Neo4j Aura Agent is now generally available, allowing users to build and deploy AI agents grounded in knowledge graphs in minutes without managing frameworks or infrastructure. By automating the connection between graph data and LLMs, the platform enables more accurate and explainable agentic workflows.
Neo4j, a graph intelligence platform, launched Aura Agent into general availability. This low-code platform lets you build and deploy AI agents grounded in a knowledge graph (a database of interconnected entities). It enables GraphRAG (graph-based retrieval) without requiring custom orchestration frameworks or complex infrastructure.
Standard RAG often fails on complex queries because vector databases lack structural context. By grounding agents in a knowledge graph, you gain higher factual accuracy and better explainability for agent decisions. This update removes the technical barrier of writing Cypher queries or managing agent loops manually.
You can now transform existing AuraDB data into production-ready agents that understand deep relationships within enterprise data. The platform is currently offering credits to early participants and has released a deep-dive course to support adoption. Access is available through the Neo4j Aura console.
