
z.enum is a Zod-native way to declare a schema with a fixed set of allowable string values. Pass the array of values directly into z.enum(). Alternatively, use as const to define your enum values as a tuple of strings. See the const assertion docs for details.
const VALUES = ["Salmon", "Tuna", "Trout"] as const;
const FishEnum = z.enum(VALUES);
This is not allowed, since Zod isn't able to infer the exact values of each element.
const fish = ["Salmon", "Tuna", "Trout"];
const FishEnum = z.enum(fish);
.enum
To get autocompletion with a Zod enum, use the .enum property of your schema:
FishEnum.enum.Salmon; // => autocompletes
FishEnum.enum;
/*
=> {
Salmon: "Salmon",
Tuna: "Tuna",
Trout: "Trout",
}
*/
You can also retrieve the list of options as a tuple with the .options property:
FishEnum.options; // ["Salmon", "Tuna", "Trout"];
.exclude/.extract()
You can create subsets of a Zod enum with the .exclude and .extract methods.
const FishEnum = z.enum(["Salmon", "Tuna", "Trout"]);
const SalmonAndTrout = FishEnum.extract(["Salmon", "Trout"]);
const TunaOnly = FishEnum.exclude(["Salmon", "Trout"]);
Zod enums are the recommended approach to defining and validating enums. But if you need to validate against an enum from a third-party library (or you don't want to rewrite your existing enums) you can use z.nativeEnum().
Numeric enums
enum Fruits {
Apple,
Banana,
}
const FruitEnum = z.nativeEnum(Fruits);
type FruitEnum = z.infer<typeof FruitEnum>; // Fruits
FruitEnum.parse(Fruits.Apple); // passes
FruitEnum.parse(Fruits.Banana); // passes
FruitEnum.parse(0); // passes
FruitEnum.parse(1); // passes
FruitEnum.parse(3); // fails
String enums
enum Fruits {
Apple = "apple",
Banana = "banana",
Cantaloupe, // you can mix numerical and string enums
}
const FruitEnum = z.nativeEnum(Fruits);
type FruitEnum = z.infer<typeof FruitEnum>; // Fruits
FruitEnum.parse(Fruits.Apple); // passes
FruitEnum.parse(Fruits.Cantaloupe); // passes
FruitEnum.parse("apple"); // passes
FruitEnum.parse("banana"); // passes
FruitEnum.parse(0); // passes
FruitEnum.parse("Cantaloupe"); // fails
Const enums
The .nativeEnum() function works for as const objects as well. ⚠️ as const requires TypeScript 3.4+!
const Fruits = {
Apple: "apple",
Banana: "banana",
Cantaloupe: 3,
} as const;
const FruitEnum = z.nativeEnum(Fruits);
type FruitEnum = z.infer<typeof FruitEnum>; // "apple" | "banana" | 3
FruitEnum.parse("apple"); // passes
FruitEnum.parse("banana"); // passes
FruitEnum.parse(3); // passes
FruitEnum.parse("Cantaloupe"); // fails
You can access the underlying object with the .enum property:
FruitEnum.enum.Apple; // "apple"
You can make any schema optional with z.optional(). This wraps the schema in a ZodOptional instance and returns the result.
const schema = z.optional(z.string());
schema.parse(undefined); // => returns undefined
type A = z.infer<typeof schema>; // string | undefined
For convenience, you can also call the .optional() method on an existing schema.
const user = z.object({
username: z.string().optional(),
});
type C = z.infer<typeof user>; // { username?: string | undefined };
You can extract the wrapped schema from a ZodOptional instance with .unwrap().
const stringSchema = z.string();
const optionalString = stringSchema.optional();
optionalString.unwrap() === stringSchema; // true
Similarly, you can create nullable types with z.nullable().
const nullableString = z.nullable(z.string());
nullableString.parse("asdf"); // => "asdf"
nullableString.parse(null); // => null
Or use the .nullable() method.
const E = z.string().nullable(); // equivalent to nullableString
type E = z.infer<typeof E>; // string | null
Extract the inner schema with .unwrap().
const stringSchema = z.string();
const nullableString = stringSchema.nullable();
nullableString.unwrap() === stringSchema; // true
// all properties are required by default
const Dog = z.object({
name: z.string(),
age: z.number(),
});
// extract the inferred type like this
type Dog = z.infer<typeof Dog>;
// equivalent to:
type Dog = {
name: string;
age: number;
};
.shapeUse .shape to access the schemas for a particular key.
Dog.shape.name; // => string schema
Dog.shape.age; // => number schema
.keyofUse .keyof to create a ZodEnum schema from the keys of an object schema.
const keySchema = Dog.keyof();
keySchema; // ZodEnum<["name", "age"]>
.extendYou can add additional fields to an object schema with the .extend method.
const DogWithBreed = Dog.extend({
breed: z.string(),
});
You can use .extend to overwrite fields! Be careful with this power!
.mergeEquivalent to A.extend(B.shape).
const BaseTeacher = z.object({ students: z.array(z.string()) });
const HasID = z.object({ id: z.string() });
const Teacher = BaseTeacher.merge(HasID);
type Teacher = z.infer<typeof Teacher>; // => { students: string[], id: string }
If the two schemas share keys, the properties of B overrides the property of A. The returned schema also inherits the "unknownKeys" policy (strip/strict/passthrough) and the catchall schema of B.
