yifanfeng97/Hyper-Extract
Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
5 releases in the last 90 days, the latest 28 days ago, with 16 contributors on GitHub.
Use Cases & Benefits
- Transforms unstructured text into structured knowledge using LLMs, supporting graphs, hypergraphs, and spatio-temporal extractions via a CLI and Python API.
- Offers a wide range of strongly-typed knowledge structures and 80+ domain-specific templates enabling zero-code, incremental, and multi-format knowledge extraction.
- Use for converting academic papers into interactive knowledge graphs to visualize key concepts, authors, and citations.
- Use for extracting financial entities and relationships from earnings reports to analyze risk factors and company data.
- Ideal for local deployment scenarios requiring on-premise LLM inference and embedding with privacy-preserving knowledge extraction workflows.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Other
GitHub could not identify this license; read the LICENSE file before depending on it.
Community files
- README: present
Watch-outs
- License not identified by GitHub; read the LICENSE file before depending on it.
General guidance from repository metadata, not legal advice. How we score
From the README
Transform documents into structured knowledge with one command.
"Stop reading. Start understanding." "告别文档焦虑，让信息一目了然"
See the full changelog in the GitHub releases.
Hyper-Extract is an intelligent, LLM-powered knowledge extraction and evolution framework. It radically simplifies transforming highly unstructured texts into persistent, predictable, and strongly-typed Knowledge Abstracts. It effortlessly extracts information into a wide spectrum of formats—ranging from simple Collections (Lists/Sets) and Pydantic Models, to complex Knowledge Graphs, Hypergraphs, and even Spatio-Temporal Graphs.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+921 stars over 79 days across 7 snapshot days; best day +194 on Aug 30.
Forks
479+91
Open issues
3+2
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| D4Vinci/Scrapling | 98.1K | +6.9K | 65 | 1 day ago |
| Leonxlnx/taste-skill | 110.7K | +5.2K | 55 | 1 day ago |
| calesthio/OpenMontage | 77.2K | +4.2K | 40 | — |
| heygen-com/hyperframes | 79.6K | +4.1K | 45 | today |
| yifanfeng97/Hyper-Extract | 4.2K | — | 36 | 28 days ago |
| Category median | 98.1K | +5.2K | 45 | — |
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