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cuopt-routing-api-python

Solve vehicle routing problems with cuOpt through a Python API.

2.8k|332|Updated Feb 25, 2026
Search Tags:#routing#python#optimization#pdp#vrp#tsp#cuopt
One-click install
npx skills add https://github.com/NVIDIA/skills --skill cuopt-routing-api-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuopt-routing-api-python
Source: https://github.com/NVIDIA/skills/tree/main/skills/cuopt/cuopt-routing-api-python
Command: npx skills add https://github.com/NVIDIA/skills --skill cuopt-routing-api-python

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) components.

What problem does it solve?

Solves vehicle routing problems (VRP, TSP, PDP) using cuOpt via a Python API.

Core Features & Use Cases

  • DataModel construction with cost and transit matrices, orders, and fleet settings.
  • Support for capacity dimensions, pickup-delivery constraints, and time windows.
  • Examples and templates for rapid prototyping of routing solutions in Python.

Quick Start

Create a DataModel, add cost and transit matrices, set orders and fleet constraints, then call Solve to obtain routing results.

Frequently Asked Questions about cuopt-routing-api-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I solve vehicle routing problems with time windows in Python?▼

To solve vehicle routing problems with time windows in Python, build a DataModel with cost and transit matrices, define fleet constraints, and call the solver to obtain routing results.

What is the best way to handle pickup and delivery constraints in a VRP?▼

Handling pickup and delivery constraints in a VRP requires defining order locations and pairing them within the DataModel before executing the solver configuration.

Can I optimize multiple vehicle capacities using cuOpt?▼

Yes, you can optimize multiple vehicle capacities using cuOpt by setting capacity dimensions within the DataModel to ensure orders match fleet constraints.

Does cuOpt support solving the Traveling Salesman Problem?▼

cuOpt supports solving the Traveling Salesman Problem by constructing a DataModel with a single vehicle and applying the solver to minimize transit costs.

What do I need to set up a routing optimization DataModel in Python?▼

Setting up a routing optimization DataModel in Python requires adding cost matrices, transit matrices, order locations, and fleet settings before calling the solver.

Are there limitations when using Python for large-scale fleet routing optimization?▼

The Python API handles fleet routing optimization by constructing DataModels and configuring solver settings, though performance depends on matrix sizes and fleet constraints.

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