Building a Neo4j Graph Agent for Gemini Enterprise
Blog post from Neo4j
The guide provides a comprehensive overview of deploying a Neo4j Graph Database Agent into a production-ready enterprise environment using Google Gemini Enterprise, emphasizing the challenges of moving beyond local prototypes to ensure security, scalability, and cost-control. It details the use of the Model Context Protocol (MCP), the Google Agent Development Kit (ADK), and the Agent-to-Agent (A2A) protocol to create a decoupled, scalable system, with key architectural features including decoupled microservices, robust app-level security, granular cost control, and extensible logic for custom business requirements. The process involves deploying two separate services on Google Cloud Run: a standalone Neo4j MCP server and a custom Python ADK application, with stringent token management and authentication measures to maintain cost efficiency and security. The architecture supports customized queries and integrates with Gemini Enterprise through OAuth validation, facilitating natural language queries on complex graph structures. The guide concludes with deployment instructions, emphasizing the importance of observability, scalability, and extensibility in building secure, enterprise-grade AI agents.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| MCP | 33 | 6,108 | 613 | 170 | +36% |
| Secrets Management | 13 | 1,821 | 338 | 111 | +22% |
| LLM | 6 | 5,932 | 1,046 | 223 | -2% |
| AI Agents | 3 | 4,430 | 1,100 | 236 | -3% |
| Real-time | 3 | 6,296 | 1,346 | 246 | -2% |
| Multi-agent systems | 1 | 460 | 170 | 68 | -20% |
| Observability | 1 | 4,496 | 812 | 176 | +40% |
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