Hyper-Extract is an open-source toolkit that uses large language models to transform unstructured text into structured knowledge. Published on GitHub, it has gathered about 4,193 stars and is under active development in 2026. Its central idea is that ordinary graphs are not always enough: many facts involve more than two entities at once, so Hyper-Extract can extract hypergraphs — richer structures that capture multi-way relationships — alongside conventional knowledge graphs and spatio-temporal extractions.
The tool works from the command line or as a Python library: point it at documents, and with essentially one command it reads the text, asks an LLM to identify entities and their relationships, and outputs a structured knowledge representation you can query, visualize, or feed into retrieval-augmented generation (RAG) pipelines. Support for spatio-temporal extraction makes it possible to track not just what relates to what, but where and when events happened — valuable for intelligence analysis, research synthesis, and domain knowledge bases. Extracted structures can be exported in standard formats for downstream tools rather than being locked inside the application.
Hyper-Extract is free and open source: you can clone the repository from GitHub and run it in your own environment at no license cost. The only expense is whatever LLM API or local model you choose to power the extraction.
Hyper-Extract is for researchers, data engineers, and AI developers who need to turn piles of unstructured documents — papers, reports, news, notes — into queryable structured knowledge for search, analysis, or retrieval-augmented generation systems.