diff --git a/graphblas/core/matrix.py b/graphblas/core/matrix.py
index a1e18152b..1ed02aed5 100644
--- a/graphblas/core/matrix.py
+++ b/graphblas/core/matrix.py
@@ -735,6 +735,7 @@ def wait(self, how="materialize"):
         else:
             raise ValueError(f'`how` argument must be "materialize" or "complete"; got {how!r}')
         call("GrB_Matrix_wait", [self, mode])
+        return self
 
     def get(self, row, col, default=None):
         """Get an element at (``row``, ``col``) indices as a Python scalar.
diff --git a/graphblas/core/scalar.py b/graphblas/core/scalar.py
index cc34e27e2..56064534d 100644
--- a/graphblas/core/scalar.py
+++ b/graphblas/core/scalar.py
@@ -465,6 +465,7 @@ def wait(self, how="materialize"):
             raise ValueError(f'`how` argument must be "materialize" or "complete"; got {how!r}')
         if not self._is_cscalar:
             call("GrB_Scalar_wait", [self, mode])
+        return self
 
     def get(self, default=None):
         """Get the internal value of the Scalar as a Python scalar.
diff --git a/graphblas/core/vector.py b/graphblas/core/vector.py
index a39a92f40..fa658b07b 100644
--- a/graphblas/core/vector.py
+++ b/graphblas/core/vector.py
@@ -624,6 +624,7 @@ def wait(self, how="materialize"):
         else:
             raise ValueError(f'`how` argument must be "materialize" or "complete"; got {how!r}')
         call("GrB_Vector_wait", [self, mode])
+        return self
 
     def get(self, index, default=None):
         """Get an element at ``index`` as a Python scalar.
diff --git a/graphblas/tests/test_matrix.py b/graphblas/tests/test_matrix.py
index 351e44e64..6d58d82c5 100644
--- a/graphblas/tests/test_matrix.py
+++ b/graphblas/tests/test_matrix.py
@@ -4234,6 +4234,12 @@ def test_ss_descriptors(A):
             (A @ A).new(nthreads=4, axb_method="dot", sort=True)
 
 
+@autocompute
+def test_wait_chains(A):
+    result = A.wait().T.wait().reduce_rowwise().wait().reduce().wait()
+    assert result == 47
+
+
 def test_subarray_dtypes():
     a = np.arange(3 * 4, dtype=np.int64).reshape(3, 4)
     A = Matrix.from_coo([1, 3, 5], [0, 1, 3], a)