EssenceBinder

THIS PROJECT IS IN ALPHA! YOU HAVE BE WARNED!

EssenceBinder is an open-source and general purpose development framework design to model, interact with and manipulate any entity - abstract or tangible - through a structured model inspired by the Suggested Upper Merged Ontology. It's core focus is on providing a rich, extensible foundation for representing and working with Things in a flexible and scalable way.

More than a typical object-oriented framework; it's a generalised, structured way to model reality itself. Whether you're building AI-driven applications knowledge graphs, automation systems, or just looking to cut down your workload. EssenceBinder provides a standardised yet flexible foundation.

PyThings is a comprehensive, extendable, and structured Python framework for representing, interacting with, and reasoning about entities, concepts, and structured data. It provides a flexible ontology-based foundation, allowing users to model relationships, attributes, and metadata with high interoperability across various domains.

Whether you're working with semantic data models, composable objects, knowledge graphs, or hierarchical systems, PyThings streamlines the process with extensible ontologies, advanced inference capabilities, and multi-format data outputs.

PyThings is a modular and extensible Python framework for working with structured, composable objects—whether they are real-world entities, abstract concepts, or system components.

Instead of reinventing data structures and relationships, PyThings provides a ready-to-use base of standardized objects while allowing you to define your own with minimal effort. Import predefined structures, enhance your own classes, or seamlessly integrate semantic tools without needing to manually build complex models.

Key features:

• Prebuilt, Modular Object System – Import rich, structured entities without having to define everything from scratch.
• Composable and Extendable – Objects can have relationships, attributes, and metadata, supporting bidirectional interactions (e.g., a Computer → HasPart → CPU, and CPU → IsPartOf → Computer).
• Flexible Data Representation – Supports a range of formats, from relational data models to knowledge graphs.
• Plug-and-Play with Python Ecosystem – Works seamlessly with NumPy, Pint, Pandas, Pydantic, and more.
• Ontology-Compatible, but Not Required – Uses SUMO as a foundation to provide structured defaults, but you don’t need to think about ontologies unless you want to.