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]

Logic Language for LLMs 🌱🐋🌍 Build Neuro-Symbolic AI for learning and reasoning

《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

Nornicdb is a low-latency, Graph + Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Uses Neo4j Bolt/Cypher and qdrant's gRPC drivers so you can switch with no changes. Then, adding intelligent features like schemas, managed embeddings, LLM reranking+inferrence, GPU acceleration, Auto-TLP, Memory Decay, and MCP server.

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

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.

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 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.

Active WIP for experimenting with GraphRAG and Knowledge Graphs

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

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

Graph-vector database that queried 1 billion edges for $2.50. Rust, OpenCypher, vector search, 14 graph algorithms. 74M nodes / 1B edges on a single machine.

[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.

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

Graph RAG with pure vector search, achieving SOTA performance in multi-hop reasoning scenarios.