{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Pyperplan is a lightweight STRIPS planner that solves classical planning problems by reading PDDL domain and problem files and computing action sequences to reach a goal state.","skillfed_tags":["planning-algorithms","educational-tool","pddl"],"use_cases":["Teaching classical planning algorithms and STRIPS semantics in an AI or computer science course.","Prototyping planning solutions for academic papers or research projects before moving to optimized solvers.","Extending the planner with custom heuristics or search strategies for experimental work.","Solving small to medium PDDL planning problems where execution speed is not a constraint.","Understanding how domain and problem specifications map to action sequences in planning."],"what_it_does":"Pyperplan is a classical planning engine that takes PDDL (Planning Domain Definition Language) specifications and computes sequences of actions to solve planning problems. It was originally developed as a teaching tool at a university planning course and deliberately prioritizes clean, understandable code over raw performance. The planner supports STRIPS (Simplified Planning Domain Representation Language) without action costs and can be invoked from the command line with a domain file and a problem file, optionally using different search algorithms and heuristics like greedy best-first search with the FF heuristic.\n\nThe package is suitable for educational use, prototyping, and research contexts where clarity and extensibility matter more than state-of-the-art speed. It runs on Python 3.6 and later, has minimal dependencies (only wheel), and is actively maintained. The authors explicitly caution that Pyperplan does not offer competitive performance for production planning tasks, making it most valuable for learning how planning algorithms work or for small-scale experimental work.","worth_installing":"Yes, if you are learning planning, doing research, or prototyping. The low install friction, active maintenance, and clean codebase make it ideal for education and experimentation. No, if you need production-grade performance or are solving large-scale planning problems\u2014the authors explicitly warn against using Pyperplan for performance-critical work. The GPLv3+ license is permissive for academic and open-source use but restricts proprietary applications."},"id":"pyperplan","links":{"html":"https://skillfed.io/packages/pyperplan","md":"https://skillfed.io/packages/pyperplan.md","pypi":"https://pypi.org/project/pyperplan/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2022-01-17","license_spdx":null,"license_treatment":"copyleft","name":"pyperplan","python_support":"supports_current","summary":"A lightweight STRIPS planner written in Python."},"popularity":{"monthly_downloads":113977,"position":12320,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1"}