diff --git a/meson.build b/meson.build
index 1d3accf4..e895bb25 100644
--- a/meson.build
+++ b/meson.build
@@ -61,6 +61,11 @@ sources_raw = {
     'src/array_api_compat/torch/fft.py',
     'src/array_api_compat/torch/linalg.py',
   ],
+  'array_api_compat/mlx': [
+    'src/array_api_compat/mlx/__init__.py',
+    'src/array_api_compat/mlx/_aliases.py',
+    'src/array_api_compat/mlx/_typing.py',
+  ],
 }
 
 sources = {}
diff --git a/pyproject.toml b/pyproject.toml
index 4ca2f429..477be4cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -55,6 +55,7 @@ dev = [
     "pytest",
     "torch",
     "sparse>=0.15.1",
+    "mlx; sys_platform == 'darwin'",
 ]
 
 
diff --git a/src/array_api_compat/mlx/__init__.py b/src/array_api_compat/mlx/__init__.py
new file mode 100644
index 00000000..4e678679
--- /dev/null
+++ b/src/array_api_compat/mlx/__init__.py
@@ -0,0 +1,15 @@
+from .._internal import clone_module
+
+__all__ = clone_module("mlx.core", globals())
+
+from . import _aliases
+from ._aliases import *  # noqa: F403
+
+# Unsure if this is needed, but it seems to be in the other backends
+# __array_api_version__: Final = "2025.12"
+
+__all__ = sorted(set(__all__) | set(_aliases.__all__))
+
+
+def __dir__() -> list[str]:
+    return __all__
diff --git a/src/array_api_compat/mlx/_aliases.py b/src/array_api_compat/mlx/_aliases.py
new file mode 100644
index 00000000..2dc7a36a
--- /dev/null
+++ b/src/array_api_compat/mlx/_aliases.py
@@ -0,0 +1,132 @@
+import mlx.core as mx
+
+from ..common._typing import NestedSequence, SupportsBufferProtocol
+from ._typing import Array, Device, DType
+
+
+def asarray(
+    obj: Array | complex | NestedSequence[complex] | SupportsBufferProtocol,
+    /,
+    *,
+    dtype: None | DType = None,
+    device: None | Device = None,  # No device or stream argument in MLX.
+    copy: bool | None = None,
+) -> Array:
+    """
+    Note on MLX Compatibility:
+        MLX does not support Ellipsis or tuple assignment in 0D or 1D arrays,
+        example:
+            >>> import mlx.core as mx
+            >>> a = mx.array([1, 2, 3])
+            >>> a[...] = 1
+    """
+    if (
+        copy is False
+        and isinstance(obj, Array)
+        and (dtype is None or dtype == obj.dtype)
+        and device is None
+    ):
+        return obj
+    return mx.asarray(obj, dtype=dtype, copy=copy)
+
+
+def arange(
+    start: int | float,
+    /,
+    stop: int | float | None = None,
+    step: int | float = 1,
+    *,
+    dtype: None | DType = None,
+    device: None | Device = None,
+):
+    """
+    Note on MLX Compatibility:
+        Native `mx.arange` rejects some Python `int` arguments above 2**31 - 1.
+        This wrapper handles empty and single-element ranges directly; larger
+        int64 and uint64 ranges remain unsupported by MLX.
+    """
+    if stop is None:
+        stop = start
+        start = 0 if isinstance(stop, int) else 0.0
+    # MLX defaults to float32 even when all arguments are integers,
+    # so we must explicitly set the dtype to int32 in that case.
+    is_float = (
+        isinstance(start, float) or isinstance(stop, float) or isinstance(step, float)
+    )
+    dtype = dtype if dtype is not None else (mx.float32 if is_float else mx.int32)
+    # MLX may overflow while converting a large step before determining that the
+    # result has at most one element (for example, arange(0, 1, 2**31) is [0]),
+    # so construct these trivial ranges directly.
+    if (step > 0 and start >= stop) or (step < 0 and start <= stop):
+        return mx.zeros((0,), dtype=dtype, stream=device)
+    if (step > 0 and step >= stop - start) or (step < 0 and step <= stop - start):
+        return mx.full((1,), start, dtype=dtype, stream=device)
+    return mx.arange(start, stop, step, dtype=dtype, stream=device)
+
+
+def empty_like(
+    x: Array,
+    /,
+    *,
+    dtype: None | DType = None,
+    device: None | Device = None,
+):
+    return mx.empty(x.shape, dtype=x.dtype if dtype is None else dtype, stream=device)
+
+
+def eye(
+    n_rows: int,
+    n_cols: int | None = None,
+    /,
+    *,
+    k: int = 0,
+    dtype: None | DType = None,
+    device: None | Device = None,
+):
+    n_cols = n_rows if n_cols is None else n_cols
+    if n_rows == 0 or n_cols == 0 or k >= n_cols or k <= -n_rows:
+        return mx.zeros((n_rows, n_cols), dtype=dtype, stream=device)
+    # mx.eye uses GPU scatter, which does not support int64 or uint64.
+    # Build from exact 0/1 values in int32, then cast to the requested dtype.
+    is_int64 = False
+    if dtype is not None:
+        is_int64 = dtype in (mx.int64, mx.uint64)
+    result = mx.eye(
+        n_rows, n_cols, k, dtype=mx.int32 if is_int64 else dtype, stream=device
+    )
+    return result.astype(dtype=dtype) if is_int64 else result
+
+
+def meshgrid(*arrays: Array, indexing: str = "xy") -> tuple[Array, ...]:
+    return tuple(mx.meshgrid(*arrays, indexing=indexing))
+
+
+def ones_like(
+    x: Array,
+    /,
+    *,
+    dtype: None | DType = None,
+    device: None | Device = None,
+):
+    return mx.ones(x.shape, dtype=x.dtype if dtype is None else dtype, stream=device)
+
+
+def zeros_like(
+    x: Array,
+    /,
+    *,
+    dtype: None | DType = None,
+    device: None | Device = None,
+):
+    return mx.zeros(x.shape, dtype=x.dtype if dtype is None else dtype, stream=device)
+
+
+__all__ = [
+    "arange",
+    "asarray",
+    "empty_like",
+    "eye",
+    "meshgrid",
+    "ones_like",
+    "zeros_like",
+]
diff --git a/src/array_api_compat/mlx/_typing.py b/src/array_api_compat/mlx/_typing.py
new file mode 100644
index 00000000..2d7d9f1a
--- /dev/null
+++ b/src/array_api_compat/mlx/_typing.py
@@ -0,0 +1,3 @@
+from mlx.core import Device, Dtype as DType, array as Array
+
+__all__ = ["Array", "DType", "Device"]