Vector search finds things that are similar. Knowledge graphs find things that are true. Agents need both, and the combination is stronger than either alone.
TL;DR
- LLMs are fluent but ungrounded; knowledge graphs are precise but inert.
- Combining them gives agents fuzzy recall and exact, relational facts.
- Graphs answer multi-hop and relational questions that vector search handles poorly.
- The pattern: retrieve broadly with vectors, verify and connect with the graph.
Two complementary weaknesses
An LLM on its own is a confident generalist with no anchor to your specific facts — it will happily invent a plausible org chart or product relationship. A knowledge graph is the opposite: a precise, structured model of entities and the relationships between them, but with no language ability of its own. Each one's weakness is the other's strength.
What graphs do that vectors cannot
Vector search excels at "find me passages about X." It struggles with questions that depend on relationships:
- Multi-hop questions. "Which customers are affected by an outage in the service that depends on this database?" is a graph traversal, not a similarity match.
- Exact relationships. "Who reports to whom," "what depends on what" — structure, not semantic proximity.
- Aggregation and constraints. Counting, filtering, and joining across entities is what graphs are built for.
Ask a vector store a relational question and you get passages that sound related. Ask a graph and you get the actual connections.
Patterns for combining them
- Graph-grounded RAG. Use vector search to find candidate entities, then traverse the graph to gather the precise, connected facts around them before generating.
- Entity-anchored retrieval. Resolve the question to specific entities in the graph, then pull their relationships as structured context.
- Verification. After the LLM drafts an answer, check its factual claims against the graph and flag or correct what does not hold.
- Graph construction. Use the LLM to build the graph from unstructured text — extracting entities and relationships — then query that structure deterministically.
Question ─▶ vector search (find candidate entities)
─▶ graph traversal (gather exact relationships)
─▶ LLM (generate, grounded in both)
─▶ graph check (verify factual claims)
Where this matters most
Domains rich in relationships gain the most: enterprise knowledge, dependency and impact analysis, compliance, healthcare, supply chains, fraud. Anywhere the right answer depends on how things connect, a graph turns guesswork into traversal.
The takeaway
The future of grounded AI is not vectors or graphs — it is both, each covering the other's blind spot. Vectors give your agent reach; the graph gives it exactness and the ability to reason over structure. For agents that must be right, not just fluent, structured knowledge is the anchor that keeps them honest.