.pick/.omitInspired by TypeScript's built-in Pick and Omit utility types, all Zod object schemas have .pick and .omit methods that return a modified version. Consider this Recipe schema:
const Recipe = z.object({
id: z.string(),
name: z.string(),
ingredients: z.array(z.string()),
});
To only keep certain keys, use .pick .
const JustTheName = Recipe.pick({ name: true });
type JustTheName = z.infer<typeof JustTheName>;
// => { name: string }
To remove certain keys, use .omit .
const NoIDRecipe = Recipe.omit({ id: true });
type NoIDRecipe = z.infer<typeof NoIDRecipe>;
// => { name: string, ingredients: string[] }
.partialInspired by the built-in TypeScript utility type Partial, the .partial method makes all properties optional.
Starting from this object:
const user = z.object({
email: z.string(),
username: z.string(),
});
// { email: string; username: string }
We can create a partial version:
const partialUser = user.partial();
// { email?: string | undefined; username?: string | undefined }
You can also specify which properties to make optional:
const optionalEmail = user.partial({
email: true,
});
/*
{
email?: string | undefined;
username: string
}
*/
.deepPartialThe .partial method is shallow — it only applies one level deep. There is also a "deep" version:
const user = z.object({
username: z.string(),
location: z.object({
latitude: z.number(),
longitude: z.number(),
}),
strings: z.array(z.object({ value: z.string() })),
});
const deepPartialUser = user.deepPartial();
/*
{
username?: string | undefined,
location?: {
latitude?: number | undefined;
longitude?: number | undefined;
} | undefined,
strings?: { value?: string}[]
}
*/
Important limitation: deep partials only work as expected in hierarchies of objects, arrays, and tuples.
.requiredContrary to the .partial method, the .required method makes all properties required.
Starting from this object:
const user = z
.object({
email: z.string(),
username: z.string(),
})
.partial();
// { email?: string | undefined; username?: string | undefined }
We can create a required version:
const requiredUser = user.required();
// { email: string; username: string }
You can also specify which properties to make required:
const requiredEmail = user.required({
email: true,
});
/*
{
email: string;
username?: string | undefined;
}
*/
.passthroughBy default Zod object schemas strip out unrecognized keys during parsing.
const person = z.object({
name: z.string(),
});
person.parse({
name: "bob dylan",
extraKey: 61,
});
// => { name: "bob dylan" }
// extraKey has been stripped
Instead, if you want to pass through unknown keys, use .passthrough() .
person.passthrough().parse({
name: "bob dylan",
extraKey: 61,
});
// => { name: "bob dylan", extraKey: 61 }
.strictBy default Zod object schemas strip out unrecognized keys during parsing. You can disallow unknown keys with .strict() . If there are any unknown keys in the input, Zod will throw an error.
const person = z
.object({
name: z.string(),
})
.strict();
person.parse({
name: "bob dylan",
extraKey: 61,
});
// => throws ZodError
.stripYou can use the .strip method to reset an object schema to the default behavior (stripping unrecognized keys).
.catchallYou can pass a "catchall" schema into an object schema. All unknown keys will be validated against it.
const person = z
.object({
name: z.string(),
})
.catchall(z.number());
person.parse({
name: "bob dylan",
validExtraKey: 61, // works fine
});
person.parse({
name: "bob dylan",
validExtraKey: false, // fails
});
// => throws ZodError
Using .catchall() obviates .passthrough() , .strip() , or .strict(). All keys are now considered "known".
const stringArray = z.array(z.string());
// equivalent
const stringArray = z.string().array();
Be careful with the .array() method. It returns a new ZodArray instance. This means the order in which you call methods matters. For instance:
z.string().optional().array(); // (string | undefined)[]
z.string().array().optional(); // string[] | undefined
.elementUse .element to access the schema for an element of the array.
stringArray.element; // => string schema
.nonemptyIf you want to ensure that an array contains at least one element, use .nonempty().
const nonEmptyStrings = z.string().array().nonempty();
// the inferred type is now
// [string, ...string[]]
nonEmptyStrings.parse([]); // throws: "Array cannot be empty"
nonEmptyStrings.parse(["Ariana Grande"]); // passes
You can optionally specify a custom error message:
// optional custom error message
const nonEmptyStrings = z.string().array().nonempty({
message: "Can't be empty!",
});
.min/.max/.lengthz.string().array().min(5); // must contain 5 or more items
z.string().array().max(5); // must contain 5 or fewer items
z.string().array().length(5); // must contain 5 items exactly
Unlike .nonempty() these methods do not change the inferred type.
Unlike arrays, tuples have a fixed number of elements and each element can have a different type.
const athleteSchema = z.tuple([
z.string(), // name
z.number(), // jersey number
z.object({
pointsScored: z.number(),
}), // statistics
]);
type Athlete = z.infer<typeof athleteSchema>;
// type Athlete = [string, number, { pointsScored: number }]
A variadic ("rest") argument can be added with the .rest method.
const variadicTuple = z.tuple([z.string()]).rest(z.number());
const result = variadicTuple.parse(["hello", 1, 2, 3]);
// => [string, ...number[]];
Zod includes a built-in z.union method for composing "OR" types.
const stringOrNumber = z.union([z.string(), z.number()]);
stringOrNumber.parse("foo"); // passes
stringOrNumber.parse(14); // passes
Zod will test the input against each of the "options" in order and return the first value that validates successfully.
