OR-Tools
Combinatorial optimization library — solves routing, scheduling, constraint satisfaction, and linear programming problems. OR-Tools features: CP-SAT solver (constraint programming with SAT), LP and MIP (linear/mixed-integer programming), Vehicle Routing Problem (VRP) solver, assignment problem solver, bin packing, knapsack, network flow, GLOP (LP solver), and Python API with CpModel/CpSolver/RoutingModel. Google's production optimization library — used for Google Maps routing, logistics optimization, and scheduling. Agent optimization for resource allocation, scheduling, and routing problems.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local optimization library — no network access, no data exfiltration. Mathematical computation only. No security concerns beyond standard Python dependency hygiene.
⚡ Reliability
Best When
Solving combinatorial optimization problems for agent systems — scheduling, routing, assignment, and resource allocation where brute-force search is infeasible and exact or near-optimal solutions are needed.
Avoid When
You need machine learning, continuous nonlinear optimization, or real-time online constraint solving.
Use Cases
- • Agent task scheduling — model = cp_model.CpModel(); start = [model.NewIntVar(0, horizon, f'start_{i}') for i in tasks]; end = [model.NewIntVar(0, horizon, f'end_{i}') for i in tasks]; model.AddNoOverlap([model.NewIntervalVar(start[i], duration[i], end[i], f'task_{i}') for i in tasks]); solver.Solve(model) — schedule agent tasks without overlap; agent resource scheduler finds optimal task sequence
- • Agent routing optimization — manager = pywrapcp.RoutingIndexManager(len(locations), num_agents, depot); routing = pywrapcp.RoutingModel(manager); routing.SetArcCostEvaluatorOfAllVehicles(callback); solution = routing.SolveWithParameters(params) — optimize agent vehicle routes; delivery scheduling, field agent territory assignment
- • Agent resource assignment — model = cp_model.CpModel(); assignments = {}; for agent, task in product(agents, tasks): assignments[agent, task] = model.NewBoolVar(f'{agent}_{task}'); for task in tasks: model.AddExactlyOne(assignments[a, task] for a in agents) — assign tasks to agents; agent workload balancing optimization
- • Agent bin packing — model = cp_model.CpModel(); bins = [model.NewBoolVar(f'item_{i}_bin_{j}') for i, j in product(items, bin_range)]; for i in items: model.AddExactlyOne(bins[i, j] for j in bin_range) — pack agent tasks into time slots or capacity bins; agent resource utilization optimization
- • Agent linear programming — solver = pywraplp.Solver.CreateSolver('GLOP'); x = solver.NumVar(0, 1, 'x'); y = solver.NumVar(0, 2, 'y'); solver.Add(x + y <= 2); solver.Maximize(solver.Objective().SetCoefficient(x, 3)); solver.Solve() — LP for agent resource allocation; maximize agent throughput subject to capacity constraints
Not For
- • Machine learning — OR-Tools is combinatorial/mathematical optimization; for ML use PyTorch or JAX
- • Continuous nonlinear optimization — OR-Tools is for combinatorial and LP problems; for nonlinear optimization use scipy.optimize
- • Real-time constraint programming — OR-Tools solves offline optimization problems; for online constraint satisfaction use different approaches
Interface
Authentication
No auth — local optimization library.
Pricing
OR-Tools is Apache 2.0 licensed by Google. Free for all use including commercial.
Agent Metadata
Known Gotchas
- ⚠ CP-SAT uses integers only — CpModel.NewIntVar works with integers; float constraints require scaling: multiply all coefficients by 1000 to avoid floats; agent optimization with floating-point costs (1.5 hours) must convert to integers (150 minutes) before model formulation
- ⚠ INFEASIBLE status means model has contradiction — solver.Solve() returning INFEASIBLE means constraints conflict or no valid assignment exists; agent code must check status before accessing solution: if solver.StatusName(status) == 'INFEASIBLE': diagnose constraints; don't call solver.Value() on INFEASIBLE result
- ⚠ Routing vs CP-SAT are separate APIs — pywrapcp.RoutingModel (VRP) and cp_model.CpModel (general CP-SAT) are different Python modules; routing.Solve() and solver.Solve(model) have different call signatures; agent scheduling uses CP-SAT; agent routing uses RoutingModel; don't mix APIs
- ⚠ Large models require time limits — CP-SAT can run indefinitely on hard combinatorial problems; agent scheduling with 1000+ tasks must set time_limit: parameters.max_time_in_seconds = 30; solver returns best solution found within time limit; check solver.ObjectiveValue() for solution quality
- ⚠ Variable bounds affect solver performance — NewIntVar(0, 1000000, 'x') with large upper bound slows CP-SAT; tighten bounds to actual problem constraints; agent scheduling with 8-hour horizon should use NewIntVar(0, 480, 'start_minutes') not NewIntVar(0, 86400, 'start_seconds')
- ⚠ from ortools.sat.python import cp_model vs from ortools.constraint_solver import pywrapcp — CP-SAT uses cp_model module; routing uses constraint_solver module; agent code mixing these gets AttributeError on wrong class; import correct module for problem type
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for OR-Tools.
Scores are editorial opinions as of 2026-03-06.