Orbifold

Orbifold / knwler

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Knwler is a lightweight, single-file Python tool that extracts structured knowledge graphs from documents using AI. Feed it a PDF or text file and receive a richly connected network of entities, relationships, and topics — complete with an interactive HTML report and exports ready for your favorite graph analytics platform.

knwler.com document-intelligence knowledge-graph
32
3
100% credibility
Found Feb 13, 2026 at 10 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Knwler is a single-file Python tool that uses AI to extract entities, relationships, and topics from PDF or text documents into interactive knowledge graphs with HTML reports and standard graph exports.

How It Works

1
🔍 Discover Knwler

You find Knwler, a simple tool that turns long confusing documents into easy-to-explore maps of ideas, people, and connections.

2
💻 Set it up on your computer

Download the one file and get everything ready with a quick preparation step.

3
Pick your AI helper
🏠
Private local AI

Use free thinking power right on your machine for total privacy.

☁️
Fast online AI

Connect a speedy web service for quicker results with a tiny fee.

4
📄 Pick your document

Choose a PDF report, legal paper, or text file you want to make sense of.

5
✨ Watch the map build

Press go and see it read the pages, spot key parts, and weave them into a connected web of insights.

6
🌐 Open your explorer page

Get a single webpage with zoomy graph, topic groups, simple summaries, and clickable links.

🎉 Unlock the document's secrets

Now grasp the big picture instantly, share with friends, or export for deeper tools – all hassle-free.

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AI-Generated Review

What is knwler?

Knwler takes PDFs or text files and extracts structured knowledge graphs—networks of entities, relationships, and topics—using local Ollama models or OpenAI. Feed it a single document, and you get a complete, connected graph in minutes, plus an interactive HTML report with Cytoscape visualization and exports to JSON, GML, or GraphML for your favorite graph analytics tools. It's a single-file Python script, installable via uv, solving the pain of manually mapping dense docs like regulations or reports.

Why is it gaining traction?

It runs fully offline with zero data leakage, auto-detects languages (English, German, French, Spanish, Dutch), and discovers schemas on the fly—no ontology tweaks needed. Caching makes re-runs or exports instant and free, while incremental mode augments graphs from new files. CLI flags let you swap models, tune concurrency, or skip steps, delivering polished outputs ready for Neo4j, Gephi, or yEd without vendor lock-in.

Who should use this?

Compliance teams parsing regulatory PDFs, legal analysts graphing contracts, or research devs extracting insights from multilingual reports. It's for backend engineers building doc-to-graph pipelines in n8n workflows, or analysts feeding entities into vector search without heavy infra.

Verdict

Try it for quick, local knowledge graph prototypes—solid CLI and exports punch above its 10 stars. But with a 1.0% credibility score, expect rough edges; treat as an early experiment, verify outputs, and contribute tests for production use.

(198 words)

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