When I started building knowledge graph systems for healthcare and enterprise data, I quickly realized there’s no single reference showing which tools actually work together. You have document extraction libraries, graph databases, visualization tools, and LLM integrations—but which ones belong in your stack?
That’s why I’ve created a comprehensive guide to 100+ graph libraries, organized by category and use case. It’s now live on GitHub, and in this post, I’ll walk you through what’s included and how to use it.
What’s in the Guide?
The Graph Libraries Guide on GitHub covers:
- Knowledge Graph Extraction — Tools that turn documents and unstructured data into graphs (docling-graph, Docs2KG, LangChain)
- Graph Databases — Where you store your graphs (Neo4j, ArangoDB, TigerGraph, JanusGraph)
- Graph Processing & Analysis — Computing and algorithms (NetworkX, PyTorch Geometric, Apache Spark)
- Visualization — Displaying your graphs (Cytoscape.js, Vis.js, D3.js)
- LLM Integration — Connecting graphs to language models (LangChain, LlamaIndex, Weaviate)
- Plus 6 more categories — Semantic web, GNNs, NLP extraction, ETL, query languages, and vector databases
Why This Matters
If you’ve read my earlier articles, you know I’ve been deep in graph technology for healthcare applications.
In A Complete Guide to Data Ingestion Approaches: From Documents to a Graph Database, I explored how to take raw documents and structure them for graph storage. That pipeline requires choosing the right extraction tool—and there are now dozens of options.
Similarly, in Graph Ontologies Design: From BT Nature Metrics to NHS England, we tackled schema design for complex healthcare data. But once your schema is defined, which database actually handles it efficiently? That’s where the reference guide comes in.
And when you’re Handling Complex Ontologies with SNOMED on Neo4j, you need to know not just Neo4j, but how it fits with extraction, visualization, and LLM tools.
The Libraries Breakdown
For Document → Graph Pipelines
If you’re starting with unstructured data (PDFs, documents, text), you’ll typically use:
- docling-graph — Transforms PDFs into validated knowledge graphs with LLM extraction
- Docs2KG — Human-LLM collaborative approach for diverse document types
- llm-graph-builder — Neo4j’s official tool for unstructured → graph conversion
These feed into graph databases where you apply the schema design principles from my ontologies article.
For Graph Databases
The database you choose depends on your use case:
- Neo4j — Industry standard for property graphs, widely used in healthcare
- ArangoDB — Multi-model (graph + document + search) in one system
- JanusGraph — Open-source, scalable, pluggable backends
- TigerGraph — High-performance analytics at scale
For SNOMED and healthcare ontologies specifically, Neo4j remains the most battle-tested option.
For LLM Integration
This is where it gets interesting. Once your knowledge graph is built, you can connect it to LLMs for reasoning and RAG:
- LangChain — Graph construction chains and entity extraction
- LlamaIndex — Knowledge graphs + vector retrieval for RAG
- Weaviate — Vector database with graph capabilities
This combination—structured knowledge graphs feeding into LLMs—is becoming the standard for enterprise AI.
For Visualization
You need to see what you’ve built:
- Cytoscape.js — Most feature-rich for web-based visualization
- Vis.js — Easy to use, good performance
- D3.js — Maximum customization, steeper learning curve
- Gephi — Desktop tool for analysis and exploration
By Use Case
For Healthcare & Complex Ontologies:
- Extract: docling-graph or Docs2KG
- Store: Neo4j (proven with SNOMED)
- Visualize: Cytoscape.js or Gephi
- Integrate with LLMs: LangChain or LlamaIndex
For Document → Knowledge Graph Pipelines:
- Extract: Check the extraction section in the guide
- Ingest: Use Apache Airflow or dbt for orchestration
- Store: Neo4j or ArangoDB
- Query: Cypher (Neo4j) or Cypher-like syntax
For LLM + Knowledge Graph Applications:
- Extract: LangChain’s graph construction chains
- Store: Neo4j or Weaviate
- Query: Cypher or GraphQL
- Integrate: LlamaIndex for RAG
How to Use the Guide
The full guide is on GitHub with:
- Quick reference tables for each category
- Links to every library (GitHub repos, documentation, official sites)
- Use case breakdowns showing which tools work together
- Quick start guides for beginners, enterprise systems, and machine learning on graphs
- Continuously updated — as new libraries emerge, the guide stays current
You can star it on GitHub to bookmark it, and I’ll keep it updated as the ecosystem evolves.
Next Steps
If you’re building a knowledge graph system:
- Read the guide — Find your use case and the recommended tools
- Review my healthcare articles — Understand schema design and implementation:
- Start with extraction — Pick a library based on your data type (PDFs, CSVs, APIs)
- Choose your database — Neo4j for healthcare, ArangoDB for multi-model needs
- Integrate with LLMs — Connect your graph to LangChain or LlamaIndex for AI applications
Download & Contribute
The guide is open source on GitHub and will stay continuously updated. If you find it useful, consider starring it.
