{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Clingo is a Python binding to the clingo Answer Set Programming solver, allowing you to model and solve combinatorial logic problems by writing logic programs and computing their answer sets.","skillfed_tags":["logic-programming","constraint-solving","combinatorial-optimization"],"use_cases":["Solve scheduling and resource allocation problems by encoding constraints as logic rules and computing valid schedules.","Perform configuration management or product configuration by modeling dependencies and constraints as ASP rules.","Solve combinatorial puzzles (Sudoku, graph coloring, N-queens) by expressing the rules and letting clingo find all solutions.","Perform planning and reasoning tasks where you need to compute all possible action sequences satisfying given goals.","Prototype constraint satisfaction problems quickly without implementing custom search algorithms."],"what_it_does":"Clingo is a Python interface to the clingo Answer Set Programming solver, part of the Potassco project. It lets you express combinatorial problems\u2014scheduling, configuration, planning, constraint satisfaction\u2014as declarative logic programs rather than imperative algorithms. You write rules describing what solutions must satisfy, and clingo computes all answer sets (solutions) that satisfy those rules. The package wraps the C++ clingo engine via CFFI, giving you access to the solver's full capabilities from Python.\n\nTypical workflows involve creating a Control object, adding logic program rules as strings, grounding the program (instantiating it with concrete data), and then calling solve() to enumerate or find solutions. It's used for problems where you want to specify constraints and let the solver find valid assignments, rather than writing search code yourself.","worth_installing":"Yes, if you need to solve combinatorial or constraint satisfaction problems and prefer declarative logic programming over imperative search. The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Medium install friction is acceptable for most platforms. Not necessary if your problems fit standard optimization libraries or if you prefer imperative constraint solvers."},"id":"clingo","links":{"html":"https://skillfed.io/packages/clingo","md":"https://skillfed.io/packages/clingo.md","pypi":"https://pypi.org/project/clingo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"clingo","python_support":"supports_current","summary":"CFFI-based bindings to the clingo solver."},"popularity":{"monthly_downloads":157813,"position":10745,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.8.2"}