Akshay 🚀

RAG vs. Graph RAG vs. Agentic RAG, clearly explained!

Standard RAG embeds documents into vectors and retrieves the most similar chunks via similarity search. For direct factual lookups, this works well.

But it breaks down when a query needs to connect facts spread across multiple documents. Similarity search retrieves individual chunks, not the relationships between them.

Graph RAG adds a knowledge graph layer on top.

→ During indexing, an LLM extracts entities and relationships from the documents.

Your agent remembers everything and understands nothing.
Agent memory started with vector databases. Store facts as chunks, retrieve by similarity.
It works until a query needs to connect facts across chunks. Then it falls apart. The problem isn't similarity. It's structure.
Knowledge graphs were the fix. Entities as nodes, relationships as edges, traversal instead of matching.
But most teams hit a different wall.
When you give an agent a knowledge graph for memory, the default behavior is that the LLM handling extraction decides the structure on its own.
It picks the entity types, the relationship labels, and the attributes.
The results are generic.
For example, you’re building a customer support agent. You feed it 50 support conversations covering customers, tickets, features, and escalation history.
You ask: “Which enterprise customers have open sev-1 tickets?”
The graph has the data. But every support ticket is stored as a “Topic” node.

Are We Ready For An Agent-Native Memory System?

First, what does that even mean?

It's memory built for an agent that keeps running, not a store you bolt on at the end. It has to hold facts across sessions, update them when they change, drop the stale ones, and hand back the right piece when the agent asks. A real data system, not a pile of saved chunks.

A new study checked whether anything out there actually does this, and what decides the answer surprised me.