ontobricks vs OpenMetadata
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 — Open metadata platform turned AI context layer: 130+ connectors feed a unified knowledge graph of lineage, quality, ownership, glossaries and contracts — served to agents via MCP and APIs.
Governed context for agents from enterprise data: OntoBricks reasons over Unity Catalog with OWL inside Databricks; OpenMetadata graphs metadata across 130+ systems without the reasoning layer.
| ontobricks | OpenMetadata | |
|---|---|---|
| Stars | 255 | 15k |
| Forks | 49 | 2.3k |
| Language | Python | TypeScript |
| License | NOASSERTION | Apache-2.0 |
| Last activity | 3 days ago | 2 days ago |
| Topics | knowledge-graphs | knowledge-graphs |
| Curated connections | 5 | 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.
OpenMetadata — the curator's take
The mature-platform play on this shelf: a decade-class data catalog (lineage, contracts, governance) that now speaks MCP, which makes it the most battle-tested 'what does this data mean' answer an agent can get. When NOT: this is a platform with platform weight — Elasticsearch, MySQL/Postgres, ingestion framework — absurd overkill if you just want a knowledge graph over one source; the AI-context framing is new even if the metadata engine isn't.