PuLP
Linear and Integer Programming modeling library for Python — formulate and solve LP/MIP problems with a simple Python API. PuLP features: LpProblem() for problem creation, LpVariable() for decision variables (continuous, integer, binary), lpSum() for objective/constraint expressions, status checking (LpStatus), solver integration (CBC default, GLPK, CPLEX, Gurobi, SCIP), LpMinimize/LpMaximize sense, variable bounds, constraint naming, results via .varValue, and JSON/MPS/LP file export. More accessible than OR-Tools for pure LP/MIP problems. Used for scheduling, routing, cutting stock, and resource allocation optimization.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local computation — no network calls. Commercial solver license keys should be in environment variables. No data exfiltration risk for local LP solving.
⚡ Reliability
Best When
Linear programming and mixed-integer programming problems (scheduling, routing, assignment, production planning) — PuLP's simple Python API is more accessible than OR-Tools for pure LP/MIP without constraint programming needs.
Avoid When
You need non-linear optimization (use CVXPY), constraint programming (use OR-Tools), or large-scale commercial MIP (use Gurobi directly).
Use Cases
- • Agent resource scheduling — prob = LpProblem('schedule', LpMinimize); x = LpVariable.dicts('assign', [(i,j) for i in workers for j in shifts], cat='Binary'); prob += lpSum(cost[i][j]*x[i,j] for i in workers for j in shifts); prob += lpSum(x[i][j] for i in workers) == 1 for j in shifts; prob.solve() — binary integer programming for shift assignment; agent workforce scheduler minimizes cost subject to coverage constraints
- • Agent production planning — prob = LpProblem('production', LpMaximize); x = {p: LpVariable(f'produce_{p}', 0) for p in products}; prob += lpSum(profit[p]*x[p] for p in products); prob += lpSum(resource[p]*x[p] for p in products) <= capacity; prob.solve(); plan = {p: x[p].varValue for p in products} — linear production planning; agent optimizes production mix given resource constraints
- • Agent portfolio LP — prob = LpProblem('portfolio', LpMaximize); w = LpVariable.dicts('weight', assets, 0, 1); prob += lpSum(returns[a]*w[a] for a in assets); prob += lpSum(w[a] for a in assets) == 1; for a in assets: prob += w[a] >= min_weight; prob.solve() — linear portfolio optimization with weight constraints; agent allocation tool for agent portfolios with budget and minimum position constraints
- • Agent cutting stock — prob = LpProblem('cutting', LpMinimize); x = LpVariable.dicts('pattern', patterns, 0, None, LpInteger); prob += lpSum(x[p] for p in patterns); for w, demand in demands.items(): prob += lpSum(pattern[p][w]*x[p] for p in patterns) >= demand; prob.solve() — integer cutting stock to minimize waste; agent manufacturing optimizer selects cutting patterns to meet demand with minimum material waste
- • Agent network flow — prob = LpProblem('flow', LpMinimize); f = LpVariable.dicts('flow', edges, 0); prob += lpSum(cost[e]*f[e] for e in edges); for node in nodes: prob += lpSum(f[e] for e in out_edges[node]) - lpSum(f[e] for e in in_edges[node]) == supply[node]; prob.solve() — minimum cost flow on network graph; agent logistics optimizer routes shipments through network at minimum cost
Not For
- • Non-linear optimization — PuLP handles only linear objectives and constraints; for non-linear use CVXPY or scipy.optimize
- • Convex quadratic programming — PuLP is LP/MIP only; for QP use CVXPY with quadratic objective
- • Large-scale MIP at commercial scale — CBC (default solver) slower than CPLEX/Gurobi for large MIP; commercial solver licenses needed for production-scale integer programs
Interface
Authentication
No auth — local optimization library. Commercial solvers (CPLEX, Gurobi) require their own license keys.
Pricing
PuLP is MIT licensed with bundled CBC solver. Free for all use. Commercial solvers provide better performance for large problems.
Agent Metadata
Known Gotchas
- ⚠ Check prob.status after solve — prob.solve() returns -1 (Infeasible), 0 (Not Solved), 1 (Optimal); agent code doing value = var.varValue without checking status gets None for infeasible problems; always: prob.solve(); if prob.status != 1: raise ValueError(f'Problem {LpStatus[prob.status]}')
- ⚠ lpSum is required for lists — sum([x[i] for i in items]) creates Python sum not LP expression; agent code must use pulp.lpSum([x[i] for i in items]) for objective and constraints; Python sum works by accident for small cases but fails for empty lists and causes wrong types
- ⚠ Variable bounds at creation not constraints — LpVariable('x', 0, 1) sets bounds [0,1]; adding constraint x >= 0 separately is redundant but valid; agent code should specify bounds in LpVariable() for cleaner formulation; CBC ignores unbounded variables causing non-optimal solutions for maximization
- ⚠ solver output floods stdout — prob.solve() with CBC prints solver log to stdout; agent pipelines must suppress with prob.solve(PULP_CBC_CMD(msg=False)) or solver = PULP_CBC_CMD(msg=False); print suppression critical for agent systems where stdout is captured for other purposes
- ⚠ LpVariable.dicts creates flat dict not nested — LpVariable.dicts('x', [(i,j) for i in A for j in B]) creates {(i,j): var} not nested dict; agent code doing x[i][j] fails; must use x[(i,j)] for tuple keys; or create nested dict manually: {i: {j: LpVariable(...)} for i in A for j in B}
- ⚠ Integer variables slow solver exponentially — every LpInteger variable makes problem NP-hard; 20+ binary variables can run for hours with CBC; agent scheduling problems with many binary variables should: add time limit prob.solve(PULP_CBC_CMD(timeLimit=60)), accept sub-optimal solution, or switch to Gurobi
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for PuLP.
Scores are editorial opinions as of 2026-03-06.