{"repo":"GramosoftAI/GSearchAI","free":true,"listed":false,"github":"https://github.com/GramosoftAI/GSearchAI","clone":"git clone https://github.com/GramosoftAI/GSearchAI.git","description":"Open-source enterprise AI search & Graph RAG platform. Self-hosted alternative to Glean, GoSearch & Guru — Neo4j + pgvector hybrid retrieval, active ontologies, multi-tenant RLS isolation, explainable multi-hop answers. MIT licensed.","language":"TypeScript","stars":23,"topics":["ai-search","enterprise-search","graph-rag","knowledge-graph","neo4j","ontology","rag","self-hosted","semantic-search"],"license":null,"category":"self-hosted-apps","readme_excerpt":"GRAG Open-Source Enterprise AI Search — a self-hosted, Graph RAG alternative to Glean, GoSearch, and Guru Turn scattered enterprise documents into deterministic, permission-aware, explainable answers — on your own infrastructure. Graph RAG · Multi-Tenant · Self-Hosted · Permission-Aware · Explainable Retrieval --- GRAG is an open-source enterprise AI search and Graph RAG platform. It connects unstructured company data — PDFs, spreadsheets, web pages — to a hybrid Neo4j + pgvector knowledge layer, so AI agents answer multi-hop questions with grounded, attributable, tenant-isolated results. If you've evaluated Glean , GoSearch , Guru , or Onyx and wanted a self-hostable, graph-native, MIT-licensed option you fully control, GRAG is built for you. 📑 Table of Contents - What is GRAG? - Why GRAG? (Open-Source Alternative) - GRAG vs Glean vs GoSearch vs Guru vs Onyx - Who is this for? - Core Capabilities - Architecture & Workflow - RDF & Ontology Support - Tech Stack - Security & Multi-Tenant Isolation - Getting Started - Use Cases - Roadmap - FAQ - Contributing - License --- 🚀 What is GRAG? GRAG ( Gr aph R etrieval- A ugmented G eneration) is a state-of-the-art backend framework for enterprise AI search that bridges raw unstructured documents and autonomous AI reasoning by enforcing strict semantic schemas. Vanilla RAG pipelines hallucinate relationships and stumble on multi-hop queries; standard graph databases lack semantic vector flexibility. GRAG unifies both. By combining th","default_branch":null,"files":null,"tree":[],"storefront":"/r/GramosoftAI","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/GramosoftAI/GSearchAI/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}