Knowledge Engine for AI Agent Memory in 6 lines of code

Neo4j graph construction from unstructured data using LLMs

A Graph RAG System for Evidenced-based Medical Information Retrieval [ACL 2025]

Neuro-Symbolic AI with Knowledge Graph | Reasoning thru AI Language 🌱🐋🌍

《动手学SpringAI》包含SSE流/Agent智能体/知识图谱RAG/FunctionCall/历史消息/图片生成/图片理解/Embedding/VectorDatabase/RAG

VeritasGraph: Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution

"Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation" by Yifan Feng, Hao Hu, Xingliang Hou, Shiquan Liu, Shihui Ying, Shaoyi Du, Han Hu, and Yue Gao.

A SQLite extension that adds graph database capabilities with Cypher query language support and built-in graph algorithms.

NornicDB is a high-performance graph + vector database built for AI agents and knowledge systems. It speaks Neo4j's (Bolt + Cypher) and qdrant's (gRPC) languages so you can use Nornic with zero code changes, while adding intelligent features including a graphql endpoint, air-gapped embeddings, GPU accelerated search, and other intelligent features.

A modular Python framework implementing the Model Context Protocol (MCP). It features a standardized client-server architecture over StdIO, integrating LLMs with external tools, real-time weather data fetching, and an advanced RAG (Retrieval-Augmented Generation) system.

Demo of knowledge graph creation and Graph RAG with BAML and Kuzu

Active WIP for experimenting with GraphRAG and Knowledge Graphs

构建一个医疗知识图谱并基于此实现 RAG，并以此实现医学试题的生成

[WWW 2025] Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning

A minimal implementation of GraphRAG, designed to quickly prototype whether you're able to get good sense-making out of a large dataset with creation of a knowledge graph.

GRACE (Graph-RAG Anchored Code Engineering): open Agent Skills for contract-driven AI code generation with semantic markup, knowledge graphs, and support for Claude Code, Codex CLI, and Kilo Code.

A hybrid retrieval system for RAG that combines vector search and graph search, integrating unstructured and structured data. It retrieves context using embeddings and a knowledge graph, then passes it to an LLM for generating accurate responses.

Graph RAG workshop using Kùzu and LanceDB for hybrid RAG

⚡️ Real-time Knowledge Graph for AI Agents. Connect LLMs to verified weather, stock, and currency data via instant tool-calling. No API keys, no scrapers, just grounded facts in <100ms.

Hybrid AI is the future of explainable intelligence. This article explores how combining vector search, knowledge graphs, and retrieval-augmented generation (RAG) creates AI systems that can reason, cite, and explain their answers with insights learned from building a real Graph-Powered RAG Engine.