Comparison
RAG vs knowledge graph: which does your agent need?
Chunk-based RAG is great at "find me passages that look like this query." It struggles with questions about relationships, history, and causation — where a knowledge graph shines. Most real systems need a hybrid, and the right routing between them.
Quick answer
RAG: RAG retrieves semantically-similar text chunks and feeds them to the model as context. Knowledge graph: A knowledge graph stores entities and the typed relationships between them, enabling multi-hop and temporal queries.
| RAG | Knowledge graph | |
|---|---|---|
| Best at | Factual lookup, "find similar" | Relationships, history, causation |
| Struggles with | Multi-hop, temporal, "why" questions | Fuzzy semantic recall |
| Data shape | Unstructured text | Entities + edges |
| Failure mode | Confident wrong answer | Missing/incomplete edges |
| Loreto skills | Routing, relevance fallback | Hybrid context layers |
Use RAG when
- Your queries are lookups over documents
- You need fast semantic recall
Use Knowledge graph when
- Your queries are about how things relate or evolved
- You need multi-hop or temporal reasoning
The verdict
Route by query type: send factual lookups to RAG and relational/temporal questions to a graph. Loreto has skills for both the routing and the hybrid layer.
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