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Monty Programming Language
Jul 23, 2021
7 minutes read


You know what the world needs? Another programming language! Not really, but anyways, it’s a fun time designing them.

After messing around with parser combinators in Haskell, a friend and I had the itch to design a language. In uni we tossed around the idea of a nondeterministic esoteric language, so we started thinking about how it would work.

Something we came up with is, every branch would possibly not do what you expected it to. So for example, the following branch statement may only evaluate correctly 99% of the time:

if True:
    print("Obviously true, you nitwit")
    print("How could this possibly happen!?")

After talking about the scope of things you could actually do with this approach of programming, we realized it’s a truly awful idea, even from an esoteric perspective. If you wanted to simulate deterministic behaviour, all you would have to do is make it extremely unlikely to evaluate incorrectly. Think “dozens of identical nested conditionals”.

What a bummer! But at this point we held a simultaneous epiphany, Haskell is a pain in the spleen to learn, but everyone knows Python these days… what if we mix them?

During a few months of quarantine, we went manic on creating the Monty programming language. Honestly, we probably put as much time into it as our actual jobs for 3 months straight.

Guess the language:

def add1(x):
  return x + 1

Python right? Yeah, but also Monty.

Guess the language:

add1 = def (x):
  return x + 1

Python right? Yeah, wait… no! What on Earth is that function assignment? In pure functional languages, functions are “first class citizens”, so they are types of values. In Monty, the two above definitions for add1 are semantically identical. Everyone and their dog has dozens of lambdas. So we also shortened the syntax for it. A natural simplification of def is to drop the leading keyword, and return the first expression.

add1 = (x): x + 1      # Monty
add1 = lambda x: x + 1 # Python

Leaning in the more Haskell direction, we also wanted currying support. It may seem a bit odd to Python aficionados, but not to our Haskell friends:

def add(a, b):
  return a + b

add1 = (x): add(1, x)
# or with sugar syntax
add1 = add(1)

We tried to be Pythonic syntactically, and Haskell semantically:

class Maybe:

instance Maybe of Functor:
  def map(Just(value), f):
    return Just(f(value))

  def map(_, _):
    return None

Check out the docs if you want to see more snippets like this, explained in proper detail.

We also wrote a JSON parser in Monty which I find “proves” this language actually works: JSON parser

Syntactic & Semantic Parsers

At first we wrote a single parser. It was purely syntactic, so it didn’t determine things like operator ordering, or know how to simplify the representation of an unwrap (for the Haskellers, unwrap is equivalent to do notation) statement in our Abstract Syntax Tree (AST).

Instead, we had all that stuff evaluated at runtime, which is a treacherous waste of resources. Once the language was in a working state, we had to fix our mess. As a layer between parsing and running, we added a “semantic parser”. It’s job is basically to convert the syntax-parser level AST into an AST more suitable for runtime. For example, unwrap expands into a tree of bind function calls.

Type Inferrence

Some of the most voodoo code I’ve ever worked on is in our type inferrence. I can still hardly believe that it works so seamlessly. Staying in line with the Pythonic way of doing things, you don’t ever tell the interpreter what types things are, with the exception of typeclasses (denoted with the type keyword).

There is a type for Applicative, with an associated wrap function, akin to the pure function in Haskell. Now, what happens when you evaluate the following code?

wrap(42).map((x): x + 1)

It maps the given function over 42, of course!… right? But wait, we don’t actually know which implementing Functor instance to use map with. List type? Maybe type? Parser type? We haven’t the foggiest idea!

So, if we can’t figure out the type yet, how could we expect the interpreter to? Via laziness! Given that expression, we don’t actually evaluate it until it’s used in a context where a type can be inferred.

For example, the following code will be able to evaluate, since the contextually implied type is a list:

wrap(42).map((x): x + 1).concat([3])

Something that helps a great deal with type inferrence is a special self keyword in type function declarations:

type Functor:
  def map(self, function)

In this definition, the interpreter knows that the first argument to map must be a functor instance.

type Equal:
  def equals(self, self)

In this definition, the interpreter knows that both arguments must be of the same type. So this is an entry point for inferrence. The following will correctly infer that wrap(42) must be a list:

equals([42], wrap(42))

Some fun type signatures, may they serve as implementation hints:

zipArgsToValues :: [Arg] -> [Value] -> Either String [(Id, Value)]
evaluateTf :: DefSignature -> HM.HashMap Id FunctionImpl -> [Value] -> Scoper Value
runFun :: Value -> [Value] -> Scoper Value


Initially, all expressions were evaluated in a huge eval function. It was a nice long list of unwieldy pattern matches. The first refactor was just pulling out the pattern match bodies into separate functions. But they were still there, silently haunting the codebase.

Through some actual, real life dark arts (aka existential quantification), we split the pattern match into modular evaluatable pieces. For example, the condition evaluatable (for if statements) lives with its AST definition:

data RCondition = RCondition
  { rConditionPos :: SourcePos,
    rConditionIf :: CondBlock ET,
    rConditionElifs :: [CondBlock ET],
    rConditionElseBody :: [ET]

instance Evaluatable RCondition where
  getPos RCondition {rConditionPos} = rConditionPos
  evaluate RCondition {rConditionIf, rConditionElifs, rConditionElseBody} =
    evalCondition rConditionIf rConditionElifs rConditionElseBody

This makes it much easier to add and remove syntax.


With these pieces in place, how does a program actually run? Via a special value called __main__. It’s required to be an instance of an IO monad, and is the main entry point to the program. Much like if __name__ == "__main__" in Python.

__main__ = unwrap:
  print("What's your name? ")
  name <- input()
  print("Hello, " + name + "!\n")

The runner will evaluate the main IO monad, which in turn can have calls to anywhere else in the program.


We wanted to have most things defined in Monty, but some things were just too slow. One of the main culprits was lists. Not much exciting to say here, except that “yay we have Haskell interoperability if need be”.

Unit is just an empty tuple

As a complete aside, unit () is just a tuple with no elements. My mind melted a little bit when I realized that.


Like any quasi-legitimate language, Monty has a standard library with loads of useful functions, types, and class definitions:

# Finds the length of a foldable 
def len(someFoldable):
  return someFoldable.foldl(0, (acc, _): acc + 1)
# Checks everything in a given foldable satisfy the predicate
def all(foldable, predicate):
  return foldable.foldl(True, (acc, it): acc and predicate(it))

# Flattens a nested Monad
def flatten(m):
  return m.bind(id)

By the power of category theory, these functions work on anything in the specified type class. Since Maybe, List, and BTree are all foldable, you can call len and all on them. If you implement your own Foldable instance, you get some free functions!

The Future

Monty is currently super slow. At some point we’d like to use a bytecode so it will be easier to do things like tail call optimization. Our approach is to slowly simplify the runtime AST until it’s at a point where we can represent it as a lower level bytecode. At that point, it will be much much much much much easier to add an optional optimization layer - after all, the runner won’t actually care if the bytecode is optimized or not, so during debug runs you can opt out of optimization.

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