Supply chain knowledge graph NL query system combining Azure-hosted Streamlit UI with multi-agent LLM orchestration via OpenRouter, routing natural language questions through Nemotron 120B primary agent with StepFun Flash fallback to generate Cypher queries. The architecture layers user interaction, AI reasoning, query generation, and Neo4j data access, with schema.py providing multi-hop reasoning rules and db.py managing bolt:// connections to Neo4j Desktop. This pattern demonstrates enterprise-grade LLM-to-graph integration, enabling supply chain analysts to query complex supplier-product-distributor-retailer networks without SQL expertise. Fork this diagram on Diagrams.so to customize agent models, add additional fallback chains, or adapt the schema for your domain knowledge graph. The config.py and .env separation ensures secure API key management while maintaining reproducible model selection across environments.