The Multi-Agent-Intelligent-Warehouse is an AI-powered, multi-agent system designed to optimize warehouse operations through intelligent automation, real-time monitoring, and natural language interaction. The system provides comprehensive support for equipment management, operations coordination, safety compliance, and document processing.
Key Value Propositions:
- Intelligent Automation: AI-powered agents handle complex operational queries and workflows
- Real-Time Visibility: Comprehensive monitoring of equipment, tasks, and safety incidents
- Natural Language Interface: Conversational AI for intuitive warehouse operations management
- Enterprise Integration: Seamless connectivity with WMS, ERP, IoT, and other warehouse systems
- Production-Ready: Scalable, secure, and monitored infrastructure
Architecture Diagram
Key Features
- Warehouse Operational Assistant: Central orchestrator managing specialized AI agents
- Agent Orchestration Framework: LangGraph for workflow orchestration + MCP (Model Context Protocol) for tool discovery
- Multi-Agent System - Five specialized agents:
- •Equipment & Asset Operations Agent - Equipment assets, assignments, maintenance, and telemetry
- •Operations Coordination Agent - Task planning and workflow management
- •Safety & Compliance Agent - Safety monitoring, incident response, and compliance tracking
- Forecasting Agent - Demand forecasting, reorder recommendations, and model performance monitoring
- Document Processing Agent - OCR, structured data extraction, and document management
- Real-Time Monitoring: Equipment status, telemetry, Prometheus metrics, Grafana dashboards, and system health
- Enterprise Security: JWT + RBAC with 5 user roles, NeMo Guardrails for content safety, and comprehensive user management
- System Integrations: WMS (SAP EWM, Manhattan, Oracle), ERP (SAP ECC, Oracle), IoT sensors, RFID/Barcode scanners, Time Attendance systems
- API Services Layer: Standardized interfaces for business logic and data access
- Data Retrieval & Processing: SQL, Vector, and Knowledge Graph retrievers
- Advanced Features: Redis caching, conversation memory, evidence scoring, intelligent query classification, automated reorder recommendations, business intelligence dashboards
Minimum System Requirements
Hardware Requirements
- Expect that you will want the NIM microservices to be self-hosted as you progress in your development. For self-hosting the blueprint with these microservices locally deployed, the recommended system requirement is 4 H100 GPUs with the Llama 3.3 Nemotron Super 49B NIM (primary LLM), Nemotron Nano 12B, the NV-EmbedQA 1B, embedding NIM, NeMo Retriever and NeMoRetriever OCR NIMs for document processing, and the Milvus database accelerated with NVIDIA cuVS.
Deployment Options
Software Used in This Blueprint
NVIDIA Technology
3rd Party Software
- LangGraph
- Redis
- Milvus database (accelerated with NVIDIA cuVS)
- PostgreSQL
- Timescale DB
- FastAPI
- React
Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this blueprint meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
License
Governing Terms: The Blueprint scripts are governed by Apache License, Version 2.0, and enables use of separate open source and proprietary software governed by their respective licenses: Llama-3.3-nemotron-super-49b-v1.5, NVIDIA Retrieval QA Llama 3.2 1B Embedding v2, NeMo Retriever Extraction, NeMo Retriever Page Elements v3, NeMo Retriever OCR v1, Nemotron Parse, NVIDIA-Nemotron-Nano-12B-v2-VL, NeMo Guardrails, and RAPIDS cuML.