For convenience, you can also use the .or method:
const stringOrNumber = z.string().or(z.number());
Optional string validation:
To validate an optional form input, you can union the desired string validation with an empty string literal.
This example validates an input that is optional but needs to contain a valid URL:
const optionalUrl = z.union([z.string().url().nullish(), z.literal("")]);
console.log(optionalUrl.safeParse(undefined).success); // true
console.log(optionalUrl.safeParse(null).success); // true
console.log(optionalUrl.safeParse("").success); // true
console.log(optionalUrl.safeParse("https://zod.dev").success); // true
console.log(optionalUrl.safeParse("not a valid url").success); // false
A discriminated union is a union of object schemas that all share a particular key.
type MyUnion =
| { status: "success"; data: string }
| { status: "failed"; error: Error };
Such unions can be represented with the z.discriminatedUnion method. This enables faster evaluation, because Zod can check the discriminator key (status in the example above) to determine which schema should be used to parse the input. This makes parsing more efficient and lets Zod report friendlier errors.
With the basic union method, the input is tested against each of the provided "options", and in the case of invalidity, issues for all the "options" are shown in the zod error. On the other hand, the discriminated union allows for selecting just one of the "options", testing against it, and showing only the issues related to this "option".
const myUnion = z.discriminatedUnion("status", [
z.object({ status: z.literal("success"), data: z.string() }),
z.object({ status: z.literal("failed"), error: z.instanceof(Error) }),
]);
myUnion.parse({ status: "success", data: "yippie ki yay" });
You can extract a reference to the array of schemas with the .options property.
myUnion.options; // [ZodObject<...>, ZodObject<...>]
To merge two or more discriminated unions, use .options with destructuring.
const A = z.discriminatedUnion("status", [
/* options */
]);
const B = z.discriminatedUnion("status", [
/* options */
]);
const AB = z.discriminatedUnion("status", [...A.options, ...B.options]);
Record schemas are used to validate types such as Record<string, number>. This is particularly useful for storing or caching items by ID.
const User = z.object({ name: z.string() });
const UserStore = z.record(z.string(), User);
type UserStore = z.infer<typeof UserStore>;
// => Record<string, { name: string }>
The schema and inferred type can be used like so:
const userStore: UserStore = {};
userStore["77d2586b-9e8e-4ecf-8b21-ea7e0530eadd"] = {
name: "Carlotta",
}; // passes
userStore["77d2586b-9e8e-4ecf-8b21-ea7e0530eadd"] = {
whatever: "Ice cream sundae",
}; // TypeError
A note on numerical keys
While z.record(keyType, valueType) is able to accept numerical key types and TypeScript's built-in Record type is Record<KeyType, ValueType>, it's hard to represent the TypeScript type Record<number, any> in Zod.
As it turns out, TypeScript's behavior surrounding [k: number] is a little unintuitive:
const testMap: { [k: number]: string } = {
1: "one",
};
for (const key in testMap) {
console.log(`${key}: ${typeof key}`);
}
// prints: `1: string`
As you can see, JavaScript automatically casts all object keys to strings under the hood. Since Zod is trying to bridge the gap between static and runtime types, it doesn't make sense to provide a way of creating a record schema with numerical keys, since there's no such thing as a numerical key in runtime JavaScript.
const stringNumberMap = z.map(z.string(), z.number());
type StringNumberMap = z.infer<typeof stringNumberMap>;
// type StringNumberMap = Map<string, number>
const numberSet = z.set(z.number());
type NumberSet = z.infer<typeof numberSet>;
// type NumberSet = Set<number>
Set schemas can be further constrained with the following utility methods.
z.set(z.string()).nonempty(); // must contain at least one item
z.set(z.string()).min(5); // must contain 5 or more items
z.set(z.string()).max(5); // must contain 5 or fewer items
z.set(z.string()).size(5); // must contain 5 items exactly
Intersections are useful for creating "logical AND" types. This is useful for intersecting two object types.
const Person = z.object({
name: z.string(),
});
const Employee = z.object({
role: z.string(),
});
const EmployedPerson = z.intersection(Person, Employee);
// equivalent to:
const EmployedPerson = Person.and(Employee);
Though in many cases, it is recommended to use A.merge(B) to merge two objects. The .merge method returns a new ZodObject instance, whereas A.and(B) returns a less useful ZodIntersection instance that lacks common object methods like pick and omit.
const a = z.union([z.number(), z.string()]);
const b = z.union([z.number(), z.boolean()]);
const c = z.intersection(a, b);
type c = z.infer<typeof c>; // => number
You can define a recursive schema in Zod, but because of a limitation of TypeScript, their type can't be statically inferred. Instead you'll need to define the type definition manually, and provide it to Zod as a "type hint".
const baseCategorySchema = z.object({
name: z.string(),
});
type Category = z.infer<typeof baseCategorySchema> & {
subcategories: Category[];
};
const categorySchema: z.ZodType<Category> = baseCategorySchema.extend({
subcategories: z.lazy(() => categorySchema.array()),
});
categorySchema.parse({
name: "People",
subcategories: [
{
name: "Politicians",
subcategories: [
{
name: "Presidents",
subcategories: [],
},
],
},
],
}); // passes
Thanks to crasite for this example.
