neo4j

RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j.

Semantic layer on top of a graph database to provide an LLM with a set of robust tools to interact with the database

Xmem is a India's First open source multi-modal, multi-agentic long‑term memory layer for AI agents.

🍽️基于图RAG技术的AI美食推荐助手 - Datawhale all-in-rag教程实战案例，集成Neo4j图数据库、Milvus向量检索与智能对话系统

Knowledge graph construction and RAG demo using Diffbot and Neo4j

Python, LlamaIndex, LangChain, 15 Property Graph, 4 RDF , 10 Vector, OpenSearch, Elasticsearch, Alfresco, Nuxeo DBs. 14 data sources (10 auto-sync), KG auto-building, Ontologies, LLMs, Docling, LlamaParse, LiteParse, GraphRAG, RAG, Hybrid Search, AI Chat. TypeScript React, Vue, Angular frontends, REST, MCP Server. Options: Langflow, CocoIndex

Supporting repository for the Essential GraphRAG book published by Manning

Extract structured data from CUAD contracts using LangChain, build a knowledge graph, and query insights through a LangGraph agent - transforming legal agreements into actionable intelligence.

Shared Memory Storage for Multi-Agent Systems

Notebooks and Code about Generative Ai, LLMs, MLOPS, NLP , CV and Graph databases

Lightweight graph database with relational, vector, and MCP support, designed to power knowledge and artificial intelligence persistence and retrieval.

A collection of personally developed projects contributing towards the advancement of Artificial General Intelligence(AGI)

Autonomous, agentic, creative story writing system that incorporates stored embeddings and Knowledge Graphs.

Self-hosted mem0 MCP server for Claude Code. Run a complete memory server against self-hosted Qdrant + Neo4j + Ollama while using Claude as the main LLM.

Examples of my tutorial on how to use Neo4j for empowering AI RAG systems

Demonstration application showing how Neo4j works with Amazon Bedrock

Healthcare RAG agent with Neo4j knowledge graphs - Query medical data using LangChain, FastAPI & Streamlit

Some Cool Semantic Kernel Plugins

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).

Async bulk data ingestion and querying in various document, graph and vector databases via their Python clients