omnigraph vs semantica
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 — Graph-native context infrastructure for accountable AI: ingest enterprise data, extract a knowledge/context graph (RDF + LPG), run graph analytics and causal reasoning with decision provenance.
Graph context for agents: omnigraph is a storage-first lakehouse runtime; Semantica is a reasoning-first platform where provenance and causality are the product.
| omnigraph | semantica | |
|---|---|---|
| Stars | 1.0k | 2.3k |
| Forks | 236 | 305 |
| Language | Rust | Python |
| License | MIT | MIT |
| Last activity | 4 days ago | 2 days ago |
| Topics | memory, knowledge-graphs | knowledge-graphs |
| Curated connections | 8 | 3 |
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.
semantica — the curator's take
The accountability angle is the real differentiator — decision provenance and deterministic reasoning aimed at regulated domains, where 'the agent decided' isn't an acceptable audit trail. W3C-standards + both RDF and LPG is rare breadth. When NOT: the README's marketing density ('open-source Palantir') outruns its documentation depth — prototype the core path before betting a compliance program on it; ★2.3k and the platform surface is huge for the team size.