Graph RAG
A retrieval-augmented generation pattern that combines knowledge graphs with vector search to provide richer, more connected context to AI agents. While standard RAG retrieves text chunks based on semantic similarity, Graph RAG also traverses relationships between entities—connecting a customer's support history to their account details, product usage, and contract terms in a single retrieval step. This produces more comprehensive, contextually accurate responses.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A support agent using standard RAG finds the relevant help article for 'billing error.' A Graph RAG agent also retrieves: this customer's subscription tier, their 3 recent billing changes, the account manager's notes, and related known issues—producing a response that addresses the root cause rather than giving a generic answer.
Frequently asked questions
- When should I use Graph RAG vs. standard RAG?
- Use standard RAG for straightforward Q&A against a document corpus (support KB, product docs). Use Graph RAG when answers require connecting information across entities and relationships—complex support cases, legal research across connected documents, or sales research where prospect data spans multiple sources. Graph RAG adds complexity and cost, so only adopt it when relationship context materially improves answer quality.