Build Knowledge Graph Agents in Minutes with Neo4j Aura Agent

Discover how to build and deploy AI agents powered by knowledge graphs in just minutes. This demo walks through Neo4j Aura Agent, showing how to transform an employee knowledge graph into an intelligent agent with GraphRAG capabilities.

**Build This Demo Yourself:**
Want to recreate this example hands-on? Follow our developer guide to build your own knowledge graph agent step-by-step:
https://neo4j.com/developer/genai-ecosystem/aura-agent-getting-started/

**Additional Resources:**
Explore documentation, tutorials, and examples:

https://neo4j.com/product/aura-agent/">Aura Agent

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**What You’ll See in This Demo:**

✓ Generate an agent from a graph schema and use case description using AI
✓ Three types of Graph RAG tools working together:
  • Cypher Templates for domain-specific queries
  • Text2-Cypher for dynamic query generation
  • Similarity Search for vector-powered semantic retrieval
✓ Advanced chain-of-thought reasoning with full explainability
✓ One-click deployment to production endpoints (REST & MCP)
✓ Integration with Claude Desktop via Model Context Protocol

**Why Ground AI Agents with Knowledge Graphs?**

Unlike vector-only RAG, Graph RAG combines semantic search with the structured relationships in your knowledge graph, delivering more accurate and contextually relevant responses. Neo4j Aura Agent makes this technology accessible through an intuitive low-code UI.

**Use Case Shown:**
Employee knowledge graph containing people, skills, projects, domains, and work types – demonstrating how complex organizational knowledge can ground an interactive AI agent.

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