When using z.ZodType with z.ZodEffects (
.refine,
.transform,
preprocess,
etc...
), you will need to define the input and output types of the schema. z.ZodType<Output, z.ZodTypeDef, Input>
const isValidId = (id: string): id is `${string}/${string}` =>
id.split("/").length === 2;
const baseSchema = z.object({
id: z.string().refine(isValidId),
});
type Input = z.input<typeof baseSchema> & {
children: Input[];
};
type Output = z.output<typeof baseSchema> & {
children: Output[];
};
const schema: z.ZodType<Output, z.ZodTypeDef, Input> = baseSchema.extend({
children: z.lazy(() => schema.array()),
});
Thanks to marcus13371337 and JoelBeeldi for this example.
If you want to validate any JSON value, you can use the snippet below.
const literalSchema = z.union([z.string(), z.number(), z.boolean(), z.null()]);
type Literal = z.infer<typeof literalSchema>;
type Json = Literal | { [key: string]: Json } | Json[];
const jsonSchema: z.ZodType<Json> = z.lazy(() =>
z.union([literalSchema, z.array(jsonSchema), z.record(jsonSchema)])
);
jsonSchema.parse(data);
Thanks to ggoodman for suggesting this.
Despite supporting recursive schemas, passing cyclical data into Zod will cause an infinite loop in some cases.
To detect cyclical objects before they cause problems, consider this approach.
const numberPromise = z.promise(z.number());
"Parsing" works a little differently with promise schemas. Validation happens in two parts:
.then and .catch methods.)..then to attach an additional validation step onto the existing Promise. You'll have to use .catch on the returned Promise to handle validation failures.numberPromise.parse("tuna");
// ZodError: Non-Promise type: string
numberPromise.parse(Promise.resolve("tuna"));
// => Promise<number>
const test = async () => {
await numberPromise.parse(Promise.resolve("tuna"));
// ZodError: Non-number type: string
await numberPromise.parse(Promise.resolve(3.14));
// => 3.14
};
You can use z.instanceof to check that the input is an instance of a class. This is useful to validate inputs against classes that are exported from third-party libraries.
class Test {
name: string;
}
const TestSchema = z.instanceof(Test);
const blob: any = "whatever";
TestSchema.parse(new Test()); // passes
TestSchema.parse(blob); // throws
Zod also lets you define "function schemas". This makes it easy to validate the inputs and outputs of a function without intermixing your validation code and "business logic".
You can create a function schema with z.function(args, returnType) .
const myFunction = z.function();
type myFunction = z.infer<typeof myFunction>;
// => ()=>unknown
Define inputs and outputs.
const myFunction = z
.function()
.args(z.string(), z.number()) // accepts an arbitrary number of arguments
.returns(z.boolean());
type myFunction = z.infer<typeof myFunction>;
// => (arg0: string, arg1: number)=>boolean
Function schemas have an .implement() method which accepts a function and returns a new function that automatically validates its inputs and outputs.
const trimmedLength = z
.function()
.args(z.string()) // accepts an arbitrary number of arguments
.returns(z.number())
.implement((x) => {
// TypeScript knows x is a string!
return x.trim().length;
});
trimmedLength("sandwich"); // => 8
trimmedLength(" asdf "); // => 4
If you only care about validating inputs, just don't call the .returns() method. The output type will be inferred from the implementation.
You can use the special
z.void()option if your function doesn't return anything. This will let Zod properly infer the type of void-returning functions. (Void-returning functions actually return undefined.)
const myFunction = z
.function()
.args(z.string())
.implement((arg) => {
return [arg.length];
});
myFunction; // (arg: string)=>number[]
Extract the input and output schemas from a function schema.
myFunction.parameters();
// => ZodTuple<[ZodString, ZodNumber]>
myFunction.returnType();
// => ZodBoolean
Zod now supports primitive coercion without the need for
.preprocess(). See the coercion docs for more information.
Typically Zod operates under a "parse then transform" paradigm. Zod validates the input first, then passes it through a chain of transformation functions. (For more information about transforms, read the .transform docs.)
But sometimes you want to apply some transform to the input before parsing happens. A common use case: type coercion. Zod enables this with the z.preprocess().
const castToString = z.preprocess((val) => String(val), z.string());
This returns a ZodEffects instance. ZodEffects is a wrapper class that contains all logic pertaining to preprocessing, refinements, and transforms.
You can create a Zod schema for any TypeScript type by using z.custom(). This is useful for creating schemas for types that are not supported by Zod out of the box, such as template string literals.
const px = z.custom<`${number}px`>((val) => {
return typeof val === "string" ? /^\d+px$/.test(val) : false;
});
type px = z.infer<typeof px>; // `${number}px`
px.parse("42px"); // "42px"
px.parse("42vw"); // throws;
If you don't provide a validation function, Zod will allow any value. This can be dangerous!
z.custom<{ arg: string }>(); // performs no validation
You can customize the error message and other options by passing a second argument. This parameter works the same way as the params parameter of .refine.
z.custom<...>((val) => ..., "custom error message");
All Zod schemas contain certain methods.
