diff --git a/keras_core/layers/__init__.py b/keras_core/layers/__init__.py
index 15fe9ede3..c4e9671aa 100644
--- a/keras_core/layers/__init__.py
+++ b/keras_core/layers/__init__.py
@@ -94,3 +94,4 @@ from keras_core.layers.reshaping.repeat_vector import RepeatVector
 from keras_core.layers.reshaping.reshape import Reshape
 from keras_core.layers.reshaping.up_sampling1d import UpSampling1D
 from keras_core.layers.rnn.rnn import RNN
+from keras_core.layers.rnn.stacked_rnn_cells import StackedRNNCells
diff --git a/keras_core/layers/rnn/stacked_rnn_cells.py b/keras_core/layers/rnn/stacked_rnn_cells.py
new file mode 100644
index 000000000..05dc16aeb
--- /dev/null
+++ b/keras_core/layers/rnn/stacked_rnn_cells.py
@@ -0,0 +1,135 @@
+from tensorflow import nest
+
+from keras_core import operations as ops
+from keras_core.api_export import keras_core_export
+from keras_core.layers.layer import Layer
+from keras_core.saving import serialization_lib
+
+
+@keras_core_export("keras_core.layers.StackedRNNCells")
+class StackedRNNCells(Layer):
+    """Wrapper allowing a stack of RNN cells to behave as a single cell.
+
+    Used to implement efficient stacked RNNs.
+
+    Args:
+      cells: List of RNN cell instances.
+
+    Examples:
+
+    ```python
+    batch_size = 3
+    sentence_length = 5
+    num_features = 2
+    new_shape = (batch_size, sentence_length, num_features)
+    x = np.reshape(np.arange(30), new_shape)
+
+    rnn_cells = [keras_core.layers.LSTMCell(128) for _ in range(2)]
+    stacked_lstm = keras_core.layers.StackedRNNCells(rnn_cells)
+    lstm_layer = keras_core.layers.RNN(stacked_lstm)
+
+    result = lstm_layer(x)
+    ```
+    """
+
+    def __init__(self, cells, **kwargs):
+        super().__init__(**kwargs)
+        for cell in cells:
+            if "call" not in dir(cell):
+                raise ValueError(
+                    "All cells must have a `call` method. "
+                    f"Received cell without a `call` method: {cell}"
+                )
+            if "state_size" not in dir(cell):
+                raise ValueError(
+                    "All cells must have a `state_size` attribute. "
+                    f"Received cell without a `state_size`: {cell}"
+                )
+        self.cells = cells
+
+    @property
+    def state_size(self):
+        return [c.state_size for c in self.cells]
+
+    @property
+    def output_size(self):
+        if getattr(self.cells[-1], "output_size", None) is not None:
+            return self.cells[-1].output_size
+        elif isinstance(self.cells[-1].state_size, (list, tuple)):
+            return self.cells[-1].state_size[0]
+        else:
+            return self.cells[-1].state_size
+
+    def get_initial_state(self, batch_size=None):
+        initial_states = []
+        for cell in self.cells:
+            get_initial_state_fn = getattr(cell, "get_initial_state", None)
+            if get_initial_state_fn:
+                initial_states.append(
+                    get_initial_state_fn(batch_size=batch_size)
+                )
+            else:
+                if isinstance(cell.state_size, int):
+                    state_size = [cell.state_size]
+                else:
+                    state_size = cell.state_size
+                initial_states.append(
+                    [
+                        ops.zeros((batch_size, d), dtype=self.compute_dtype)
+                        for d in state_size
+                    ]
+                )
+        return initial_states
+
+    def call(self, inputs, states, training=False, **kwargs):
+        # Call the cells in order and store the returned states.
+        new_states = []
+        for cell, states in zip(self.cells, states):
+            states = list(states) if nest.is_nested(states) else [states]
+            if isinstance(cell, Layer) and cell._call_has_training_arg():
+                kwargs["training"] = training
+            else:
+                kwargs.pop("training", None)
+            cell_call_fn = cell.__call__ if callable(cell) else cell.call
+            print("call", cell.__class__, "with", states)
+            inputs, states = cell_call_fn(inputs, states, **kwargs)
+            new_states.append(states)
+        return inputs, new_states
+
+    def build(self, input_shape):
+        print("build called")
+        for cell in self.cells:
+            if isinstance(cell, Layer) and not cell.built:
+                print("build cell", cell)
+                cell.build(input_shape)
+                cell.built = True
+                print(cell, len(cell.trainable_weights))
+            if getattr(cell, "output_size", None) is not None:
+                output_dim = cell.output_size
+            elif isinstance(cell.state_size, (list, tuple)):
+                output_dim = cell.state_size[0]
+            else:
+                output_dim = cell.state_size
+            batch_size = nest.flatten(input_shape)[0]
+            input_shape = (batch_size, output_dim)
+        self.built = True
+        print(self, len(self.trainable_weights))
+
+    def get_config(self):
+        cells = []
+        for cell in self.cells:
+            cells.append(serialization_lib.serialize_keras_object(cell))
+        config = {"cells": cells}
+        base_config = super().get_config()
+        return {**base_config, **config}
+
+    @classmethod
+    def from_config(cls, config, custom_objects=None):
+        cells = []
+        for cell_config in config.pop("cells"):
+            cells.append(
+                serialization_lib.deserialize_keras_object(
+                    cell_config, custom_objects=custom_objects
+                )
+            )
+        return cls(cells, **config)