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Volodymyr Pavlyshyn

Draw the model. Ship the text.

Three VS Code extensions, one conviction. You model visually because a diagram is how a person understands structure — and then you commit plain, schema-validated JSON, because text is what diffs, merges, reviews and generates. The picture is a projection you can throw away and redraw. The model is the asset.

Person Company works for “Each Person works for at most one Company.” FORML verbalization · read back to a domain expert

Factum ORM

Conceptual schemas in ORM 2 — drawn, verbalized, and checked.

Object-Role Modeling describes a domain as elementary facts — *Person works for Company* — instead of tables or classes. Because those facts are attribute-free, every constraint is explicit and the whole model reads back as plain English sentences a domain expert can confirm or reject. Draw the schema, read the FORML verbalization, validate it, then map it to a relational or a property graph schema. In the spirit of NORMA, but native to VS Code.

Install Documentation Source

one model LadybugDB DDL Neo4j constraints SHACL shapes OWL ontology four artifacts, one source of truth — not four that drift apart —

LPG Modeler

Design a property graph once. Generate every schema from it.

Author a Labeled Property Graph schema as text, view it as an ERD-like diagram, and generate the artifacts from that single model: LadybugDB DDL, Neo4j constraints, SHACL shapes, and an OWL ontology. One source of truth instead of four drifting ones. VS Code extension and CLI.

Install Documentation Source

maternal smoking mortality birth weight collider — do not adjust the birth weight paradox, caught before it is published

Causal Canvas

Causal models that tell you when you are wrong.

A visual editor for causal models whose real output is a plain, schema-validated JSON file — not a picture. It also knows what a causal model means: it will tell you when you are adjusting for a collider, when an instrument violates the exclusion restriction, and when your exposure has no path to your outcome at all.

Install Documentation Source

Research & reference

Not products — reference implementations and research, kept public because the results are more useful in the open.

  • agentic-memory 11★

    Multi-layered memory architectures for agents — episodic, semantic, procedural.

  • ladybug-rag 9★

    Reference implementation of Hybrid Graph RAG with LadybugDB, in Python.

  • ladybug-rag-rs 5★

    Four retrieval modes in one query — vector search, graph traversal, PageRank and community detection. +109% on multi-hop questions against vector-only RAG.

There is a good deal more at github.com/Volland — ontology editors, graph engines, and a quantity of half-finished ideas.