diff --git a/bayesflow/wrappers/__init__.py b/bayesflow/wrappers/__init__.py
new file mode 100644
index 000000000..efd3a2a3e
--- /dev/null
+++ b/bayesflow/wrappers/__init__.py
@@ -0,0 +1 @@
+from .mamba import Mamba
diff --git a/bayesflow/wrappers/mamba/__init__.py b/bayesflow/wrappers/mamba/__init__.py
new file mode 100644
index 000000000..efd3a2a3e
--- /dev/null
+++ b/bayesflow/wrappers/mamba/__init__.py
@@ -0,0 +1 @@
+from .mamba import Mamba
diff --git a/bayesflow/wrappers/mamba/mamba.py b/bayesflow/wrappers/mamba/mamba.py
new file mode 100644
index 000000000..0dba70d43
--- /dev/null
+++ b/bayesflow/wrappers/mamba/mamba.py
@@ -0,0 +1,122 @@
+from collections.abc import Sequence
+
+import keras
+from keras.saving import register_keras_serializable as serializable
+
+from bayesflow.networks.summary_network import SummaryNetwork
+from bayesflow.types import Tensor
+from bayesflow.utils import keras_kwargs
+from bayesflow.utils.decorators import sanitize_input_shape
+
+from .mamba_block import MambaBlock
+
+
+@serializable("bayesflow.wrappers")
+class Mamba(SummaryNetwork):
+    """
+    Wraps a sequence of Mamba modules using the simple Mamba module from:
+    https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py
+
+    Copyright (c) 2023, Tri Dao, Albert Gu.
+
+    Example usage in a BayesFlow workflow as a summary network:
+
+    `summary_net = bayesflow.wrappers.Mamba(summary_dim=32)`
+    """
+
+    def __init__(
+        self,
+        summary_dim: int = 16,
+        feature_dims: Sequence[int] = (64, 64),
+        state_dims: Sequence[int] = (64, 64),
+        conv_dims: Sequence[int] = (64, 64),
+        expand_dims: Sequence[int] = (2, 2),
+        bidirectional: bool = True,
+        dt_min: float = 0.001,
+        dt_max: float = 0.1,
+        dropout: float = 0.05,
+        device: str = "cuda",
+        **kwargs,
+    ):
+        """
+        A time-series summarization network using Mamba-based State Space Models (SSM). This model processes
+        sequential input data using a sequence of Mamba SSM layers (determined by the length of the tuples),
+        followed by optional pooling, dropout, and a dense layer for extracting summary statistics.
+
+        Parameters
+        ----------
+        summary_dim : Sequence[int], optional
+            The output dimensionality of the summary statistics layer (default is 16).
+        feature_dims : Sequence[int], optional
+            The feature dimension for each mamba block, default is (64, 64),
+        state_dims : Sequence[int], optional
+            The dimensionality of the internal state in each Mamba block, default is (64, 64)
+        conv_dims : Sequence[int], optional
+            The dimensionality of the convolutional layer in each Mamba block, default is (32, 32)
+        expand_dims : Sequence[int], optional
+            The expansion factors for the hidden state in each Mamba block, default is (2, 2)
+        dt_min : float, optional
+            Minimum dynamic state evolution over time (default is 0.001).
+        dt_max : float, optional
+            Maximum dynamic state evolution over time (default is 0.1).
+        pooling : bool, optional
+            Whether to apply global average pooling (default is True).
+        dropout : int, float, or None, optional
+            Dropout rate applied before the summary layer (default is 0.5).
+        dropout: float, optional
+            Dropout probability; dropout is applied to the pooled summary vector.
+        device : str, optional
+            The computing device. Currently, only "cuda" is supported (default is "cuda").
+        **kwargs :
+            Additional keyword arguments passed to the `SummaryNetwork` parent class.
+        """
+
+        super().__init__(**keras_kwargs(kwargs))
+
+        if device != "cuda":
+            raise NotImplementedError("MambaSSM only supports cuda as `device`.")
