RAG vs. Graph RAG vs. Agentic RAG

Standard RAG embeds documents into vectors and retrieves similar chunks.
Effective for direct factual lookups, but fails when queries require connecting facts across multiple documents, as similarity search misses relationships between chunks.

Graph RAG adds a knowledge graph layer. An LLM extracts entities and relationships during indexing; retrieval traverses these connections rather than relying solely on embedding similarity. This enables multi-hop queries.

Example: Vector search retrieves "checkout service uses payments API" and "cluster-3 maintenance Friday" but misses "payments API runs on cluster-3" because the middle fact lacks query keywords. Graph traversal connects these entities, finding the full path in one query.

Agentic RAG uses an LLM agent to dynamically decide which tools to invoke, which sources to query, and in what order, rather than using a fixed pipeline.