Handy python wrapper around Potassco's Clingo ASP solver.
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Handy python wrapper around Potassco's Clingo ASP solver.


Clyngor offers multiple interfaces. The followings are all equivalent. (they search for formal concepts)

from clyngor import ASP, solve

answers = ASP("""
rel(a,(c;d)). rel(b,(d;e)).
obj(X):- rel(X,_) ; rel(X,Y): att(Y).
att(Y):- rel(_,Y) ; rel(X,Y): obj(X).
:- not obj(X):obj(X).
:- not att(Y):att(Y).
for answer in answers:

The same, but with the lower level function expecting files:

answers = solve(inline="""
rel(a,(c;d)). rel(b,(d;e)).
obj(X):- rel(X,_) ; rel(X,Y): att(Y).
att(Y):- rel(_,Y) ; rel(X,Y): obj(X).
:- not obj(X):obj(X).
:- not att(Y):att(Y).

More traditional interface, using file containing the ASP source code:

answers = solve('concepts.lp'):  # also accepts an iterable of file

More examples are available in the unit tests.


Once you get your answers, clyngor allows you to specify the answer sets format using builtin methods:

for answer in answers.by_predicate.first_arg_only:
    print('{' + ','.join(answer['obj']) + '} × {' + ','.join(answer['att']) + '}')

And if you need a pyasp-like interface:

for answer in answers.as_pyasp:
    print('{' + ','.join(a.args()[0] for a in answer if a.predicate == 'obj')
          + '} × {' + ','.join(a.args()[0] for a in answer if a.predicate == 'att') + '}')

Currently, there is only one way to see all chaining operator available: the source code of the Answers object. (or help(clyngor.Answers))

Official Python API

If the used version of clingo is compiled with python, you can put python code into your ASP code as usual. But if you also have the clingo package installed and importable, clyngor can use it for various tasks.

Using the official API leads to the following changes :

  • both robust and quick parsing, instead of the simple vs slow method
  • some options are not supported : constants, time-limit, parsing error handling

You can activate the use of the clingo module by calling once clyngor.activate_clingo_module() or calling clyngor.solve with argument use_clingo_module set to True.

Python embedding

For users putting some python in their ASP, clyngor may help. The only condition is to have clingo compiled with python support, and having clyngor installed for the python used by clingo.

Easy ASP functors

Clyngor provides converted_types function, allowing one to avoid boilerplate code based on type annotation when calling python from inside ASP code.

Example (see tests for more):

from clyngor.upapi import converted_types
def f(a:str, b:int):
    yield a * b
    yield len(a) * b

p(X):- (X)=@f("hello",2).
p(X):- (X)=@f(1,2).  % ignored, because types do not match

Without converted_types, user have to ensure that f is a function returning a list, and that arguments are of the expected type.

Generalist propagators

Propagators are presented in this paper. They are basically active observers of the solving process, able for instance to modify truth assignment and invalidate models.

As shown in clyngor/test/test_propagator_class.py, a high-level propagator class built on top of the official API is available, useful in many typical use-cases.

Python constraint propagators

As shown in examples/pyconstraint.lp, clyngor also exposes some helpers for users wanting to create propagators that implement an ASP constraint, but written in Python:

from clyngor import Constraint, Variable as V, Main

# Build the constraint on atom b
def formula(inputs) -> bool:
    return inputs['b', (2,)]

constraint = Constraint(formula, {('b', (V,))})

# regular main function that register given propagator.
main = Main(propagators=constraint)


% ASP part, computing 3 models, b(1), b(2) and b(3).


pyasp comes into mind, but does not supports clingo alone.


pip install clyngor

You must have clingo in your path. Depending on your OS, it might be done with a system installation, or through downloading and (compilation and) manual installation.

You may also want to install the python clingo module, which is an optional dependancy.


Careful parsing

By default, clyngor uses a very simple parser (yeah, str.split) in order to achieve time efficiency in most time. However, when asked to compute a particular output format (like parse_args) or an explicitely careful parsing, clyngor will use a much more robust parser (made with an arpeggio grammar).


See the utils module and its tests, which provides high level routines to save and load answer sets.

Define the path to clingo binary

import clyngor
clyngor.CLINGO_BIN_PATH = 'path/to/clingo'

Note that it will be the very first parameter to subprocess.Popen.

clyngor.solve parameters

The solve functions allow to pass explicitely some parameters to clingo (including number of models to yield, time-limit, and constants). Using the options parameter is just fine, but with the explicit parameters some verifications are made against data (mostly about type).

Therefore, the two followings are equivalent ; but the first is more readable and will crash earlier with a better error message if n is not valid:

solve('file.lp', nb_model=n)
solve('file.lp', options='-n ' + str(n))


Dinopython support ?


Contributions ?


Why clyngor ?

No, it's pronounced clyngor.

Explain me again the thing with the official module

Clyngor was designed to not require the official module, because it required a manual compilation and installation of clingo. However, because of the obvious interest in features and performances, the official module can be used by clyngor if it is available.

Further ideas


  • 0.4.0
    • predicat to know if python/lua are available with used clingo binary
    • easy interface for most use cases using type hint for embedded python
    • easy python constraints in ASP with Constraint type
    • add support for propagators
    • add support for clingo official python module

from pyasp to clyngor

If you have a project that makes use of pyasp, but need clingo instead of gringo+clasp, one way to go is to use clyngor instead.

Here was my old code:

from pyasp import asp

def solving(comp, graph):
    programs = [comp, graph]
    clasp_options = ['--opt-mode=optN', '--parallel-mode=4', '--project']
    solver = asp.Gringo4Clasp(clasp_options=clasp_options)
    print("solver run as: `clingo {} {}`".format(' '.join(programs), clasp_options))
    at_least_one_solution = False
    for answerset in solver.run(programs, collapseAtoms=False):
        yield answerset

def find_direct_inclusions(model) -> dict:
    programs = [ASP_SRC_INCLUSION]
    solver = asp.Gringo4Clasp()
    add_atoms = ''.join(str(atom) + '.' for atom in model)
    answers = tuple(solver.run(programs, collapseAtoms=False,
    return answers

And here is the version using clyngor, that pass the exact same unit tests:

import clyngor

def solving(comp, graph):
    programs = [comp, graph]
    clasp_options = '--opt-mode=optN', '--parallel-mode=4', '--project'
    answers = clyngor.solve(programs, options=clasp_options)
    print("solver run as: `{}`".format(answers.command))
    for answerset in answers.as_pyasp.parse_args.int_not_parsed:
        yield answerset

def find_direct_inclusions(model) -> dict:
    programs = [ASP_SRC_INCLUSION]
    add_atoms = ''.join(str(atom) + '.' for atom in model)
    answers = tuple(clyngor.solve(programs, inline=add_atoms).as_pyasp.parse_args)
    return answers