omnigraph vs ontobricks
Lakehouse graph database for agent context — graph, vector and full-text retrieval fused in one runtime on branchable Lance/S3 storage; agent fleets write on isolated branches and merge Git-style. — versus — 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.
Both bet on lakehouse-native graph context for agents. OntoBricks adds OWL reasoning but locks you to Databricks; omnigraph runs standalone on Lance/S3 with vector + full-text fused in.
| omnigraph | ontobricks | |
|---|---|---|
| Stars | 1.0k | 254 |
| Forks | 206 | 49 |
| Language | Rust | Python |
| License | MIT | NOASSERTION |
| Last activity | 2 days ago | yesterday |
| Topics | memory, rag | rag |
| Curated connections | 6 | 2 |
omnigraph — the curator's take
Reach for it when many agents must share one evolving knowledge store and you need blame, rollback and review on their writes — branch-per-agent with merge gates is the feature nothing else in this space has; also strong when retrieval genuinely needs graph + vector + full-text fused, not a vector store with metadata filters. NOT a drop-in vector DB: you take on a server, cluster.yaml, schemas and Cedar policy — for plain RAG recall, Chroma is answering queries before you've finished omnigraph's docs. Young project on a credible Rust/Lance foundation; expect sharp edges and a moving API.
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.