Hyper-Extract vs knowledge_graph
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 — Notebook recipe that turns any text corpus into a concept graph with a local Mistral 7B via Ollama — chunk, extract concepts and relations, add proximity edges — for Graph RAG and KG QA.
Same idea, grown up: hyper-extract is a CLI that has LLMs turn documents into graphs and hypergraphs with structure; knowledge_graph is the one-notebook version that shows the mechanics.
| Hyper-Extract | knowledge_graph | |
|---|---|---|
| Stars | 3.9k | 4.0k |
| Forks | 446 | 627 |
| Language | Python | Jupyter Notebook |
| License | NOASSERTION | MIT |
| Last activity | 2 days ago | 21 days ago |
| Topics | knowledge-graphs, rag | knowledge-graphs, rag, local |
| Curated connections | 9 | 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.
knowledge_graph — the curator's take
Read this to *learn* how a text-to-knowledge-graph pipeline works: it is a single notebook that chunks a PDF, asks a local model for concepts per chunk (concepts, not NER entities — the author's good point), treats co-occurrence as a weighted edge, and visualises with NetworkX/pyvis. Free to run, GPU-free, honest about being simple. NOT a library: no schema, no incremental updates, no store, no retrieval layer — 4k stars are for the tutorial, not for shipping. When you've understood the recipe, move to a tool that validates against a schema and persists.