NVIDIA Open Sources Internal Agentic Workflows for Supply Chain Optimization

May 5, 2026 · Updated May 13, 2026

NVIDIA open-sourced the agentic workflow it uses to manage its own supply chain, combining its cuOpt (a GPU-accelerated mathematical solver) with a large language model reasoning layer. The system uses agent skills and LangChain Deep agent orchestration to coordinate specialized agents, turning natural language into optimized logistical plans.

Optimization engine: NVIDIA cuOpt
Orchestration framework: LangChain Deep
Optimization speed: Minutes vs weeks
Deployment method: Brev Launchable
Developer incentive: Free credits for first 50 users

Traditional supply chain optimization often requires weeks of manual modeling. This release follows the NVIDIA Agent Toolkit and extends the NVIDIA Dynamo inference stack, matching the multi-step coordination seen in the Nemotron 3 Super model. By combining these layers, the workflow bridges the gap between high-level reasoning and low-level hardware optimization.

You can deploy the workflow using a Brev Launchable, a preconfigured GPU environment that bypasses manual setup. The open-source code is available for enterprise integration, and NVIDIA is offering free developer credits to the first 50 users. This enables near-real-time decision-making for complex routing and inventory management.

View the full update on developer.nvidia.com

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Internally at NVIDIA, we use cuOpt based agentic workflows with agent skills to optimize our supply chains. Since it’s open source, you can too. With optimizations ready in minutes instead of weeks, the workflow uses multi-agent LangChain Deep agent orchestration and GPU-accelerated solvers to turn natural language into optimized decisions. Spin it up instantly with a Brev Launchable (preconfigured GPU environment) and grab free developer credits while they last.

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Still wondering? A few quick answers below.

NVIDIA cuOpt is an open-source, GPU-accelerated engine designed for decision optimization. It specializes in solving complex mathematical problems like linear programming and mixed-integer programming. By using GPU acceleration, it can process logistical and routing challenges in near-real time, making it significantly faster than traditional CPU-based solvers for large-scale supply chain operations.

The workflow uses a multi-agent system orchestrated by LangChain Deep to bridge the gap between natural language and mathematical optimization. It utilizes specialized agent skills to interpret user requests and then invokes GPU-accelerated solvers to generate decisions. This approach allows users to describe complex supply chain scenarios in plain English and receive optimized results in minutes.

Yes, NVIDIA has open-sourced the agentic workflow it uses internally for its own supply chain management. Developers can access the reference workflow to build their own decision systems. This includes the integration logic between large language models and the cuOpt optimization engine, allowing organizations to implement similar high-speed logistical planning within their own infrastructure.

Developers can instantly deploy the workflow using a Brev Launchable, which provides a preconfigured GPU environment specifically set up for this agentic system. This eliminates the need for manual infrastructure configuration. Additionally, NVIDIA is offering free developer credits to the first 50 users who deploy the workflow through the provided Brev link to encourage early adoption.

Agent skills are reusable, domain-specific capabilities that allow AI agents to perform specialized tasks. In this supply chain workflow, these skills act as the interface between the reasoning capabilities of a language model and the technical requirements of the cuOpt solver. They enable the agent to understand how to structure data and parameters for complex optimization problems.