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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Turn any collection of documents into a knowledge graph. Extract entities and relationships via LLM, deduplicate with your approval. Map domains, find hidden connections, spot patterns across documents — knowledge that persists and compounds, for you and your AI agents. All from the CLI.
| Date | Stars |
|---|---|
| 2026-07-31 | 663 |
| 2026-08-02 | 664 |
| 2026-08-06 | 664 |
Today
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growth rate 0.00%/day
# sift-kg
**Turn any collection of documents into a knowledge graph.**
No code, no database, no infrastructure — just a CLI and your documents. Drop in PDFs, papers, articles, or records — get a browsable knowledge graph that shows how everything connects, in minutes. sift-kg extracts entities and relationships via LLM, deduplicates with your approval, and generates an interactive viewer you can explore in your browser. Concept maps for anything, at your fingertips.
The same graph that powers your visualizations also works as an [**AI second brain**](#ai-knowledge-base). Everyone's spending months building knowledge bases in Notion and Obsidian. Who has time for that? sift-kg is the structured memory you build in 2 minutes instead of 2 years. Just point at your docs and your AI has a structured understanding of how everything connects.
**[Live demos →](https://juanceresa.github.io/sift-kg/)** graphs generated entirely by sift-kg
```bash
pip install sift-kg
sift init # create sift.yaml + .env.example
sift extract ./documents/ # extract entities & relations
sift build # build knowledge graph
sift resolve # find duplicate entities
sift review # approve/reject merges interactively
sift apply-merges # apply your decisions
sift narrate # generate narrative summary
sift view # interactive graph in your browser
sift export graphml # export to Gephi, yEd, Cytoscape, SQLite, etc.
```
## How It Works
```
Documents (PDF, DOCX, text, HTML, and 75+ formats)
↓
Text Extraction (Kreuzberg, local) — with optional OCR (Tesseract, EasyOCR, PaddleOCR, or Google Cloud Vision)
↓
Schema Discovery (LLM designs entity/relation types from your data — or use a predefined domain)
↓
Entity & Relation Extraction (LLM, using discovered or predefined schema)
↓
Knowledge Graph (NetworkX, JSON)
↓
Entity Resolution (LLM proposes → you review)
↓
Narrative Generation (LLM)
↓
Interactive Viewer (browser) / Export (GraphML, GEXF, CSV, SQLite)
```
Every entity and relation links back to the source document and passage. You control what gets merged. The graph is yours.
## Features
- **Zero-config start** — point at a folder, get a knowledge graph. Or drop a `sift.yaml` in your project for persistent settings
- **Any LLM provider** — OpenAI, Anthropic, Mistral, Ollama (local/private), or any LiteLLM-compatible provider
- **Schema-free by default** — one LLM call samples your documents and designs a schema tailored to the corpus, saved as `discovered_domain.yaml` for reuse and editing. Or use a structured domain (`general`, `osint`, `academic`) for fixed schemas, or define your own in YAML
- **Human-in-the-loop** — sift proposes entity merges, you approve or reject in an interactive terminal UI
- **CLI search** — `sift search "SBF"` finds entities by name or alias, with optional relation and description output
- **Interactive viewer** — explore your graph in-browser with community regions (colored zones showing graph structure), hover preview, focus mode (double-click to isolate neighborhoods), keyboard navigation (arrow keys to step through connections), trail breadcrumb (persistent path that tracks your exploration — trace back through every node you visited), search, type/community/relation toggles, source document filter, and degree filtering. Pre-filter with CLI flags: `--neighborhood`, `--top`, `--community`, `--source-doc`, `--min-confidence`
- **Export anywhere** — GraphML (yEd, Cytoscape), GEXF (Gephi), SQLite, CSV, or native JSON for advanced analysis
- **Narrative generation** — prose reports with relationship chains, timelines, and community-grouped entity profiles
- **Source provenance** — every extraction links to the document and passage it came from
- **Multilingual** — extracts from documents Excerpt of 30,468 characters
Read on GitHub
141
Mark Zhang · United States
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:304fcc863521a05f, desc:ai agents