Memory for AI Agents in 6 lines of code

Neo4j graph construction from unstructured data using LLMs

Neuro-Symbolic AI with Pythonic AI Language 🌱🐋🌍

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

《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

"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.

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

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.

⚡️ 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.

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

An opinionated development framework for building production-ready AI agents with LangGraph. It grounds AI coding assistants (Cursor, Windsurf, Cline) and guides them to use local, official documentation, ensuring reliable, secure, and observable agentic workflows.

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.