.parse.parse(data: unknown): T
Given any Zod schema, you can call its .parse method to check data is valid. If it is, a value is returned with full type information! Otherwise, an error is thrown.
IMPORTANT: The value returned by
.parseis a deep clone of the variable you passed in.
const stringSchema = z.string();
stringSchema.parse("fish"); // => returns "fish"
stringSchema.parse(12); // throws error
.parseAsync.parseAsync(data:unknown): Promise<T>
If you use asynchronous refinements or transforms (more on those later), you'll need to use .parseAsync.
const stringSchema = z.string().refine(async (val) => val.length <= 8);
await stringSchema.parseAsync("hello"); // => returns "hello"
await stringSchema.parseAsync("hello world"); // => throws error
.safeParse.safeParse(data:unknown): { success: true; data: T; } | { success: false; error: ZodError; }
If you don't want Zod to throw errors when validation fails, use .safeParse. This method returns an object containing either the successfully parsed data or a ZodError instance containing detailed information about the validation problems.
stringSchema.safeParse(12);
// => { success: false; error: ZodError }
stringSchema.safeParse("billie");
// => { success: true; data: 'billie' }
The result is a discriminated union, so you can handle errors very conveniently:
const result = stringSchema.safeParse("billie");
if (!result.success) {
// handle error then return
result.error;
} else {
// do something
result.data;
}
.safeParseAsyncAlias:
.spa
An asynchronous version of safeParse.
await stringSchema.safeParseAsync("billie");
For convenience, this has been aliased to .spa:
await stringSchema.spa("billie");
.refine.refine(validator: (data:T)=>any, params?: RefineParams)
Zod lets you provide custom validation logic via refinements. (For advanced features like creating multiple issues and customizing error codes, see .superRefine.)
Zod was designed to mirror TypeScript as closely as possible. But there are many so-called "refinement types" you may wish to check for that can't be represented in TypeScript's type system. For instance: checking that a number is an integer or that a string is a valid email address.
For example, you can define a custom validation check on any Zod schema with .refine :
const myString = z.string().refine((val) => val.length <= 255, {
message: "String can't be more than 255 characters",
});
⚠️ Refinement functions should not throw. Instead they should return a falsy value to signal failure.
As you can see, .refine takes two arguments.
T — the inferred type of the schema) and returns any. Any truthy value will pass validation. (Prior to zod@1.6.2 the validation function had to return a boolean.)type RefineParams = {
// override error message
message?: string;
// appended to error path
path?: (string | number)[];
// params object you can use to customize message
// in error map
params?: object;
};
For advanced cases, the second argument can also be a function that returns RefineParams.
const longString = z.string().refine(
(val) => val.length > 10,
(val) => ({ message: `${val} is not more than 10 characters` })
);
const passwordForm = z
.object({
password: z.string(),
confirm: z.string(),
})
.refine((data) => data.password === data.confirm, {
message: "Passwords don't match",
path: ["confirm"], // path of error
});
passwordForm.parse({ password: "asdf", confirm: "qwer" });
Because you provided a path parameter, the resulting error will be:
ZodError {
issues: [{
"code": "custom",
"path": [ "confirm" ],
"message": "Passwords don't match"
}]
}
Refinements can also be async:
const userId = z.string().refine(async (id) => {
// verify that ID exists in database
return true;
});
⚠️ If you use async refinements, you must use the
.parseAsyncmethod to parse data! Otherwise Zod will throw an error.
Transforms and refinements can be interleaved:
z.string()
.transform((val) => val.length)
.refine((val) => val > 25);
.superRefineThe .refine method is actually syntactic sugar atop a more versatile (and verbose) method called superRefine. Here's an example:
const Strings = z.array(z.string()).superRefine((val, ctx) => {
if (val.length > 3) {
ctx.addIssue({
code: z.ZodIssueCode.too_big,
maximum: 3,
type: "array",
inclusive: true,
message: "Too many items 😡",
});
}
if (val.length !== new Set(val).size) {
ctx.addIssue({
code: z.ZodIssueCode.custom,
message: `No duplicates allowed.`,
});
}
});
You can add as many issues as you like. If ctx.addIssue is not called during the execution of the function, validation passes.
Normally refinements always create issues with a ZodIssueCode.custom error code, but with superRefine it's possible to throw issues of any ZodIssueCode. Each issue code is described in detail in the Error Handling guide: ERROR_HANDLING.md.
