diff --git a/python/python/lancedb/integrations/torch.py b/python/python/lancedb/integrations/torch.py
new file mode 100644
index 000000000..11aac3626
--- /dev/null
+++ b/python/python/lancedb/integrations/torch.py
@@ -0,0 +1,230 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""
+PyTorch integration for LanceDB.
+
+Exposes ``LanceTorchDataset`` (map-style) and ``LanceIterableTorchDataset``
+(iterable-style) wrappers that adapt a LanceDB table or permutation to the
+PyTorch ``torch.utils.data`` API, while transparently handling the bits
+that make a hand-rolled subclass tricky:
+
+* The underlying Lance reader holds Rust state that is not picklable, but
+  ``DataLoader(num_workers > 0)`` needs to fork the dataset to its workers.
+  These classes strip the reader on pickle and re-open it in the worker on
+  first read.
+* Constructing a permutation from a table involves several steps
+  (``permutation_builder``/``Permutation.from_tables``/``select_columns``
+  /``with_format``/...). The wrapper takes those as constructor arguments
+  and applies them once the dataset is opened in the worker.
+
+Example
+-------
+>>> import lancedb, torch                                   # doctest: +SKIP
+>>> from lancedb.integrations.torch import LanceTorchDataset
+>>> db = lancedb.connect(uri)                               # doctest: +SKIP
+>>> tbl = db.open_table("images_224")                       # doctest: +SKIP
+>>> ds = LanceTorchDataset(                                 # doctest: +SKIP
+...     tbl, columns=["image_bytes", "label"], format="torch"
+... )
+>>> loader = torch.utils.data.DataLoader(                   # doctest: +SKIP
+...     ds, batch_size=64, num_workers=4, shuffle=True,
+... )
+"""
+
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import torch.utils.data as _torch_data
+
+from ..permutation import Permutation
+from ..table import LanceTable
+
+
+def _capture_table_state(table: LanceTable) -> Dict[str, Any]:
+    """Pull just enough state out of a LanceTable so we can re-open the same
+    table in a forked worker process where the Rust handle isn't valid."""
+    conn = table._conn
+    connect_kwargs: Dict[str, Any] = {}
+    storage_options = getattr(conn, "storage_options", None)
+    if storage_options is not None:
+        connect_kwargs["storage_options"] = storage_options
+    return {
+        "uri": conn.uri,
+        "table_name": table.name,
+        "connect_kwargs": connect_kwargs,
+    }
+
+
+def _open_permutation(state: Dict[str, Any]) -> Permutation:
+    """Reconstruct a Permutation from a captured state dict."""
+    import lancedb
+
+    db = lancedb.connect(state["uri"], **state["connect_kwargs"])
+    base = db.open_table(state["table_name"])
+
+    perm_table_name = state.get("perm_table_name")
+    if perm_table_name is not None:
+        perm_tbl = db.open_table(perm_table_name)
+        perm = Permutation.from_tables(base, perm_tbl, state.get("split"))
+    else:
+        perm = Permutation.identity(base)
+
+    columns = state.get("columns")
+    fmt = state.get("format")
+    transform = state.get("transform")
+    batch_size = state.get("batch_size")
+
+    if columns is not None:
+        perm = perm.select_columns(columns)
+    if fmt is not None:
+        perm = perm.with_format(fmt)
+    if transform is not None:
+        perm = perm.with_transform(transform)
+    if batch_size is not None:
+        perm = perm.with_batch_size(batch_size)
+    return perm
+
+
+class LanceTorchDataset(_torch_data.Dataset):
+    """
+    A PyTorch map-style ``Dataset`` backed by a LanceDB table or permutation.
+
+    Pass the same ``LanceTable`` you already opened (and, optionally, a
+    permutation table / split / column selection / output format) and use
+    the result anywhere a ``torch.utils.data.Dataset`` is expected.
+
+    The wrapper:
+
+    * Stores the URI / table name / storage options needed to re-open the
+      table, not the Rust reader handle. Pickling keeps only the rebuild
+      recipe, so ``DataLoader(num_workers > 0)`` works out of the box.
+    * Implements both ``__getitem__`` and PyTorch's ``__getitems__`` dunder
+      so the underlying batched ``Permutation.fetch`` is used when the
+      DataLoader fetches a batch of indices.
+
+    Parameters
+    ----------
+    table : LanceTable, optional
+        The base table to read from. Either ``table`` or both ``uri`` and
+        ``table_name`` must be provided.
+    uri : str, optional
+        Database URI to reconnect to. Required if ``table`` is not given.
+    table_name : str, optional
+        Name of the base table within ``uri``.
+    connect_kwargs : dict, optional
+        Extra keyword arguments forwarded to ``lancedb.connect`` when
+        re-opening the database in a worker.
