status: WIP version: 0.0.0 license: MIT

Letlang is a general purpose functional programming language.

Implemented in Rust and Python, it compiles to Rust, allowing you to target any platform supported by LLVM.

Work In Progress

Letlang is opensource but has not yet been published because of its very early stage.

The documentation and code examples you may find on this website are NOT definitive and may be subject to change.

If you’d like to contribute, feel free to contact me by mail.

Useful resources


Read more about Letlang’s features and design. A good resource to learn about the language.

Code Examples

Discover code samples from the standard library or implentations of simple UNIX. tools.

Language Specification

Read the language specification by exploring the different LEP (Letlang Enhancement Proposal) documents.


Github repository containing the source code and setup instructions to get started with Letlang.

Key features


Letlang is inspired by mathematics and the following languages:

module "example.main";

import "std";

func main(args: list<string>) -> @ok {
  std.print("Hello World");

Working with undefined

First, let’s clarify a few things:

  • there is no undefined or null value in Letlang
  • every variable has a value
  • you don’t need to define its value to use the variable

Using the let keyword, we can define properties:

  • n is a number, no specific value defined
let n: number;
  • n still has no value, but it must be a positive number
let n > 0;
  • ⚠ incompatible with known properties
let n < 0;

Using the = comparison operator and the solvable {} block, we can write equations:

  • the equation has one or more solutions, so it is true
let x: number;
solvable { 3 + x = 0 };
  • the equation has no solution, so it is false
let x: number;
solvable { 1 / x = 0 };
  • if we try to assign the undefined variable to another, a compilation error is thrown:
let x: number;
y := 2 * x + 1;

NB: A SAT solver is included in the runtime to solve the equations.

Expressive Type System

In mathematics, a value does not have a single type. Instead, it belongs to one or more sets:

  • 42 is an integer, a real number, a scalar, …
  • (2, 1) is a 2D vector, a 2x1 matrix, …

This concept is at the core of Letlang’s type system. New types can be defined using the class statement.

A class has a structure, every value matching this structure belongs to the class.

Optionnaly, a class can define a predicate. Every value belonging to the class must validate the predicate.

module "example.main";

import "std";

class int(n: number) {
  frac(n) = 0;

class even(n: int) {
  solvable {
    thereis k: int, n = 2 * k;

class odd(n: int & !even);

class vector(xy: (number, number));

Using the above definitions, we can assert:

0.3 is int;    # false
42 is number;  # true
42 is int;     # true
42 is even;    # true
42 is odd;     # false
43 is odd;     # true
43 is even;    # false

Furthermore, we can compose types together with the operators |, & and !:

let var: number | string;
  • var is either a number or a string
let var: int & !even;
  • var is an integer and must not be even


Classes and functions can require type parameters.

Those parameters can then be used within the definition of the class or function.

Multiple parameters can be specified.

class vector<T>(xy: (T, T));

func swap<A, B>(a: A, b: B) -> (B, A) {
  (b, a);

These parameters need to be specified to use the generic class or function.

But most of the time, the compiler can infer the types.

let v: vector<number>;

x, s := swap("hello", 42);
Thanks to this feature, a Result type (similar to Rust’s) can be defined as follows:
class ok<T>(v: (@ok, T));
class err<T>(v: (@error, T));
class result<T, E>(v: ok<T> | err<E>);

Leading to the following equalities:

(@ok, 42) is result<number, string>;
  • this is true
(@ok, 42) is ok<number>;
  • this is true
(@error, "wrong") is result<number, string>;
  • this is true
(@error, "wrong") is ok<number>;
  • this is false

Type parameters can also be constrained:

class blittable(v: number | boolean | atom);

func dump<T: blittable>(v: T) -> @ok {
  # do stuff

dump::<string>("won't compile")

Recursion and Tail Call Optimization

A recursive function is a function which calls itself.

If the self call is the last instruction in the function’s body, it is called a tail call.

Such functions can be unrolled into a simple loop, this is called Tail Call Optimization:

func count<T>(acc: number, l: list<T>) -> int {
  match l {
    [] => acc,
    list<T> => {
      [_head | tail] := l;
      count(acc + 1, tail);

count(0, [1, 2, 3, 4, 5]) = 5;

Coroutines and streams

Functions have no color, any function can be run asynchronously or not.

The keyword coro followed by a function call will run the function asynchronously and return a coroutine value.

The keyword join takes a coroutine value as argument and will block until the coroutine is finished and finally returns the coroutine’s result.

This allows the program to work on something else while the coroutine’s value is being computed, and introduce a synchronization point later on when the value is needed.

module "example.main";

import "std";

func count(msg: string) -> string {

func main(args: list<string>) -> @ok {
  c := coro echo("foo");
  # do other stuff
  let 3 = join c;


Streams can then be used for communication between coroutines.

A stream is a special object for bidirectionnal Input/Output of values.

