Graph-R1. New RAG framework just dropped!. Combines agents, GraphRAG, and RL. Here are my notes:

【Graph‑R1：従来RAGの構造的な意味情報の欠落やGraphRAGの設計上の課題を解決】. Graph‑R1は、チャンク分割による文脈や意味関係の分断によって構造的な意味情報が欠落してしまう従来のRAGの課題を解決するフレームワーク。

跟大家分享 GitHub 上一个结合 GraphRAG 与 DeepSearch 的智能问答系统框架：Graph RAG Agent。. 该系统框架不仅能回答问题，还能完整展示每一步的思考轨迹和证据来源，让 AI 的推理过程变得可见可信。. GitHub： 主要特性：. - 从零复现 GraphRAG

🔍🗃️ GraphRAG Chatbot Tutorial. Build a powerful GenAI chatbot combining vector search and graph knowledge with SurrealDB and LangChain. This health symptoms demo shows how GraphRAG delivers more contextual AI responses. Learn to build your own GraphRAG chatbot 🚀

Want AI agents that are accurate, explainable, and production-ready?. @Google's MCP Toolbox now supports Neo4j, making GraphRAG easier to build and deploy. Kurtis Van Gent & @mesirii break it down in this live session: #Neo4j #RAG #LLMs #GenAI #MCP

Congrats to @aiDotEngineer 2025 Best Speakers!. MCP: @zeeg.Tiny Teams: @alxai_.LLM Recsys: @devanshtandon_.GraphRAG: @danielchalef.Fortune 500 Day 1: @hwchase17.Architects Day 1: @denyslinkov.Infra: @dylan522p.Voice: @bnicholehopkins.Product Management: @bbalfour.Agent

Is your RAG system connecting the dots, or just finding them?. Here’s why GraphRAG might be the missing piece in your AI retrieval system:. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 ("𝗡𝗮𝗶𝘃𝗲") 𝗥𝗔𝗚. • Uses semantic search to find relevant documents based on meaning.• Each piece of content

LLMs are amazing… but let’s face it — they sometimes just make stuff up 🤯. That’s where GraphRAG comes in: .it makes AI answers smarter, more accurate, and easier to trust.

MCP + GraphRAG is the state of the art for the agentic economy of the future @aiDotEngineer

customers are loving graphs inside @supermemoryai . You can now view how the supermemories of your users evolve, as documents and memories are added to it, with:.temporal understanding,.forgetfulness.relevancy bias (. & more). literally just postgres btw. no graphRAG bullshit

Traditional RAG is good. #GraphRAG is better. 😎.Explore how GraphRAG and #agentic architecture can supercharge #LLMs with richer context, dynamic reasoning, and smarter autonomy. Featuring: NeoConverse - a hands-on experiment by #Neo4j. 👀 Check it out:

Graph-R1: When GraphRAG Meets Reinforcement Learning. Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. GraphRAG methods improve RAG by modeling

معرفی یک ریپوی عالی دیگه. که یک پیاده سازی سریع و سبک از روش GraphRAG هست. اگه نمیدونید GraphRAG چیه، یک روش پیشرفته برای پیاده سازی RAG هست، که به‌جای استفاده از اسناد به صورت جداگانه، از ساختار گراف/ knowledge graph استفاده میکنه. تو این ریپو میتونید از مدلهای اپن سورس هم

Graph databases are becoming the backbone for accurate AI apps!.@furrier with @jimwebbe on why GraphRAG is an essential part of the AI ecosystem. @SiliconANGLE @theCUBE #GenAI #llms #graphdatabase

📄🔍 GraphRAG Contract Analysis. A powerful solution combining GraphRAG and LangGraph agents to analyze legal contracts using Neo4j knowledge graphs, featuring benchmarks across multiple LLMs. Check out the full implementation guide ➡️

Thrilled to announce this short course on why graphRAG is hyped

quick update guys worked on graphrag for the last few days here's a quick update blog. lmk what you think, suggestions are welcome 👇

🚀 Transform your AI's accuracy with knowledge graphs! Free download!. The new Manning book Essential GraphRAG, authored by Tomaz Bratanic and Oskar Hane, is key to learning how to build accurate LLMs with the GraphRAG system from scratch. Get it now:.

Build smarter LLMs with knowledge graphs 😎 . We had a wonderful time at AI Engineer World's Fair 2025! @mesirii and @steveonjava delivered an insightful talk exploring #GraphRAG challenges, implementation patterns, and real-world agentic examples, showing how GraphRAG delivers