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What is Graph RAG?

Definition

Graph Retrieval-Augmented Generation (Graph RAG) is an advanced artificial intelligence architecture that combines the semantic understanding of Large Language Models (LLMs) with the structured relationships of a Knowledge Graph. Unlike traditional vector-based Retrieval-Augmented Generation (RAG), Graph RAG doesn't simply retrieve semantically similar text chunks. Instead, it understands entities and the relationships between them, enabling multi-hop reasoning across people, projects, documents, meetings, repositories, and business processes. This approach allows AI systems to generate more accurate, contextual, and explainable responses while maintaining strong traceability to the original sources.

How Memora uses Graph RAG

Memora links related conversations, PRs, tickets, docs and meetings, and uses those connections when answering questions. Answers link back to their sources and only draw on content the asker can already access.

Why Traditional RAG Falls Short

Traditional vector-based RAG works well when answering questions from a single document. However, enterprise knowledge is fragmented across dozens of platforms, making relationship-aware retrieval essential.

Graph RAG connects this information into a unified knowledge graph, allowing AI to understand how information is related instead of simply where it appears.

Related concepts

Terms that connect to Graph RAG.

View the full glossary

Agentic RAG

Agentic RAG (Retrieval-Augmented Generation) is an advanced AI architecture where autonomous agents actively formulate retrieval strategies, evaluate retrieved context quality, dynamically route queries across vector and graph indexes, and execute iterative tool loops to reduce unsupported answers in enterprise decision-making.

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Hybrid RAG

Hybrid RAG (Retrieval-Augmented Generation) is an enterprise search architecture that unifies dense vector semantic embeddings with sparse keyword search (BM25) and topological knowledge graph traversal (Graph RAG), merging ranked results via Reciprocal Rank Fusion (RRF) for well-grounded, citable context delivery.

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Agentic AI

Agentic AI represents an advanced operational paradigm where artificial intelligence models operate as semi-autonomous or fully autonomous software agents.

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AI Agent

An AI Agent is an autonomous operational software entity driven by large cognitive reasoning models that can perceive its corporate environment, maintain memory states, make logical decisions, design step-by-step action paths, and invoke external APIs or software tools to execute specific business goals with minimal human intervention.

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AI Alignment

AI Alignment is the technical discipline of configuring, tuning, and structuring machine learning models to ensure their generated answers, reasoning processes, and behavioral outputs closely match intended human values, safety definitions, and strict corporate execution rules.

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AI Assistant

An AI Assistant is a context-aware conversational software interface designed to help enterprise workforces discover information, summarize data, configure administrative tasks, and manage day-to-day software operations using standard, natural human language.

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