ontobricks vs pgGraph
Turns Databricks Unity Catalog tables into a materialized knowledge graph: OWL ontology design, R2RML mapping, OWL 2 RL/SWRL/SHACL reasoning, auto-generated GraphQL — exposed to agents over MCP. — versus — PostgreSQL extension adding graph search, traversal and shortest-path over your existing tables — a derived graph index queried from plain SQL, no separate graph DB or query language. Rust.
Both build a knowledge graph over relational data you already govern. OntoBricks maps Unity Catalog with OWL reasoning inside Databricks; pgGraph is one CREATE EXTENSION on plain Postgres.
| ontobricks | pgGraph | |
|---|---|---|
| Stars | 254 | 898 |
| Forks | 49 | 76 |
| Language | Python | Rust |
| License | NOASSERTION | NOASSERTION |
| Last activity | yesterday | 3 days ago |
| Topics | knowledge-graphs | knowledge-graphs, rag |
| Curated connections | 4 | 2 |
ontobricks — the curator's take
The only tool here that gives agents a REASONED graph — OWL 2 RL/SWRL inference over your warehouse, not just edges — and the four-click LLM-assisted pipeline from table metadata to queryable ontology is genuinely novel. When NOT: anywhere outside Databricks — it hard-requires Unity Catalog, Lakebase Postgres and Databricks Apps. Labs project: no SLA, ★254 young. For lakehouse graph context without the platform lock-in, look at omnigraph.
pgGraph — the curator's take
The zero-migration bet: your tables stay the source of truth and the graph is a derived index, which makes it the cheapest possible on-ramp to GraphRAG over data already in Postgres. When NOT: the license is NOASSERTION with a managed-service funnel (polygres.com) — read the terms before production; graph-native workloads at scale still favor a dedicated store; ★898 and v1.0-young.