+
+        self.mamba_blocks = []
+        for feature_dim, state_dim, conv_dim, expand in zip(feature_dims, state_dims, conv_dims, expand_dims):
+            mamba = MambaBlock(feature_dim, state_dim, conv_dim, expand, bidirectional, dt_min, dt_max, device)
+            self.mamba_blocks.append(mamba)
+
+        self.pooling_layer = keras.layers.GlobalAveragePooling1D()
+        self.dropout = keras.layers.Dropout(dropout)
+        self.summary_stats = keras.layers.Dense(summary_dim)
+
+    def call(self, time_series: Tensor, training: bool = True, **kwargs) -> Tensor:
+        """
+        Apply a sequence of Mamba blocks, followed by pooling, dropout, and summary statistics.
+
+        Parameters
+        ----------
+        time_series : Tensor
+            Input tensor representing the time series data, typically of shape
+            (batch_size, sequence_length, feature_dim).
+        training : bool, optional
+            Whether the model is in training mode (default is True). Affects behavior of
+            layers like dropout.
+        **kwargs : dict
+            Additional keyword arguments (not used in this method).
+
+        Returns
+        -------
+        Tensor
+            Output tensor after applying Mamba blocks, pooling, dropout, and summary statistics.
+        """
+
+        summary = time_series
+        for mamba_block in self.mamba_blocks:
+            summary = mamba_block(summary, training=training)
+
+        summary = self.pooling_layer(summary)
+        summary = self.dropout(summary, training=training)
+        summary = self.summary_stats(summary)
+
+        return summary
+
+    @sanitize_input_shape
+    def build(self, input_shape):
+        super().build(input_shape)
+        self.call(keras.ops.zeros(input_shape))
diff --git a/bayesflow/wrappers/mamba/mamba_block.py b/bayesflow/wrappers/mamba/mamba_block.py
new file mode 100644
index 000000000..fcc3d8dce
--- /dev/null
+++ b/bayesflow/wrappers/mamba/mamba_block.py
@@ -0,0 +1,114 @@
+import keras
+from keras.saving import register_keras_serializable as serializable
+
+from bayesflow.types import Tensor
+from bayesflow.utils import logging, keras_kwargs
+from bayesflow.utils.decorators import sanitize_input_shape
+
+try:
+    from mamba_ssm import Mamba
+except ImportError:
+    logging.error("Mamba class is not available. Please, install the mamba-ssm library via `pip install mamba-ssm`.")
+
+
+@serializable("bayesflow.wrappers")
+class MambaBlock(keras.Layer):
+    """
+    Wraps the original Mamba module from, with added functionality for bidirectional processing:
+    https://github.com/state-spaces/mamba/blob/main/mamba_ssm/modules/mamba_simple.py
+
+    Copyright (c) 2023, Tri Dao, Albert Gu.
+    """
+
+    def __init__(
+        self,
+        state_dim: int,
+        conv_dim: int,
+        feature_dim: int = 16,
+        expand: int = 2,
+        bidirectional: bool = True,
+        dt_min: float = 0.001,
+        dt_max: float = 0.1,
+        device: str = "cuda",
+        **kwargs,
+    ):
+        """
+        A Keras layer implementing a Mamba-based sequence processing block.
+
+        This layer applies a Mamba model for sequence modeling, preceded by a
+        convolutional projection and followed by layer normalization.
+
+        Parameters
+        ----------
+        state_dim : int
+            The dimension of the state space in the Mamba model.
+        conv_dim : int
+            The dimension of the convolutional layer used in Mamba.
+        feature_dim : int, optional
+            The feature dimension for input projection and Mamba processing (default is 16).
+        expand : int, optional
+            Expansion factor for Mamba's internal dimension (default is 1).
+        dt_min : float, optional
+            Minimum delta time for Mamba (default is 0.001).
+        dt_max : float, optional
+            Maximum delta time for Mamba (default is 0.1).
+        device : str, optional
+            The device to which the Mamba model is moved, typically "cuda" or "cpu" (default is "cuda").
+        **kwargs :
+            Additional keyword arguments passed to the `keras.layers.Layer` initializer.
