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A modern programming language featuring formal proofs. Now written in itself!

Why formal proofs?

When most people hear about formal proofs, they naturally think about mathematics and security, or, "boring stuff". While it is true that formal proofs can be used to formalize theorems and verify software correctness, Formality's approach is different: we focus in using proofs as a tool to enhance developer productivity.

There is little doubt left that adding types to untyped languages greatly increases productivity, specially when the codebase grows past a certain point: just see the surge of TypeScript. Formal proofs are, in a way, an evolution of the simple types used in common languages.

We believe that proofs are superpowers waiting to be explored, and the proper usage of them can enhance the productivity of a developer in a disruptive manner: think of Haskell's Hackage on steroids. Formality was designed to explore and enable that side of formal proofs, and we'll be publishing more about that soon.

Why Formality?

There are some interesting proof languages, or theorem provers, as they're often called, in the market. Agda, Coq, Lean, Idris, to name a few. But these (perhaps with exception of Idris, which we love!) aren't aligned with the vision highlighted above, in some key aspects:


Formality is entirely compiled to a small trusted core that has 700 lines of code. This is 1 to 2 orders of magnitude smaller than existing alternatives. Because of that, auditing Formality is much easier, decreasing the need for trust and solving the "who verifies that the verifier" problem.


Being compiled to such a small core also allows Formality to be easily compiled to multiple targets, making it very portable. For example, out Formality-to-Haskell compiler was developed in an evening and has less than 1000 lines of code. This allows Formality to be used as a a lazy, pure functional language that is compiled directly by Haskell's GHC.


Formality has a long-term approach to performance: make the language fast in theory, then build great compilers for each specific target. Our JavaScript compiler, for example, is tuned to generate small, fast JS, allowing Formality to be used for web development. Other targets may have different optimizations, and we're constantly researching new ways of evaluating functional programs; see our post about interaction nets and optimal reduction.


For a programming language to be fun, it can't be too serious. It must have great supporting tools such as a package manager, an editor. It must have friendly error messages. It must have a fast type-checker. It must have a non-cryptic syntax that everyone can use and understand. It must be stable and provide long term support. All of these are non-goals for many of the existing alternatives, but are high priorities for Formaltiy.


  1. Install

    Using the JavaScript release (fmjs):

    npm i -g formality-js

    Using the Haskell release (uses fmhs instead of fmjs):

    git clone
    cd formality/bin/hs
    cabal install
  2. Clone the base libraries

    git clone
    cd formality/src
  3. Edit, check and run

    Edit on formality/src to add your code:

    Main: IO(Unit)
      do IO {
        IO.print("Hello, world!")

    Type-check to see errors and goals:


    Run to see results:

    fmjs Main --run

    Since Formality doesn't have a module system yet, you must be at formality/src to use the base types (lists, strings, etc.). In this early phase, we'd like all the development to be contained in that directory. Feel encouraged to send your programs and proofs as a PR!

Quick Introduction

A simple, clear and fun syntax

If you can't explain it simply, you don't understand it well enough.

Why make it hard? Formality aims to frame advanced concepts in ways that everyone can understand. For example, if you ask a Haskeller to sum a list of positive ints (Nats), he might write:

sum(list: List(Nat)): Nat
  case list {
    nil  : 0
    cons : list.head + sum(list.tail)

Main: IO(Unit)
  do IO {
    IO.print("Sum is: " |[1, 2, 3])))

Or, if he is enlightened enough:

sum(list: List(Nat)): Nat
  List.fold<_>(list)<_>(0, Nat.add)

Main: IO(Unit)
  do IO {
    IO.print("Sum is: " |[1, 2, 3])))

But, while recursion and folds are nice, this is fine too:

sum(list: List(Nat)): Nat
  let sum = 0
  for x in list:
    sum = x + sum

The code above isn't impure, Formality translates loops to pure folds. It is just written in a way that is more familiar to some. Proof languages are already hard enough, so why make syntax yet another obstacle?

(You can test the examples above by editing, and typing fmjs and fmjs Main --run on the Formality/src directory.)

Powerful types

Let's now see how to write structures with increasingly complex types. Below is the simple list, a "variant type" with two constructors, one for the empty list, and one to push a positive number (Nat) to another list:

// NatList is a linked list of Nats
type NatList {
  push(head: Nat, tail: NatList)

As usual, we can make it more generic with polymorphic types:

// List is a linked list of A's (for any type A)
type List (A: Type) {
  push(head: A, tail: List(A))

But we can make it more specific with indexed types:

// Vector is a linked list of Nats with a statically known size
type Vector ~ (len: Nat) {
  empty                                        ~ (len: 0) 
  push(len: Nat, head: Nat, tail: Vector(len)) ~ (len: 1 + len)

The type above isn't of a fixed length list, but of one that has a length that is statically known. The difference is that we can still grown and shrink it, but we can't, for example, get the the head of an empty list. For example:

Main: IO(Unit)
  def list = [1,2,3]
  def vect = Vector.from_list<Nat>(list)
  def head = Vector.head<Nat,_>(vect)
  do IO {
    IO.print("First is: " |

Works fine, but, if you change the list to be empty, it will result in a type error! This is in contrast to Haskell, where head [] results in a runtime crash. Formality programs can't crash. Ever!

(You can also check the program above by editing

Theorem proving

Proof languages go beyound checking lengths though. Everything you can think of can be statically verified by the type system. With subset types, written as {x: A} -> B(x), you can restrict a type arbitrarily. For example, here we use subsets to represent even numbers:

// An "EvenNat" is a Nat `x`, such that `(x % 2) == 0`
EvenNat: Type
  {x: Nat} (x % 2) == 0

six_as_even: EvenNat
  6 ~ refl

This program only type-checks because 6 is even: try changing it to 7 and it will be a type error! But what about ~ refl? This is a proof that 6 is indeed even. Since 6 is a compile-time constant, it is very easy for Formality to verify that it is even (it just needs to run 6 % 2), so we write refl, which stands for "reflexive", or "just reduce it".

But what if it was an expression instead? For example, what if we wanted to write a function that receives a Nat x, and returns x*2 as an EvenNat? It makes sense, because the double of every number is even. But if we just write:

double_as_even(n: Nat): EvenNat
  (2 * n) ~ refl

Formality will complain:

Type mismatch.
- Expected: Nat.mod(Nat.double(n),2) == 0
- Detected: 0 == 0

That's because Formality doesn't know that (n*2)%2 == 0 is necessarily true for every n. We need to convince the type-checker by proving it. Proofs are like functions, we just create a separate function that, given a n: Nat, returns a proof that ((n*2)%2)==0. That proof will be done by case analysis and induction, but we won't get into details on how it works; for now, suffice to say it is just pattern-matching and recursion. Here is it:

EvenNat: Type
  {x: Nat} (x % 2) == 0

six_as_even: EvenNat
  6 ~ refl

double_as_even(n: Nat): EvenNat
  (2 * n) ~ double_is_even(n)

double_is_even(n: Nat): ((2 * n) % 2) == 0
  case n {
    zero: refl
    succ: double_is_even(n.pred)

To sum up, EvenNat is the type of Nats that are even. six_as_even is just the number 6, viewed as an EvenNat; since Formality can verify that 6 is even, we write ~ refl on it. double_as_even is a function that, for any Nat n, returns n*2 as an EvenNat. Formality can't verify that n*2 is always even by itself, so, to convince it, we write a separate proof called double_is_even(n).

(Check this program too!)


An efficient proof language




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