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A comparison of vector databases and graph RAG as memory architectures for AI agents. Vector databases use dense embeddings for semantic similarity search, making them easy to set up and well-suited for unstructured data like chat logs and documentation. Graph RAG combines knowledge graphs with LLMs to represent entities and explicit relationships, enabling precise multi-hop retrieval and explainable reasoning at the cost of higher implementation complexity. The piece provides a decision framework: use vector databases for broad, fuzzy recall on unstructured data; use graph RAG when queries require precise relational answers, factual accuracy, or auditability. A hybrid approach—using vector search to find entry nodes in a knowledge graph, then traversing the graph for precise context—is presented as the direction for advanced agentic systems.

Vector Databases vs. Graph RAG for Agent Memory: When to Use Which

Vector Databases: The Foundation of Semantic Agent Memory

Graph RAG: Structured Context and Relational Memory

The Comparison Framework: When to Use Which