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cuopt-routing-api-python

Solve vehicle routing problems using cuOpt's Python API.

Updated Apr 16, 2026
Search Tags:#routing#python#pdp#vrp#tsp#cuopt
One-click install
npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill cuopt-routing-api-python-sayalinvidia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cuopt-routing-api-python
Source: https://github.com/sayalinvidia/sayali-skills-test/tree/main/skills/cuopt-routing-api-python
Command: npx skills add https://github.com/sayalinvidia/sayali-skills-test --skill cuopt-routing-api-python-sayalinvidia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Enables developers to model and solve vehicle routing problems (VRP), traveling salesman problems (TSP), and pickup-delivery problems (PDP) using NVIDIA cuOpt's Python API.

It supports defining costs, time windows, capacities, and pickup-delivery constraints, enabling realistic logistics optimization in Python workflows.

Core Features & Use Cases

  • Python-only routing API: Use cuopt.routing DataModel, add_cost_matrix, set_order_locations, and Solve to generate optimized routes.
  • Time windows, capacity, and PDP support: Model complex constraints for real-world logistics scenarios.
  • Multi-depot and fleet configurations: Configure multiple vehicles with individual start/end locations and time windows.

Quick Start

Start by constructing a cuOpt routing DataModel in Python, configure matrices and constraints, and call routing.Solve to retrieve an optimized route.

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 and capacity constraints in Python?▼

To solve vehicle routing problems with time windows and capacities in Python, use cuOpt's Python API to construct a DataModel, add cost matrices, set order locations, and call Solve to retrieve optimized routes.

Can I model pickup and delivery problems with multiple depots using Python?▼

Yes, you can model pickup and delivery problems with multiple depots in Python by configuring the cuOpt routing DataModel with individual vehicle start and end locations, time windows, and PDP constraints.

What is the best way to optimize multi-depot fleet routing configurations?▼

The best way to optimize multi-depot fleet routing configurations is using cuOpt's Python API to define fleet parameters, cost matrices, and constraints within the DataModel before executing the Solve function.

Does the cuOpt Python API support adding custom cost matrices for logistics optimization?▼

Yes, the cuOpt Python API supports adding custom cost matrices for logistics optimization by utilizing the add_cost_matrix function within the cuopt.routing DataModel structure.

Do I need specific Python libraries to execute traveling salesman problem routing tasks?▼

Yes, executing traveling salesman problem routing tasks requires Python, the cuOpt Python libraries, and the cuopt.routing module to construct the DataModel and obtain optimized solutions.

What are the limitations of using Python for complex vehicle routing problem constraints?▼

Complex vehicle routing problem constraints in Python are limited to the cuOpt API's supported parameters, which include time windows, capacities, pickup-delivery rules, and multi-depot fleet configurations.

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