nano-graphrag and VeritasGraph
One implementation is a simplified, easily modifiable GraphRAG framework, while the other is an enterprise-grade, on-premise Graph RAG solution with verifiable attribution; thus, they are competitors, with the choice depending on whether the user prioritizes ease of hacking and simplicity or robust, secure, and attributable enterprise deployment.
About nano-graphrag
gusye1234/nano-graphrag
A simple, easy-to-hack GraphRAG implementation
Builds knowledge graphs from text by extracting entities and relationships, then performs retrieval-augmented generation through both global and local graph traversal modes. Supports pluggable components including multiple LLM providers (OpenAI, Bedrock, Ollama), vector databases (FAISS, Milvus, HNSWlib), and graph backends (Neo4j, NetworkX), with full async/await support and MD5-based deduplication for incremental inserts.
About VeritasGraph
bibinprathap/VeritasGraph
VeritasGraph: Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution
Combines hierarchical tree-based navigation with semantic knowledge graph reasoning to enable multi-hop retrieval and 100% verifiable attribution. Supports multiple deployment modes—lite with cloud APIs (OpenAI/Anthropic), local with Ollama (~8GB RAM), or full production with Neo4j—while including built-in vision RAG for extracting structured data from charts and tables. Integrates with optional UI (Gradio), Microsoft GraphRAG, and ingestion tools (YouTube/web articles).
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