Write are instantaneous but reads will block until a value has been written.

let s: stream<int>;
let v: int;

# write a value to the stream
s |<< 42;

# read a value from the stream to a variable
s |>> v;

Streams used with coroutines offer a simple synchronization method between parallel tasks:

module "example.main";

import "std";

func double(s: stream<int>) -> @ok {
  let x: int;

  s |>> x;

  match x {
    -1 => @ok,
    int => {
      s |<< (x * 2);

func main(args: list<string>) -> @ok {
  let s: stream<int>;

  c := coro double(s);

  s |<< 1 |<< 2 |<< 3 |<< 4 |<< -1;

  let @ok = join c;

  let (a, b, c, d): (int, int, int, int);

  s |>> a |>> b |>> c |>> d;
  let a = 2;
  let b = 4;
  let c = 6;
  let d = 8;


Algebraic Effects

In mathematics, functions have no side effects.

They will always return the same result given the same parameters.

f(x) = 2x + 1
f(0) = 1
f(1) = 3

In software development, such functions are called pure.

Yet, not every functions can be pure, like:

  • getting input from the user
  • getting the current time
  • performing a request to an external service

Such impure functions have side effects.

Letlang provides a mechanism to decouple the handling of a side effect from a function.

Using the effect statement, you can declare a new type of side effect:

class log_level(lvl: "debug" | "info");

effect log(log_level, string) -> @ok;

Inside a function, you can then trigger the effect, delegating its handling to the caller.

This is done with the perform keyword followed by a call to the effect:

func greet(name: string) -> @ok {
  res := perform log("info", "Hello ${name}");

Using a do {} block and one or more intercept clause, the caller can handle the side effect.

An intercept clause uses pattern matching to select which effect to handle.

The last expression of an intercept clause will be used as return value of the perform keyword.

NB: Types are also checked at runtime to ensure type safety.

Unhandled effects are then propagated to the builtin runtime Letlang includes during compile time.

If the effect is unknown to the runtime, the program will crash with a stacktrace.

module "example.main";

import "std";

func main(args: list<string>) -> @ok {
  let @ok = do {
  intercept log("debug", _message) {
    # silenced
  intercept log("info", message) {

  perform log("fatal", "won't compile");
  perform log("debug", "will crash");

The builtin runtime provides a few effect handlers out of the box:

effect gettime() -> number;

effect println(string, @stdout | @stderr) -> @ok;
effect readline() -> std.result<string, std.errno>;

class fmode(m: @w | @wb | @r | @rb | @a);
effect fopen(string, fmode) -> std.result<std.file, std.errno>;
effect fwrite(std.file, string) -> std.result<(), std.errno>;
effect fread(std.file, number) -> std.result<string, std.errno>;
effect fclose(std.file) -> std.result<(), std.errno>;

effect sockbind(std.socket) -> std.result<(), std.errno>;
effect sockaccept(std.socket) -> std.result<std.socket, std.errno>;
effect socksend(std.socket, string) -> std.result<number, std.errno>;
effect sockread(std.socket, number) -> std.result<string, std.errno>;
effect sockclose(std.socket) -> std.result<(), std.errno>;

Exceptions are a special kind of effect: they do not resume. This is a form of early return from a function.

Exceptions can be of any type and are raised with the throw keyword.

The caller can handle such exceptions with the catch clause in a do {} block.

The value returned by the do {} block will be the value returned by the catch clause that handled the exception.

NB: Unhandled exceptions (like any other effects) are propagated to the runtime environment and crash the program.

func main(args: list<string>) -> @ok {
  err := do {
    throw (@error, @not_implemented);
  catch (@error, reason) {

  err = @not_implemented;

  throw @will_crash;


A do{} block can have a finally clause to execute code afterwards.

This is useful to free resources allocated within the function in case of error.

The finally clause will be executed after all other clauses are done.

func will_fail() -> @ok {
  resource := create_resource();

  do {
    throw @did_fail;
  finally {


Set Builder Notation

Letlang has infinite sets. They are described using a variable and a predicate indicating which values are included:
Sa := { x: number | x > 0 };
Sb := { x: Sa | x < 10 };

The above definitions leads to the following equalities:

Sa is set<number>
  • this is true
Sb is set<number>;
  • this is true
42 in Sa;
  • this is true
42 in Sb;
  • this is false

NB: There is no infinite lists, because you can’t always define a beginning.

Consider the definition on the right, what would be the value of l[0]?
l := [ x: number | x != 42 ];

About Russel’s paradox:

set is a generic types, they can only contain elements of the underlying type. Therefore, you cannot build the set of all sets.

If you could build such a set, it would lead to a conflicting definition such as:

  • The set of all sets that do not contain themselves
S := { x: set | x not in x };

Does the following expression S in S returns true or false?

  • if S not in S, then it would match the predicate x not in x, implying S in S
  • if S in S, then it would not match the predicate x not in x, implying S not in S

This is why the type set do not exists, only set<T>.

x is a set that can only contain numbers, not sets of numbers, therefore x not in x would lead to a type mismatch error:

  • number in set<number> is valid
  • set<number> in set<number> is not
# this will lead to a compilation error
S := { x: set<number> | x not in x };

Pipeline operator

The pipeline operator is used to chain operations by injecting the value in the lefthand side as first argument to the function call in the righthand side:
x |> add(5) |> mul(2);
# equivalent to
mul(add(x, 5), 2);

Logical operators

On top of the common boolean operators not, and, or, Letlang introduces the following ones:

  • ==>: implication operator
  • <==>: biconditional operator

Here is their truthtable:

PQP ==> QP <==> Q