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 Answer: Use Both

ODEI uses both in a hybrid approach:

1. Graph-native queries: constitutional checks, authority, deduplication
2. Semantic search: "find nodes similar to X" via vector index on Neo4j

This outperforms pure vector or pure graph for agent memory tasks.

Practical Decision Guide

Need	Tool
Search documents	RAG/Vector
Track decisions	Knowledge Graph
Validate actions	Knowledge Graph
Semantic similarity	RAG/Vector
Causal chains	Knowledge Graph
Large text corpus	RAG/Vector

Production

ODEI runs Neo4j with hybrid retrieval since January 2026. API: https://api.odei.ai
Research: https://github.com/odei-ai/research/blob/main/agent-memory-comparison.md