By default, parsing will continue even after a refinement check fails. For instance, if you chain together multiple refinements, they will all be executed. However, it may be desirable to abort early to prevent later refinements from being executed. To achieve this, pass the fatal flag to ctx.addIssue and return z.NEVER.
const schema = z.number().superRefine((val, ctx) => {
if (val < 10) {
ctx.addIssue({
code: z.ZodIssueCode.custom,
message: "should be >= 10",
fatal: true,
});
return z.NEVER;
}
if (val !== 12) {
ctx.addIssue({
code: z.ZodIssueCode.custom,
message: "should be twelve",
});
}
});
If you provide a type predicate to .refine() or .superRefine(), the resulting type will be narrowed down to your predicate's type. This is useful if you are mixing multiple chained refinements and transformations:
const schema = z
.object({
first: z.string(),
second: z.number(),
})
.nullable()
.superRefine((arg, ctx): arg is { first: string; second: number } => {
if (!arg) {
ctx.addIssue({
code: z.ZodIssueCode.custom, // customize your issue
message: "object should exist",
});
}
return z.NEVER; // The return value is not used, but we need to return something to satisfy the typing
})
// here, TS knows that arg is not null
.refine((arg) => arg.first === "bob", "`first` is not `bob`!");
⚠️ You must use
ctx.addIssue()instead of returning a boolean value to indicate whether the validation passes. Ifctx.addIssueis not called during the execution of the function, validation passes.
.transformTo transform data after parsing, use the transform method.
const stringToNumber = z.string().transform((val) => val.length);
stringToNumber.parse("string"); // => 6
Note that stringToNumber above is an instance of the ZodEffects subclass. It is NOT an instance of ZodString. If you want to use the built-in methods of ZodString (e.g. .email()) you must apply those methods before any transforms.
const emailToDomain = z
.string()
.email()
.transform((val) => val.split("@")[1]);
emailToDomain.parse("colinhacks@example.com"); // => example.com
The .transform method can simultaneously validate and transform the value. This is often simpler and less duplicative than chaining transform and refine.
As with .superRefine, the transform function receives a ctx object with an addIssue method that can be used to register validation issues.
const numberInString = z.string().transform((val, ctx) => {
const parsed = parseInt(val);
if (isNaN(parsed)) {
ctx.addIssue({
code: z.ZodIssueCode.custom,
message: "Not a number",
});
// This is a special symbol you can use to
// return early from the transform function.
// It has type `never` so it does not affect the
// inferred return type.
return z.NEVER;
}
return parsed;
});
Transforms and refinements can be interleaved. These will be executed in the order they are declared.
const nameToGreeting = z
.string()
.transform((val) => val.toUpperCase())
.refine((val) => val.length > 15)
.transform((val) => `Hello ${val}`)
.refine((val) => val.indexOf("!") === -1);
Transforms can also be async.
const IdToUser = z
.string()
.uuid()
.transform(async (id) => {
return await getUserById(id);
});
⚠️ If your schema contains asynchronous transforms, you must use .parseAsync() or .safeParseAsync() to parse data. Otherwise Zod will throw an error.
.defaultYou can use transforms to implement the concept of "default values" in Zod.
const stringWithDefault = z.string().default("tuna");
stringWithDefault.parse(undefined); // => "tuna"
Optionally, you can pass a function into .default that will be re-executed whenever a default value needs to be generated:
const numberWithRandomDefault = z.number().default(Math.random);
numberWithRandomDefault.parse(undefined); // => 0.4413456736055323
numberWithRandomDefault.parse(undefined); // => 0.1871840107401901
numberWithRandomDefault.parse(undefined); // => 0.7223408162401552
Conceptually, this is how Zod processes default values:
undefined, the default value is returned.describeUse .describe() to add a description property to the resulting schema.
const documentedString = z
.string()
.describe("A useful bit of text, if you know what to do with it.");
documentedString.description; // A useful bit of text…
This can be useful for documenting a field, for example in a JSON Schema using a library like zod-to-json-schema).
.catchUse .catch() to provide a "catch value" to be returned in the event of a parsing error.
const numberWithCatch = z.number().catch(42);
numberWithCatch.parse(5); // => 5
numberWithCatch.parse("tuna"); // => 42
Optionally, you can pass a function into .catch that will be re-executed whenever a default value needs to be generated. A ctx object containing the caught error will be passed into this function.
const numberWithRandomCatch = z.number().catch((ctx) => {
ctx.error; // the caught ZodError
return Math.random();
});
numberWithRandomCatch.parse("sup"); // => 0.4413456736055323
numberWithRandomCatch.parse("sup"); // => 0.1871840107401901
numberWithRandomCatch.parse("sup"); // => 0.7223408162401552
Conceptually, this is how Zod processes "catch values":
.optionalA convenience method that returns an optional version of a schema.
const optionalString = z.string().optional(); // string | undefined
// equivalent to
z.optional(z.string());
.nullableA convenience method that returns a nullable version of a schema.
const nullableString = z.string().nullable(); // string | null
// equivalent to
z.nullable(z.string());
.nullishA convenience method that returns a "nullish" version of a schema. Nullish schemas will accept both undefined and null. Read more about the concept of "nullish" in the TypeScript 3.7 release notes.
const nullishString = z.string().nullish(); // string | null | undefined
// equivalent to
z.string().nullable().optional();
.arrayA convenience method that returns an array schema for the given type:
const stringArray = z.string().array(); // string[]
// equivalent to
z.array(z.string());
.promiseA convenience method for promise types:
const stringPromise = z.string().promise(); // Promise<string>
// equivalent to
z.promise(z.string());
.orA convenience method for union types.
const stringOrNumber = z.string().or(z.number()); // string | number
// equivalent to
z.union([z.string(), z.number()]);
.andA convenience method for creating intersection types.