+        """
+
+        super().__init__(**keras_kwargs(kwargs))
+
+        if keras.backend.backend() != "torch":
+            raise RuntimeError("Mamba is only available using torch backend.")
+
+        self.bidirectional = bidirectional
+
+        self.mamba = Mamba(
+            d_model=feature_dim, d_state=state_dim, d_conv=conv_dim, expand=expand, dt_min=dt_min, dt_max=dt_max
+        ).to(device)
+
+        self.input_projector = keras.layers.Conv1D(
+            feature_dim,
+            kernel_size=1,
+            strides=1,
+        )
+        self.layer_norm = keras.layers.LayerNormalization()
+
+    def call(self, x: Tensor, training: bool = False, **kwargs) -> Tensor:
+        """
+        Applies the Mamba layer to the input tensor `x`, optionally in a bidirectional manner.
+
+        Parameters
+        ----------
+        x : Tensor
+            Input tensor of shape `(batch_size, sequence_length, input_dim)`.
+        training : bool, optional
+            Whether the layer should behave in training mode (e.g., applying dropout). Default is False.
+        **kwargs : dict
+            Additional keyword arguments passed to the internal `_call` method.
+
+        Returns
+        -------
+        Tensor
+            Output tensor of shape `(batch_size, sequence_length, feature_dim)` if unidirectional,
+            or `(batch_size, sequence_length, 2 * feature_dim)` if bidirectional.
+        """
+
+        out_forward = self._call(x, training=training, **kwargs)
+        if self.bidirectional:
+            out_backward = self._call(keras.ops.flip(x, axis=-2), training=training, **kwargs)
+            return keras.ops.concatenate((out_forward, out_backward), axis=-1)
+        return out_forward
+
+    def _call(self, x: Tensor, training: bool = False, **kwargs) -> Tensor:
+        x = self.input_projector(x)
+        h = self.mamba(x)
+        out = self.layer_norm(h + x, training=training, **kwargs)
+        return out
+
+    @sanitize_input_shape
+    def build(self, input_shape):
+        super().build(input_shape)
+        self.call(keras.ops.zeros(input_shape))
diff --git a/tests/test_wrappers/__init__.py b/tests/test_wrappers/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/test_wrappers/conftest.py b/tests/test_wrappers/conftest.py
new file mode 100644
index 000000000..8a336a41b
--- /dev/null
+++ b/tests/test_wrappers/conftest.py
@@ -0,0 +1,22 @@
+import pytest
+
+
+@pytest.fixture()
+def inference_network():
+    from bayesflow.networks import CouplingFlow
+
+    return CouplingFlow(depth=2)
+
+
+@pytest.fixture()
+def random_time_series():
+    import keras
+
+    return keras.random.normal(shape=(2, 80, 2))
+
+
+@pytest.fixture()
+def mamba_summary_network():
+    from bayesflow.wrappers.mamba import Mamba
+
+    return Mamba(summary_dim=4, feature_dims=(2, 2), state_dims=(4, 4), conv_dims=(8, 8))
diff --git a/tests/test_wrappers/test_mamba.py b/tests/test_wrappers/test_mamba.py
new file mode 100644
index 000000000..4d7dcfb6b
--- /dev/null
+++ b/tests/test_wrappers/test_mamba.py
@@ -0,0 +1,25 @@
+import pytest
+
+import bayesflow as bf
+
+
+@pytest.mark.torch
+def test_mamba_summary(random_time_series, mamba_summary_network):
+    out = mamba_summary_network(random_time_series)
+    # Batch size 2, summary dim 4
+    assert out.shape == (2, 4)
+
+
+@pytest.mark.torch
+def test_mamba_trains(random_time_series, inference_network, mamba_summary_network):
+    workflow = bf.BasicWorkflow(
+        inference_network=inference_network,
+        summary_network=mamba_summary_network,
+        inference_variables=["parameters"],
+        summary_variables=["observables"],
+        simulator=bf.simulators.SIR(),
+    )
+
+    history = workflow.fit_online(epochs=2, batch_size=8, num_batches_per_epoch=2)
+    assert "loss" in list(history.history.keys())
+    assert len(history.history["loss"]) == 2