GitNexus: The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a GitHub repo or ZIP file, and get an interactive knowledge graph wit a built in Graph RAG Agent. Perfect for code exploration

A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and amnesia. Empower your AI with persistent, graph-like structured memory across any model, session, or tool. Drop-in replacement for OpenClaw.

Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat, and inline citations. Powered by Gemini or local Ollama models.

An Open Source, Claude Code Like Tool, With RAG + Graph RAG + MCP Integration, and Supports Most LLMs (Incomplete But Functional & Usable)Autonomous LLM agent: ChromaDB semantic RAG + Neo4j knowledge-graph sync + MCP tool-call gateway.

A scalable RAG platform combining LangGraph agents, hybrid retrieval (Vector+Graph), and Ray orchestration on Kubernetes.

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.

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

12-layer memory architecture for OpenClaw agents — knowledge graph (3K+ facts), semantic search (multilingual, 7ms GPU), continuity + stability + graph-memory plugins, activation/decay system, domain RAG. Agents reconstruct themselves from files on every boot.

👨🏻‍💻 Meet Lumina – my personal AI assistant powered by hybrid RAG with Pinecone vector search and Neo4j graph traversal, Google AI, LangChain, and an MCP server with 30+ tools. Features real-time streaming, conversation branching, and a multi-agent agentic AI pipeline for intelligent, grounded responses with inline citations.

Agentic Graph RAG: Skeleton Indexing + VectorCypher + Agentic Router with Self-Correction

Interactive LLM Chatbot that constructs direct and transitive software dependencies as a knowledge graph and answers user's questions leveraging RAG and critic-agent approach

Building production-ready Retrieval-Augmented Generation (RAG) systems with LangGraph orchestration and local Ollama models for privacy-preserving AI applications.

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).

使用langraph构建Agentic-RAG

agentic_graph_rag

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

A local-LLM based Graph RAG agent using FalkorDB

GenAI Series - RAG, Fine tuning, Agents, Knowledge Graph

A LangGraph-based conversational RAG system: Multi-turn retrieval and adaptive query optimization

This project demonstrates the PathRAG implementation using a team of 6 AI agents at PhD level to show how PathRAG prunes graph-based RAG using relational paths.

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In this story, I have a super quick tutorial showing you how to create a multi-agent chatbot using LangGraph, RAG, and long-term memory to build a powerful agent chatbot for your business or personal use.

A RAG-based chatbot withmulti-tenant project support, where specialized agents collaborate over knowledge graphs to deliver accurate, context-aware answers.

Subgrapher is a local-first semantic browser workspace where references become runnable memory apps with graph context, agent execution, and hybrid RAG + Telegram Remote.

도서 Building AI Agents with LLMs, RAG, and Knowledge Graphs, First Edition의 한글판의 깃허브 레포지토리입니다.

A high-performance agentic RAG system combining Graphiti's temporal knowledge graphs with LangGraph's multi-agent orchestration to achieve 100x faster retrieval speeds than traditional RAG through intelligent graph-based indexing and parallel agent processing.

MCP server combining Qdrant vector search, Neo4j knowledge graphs, and Crawl4AI web intelligence with agentic RAG capabilities. FastMCP 2.0 architecture with enterprise security, monitoring, and Kubernetes deployment. AI/ML engineering powerhouse.

Advanced GraphRAG implementation featuring automated schema generation, optimized batch processing, and comprehensive visualization tools for building intelligent knowledge graphs from documents

Async-native Python framework for building LLM applications — RAG pipelines, tool-using agents, and graph workflows. Streaming-first, transparent API, 2 hard deps. 27 providers · 43 tools · 26 loaders · 9 vector stores.

PostgreSQL Copilot - Code Property Graph Analysis Suite using Joern and RAG. 100% valid CPGQL query generation with 4-agent architecture.