const nameAndAge = z
.object({ name: z.string() })
.and(z.object({ age: z.number() })); // { name: string } & { age: number }
// equivalent to
z.intersection(z.object({ name: z.string() }), z.object({ age: z.number() }));
.brand.brand<T>() => ZodBranded<this, B>
TypeScript's type system is structural, which means that any two types that are structurally equivalent are considered the same.
type Cat = { name: string };
type Dog = { name: string };
const petCat = (cat: Cat) => {};
const fido: Dog = { name: "fido" };
petCat(fido); // works fine
In some cases, its can be desirable to simulate nominal typing inside TypeScript. For instance, you may wish to write a function that only accepts an input that has been validated by Zod. This can be achieved with branded types (AKA opaque types).
const Cat = z.object({ name: z.string() }).brand<"Cat">();
type Cat = z.infer<typeof Cat>;
const petCat = (cat: Cat) => {};
// this works
const simba = Cat.parse({ name: "simba" });
petCat(simba);
// this doesn't
petCat({ name: "fido" });
Under the hood, this works by attaching a "brand" to the inferred type using an intersection type. This way, plain/unbranded data structures are no longer assignable to the inferred type of the schema.
const Cat = z.object({ name: z.string() }).brand<"Cat">();
type Cat = z.infer<typeof Cat>;
// {name: string} & {[symbol]: "Cat"}
Note that branded types do not affect the runtime result of .parse. It is a static-only construct.
.readonly.readonly() => ZodReadonly<this>
This method returns a ZodReadonly schema instance that parses the input using the base schema, then calls Object.freeze() on the result. The inferred type is also marked as readonly.
const schema = z.object({ name: z.string() }).readonly();
type schema = z.infer<typeof schema>;
// Readonly<{name: string}>
const result = schema.parse({ name: "fido" });
result.name = "simba"; // error
The inferred type uses TypeScript's built-in readonly types when relevant.
z.array(z.string()).readonly();
// readonly string[]
z.tuple([z.string(), z.number()]).readonly();
// readonly [string, number]
z.map(z.string(), z.date()).readonly();
// ReadonlyMap<string, Date>
z.set(z.string()).readonly();
// ReadonlySet<string>
.pipeSchemas can be chained into validation "pipelines". It's useful for easily validating the result after a .transform():
z.string()
.transform((val) => val.length)
.pipe(z.number().min(5));
The .pipe() method returns a ZodPipeline instance.
.pipe() to fix common issues with z.coerce.You can constrain the input to types that work well with your chosen coercion. Then use .pipe() to apply the coercion.
without constrained input:
const toDate = z.coerce.date();
// works intuitively
console.log(toDate.safeParse("2023-01-01").success); // true
// might not be what you want
console.log(toDate.safeParse(null).success); // true
with constrained input:
const datelike = z.union([z.number(), z.string(), z.date()]);
const datelikeToDate = datelike.pipe(z.coerce.date());
// still works intuitively
console.log(datelikeToDate.safeParse("2023-01-01").success); // true
// more likely what you want
console.log(datelikeToDate.safeParse(null).success); // false
You can also use this technique to avoid coercions that throw uncaught errors.
without constrained input:
const toBigInt = z.coerce.bigint();
// works intuitively
console.log(toBigInt.safeParse("42")); // true
// probably not what you want
console.log(toBigInt.safeParse(null)); // throws uncaught error
with constrained input:
const toNumber = z.number().or(z.string()).pipe(z.coerce.number());
const toBigInt = z.bigint().or(toNumber).pipe(z.coerce.bigint());
// still works intuitively
console.log(toBigInt.safeParse("42").success); // true
// error handled by zod, more likely what you want
console.log(toBigInt.safeParse(null).success); // false
You can extract the TypeScript type of any schema with z.infer<typeof mySchema> .
const A = z.string();
type A = z.infer<typeof A>; // string
const u: A = 12; // TypeError
const u: A = "asdf"; // compiles
What about transforms?
In reality each Zod schema internally tracks two types: an input and an output. For most schemas (e.g. z.string()) these two are the same. But once you add transforms into the mix, these two values can diverge. For instance z.string().transform(val => val.length) has an input of string and an output of number.
You can separately extract the input and output types like so:
const stringToNumber = z.string().transform((val) => val.length);
// ⚠️ Important: z.infer returns the OUTPUT type!
type input = z.input<typeof stringToNumber>; // string
type output = z.output<typeof stringToNumber>; // number
// equivalent to z.output!
type inferred = z.infer<typeof stringToNumber>; // number
With TypeScript generics, you can write reusable functions that accept Zod schemas as parameters. This enables you to create custom validation logic, schema transformations, and more, while maintaining type safety and inference.
When attempting to write a function that accepts a Zod schema as an input, it's tempting to try something like this:
function inferSchema<T>(schema: z.ZodType<T>) {
return schema;
}
This approach is incorrect, and limits TypeScript's ability to properly infer the argument. No matter what you pass in, the type of schema will be an instance of ZodType.
inferSchema(z.string());
// => ZodType<string>
This approach loses type information, namely which subclass the input actually is (in this case, ZodString). That means you can't call any string-specific methods like .min() on the result of inferSchema.
