Generating Python bindings for OCaml with pyml_bindgen

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Hi! I'm Ryan Moore, NBA fan & PhD candidate in Eric Wommack's viral ecology lab @ UD. Follow me on Twitter!

pyml_bindgen is a command line app that generates Python bindings via pyml directly from OCaml value specifications. While you could write pyml bindings by hand, it can get repetitive, especially if you are binding a decent sized Python library.

In this post, I will introduce pyml_bindgen and go through a couple of common tasks.


To get started with pyml_bindgen, you will need to install it. It is available on opam (opam install pyml_bindgen). However, to follow along with this blog, you will need to install from the main branch on GitHub.

$ git clone --depth 1
$ opam install .

(As of Apr 12, 2022, you will need to install from the main branch rather than opam to follow along with this post.)

A simple example

Let’s start with a simple example.

Python code

Here is the Python class that we want to bind (

class Hobbit:
    def __init__(self, name, age): = name
        self.age = age

    def __str__(self):
        return f'Hobbit -- {}, {self.age}'

As you see, it’s pretty simple! It’s just the __init__ method to create the class and the __str__ method for converting it to a string with the Python str or print functions.

Here’s an example of using it in Python.

from hobbit import Hobbit
bilbo = Hobbit('Bilbo', 111)
#=> Hobbit -- Bilbo, 111

Write value specifications

To bind Python classes with pyml_bindgen, you first need to write value specifications to define the OCaml interface for the Python code we are binding.

To start, we will keep the functions and argument names the same.

val __init__ : name:string -> age:int -> unit -> t
val __str__ : t -> unit -> string
val name : t -> string
val age : t -> int

There are a couple things call your attention to here:

  • I haven’t defined type t anywhere yet. Depending on the command line arguments you pass to pyml_bindgen, it will take care of this for you.
  • For the __init__ function, I have used all named arguments plus the unit argument. The unit argument tells pyml_bindgen that you are binding a normal Python method or function call (as opposed to a Python attribute or property).
  • The __str__ function takes t as the first argument. Value specifications that start with t, will bind to object method calls on the Python side.
  • name and age both take t as the first and only argument. If a value specification takes t and nothing else, it binds to the Python attribute of that name.

Save the above in a file called hobbit.txt.

Generate bindings

Now, we’re ready to generate the OCaml bindings.

Here’s how you would run pyml_bindgen for this example.

$ pyml_bindgen hobbit.txt hobbit Hobbit \
  --of-pyo-ret-type no_check \

Let’s unpack that.

  • The first three arguments are the path to the OCaml value specifications, the name of the Python module we are binding, and the Python class name.
    • Since we named the Python file, its module name is hobbit.
    • Depending on the directory structure you’re using, this may change.
  • --of-pyo-ret-type specifies the return type for functions that generate Python objects.
    • Using no_check means the generated functions will assume the Python object is the correct type.
    • You can also use option and or_error as well.
  • The output is redirected to a file called Thus, our generated code will be in a module called Hobbit.
  • We did not tell pyml_bindgen that it should generate a full module with a signature, so it will just write the implementation.
    • In this example it is fine, but you will often want to generate the module and signature, so that your types will be abstract.
    • For example, you could use --caml-module Hobbit --split-caml-module to generate both an ml and mli file.
  • If you look at the generated code, it will be kind of messy. I usually run the output through ocamlformat if I need to edit the output, or check the generated code into version control or something like that.

Test it out

Now we can make a program to test it out. Don’t forget to call initialize before running the rest of your code!

let () = Py.initialize ()

let bilbo = Hobbit.__init__ ~name:"Bilbo" ~age:111 ()

let () =
  assert ("Hobbit -- Bilbo, 111" = Hobbit.__str__ bilbo ());
  assert ("Bilbo" = bilbo);
  assert (111 = Hobbit.age bilbo)

Since we didn’t generate a signature to go with our implementation, the type of the value returned by Hobbit.__init__ will be Pytypes.pyobject. In this way, we can pass any pyobject to the Hobbit.__str__ function. Let’s see.

let x = Py.Int.of_int 1234

let () = print_endline @@ Hobbit.__str__ x ()

If you run that, it will print 1234. Huh? Well, if you look at the generated code for the Hobbit.__str__ function, it looks something like this:

let __str__ t () =
  let callable = Py.Object.find_attr_string t "__str__" in
  let kwargs = filter_opt [] in
  @@ Py.Callable.to_function_with_keywords callable [||] kwargs

Without going into too much detail, essentially all it is doing is calling the __str__ method on the Python object passed in. While this is fine on the Python side, it doesn’t work the way we might want it to on the OCaml side. Ideally, we only want the Hobbit module functions to work on values of type Hobbit.t.

Generating abstract types

If we were writing the bindings by hand, we would make Hobbit.t abstract. With pyml_bindgen, we can do that using the --caml-module option.

$ pyml_bindgen hobbit_specs.txt hobbit Hobbit \
  --of-pyo-ret-type no_check \
  --caml-module Hobbit \
  --split-caml-module . \

Notice that I also used --split-caml-module . which tells pyml_bindgen to split the implementation and signature into separate ml and mli files, and to put the output in the directory in which the command is run. You can pass in whatever directory you want to this option.

Now if we tried something like this:

let x = Py.Int.of_int 1234

let () = print_endline @@ Hobbit.__str__ x ()

It would be a compile-time error.