+    permutation_table : LanceTable, optional
+        A pre-built permutation table (see ``permutation_builder``) used to
+        define the row ordering. If omitted, the identity permutation is
+        used (rows in physical order).
+    split : str or int, optional
+        Split selector when ``permutation_table`` defines splits.
+    columns : list[str], optional
+        Subset of columns to read.
+    format : str, optional
+        Output format, forwarded to ``Permutation.with_format`` (e.g.
+        ``"torch"`` for HuggingFace-style ``dict[str, Tensor]`` batches).
+    transform : Callable, optional
+        Custom batch transform, forwarded to ``Permutation.with_transform``.
+        Must be picklable to work with ``num_workers > 0``.
+    batch_size : int, optional
+        Forwarded to ``Permutation.with_batch_size`` for direct iteration.
+        DataLoader controls its own batching, so this only matters if the
+        dataset is iterated directly.
+    """
+
+    def __init__(
+        self,
+        table: Optional[LanceTable] = None,
+        *,
+        uri: Optional[str] = None,
+        table_name: Optional[str] = None,
+        connect_kwargs: Optional[Dict[str, Any]] = None,
+        permutation_table: Optional[LanceTable] = None,
+        split: Optional[Union[str, int]] = None,
+        columns: Optional[List[str]] = None,
+        format: Optional[str] = None,
+        transform: Optional[Callable] = None,
+        batch_size: Optional[int] = None,
+    ):
+        if table is None and (uri is None or table_name is None):
+            raise ValueError(
+                "Provide either `table` or both `uri` and `table_name`."
+            )
+
+        if table is not None:
+            state = _capture_table_state(table)
+            if connect_kwargs is not None:
+                state["connect_kwargs"] = connect_kwargs
+        else:
+            state = {
+                "uri": uri,
+                "table_name": table_name,
+                "connect_kwargs": connect_kwargs or {},
+            }
+
+        state["perm_table_name"] = (
+            permutation_table.name if permutation_table is not None else None
+        )
+        state["split"] = split
+        state["columns"] = columns
+        state["format"] = format
+        state["transform"] = transform
+        state["batch_size"] = batch_size
+
+        self._state: Dict[str, Any] = state
+        self._perm: Optional[Permutation] = None
+
+    def __getstate__(self) -> Dict[str, Any]:
+        # Strip the Rust-backed reader so the dataset is picklable. Workers
+        # rebuild it on first read via _ensure_open().
+        d = self.__dict__.copy()
+        d["_perm"] = None
+        return d
+
+    def __setstate__(self, d: Dict[str, Any]) -> None:
+        self.__dict__.update(d)
+
+    def _ensure_open(self) -> None:
+        if self._perm is None:
+            self._perm = _open_permutation(self._state)
+
+    def __len__(self) -> int:
+        self._ensure_open()
+        return len(self._perm)
+
+    def __getitem__(self, index: int) -> Any:
+        self._ensure_open()
+        return self._perm[index]
+
+    def __getitems__(self, indices: List[int]) -> Any:
+        self._ensure_open()
+        return self._perm.fetch(indices)
+
+
+class LanceIterableTorchDataset(_torch_data.IterableDataset):
+    """
+    PyTorch iterable-style ``IterableDataset`` over a LanceDB permutation.
+
+    Yields batches in the order defined by the underlying ``Permutation``.
+    With ``num_workers > 1`` each worker iterates the permutation
+    independently — for sharded iteration use the map-style
+    ``LanceTorchDataset`` together with a sampler.
+
+    Constructor arguments mirror ``LanceTorchDataset``.
+    """
+
+    def __init__(self, *args, **kwargs):
+        self._inner = LanceTorchDataset(*args, **kwargs)
+
+    def __getstate__(self) -> Dict[str, Any]:
+        return {"_inner": self._inner.__getstate__()}
+
+    def __setstate__(self, d: Dict[str, Any]) -> None:
+        self._inner = LanceTorchDataset.__new__(LanceTorchDataset)
+        self._inner.__setstate__(d["_inner"])
+
+    def __iter__(self):
+        self._inner._ensure_open()
+        return iter(self._inner._perm)
diff --git a/python/python/tests/test_torch_dataset.py b/python/python/tests/test_torch_dataset.py
new file mode 100644
index 000000000..be53a61cb
--- /dev/null
+++ b/python/python/tests/test_torch_dataset.py
@@ -0,0 +1,140 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import pickle
+
+import pyarrow as pa
+import pytest
+
+from lancedb import connect
+from lancedb.permutation import permutation_builder
+
+torch = pytest.importorskip("torch")
+from lancedb.integrations.torch import (  # noqa: E402
+    LanceIterableTorchDataset,
+    LanceTorchDataset,
+)
+
+
+@pytest.fixture
+def db_path(tmp_path):
+    """LanceTorchDataset needs a real, on-disk DB so workers can re-open it."""