A better approach is to infer the schema as a whole instead of merely its inferred type. You can do this with a utility type called z.ZodTypeAny.
function inferSchema<T extends z.ZodTypeAny>(schema: T) {
return schema;
}
inferSchema(z.string());
// => ZodString
ZodTypeAnyis just a shorthand forZodType<any, any, any>, a type that is broad enough to match any Zod schema.
The Result is now fully and properly typed, and the type system can infer the specific subclass of the schema.
If you follow the best practice of using z.ZodTypeAny as the generic parameter for your schema, you may encounter issues with the parsed data being typed as any instead of the inferred type of the schema.
function parseData<T extends z.ZodTypeAny>(data: unknown, schema: T) {
return schema.parse(data);
}
parseData("sup", z.string());
// => any
Due to how TypeScript inference works, it is treating schema like a ZodTypeAny instead of the inferred type. You can fix this with a type cast using z.infer.
function parseData<T extends z.ZodTypeAny>(data: unknown, schema: T) {
return schema.parse(data) as z.infer<T>;
// ^^^^^^^^^^^^^^ <- add this
}
parseData("sup", z.string());
// => string
The ZodType class has three generic parameters.
class ZodType<
Output = any,
Def extends ZodTypeDef = ZodTypeDef,
Input = Output
> { ... }
By constraining these in your generic input, you can limit what schemas are allowable as inputs to your function:
function makeSchemaOptional<T extends z.ZodType<string>>(schema: T) {
return schema.optional();
}
makeSchemaOptional(z.string());
// works fine
makeSchemaOptional(z.number());
// Error: 'ZodNumber' is not assignable to parameter of type 'ZodType<string, ZodTypeDef, string>'
Zod provides a subclass of Error called ZodError. ZodErrors contain an issues array containing detailed information about the validation problems.
const result = z
.object({
name: z.string(),
})
.safeParse({ name: 12 });
if (!result.success) {
result.error.issues;
/* [
{
"code": "invalid_type",
"expected": "string",
"received": "number",
"path": [ "name" ],
"message": "Expected string, received number"
}
] */
}
For detailed information about the possible error codes and how to customize error messages, check out the dedicated error handling guide: ERROR_HANDLING.md
Zod's error reporting emphasizes completeness and correctness. If you are looking to present a useful error message to the end user, you should either override Zod's error messages using an error map (described in detail in the Error Handling guide) or use a third-party library like zod-validation-error
You can use the .format() method to convert this error into a nested object.
const result = z
.object({
name: z.string(),
})
.safeParse({ name: 12 });
if (!result.success) {
const formatted = result.error.format();
/* {
name: { _errors: [ 'Expected string, received number' ] }
} */
formatted.name?._errors;
// => ["Expected string, received number"]
}
There are a handful of other widely-used validation libraries, but all of them have certain design limitations that make for a non-ideal developer experience.
Doesn't support static type inference 😕
https://github.com/jquense/yup
Yup is a full-featured library that was implemented first in vanilla JS, and later rewritten in TypeScript.
https://github.com/gcanti/io-ts
io-ts is an excellent library by gcanti. The API of io-ts heavily inspired the design of Zod.
In our experience, io-ts prioritizes functional programming purity over developer experience in many cases. This is a valid and admirable design goal, but it makes io-ts particularly hard to integrate into an existing codebase with a more procedural or object-oriented bias. For instance, consider how to define an object with optional properties in io-ts:
import * as t from "io-ts";
const A = t.type({
foo: t.string,
});
const B = t.partial({
bar: t.number,
});
const C = t.intersection([A, B]);
type C = t.TypeOf<typeof C>;
// returns { foo: string; bar?: number | undefined }
You must define the required and optional props in separate object validators, pass the optionals through t.partial (which marks all properties as optional), then combine them with t.intersection .
Consider the equivalent in Zod:
const C = z.object({
foo: z.string(),
bar: z.number().optional(),
});
type C = z.infer<typeof C>;
// returns { foo: string; bar?: number | undefined }
This more declarative API makes schema definitions vastly more concise.
io-ts also requires the use of gcanti's functional programming library fp-ts to parse results and handle errors. This is another fantastic resource for developers looking to keep their codebase strictly functional. But depending on fp-ts necessarily comes with a lot of intellectual overhead; a developer has to be familiar with functional programming concepts and the fp-ts nomenclature to use the library.
fp-ts compatibility[T, ...T[]])https://github.com/pelotom/runtypes
Good type inference support.
[T, ...T[]])https://github.com/sindresorhus/ow
Ow is focused on function input validation. It's a library that makes it easy to express complicated assert statements, but it doesn't let you parse untyped data. They support a much wider variety of types; Zod has a nearly one-to-one mapping with TypeScript's type system, whereas ow lets you validate several highly-specific types out of the box (e.g. int32Array , see full list in their README).
If you want to validate function inputs, use function schemas in Zod! It's a much simpler approach that lets you reuse a function type declaration without repeating yourself (namely, copy-pasting a bunch of ow assertions at the beginning of every function). Also Zod lets you validate your return types as well, so you can be sure there won't be any unexpected data passed downstream.
View the changelog at CHANGELOG.md
FAQs
TypeScript-first schema declaration and validation library with static type inference
We found that zod demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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