Cuopt Routing API Python Agent Skill
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/NVIDIA/skills --skill cuopt-routing-api-pythonWhat comes with it
32 686 bytes besides the instructionThe instruction itself
11 sections, as written by the authorcuOpt Routing — Python API
This skill is Python only. Routing has no C API in cuOpt.
Required questions
Ask these if not already clear:
- Problem type — TSP, VRP, or PDP?
- Locations — How many? Depot(s)? Cost or distance between pairs (matrix or derived)?
- Orders / tasks — Which locations must be visited? Demand or service per stop?
- Fleet — Number of vehicles, capacity per vehicle (and per dimension if multiple), start/end locations?
- 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
| Problem | Fix |
|---------|-----|
| Empty solution | Widen time windows or check travel times |
| Infeasible orders | Increase fleet or capacity |
| Status != 0 with time windows | Add add_transit_time_matrix() |
| Wrong cost | Check cost_matrix is symmetric |
| compute_waypoint_sequence alters route_df | It 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
- examples.md — VRP, PDP, multi-depot
- server_examples.md — REST client (curl, Python)
- Reference models: This skill's
assets/— vrp_basic, pdp_basic. See assets/README.md.
Escalate
For contribution or build-from-source, see the developer skill.
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How to use it
Take nvidia/cuopt-routing-api-python from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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