Controlling the bindings

Let’s clean up this example a little bit.

Using different function names

While __init__ and __str__ are fine for OCaml function names, they don’t feel quite right. pyml_bindgen lets you bind Python functions to different names on the OCaml side using attributes on the value specifications. To bind to a different function name, we use the py_fun_name attribute. Check it out.

val create : name:string -> age:int -> unit -> t
[@@py_fun_name __init__]

val to_string : t -> unit -> string
[@@py_fun_name __str__]

We bind the __init__ function to an OCaml function called create, and the Python function __str__ to the OCaml function to_string. That’s much more natural!

As you can see, the syntax is like this: [@@attr-id attr-payload]. In this case, the attribute id is py_fun_name and the payload is the name of the Python function that we want to bind. Put another way, the attribute payload should be the name of the function as it is defined in the Python library you are binding to (i.e., __init__ is the name of the function on the Python side, not create).

Putting it together, you get [@@py_fun_name __init__] for the Python __init__ function and [@@py_fun_name __str__] for the Python __str__ function.

Using different argument names

The other available attribute is py_arg_name. With this, we can bind arguments to different names on the OCaml and Python sides. This can be useful in situations in which Python argument names use reserved OCaml keywords, or simply to make the generated API feel more natural for use in OCaml.

For example, you may have a Python function that has an argument name method.

def cluster(method='ward'):

Since method is a reserved keyword in OCaml, we can’t use it directly. Instead, we want to name it method_ in our OCaml code.

val cluster : method_:string -> ...
[@@py_arg_name method_ method]

In this case, the payload is two items: the first is the argument name on the OCaml side, and the second is the argument name on the Python side.

Note that in cases in which you need multiple attributes per specification, they must be placed one per line. (This is a pyml_bindgen specific restriction.) E.g., something like this:

val run_clustering : method_:string -> ...
[@@py_fun_name cluster]
[@@py_arg_name method_ method]

This will bind the OCaml function run_clustering to the corresponding Python function cluster.

Binding cyclic Python classes

Often you will need to bind Python classes that refer to each other. One way to bind these is to use recursive modules. Let’s update our Hobbit example to show how you can do this in pyml_bindgen.

class Hobbit:
    def __init__(self, name, age): = name
        self.age = age = None

    def __str__(self):
        return f'Hobbit -- {}, age: {self.age}, house: {}'

    def buy_house(self, house): = house = self

class House:
    def __init__(self, name): = name
        self.owner = None

    def __str__(self):
        return f'House -- {}, owner: {}'

So this is a pretty silly example, but it’s just to illustrate the point. In this case, a Hobbit can own a House and a House can have a Hobbit for an owner.

To bind these classes, I will use the gen_multi and combine_rec_modules helper programs that come with pyml_bindgen.


gen_multi is a wrapper script that runs pyml_bindgen multiple times to generate multiple OCaml modules in one go. It takes a tsv file specifying the same set of options that you would pass in to pyml_bindgen if you used it directly.

Assume this is in a file called gen_multi_cli.tsv.

signatures py_module py_class associated_with caml_module split_caml_module embed_python_source of_pyo_ret_type
hobbit.txt hobbit Hobbit class Hobbit NA no_check
house.txt house House class House NA no_check

The order of the columns must as shown above. (For more info on each of these options, run pyml_bindgen --help.)

You will see that we refer to hobbit.txt and house.txt. These are the value specifications for each of the Python classes. Here are there contents.


val create : name:string -> age:int -> unit -> t
[@@py_fun_name __init__]

val to_string : t -> unit -> string
[@@py_fun_name __str__]

val buy_house : t -> house:House.t -> unit -> unit


val create : name:string -> unit -> t
[@@py_fun_name __init__]

val to_string : t -> unit -> string
[@@py_fun_name __str__]


combine_rec_modules takes a file of OCaml modules and “converts” them into recursive modules. It does this using a simple text transformation.

Often you will want to pipe the output of gen_multi directly into combine_rec_modules.

Generate the modules & test it out

Now let’s see it in action.

$ gen_multi gen_multi_cli.tsv | combine_rec_modules /dev/stdin >

We put that in a module called Lib. And here is how we might use that.

open Lib

let () = Py.initialize ()

let bilbo = Hobbit.create ~name:"Bilbo" ~age:111 ()

let bag_end = House.create ~name:"Bag End" ()

let () = Hobbit.buy_house bilbo ~house:bag_end ()

let () =
  assert (
    "Hobbit -- Bilbo, age: 111, house: Bag End" = Hobbit.to_string bilbo ())

Other stuff

Let me mention a couple of other things before we go…

  • In this post we ran pyml_bindgen (or its helper scripts) manually, it’s not too hard to set up Dune rules to automatically generate bindings. See the dune files in the example directory on the pyml_bindgen GitHub for more information.
  • While I only showed how to bind to Python classes, you can also bind to functions associated with modules rather than with classes.
  • Another cool feature is that you can embed Python source code directly into your generated OCaml modules. See here for more details.


pyml_bindgen is a command line app for generating Python bindings using pyml. It makes incorporating Python libraries into your OCaml projects as easy as writing regular OCaml value specifications.

To get more information on setting up and using pyml_bindgen, including ideas on how to structure your projects, check out the examples, tests, and docs.

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