Hyper-ExtractKnowledge-extraction CLI: LLMs turn documents into structured graphs, hypergraphs and spatio-temporal knowledge — with an MCP server for agents and Obsidian vault export.
Why switchBoth turn documents into knowledge graphs with an LLM. hyper-extract goes wide — hypergraphs, spatio-temporal knowledge, MCP, Obsidian export; OntoCast goes deep on correctness: ontology co-evolution, provenance, disambiguation and SHACL validation.
Full comparison → docling-graphDocuments to validated knowledge graphs: Docling parses, an LLM or VLM fills Pydantic schemas, and you get a directed NetworkX graph with provenance, Cypher/CSV export and HTML views.
Why switchBoth turn documents into validated graphs with an LLM in the loop; docling-graph fills Pydantic schemas you define up front, ontocast co-evolves the ontology alongside the facts as it reads. Fixed schema vs emergent one.
Full comparison → context-ontology-acceleratorAWS's ontology-based context layer: scan your sources, induce ontologies, then serve validated context to agents over MCP — SPARQL federation, a virtual knowledge graph and OWL reasoning.
Why switchOntology induction from two directions. The AWS accelerator is a deployed platform that scans your warehouse, reasons and serves context over MCP; OntoCast is a library-shaped pipeline that grows the ontology out of the documents themselves. Platform versus pipeline.
Full comparison → semanticaGraph-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.
Why switchBoth build accountable RDF context with provenance. Semantica is the enterprise runtime with graph analytics and causal reasoning on top; OntoCast is the focused extraction half you can embed in a LangGraph agent.
Full comparison →