Memory for AI Agents in 6 lines of code

Neo4j graph construction from unstructured data using LLMs

Neuro-Symbolic AI with Pythonic Logic 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

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

Active WIP for experimenting with GraphRAG and Knowledge Graphs

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.

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

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.

Code from the ODSC Agentic Graph RAG workshop combining vector, FTS & graph retrieval for RAG. Includes observability and guardrails for evaluating outputs.

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.

AMG-RAG (Agentic Medical Graph-RAG) is a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates reasoning, and retrieves current external evidence for medical Question Answering (QA).

AI Causal and fastest memory

Structural RAG (Retrieval-Augmented Generation) system for large codebases

Experiments and benchmarks with Text2Cypher for Graph RAG