edgextract vs Hyper-Extract
Markdown to knowledge graph with an ontology you write: a small local decision model answers closed yes/no questions per candidate, and your cutoff keeps, queues for review or drops each link. — versus — Knowledge-extraction CLI: LLMs turn documents into structured graphs, hypergraphs and spatio-temporal knowledge — with an MCP server for agents and Obsidian vault export.
Both turn documents into knowledge graphs; hyper-extract lets an LLM generate the structure, edgextract asks closed yes/no questions under your ontology with a cutoff you own.
| edgextract | Hyper-Extract | |
|---|---|---|
| Stars | 100 | 4.1k |
| Forks | 11 | 468 |
| Language | TypeScript | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 7 days ago | 12 days ago |
| Topics | knowledge-graphs, decision-models | knowledge-graphs, rag |
| Curated connections | 4 | 11 |
edgextract — the curator's take
Use it when a wrong edge in your graph costs more than a missing one — every link is a probability you threshold, uncertain ones go to a human queue, and nothing is free-form JSON. Runs fully local (tev1 via Ollama, or WebGPU in the browser through its Rust/WASM core). Small project (~100 stars) and it extracts only what your ontology allows; for open-ended extraction over messy corpora an LLM extractor covers more ground.
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.