ai · 1 min read
Knowledge-Graph Agents: Combining LLMs with Structured Reasoning
When pure RAG is not enough, knowledge graphs give agents the structured backbone they need for multi-hop reasoning. Here is how Bhogar AI combines them.
BABhogar AI TeamProduct & Engineering
Vector RAG is great at "find similar text". It struggles when the answer requires connecting facts across documents - multi-hop reasoning. Knowledge graphs solve that, and combining them with LLMs is one of the highest-use architectures of 2026.
Why it matters
Most knowledge-graph projects die in modeling. Building an ontology by hand is slow, and most enterprises do not have one. The new generation of LLM-assisted graph construction makes this dramatically faster.
How Bhogar AI approaches it
Bhogar AI ships a hybrid retrieval surface: vector search for fuzzy recall, plus a graph store for entity-anchored multi-hop queries. We also offer LLM-assisted graph builders that bootstrap your ontology from existing documents.
- Hybrid vector + graph retrieval in a single API
- LLM-assisted entity and relationship extraction
- Per-tenant graph isolation
- Cypher and SPARQL pass-through for advanced users
- Citations: every claim links back to source documents and graph paths
What you get
On benchmarks requiring multi-hop reasoning, hybrid graph + vector retrieval improves answer accuracy 18-35 points over vector-only RAG.