---
title: "RAG vs. Graph RAG vs. Agentic RAG"
url: https://daily.dev/posts/rag-vs-graph-rag-vs-agentic-rag-hsq59ngy1
source_url: https://blog.dailydoseofds.com/p/rag-vs-graph-rag-vs-agentic-rag
type: article
source: "Daily Dose of Data Science | Avi Chawla | Substack"
published: 2026-05-28T22:40:35.383Z
updated: 2026-05-28T22:45:30.263Z
tags: ["llm", "rag", "agentic-ai"]
reading_time: 3
upvotes: 4
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# RAG vs. Graph RAG vs. Agentic RAG

**[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 3 min read · 4 upvotes · 0 comments

## Summary

A visual comparison of three RAG architectures: standard RAG uses vector similarity search for single-hop factual lookups; Graph RAG adds a knowledge graph layer enabling multi-hop queries by traversing entity relationships; Agentic RAG uses an LLM agent to dynamically decide which tools and sources to query at runtime. The key insight is that these aren't a progression of sophistication but rather solutions to different query types — factual lookups, relationship queries, and dynamic multi-source tasks respectively.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.dailydoseofds.com/p/rag-vs-graph-rag-vs-agentic-rag>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#rag](https://daily.dev/tags/rag), [#agentic-ai](https://daily.dev/tags/agentic-ai)

[View this post on daily.dev](https://daily.dev/posts/rag-vs-graph-rag-vs-agentic-rag-hsq59ngy1)