Knowledge Graphs vs RAG: When to Use Each for AI Agents

Knowledge Graphs vs RAG: When to Use Each for AI Agents Both retrieve context for LLMs. But they solve different problems. Here is when to use each. What RAG Does Well RAG (retrieval-augmented generation) excels at: "What does the documentation say about X?" Fuzzy semantic similarity Unstructured text retrieval One-off question answering Vector databases are fast, cheap, and easy to use. For document Q&A, RAG is the right tool. Where RAG Falls Short RAG struggles with: "What decisions led to this outcome?" (causal chains) "Has this action already been taken?" (exact deduplication) "Is this agent authorized for this?" (authority hierarchy) "What was true two weeks ago?" (temporal state) These require structure that vectors cannot naturally express. Where Knowledge Graphs Excel Knowledge graphs handle: Explicit relationships (BLOCKS, INFORMS, AUTHORIZED_BY) Exact deduplication via content hashing Authority chains via graph traversal Temporal validity via node properties The Production An