diff --git a/ortools/linear_solver/python/linear_solver.i b/ortools/linear_solver/python/linear_solver.i
index c73d11807c2..6956dd70cbb 100644
--- a/ortools/linear_solver/python/linear_solver.i
+++ b/ortools/linear_solver/python/linear_solver.i
@@ -168,6 +168,33 @@ from ortools.linear_solver.python.linear_solver_natural_api import VariableExpr
     $self->SetHint(hint);
   }
 
+  // We have to define this custom method to handle conversion between int & enum
+  // from Python to C++
+  /// Advanced usage: Incrementality.
+  ///
+  /// This function takes a starting basis to be used in the next LP Solve()
+  /// call. The statuses of a current solution can be retrieved via the
+  /// basis_status() function of a MPVariable or a MPConstraint (int between
+  /// 0 and 4: FREE = 0, AT_LOWER_BOUND = 1, AT_UPPER_BOUND = 2, FIXED_VALUE = 3,
+  //  BASIC = 4)
+  ///
+  /// WARNING: With Glop, you should disable presolve when using this because
+  /// this information will not be modified in sync with the presolve and will
+  /// likely not mean much on the presolved problem.
+  void SetStartingLpBasis(
+      const std::vector<int>& variable_statuses,
+      const std::vector<int>& constraint_statuses) {
+    std::vector<operations_research::MPSolver::BasisStatus> variable_statuses_enum(variable_statuses.size());
+    std::vector<operations_research::MPSolver::BasisStatus> constraint_statuses_enum(constraint_statuses.size());
+    for (int i = 0; i < variable_statuses.size(); ++i) {
+      variable_statuses_enum[i] = static_cast<operations_research::MPSolver::BasisStatus>(variable_statuses[i]);
+    }
+    for (int i = 0; i < constraint_statuses.size(); ++i) {
+      constraint_statuses_enum[i] = static_cast<operations_research::MPSolver::BasisStatus>(constraint_statuses[i]);
+    }
+    $self->SetStartingLpBasis(variable_statuses_enum, constraint_statuses_enum);
+  }
+
   /// Sets the number of threads to be used by the solver.
   bool SetNumThreads(int num_theads) {
     return $self->SetNumThreads(num_theads).ok();
diff --git a/ortools/linear_solver/python/set_starting_basis_test.py b/ortools/linear_solver/python/set_starting_basis_test.py
new file mode 100644
index 00000000000..f49a1762af8
--- /dev/null
+++ b/ortools/linear_solver/python/set_starting_basis_test.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# Copyright 2010-2024 Google LLC
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Simple unit tests for python LP Basis API."""
+
+import unittest
+import random
+from ortools.linear_solver import pywraplp
+
+class TestSetStartingBasis(unittest.TestCase):
+    def build_large_lp(self, solver):
+        n_vars = 10
+        if not solver:
+            return
+        random.seed(123)
+        objective = solver.Objective()
+        objective.SetMaximization()
+        for i in range(0, n_vars):
+            x = solver.IntVar(-random.random() * 200, random.random() * 200, 'x_' + str(i))
+            objective.SetCoefficient(x, random.random() * 200 - 100)
+            if i == 0:
+                continue
+            rand1 = -random.random() * 2000
+            rand2 = random.random() * 2000
+            c = solver.Constraint(min(rand1, rand2), max(rand1, rand2))
+            c.SetCoefficient(x, random.random() * 200 - 100)
+            for j in range(0, i):
+                c.SetCoefficient(solver.variable(j), random.random() * 200 - 100)
+
+    def test_xpress(self):
+        # Build an LP and solve it, then fetch LP basis
+        solver = pywraplp.Solver.CreateSolver("XPRESS_LP")
+        self.build_large_lp(solver)
+        solver.Solve()
+        assert solver.iterations() >= 1
+
+        var_basis = []
+        con_basis = []
+        for var in solver.variables():
+            var_basis.append(var.basis_status())
+        for con in solver.constraints():
+            con_basis.append(con.basis_status())
+
+        # Re-build the same optimization problem in another MPSolver
+        solver_with_basis = pywraplp.Solver.CreateSolver("XPRESS_LP")
+        self.build_large_lp(solver_with_basis)
+        # Set same basis as previous Solver
+        solver_with_basis.SetStartingLpBasis(var_basis, con_basis)
+        # Solve and check that it finds the same solution with no iterations at all
+        solver_with_basis.Solve()
+        self.assertAlmostEqual(solver.Objective().Value(), solver_with_basis.Objective().Value(), delta=1)
+        assert solver_with_basis.iterations() == 0
+
+if __name__ == "__main__":
+    unittest.main()