cuOpt Routing — Python API

SkillDev tools

Lets your agent solve vehicle routing problems like TSP and VRP in Python using cuOpt.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the cuOpt Routing — Python API skill

About this capability

Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.

What this skill tells your AI

The instructions your AI receives, as published by nvidia/skills in skills/cuopt-routing-api-python/SKILL.md and read by ahel’s review.

This skill is Python only. Routing has no C API in cuOpt.

Required questions

Ask these if not already clear:

  1. Problem type — TSP, VRP, or PDP?
  2. Locations — How many? Depot(s)? Cost or distance between pairs (matrix or derived)?
  3. Orders / tasks — Which locations must be visited? Demand or service per stop?
  4. Fleet — Number of vehicles, capacity per vehicle (and per dimension if multiple), start/end locations?
  5. Constraints — Time windows (earliest/latest arrival), service times, precedence (order A before B)?

Minimal VRP Example

import cudf
from cuopt import routing

cost_matrix = cudf.DataFrame([...], dtype="float32")
dm = routing.DataModel(n_locations=4, n_fleet=2, n_orders=3)
dm.add_cost_matrix(cost_matrix)
dm.set_order_locations(cudf.Series([1, 2, 3], dtype="int32"))
solution = routing.Solve(dm, routing.SolverSettings())

if solution.get_status() == 0:
    solution.display_routes()

Adding Constraints

# Time windows
dm.add_transit_time_matrix(transit_time_matrix)
dm.set_order_time_windows(earliest_series, latest_series)

# Capacities
dm.add_capacity_dimension("weight", demand_series, capacity_series)
dm.set_order_service_times(service_times)
dm.set_vehicle_locations(start_locations, end_locations)
dm.set_vehicle_time_windows(earliest_start, latest_return)

# Pickup-delivery pairs
dm.set_pickup_delivery_pairs(pickup_indices, delivery_indices)

# Precedence
dm.add_order_precedence(node_id=2, preceding_nodes=np.array([0, 1]))

Solution Checking

status = solution.get_status()  # 0=SUCCESS, 1=FAIL, 2=TIMEOUT, 3=EMPTY
if status == 0:
    route_df = solution.get_route()
    total_cost = solution.get_total_objective()
else:
    print(solution.get_error_message())
    print(solution.get_infeasible_orders().to_list())

Data Types (use explicit dtypes)

cost_matrix = cost_matrix.astype("float32")
order_locations = cudf.Series([...], dtype="int32")
demand = cudf.Series([...], dtype="int32")

Solver Settings

ss = routing.SolverSettings()
ss.set_time_limit(30)
ss.set_verbose_mode(True)
ss.set_error_logging_mode(True)

Common Issues

ProblemFix
Empty solutionWiden time windows or check travel times
Infeasible ordersIncrease fleet or capacity
Status != 0 with time windowsAdd add_transit_time_matrix()
Wrong costCheck cost_matrix is symmetric
compute_waypoint_sequence alters route_dfIt replaces the location column with waypoint ids in place — pass route_df.copy() if you still need cost-matrix indices (e.g. when iterating per truck)

Debugging

When status != 0: print(solution.get_error_message()) and print(solution.get_infeasible_orders().to_list()) to see which orders are infeasible.

Data types: Use explicit dtypes (float32, int32) for matrices and series to avoid silent errors.

Examples

Escalate

For contribution or build-from-source, see the developer skill.

Signals

GitHub stars
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Forks
387
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
cuopt-routing-api-python
Source
github.com/nvidia/skills