OpenMetadata vs semantica
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. — 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.
Enterprise context layers for AI: OpenMetadata catalogs what data IS across 130+ systems; Semantica reasons over what it MEANS with provenance and causality. Catalog-first vs reasoning-first.
| OpenMetadata | semantica | |
|---|---|---|
| Stars | 15k | 2.3k |
| Forks | 2.3k | 305 |
| Language | TypeScript | Python |
| License | Apache-2.0 | MIT |
| Last activity | 2 days ago | 2 days ago |
| Topics | knowledge-graphs | knowledge-graphs |
| Curated connections | 2 | 3 |
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.
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.