+    return tmp_path
+
+
+def _make_table(db_path, name="imgs", n=20):
+    db = connect(db_path)
+    return db.create_table(
+        name,
+        pa.table({"x": [float(i) for i in range(n)], "y": list(range(n))}),
+    )
+
+
+def test_basic_len_and_getitem(db_path):
+    tbl = _make_table(db_path)
+    ds = LanceTorchDataset(tbl)
+    assert len(ds) == 20
+    row = ds[0]
+    # Default ("python") format = list of dicts; __getitem__ wraps a single index.
+    assert isinstance(row, list)
+    assert row[0] == {"x": 0.0, "y": 0}
+
+
+def test_getitems_uses_fetch(db_path):
+    tbl = _make_table(db_path)
+    ds = LanceTorchDataset(tbl)
+    rows = ds.__getitems__([0, 2, 4])
+    assert rows == [
+        {"x": 0.0, "y": 0},
+        {"x": 2.0, "y": 2},
+        {"x": 4.0, "y": 4},
+    ]
+
+
+def test_dataloader_default_collate(db_path):
+    tbl = _make_table(db_path, n=40)
+    ds = LanceTorchDataset(tbl)
+    loader = torch.utils.data.DataLoader(ds, batch_size=8, shuffle=False)
+    batch = next(iter(loader))
+    # default collate stacks list-of-dicts into dict-of-tensors
+    assert isinstance(batch, dict)
+    assert batch["x"].size() == (8,)
+    assert batch["y"].size() == (8,)
+
+
+def test_picklable(db_path):
+    tbl = _make_table(db_path)
+    ds = LanceTorchDataset(tbl, columns=["x"])
+
+    # Force open then ensure pickle drops the Rust handle.
+    _ = len(ds)
+    blob = pickle.dumps(ds)
+    restored: LanceTorchDataset = pickle.loads(blob)
+    # Rust state should not survive pickling.
+    assert restored._perm is None
+    # …but the dataset must work after re-opening transparently.
+    assert len(restored) == 20
+    assert restored[0] == [{"x": 0.0}]
+
+
+def test_dataloader_with_workers(db_path):
+    tbl = _make_table(db_path, n=32)
+    ds = LanceTorchDataset(tbl)
+    loader = torch.utils.data.DataLoader(
+        ds, batch_size=4, num_workers=2, shuffle=False
+    )
+    batches = list(loader)
+    seen = []
+    for b in batches:
+        seen.extend(b["x"].tolist())
+    assert sorted(seen) == [float(i) for i in range(32)]
+
+
+def test_with_permutation_table(db_path):
+    tbl = _make_table(db_path, n=30)
+    db = connect(db_path)
+    perm_tbl = (
+        permutation_builder(tbl)
+        .split_random(ratios=[0.5, 0.5], seed=1, split_names=["train", "test"])
+        .persist(db, "imgs_perm")
+        .execute()
+    )
+    ds = LanceTorchDataset(tbl, permutation_table=perm_tbl, split="train")
+    # Should pickle/restore the permutation table reference too.
+    blob = pickle.dumps(ds)
+    restored = pickle.loads(blob)
+    assert len(restored) == 15
+
+
+def test_format_passthrough_dataloader(db_path):
+    """Custom `format` is forwarded to the underlying Permutation."""
+    tbl = _make_table(db_path, n=20)
+    ds = LanceTorchDataset(tbl, format="arrow")
+    # Arrow batches don't go through default_collate, so use a no-op collate.
+    loader = torch.utils.data.DataLoader(
+        ds, batch_size=5, shuffle=False, collate_fn=lambda x: x
+    )
+    batch = next(iter(loader))
+    assert isinstance(batch, pa.RecordBatch)
+    assert batch.num_rows == 5
+
+
+def test_iterable_dataset(db_path):
+    tbl = _make_table(db_path, n=20)
+    ds = LanceIterableTorchDataset(tbl, batch_size=5)
+    batches = list(ds)
+    # default batch size + skip_last_batch=True yields full-size batches only
+    assert len(batches) == 4
+    assert all(len(b) == 5 for b in batches)
+
+
+def test_uri_table_name_constructor(db_path):
+    _make_table(db_path)
+    ds = LanceTorchDataset(uri=str(db_path), table_name="imgs")
+    assert len(ds) == 20
+    assert ds[0] == [{"x": 0.0, "y": 0}]
+
+
+def test_constructor_validates_args():
+    with pytest.raises(ValueError, match="table"):
+        LanceTorchDataset()