Graph Database

Graph databases store entities and the relationships between them as first-class data. For AI agents, this matters because many of the hardest business questions are relational by nature: which customers are connected to a fraudulent account, how a supplier disruption propagates through a bill of materials, or which tables feed a broken dashboard. Answering these with SQL means chains of self-joins; in a graph they become a single traversal that an agent can express, inspect, and explain.

Graphs have also become a core building block of agent memory and retrieval. GraphRAG pipelines extract entities and relationships from documents into a knowledge graph, then combine vector similarity with graph traversal so an agent retrieves not just similar chunks but connected context. The same structure works as long-term agent memory, recording facts, decisions, and their provenance in a form that can be queried and audited.

The landscape spans several architectures. Native graph databases such as Neo4j optimize storage for traversal, distributed engines such as TigerGraph and NebulaGraph scale deep-link queries across clusters, and zero-ETL engines such as PuppyGraph query existing lakehouse and warehouse tables as a graph without moving data. Most now ship MCP servers that let agents inspect graph schemas and run Cypher, GSQL, or nGQL directly, making the graph layer a natural complement to SQL engines in the agentic data stack.