diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index aa185923d02e..bf6293a2a90c 100644
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -896,6 +896,7 @@ def _convert_lstm(inexpr, keras_layer, etab):
     in_data = _op.squeeze(in_data, axis=[0])
     in_data = _op.split(in_data, indices_or_sections=time_steps, axis=0)
     # loop for the number of time_steps
+    out_list = []  # store h outputs in case return_sequences is True
     for data in in_data:
         ixh1 = _op.nn.dense(data, kernel_weight, units=units)
         ixh2 = _op.nn.bias_add(_op.nn.dense(next_h, recurrent_weight, units=units), bias=in_bias)
@@ -906,8 +907,11 @@ def _convert_lstm(inexpr, keras_layer, etab):
         next_c = in_transform * next_c + in_gate * _convert_activation(gates[2], keras_layer, None)
         out_gate = _convert_recurrent_activation(gates[3], keras_layer)
         next_h = out_gate * _convert_activation(next_c, keras_layer, None)
+        if keras_layer.return_sequences:
+            out_list.append(_op.expand_dims(next_h, axis=1))
+    out = _op.concatenate(out_list, axis=1) if keras_layer.return_sequences else next_h
     out_shape = tuple(dim if dim else 1 for dim in _as_list(keras_layer.output_shape)[0])
-    out = _op.reshape(next_h, newshape=out_shape)
+    out = _op.reshape(out, newshape=out_shape)
     return [out, next_h, next_c]
 
 
diff --git a/tests/python/frontend/keras/test_forward.py b/tests/python/frontend/keras/test_forward.py
index 26bf58cbf384..4dfe89fe40e5 100644
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -417,6 +417,17 @@ def test_forward_reuse_layers(self, keras):
         keras_model = keras.models.Model(data, z)
         verify_keras_frontend(keras_model)
 
+    def test_forward_lstm(self, keras):
+        data = keras.layers.Input(shape=(10, 32))
+        rnn_funcs = [
+            keras.layers.LSTM(16),
+            keras.layers.LSTM(16, return_sequences=True),
+        ]
+        for rnn_func in rnn_funcs:
+            x = rnn_func(data)
+            keras_model = keras.models.Model(data, x)
+            verify_keras_frontend(keras_model, need_transpose=False)
+
     def test_forward_rnn(self, keras):
         data = keras.layers.Input(shape=(1, 32))
         rnn_funcs = [
@@ -613,6 +624,7 @@ def test_forward_nested_layers(self, keras):
         sut.test_forward_multi_inputs(keras=k)
         sut.test_forward_multi_outputs(keras=k)
         sut.test_forward_reuse_layers(keras=k)
+        sut.test_forward_lstm(keras=k)
         sut.test_forward_rnn(keras=k)
         sut.test_forward_vgg16(keras=k)
         sut.test_forward_vgg16(keras=k, layout="NHWC")