Hyper-Extract vs semantica
Knowledge-extraction CLI: LLMs turn documents into structured graphs, hypergraphs and spatio-temporal knowledge — with an MCP server for agents and Obsidian vault export. — 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.
Both LLM-extract knowledge graphs from enterprise sources; hyper-extract stops at extraction with an MCP server, Semantica wraps extraction in governance, analytics and causal reasoning.
| Hyper-Extract | semantica | |
|---|---|---|
| Stars | 3.2k | 2.3k |
| Forks | 384 | 305 |
| Language | Python | Python |
| License | NOASSERTION | MIT |
| Last activity | 6 days ago | 2 days ago |
| Topics | knowledge-graphs, rag | knowledge-graphs |
| Curated connections | 5 | 3 |
Hyper-Extract — the curator's take
The pitch beyond ordinary KG extraction is the hypergraph: relations that connect MORE than two entities survive instead of being flattened into pairwise triples. One command per document, query the abstracts over MCP from Claude Desktop or your IDE, export to Obsidian wikilinks. NOT a graph database (it extracts, storage stays simple) and no standard license resolution at review time — verify before building on it; extraction quality tracks the LLM you plug in.
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.