When I first started working with LLMs, retrieval felt like magic.
You drop in a vector database, point it at your documents, and suddenly the model can “remember” everything it couldn’t fit in the context window.
But the more I used it, the more I realized retrieval alone isn’t the endgame.
It’s like giving a student a stack of textbooks. Yes, they can look things up. But what you really want is for them to understand, reason, and make connections on their own.
That’s where two new approaches come in: agentic RAG and graph RAG.
Both take retrieval and stretch it in new directions. Both are attempts to get closer to actual intelligence.
And they couldn’t be more different.
What agentic RAG does
Agentic RAG is about giving models the ability to act.
Instead of just fetching documents, the model becomes an agent that decides what to look for, how to look for it, and when to stop.
It’s like the difference between a librarian fetching you a single book and a research assistant who knows your goal, can read the books, summarize them, and then run off to find the next lead.
This kind of system feels alive in a way plain RAG doesn’t. It’s iterative, goal-driven, and flexible.
The downside is complexity. Once you make the model an agent, you also inherit the messiness of agents: loops, dead ends, hallucinations, and cost.
But when it works, it feels like magic.
Building agentic RAG becomes easier with Langbase SDK. Here's a guide if you are interested.
What graph RAG does
Graph RAG goes in the opposite direction.
Instead of making the model act like an agent, it structures the knowledge itself.
Imagine taking all your data and turning it into a graph of entities, relationships, and connections. Instead of raw chunks of text, you have a map of how ideas fit together.
When the model queries this graph, it’s no longer just pulling a paragraph. It’s pulling an entire web of meaning.
This makes answers more grounded and less brittle. You don’t have to hope the right chunk happens to be retrieved. The graph gives you the relationships directly.
Graph RAG feels less flashy than agentic RAG, but it’s sturdier. It’s the difference between a curious assistant and a well-organized library.
Choosing between the two
The funny thing is you don’t actually have to choose.
Agentic RAG and graph RAG are two different bets on the same problem: how do we get models to reason over knowledge instead of just parroting it back?
If you care about exploration and discovery, agentic RAG will take you further.
If you care about accuracy and structure, graph RAG is safer.
The smartest systems I’ve seen combine both. An agent that can reason and plan, but also a knowledge graph to keep it grounded.
One gives you flexibility, the other gives you stability.
Why this matters now
I don’t think we’ve seen the final form of RAG yet.
Right now, everyone is experimenting. Some are pushing towards agentic systems. Others are betting on graphs.
The reason it matters is simple. Retrieval is the foundation of every serious AI system. If you can make retrieval smarter, you make everything smarter.
That’s why I think agentic RAG and graph RAG are more than passing fads. They’re the first real attempts to move beyond raw text search and into reasoning.
The next decade of AI might be decided by which of these paths works best—or how we combine them.
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