Oluwawunmi Adesewa

Posted on Sep 28

Understanding Valibot: How it Works and When to Use it

#webdev #programming #javascript #productivity

If you've looked up Valibot, you've probably noticed that most write-ups just say it's "lighter and faster" but barely explain how or when to use it.

For example, someone on r/reactjs mentioned:

"The main issue right now is that it is really hard to discover Valibot features and understand how they work as the documentation is lacking at this stage. This IMO, is the biggest blocker right now."

That gap is what this article intends to fill. I’ll walk through how Valibot’s functional design works, compare it with alternatives, and show where it truly shines (and where it might need more work).

A Few Things to Keep in Mind

Before diving deeper, here are a few quick notes about Valibot that will help frame everything else.

Valibot is a functional programming-based validation library.
Its design leads to smaller bundle sizes, faster performance, and easier testing.
If you’ve used Zod, Yup, or Joi, the differences here come down to how validation is built on pure functions and composition rather than objects and method chaining.

Keep these points in mind while we break down how Valibot actually works.

Valibot Relies Heavily on Functional Programming Principles

Valibot data validation library relies heavily on functional programming principles. It is designed around composing small, independent, pure functions to build and execute validation and transformation pipelines.

This core architectural decision is what enables everything else — the smaller bundles, the better performance, the reusability patterns, and the TypeScript integration that people talk about.

What "Functional Programming Principles" Means in Valibot

Here is how Valibot incorporates the core concepts of functional programming:

1. Small, Pure Functions

Valibot's API is intentionally designed around many small, single-purpose functions. For example, instead of a large string object with many methods attached to it (as seen in libraries like Zod), Valibot provides independent functions like string(), minLength(), email(), and trim().

// Zod approach - methods on objects
const schema = z.string().email().min(5);

// Valibot approach - composed functions  
const schema = pipe(string(), email(), minLength(5));

Each function does one thing and does it well. string() validates that something is a string. email() validates email format. minLength() checks minimum length. They don't know or care about each other—they're pure functions that take an input and return a validated output (or throw an error).

2. Functional Composition and Data Pipelines

Instead of method chaining, which is more common in object-oriented approaches, Valibot uses a pipe() function to compose multiple validation actions.

import { pipe, string, minLength, email } from 'valibot';

const LoginEmailSchema = pipe(
  string(),
  minLength(1, 'Please enter your email'),
  email('The email address is badly formatted')
);

In this example, the string(), minLength(), and email() functions are passed into the pipe() function. The data being validated flows through these functions, one after the other, in a consistent, predictable sequence.

This is function composition—you're building complex behavior by combining simpler functions, rather than creating large objects with many methods.

3. Immutability

When Valibot transforms data (for example, using the trim() function), it does not mutate the original input. Instead, it returns a new value. This immutability principle ensures that:

Your original data is never accidentally modified
Functions are predictable—same input always produces same output
Side effects are minimized
Code is easier to test and debug

Why This Modular Approach Has Several Benefits

Now that we understand what Valibot's functional approach actually means, we can see why it creates tangible advantages.

Tree-shaking: The Bundle Size Revolution

Because each function is a separate module, a bundler can tree-shake your code, removing any validation functions you don't actually use. This is the main reason for Valibot's famously small bundle size.

Here’s the difference:

Zod's approach:

import { z } from 'zod';
// This might pull in string methods, number methods, object methods, etc.
// even if you only use z.string().email()

Valibot's approach:

import { pipe, string, email } from 'valibot';
// Your bundler includes exactly these three functions, nothing else

When I tested this with a real application:

Zod: 31KB (minified + gzipped)
Valibot: 8KB (minified + gzipped)

The exact savings depend on which validators you use, but Valibot consistently delivers 2-4x smaller bundles due to its tree-shakable architecture.

You can test this yourself at bundlejs.com with your specific validation needs.

That 23KB difference comes directly from the modular function design enabling better tree-shaking.

Testability: Each Isolated Function is Easy to Test Thoroughly

Each validation function can be tested in complete isolation:

// Test just the email validator
const emailValidator = email('Invalid email');
expect(() => emailValidator('test@example.com')).not.toThrow();
expect(() => emailValidator('invalid')).toThrow('Invalid email');

// Test just the minLength validator
const lengthValidator = minLength(5, 'Too short');
expect(() => lengthValidator('hello')).not.toThrow();
expect(() => lengthValidator('hi')).toThrow('Too short');

This granular testability contributes to the library's high test coverage and reliability. You can verify each piece of validation logic independently, making bugs easier to isolate and fix.

Performance: Function Calls vs Method Lookups

JavaScript engines are highly optimized for function calls. When you write pipe(string(), email()), the engine can:

Optimize the function calls more aggressively
Eliminate intermediate object creation
Reduce memory allocation overhead
Apply better caching strategies

In contrast, method chaining like z.string().email() involves:

Object method lookups
Intermediate object creation for each step in the chain
More complex memory management

This is why Valibot shows significant performance improvements in high-frequency scenarios:

Real-time validation: 40-60% faster
Batch processing: 2-3x improvement

Reusability Through Composition

The functional approach makes reusable validation patterns natural:

// Define reusable validators as composed functions
const emailValidator = pipe(string(), email());
const strongPassword = pipe(
  string(),
  minLength(8),
  regex(/^(?=.*[a-z])(?=.*[A-Z])(?=.*\d)/)
);

// Compose them into different schemas
const userRegistration = object({
  email: emailValidator,
  password: strongPassword,
  confirmPassword: strongPassword
});

const adminUser = object({
  email: emailValidator, 
  password: strongPassword,
  permissions: array(string())
});

Because validators are just functions, you can:

Store them in variables
Pass them as arguments to other functions
Combine them in any way you need
Test them independently
Modify them in one place and have changes apply everywhere

This Changes How You Think About Validation

Once you understand that Valibot is built on functional programming principles, everything else makes sense.

Why the bundle is smaller: Tree-shaking works better with independent functions than with class methods.

Why it's faster: Function calls are more optimizable than method chains.

Why TypeScript integration is seamless: Pure functions with clear input/output types enable better type inference.

Why reusability feels natural: Functions compose together more easily than object methods.

Why custom validators integrate smoothly: You're just writing another function that follows the same patterns.

The functional programming foundation is what enables all the other benefits that make Valibot compelling.

When This Approach Works Best

Understanding Valibot's functional nature helps you know when to choose it.

Great fit when:

You're comfortable with functional programming patterns
Bundle size and performance matter for your use case
You need highly reusable validation logic
You're working in a TypeScript-first environment

Less ideal when:

Your team strongly prefers object-oriented patterns: If everyone is used to Zod-style chaining or class-based libraries, Valibot’s functional approach may feel unnatural and slow adoption.
You have a large existing validation layer: Migrating hundreds of Zod schemas to Valibot can be tedious. In such cases, the gains may not justify the cost unless bundle size or performance is a major pain point.
Ecosystem integrations are important: Right now, Valibot doesn’t have the same level of adoption as Zod or Yup. That means fewer ready-made adapters for form libraries (like React Hook Form), frameworks, and API tools. You may need to write your own glue code until the ecosystem matures.

The Bottom Line

Valibot isn't just "Zod but smaller." It's a fundamentally different approach to validation based on functional programming principles.

The small bundle size, better performance, excellent TypeScript integration, and reusability patterns all flow from this core architectural choice. Whether those benefits matter for your specific project is what should guide your decision to adopt it.

But now you understand why those benefits exist and what tradeoffs they involve.

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