diff --git a/src/sklearn_ann/kneighbors/hannoy.py b/src/sklearn_ann/kneighbors/hannoy.py
new file mode 100644
index 0000000..2f44f5f
--- /dev/null
+++ b/src/sklearn_ann/kneighbors/hannoy.py
@@ -0,0 +1,126 @@
+# hannoy needs a filesystem path (LMDB-backed)
+import tempfile
+
+import hannoy
+import numpy as np
+from hannoy import Metric
+from scipy.sparse import csr_matrix
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.utils import Tags, TargetTags, TransformerTags
+from sklearn.utils.validation import validate_data
+
+from ..utils import TransformerChecksMixin
+
+# what other metrics should I include?
+# it has cosine, manhattan, hamming and quantize counterparts:
+# like binary quantitized consine, bq euclidean, and bq manhattan
+METRIC_MAP = {
+    "euclidean": Metric.EUCLIDEAN,
+}
+
+
+class HannoyTransformer(TransformerChecksMixin, TransformerMixin, BaseEstimator):
+    # known issue where multiple Database instances silently share the first one's LMDB env
+
+    def __init__(
+        self,
+        n_neighbors=5,
+        *,
+        metric="euclidean",
+        path=None,
+        m=16,
+        ef_construction=96,
+        ef_search=200,
+    ):
+        self.n_neighbors = n_neighbors
+        self.metric = metric
+        # LMDB directory for the index; if None = auto-create a temp dir
+        self.path = path
+        # edges per node in the HNSW graph; hannoy default is 16
+        self.m = m
+        # hannoy default is 96 (higher = better graph but slower build)
+        self.ef_construction = ef_construction
+        # hannoy default is 200 (higher = better recall, slower search)
+        self.ef_search = ef_search
+
+    def fit(self, X, y=None):
+        X = validate_data(self, X)
+        self.n_samples_fit_ = X.shape[0]
+
+        # storing validated X for fit_transform as hannoy doesn't have by_item yet
+        self.fit_X = X
+        # path to LMDB
+        path = (
+            self.path if self.path is not None else tempfile.mkdtemp(prefix="hannoy_")
+        )
+
+        # converting to the metric names used by hannoy
+        hannoy_metric = METRIC_MAP[self.metric]
+
+        # metric is fixed for the entire database
+        self.hannoy_db_ = hannoy.Database(path, hannoy_metric)
+        with self.hannoy_db_.writer(
+            X.shape[1], m=self.m, ef=self.ef_construction
+        ) as writer:
+            for i, x in enumerate(X):
+                # convert to list as hannoy's Rust code expects that type
+                writer.add_item(i, x.tolist())
+        # opening a reader query
+        self.hannoy_reader_ = self.hannoy_db_.reader()
+        return self
+
+    def transform(self, X):
+        # verify that fit was called and + that X has the right number of features
+        X = self._transform_checks(X, "hannoy_reader_")
+        return self._transform(X)
+
+    def _transform(self, X):
+        # how many points
+        n_samples_transform = X.shape[0]
+
+        n_neighbors = self.n_neighbors + 1
+        # pre allocating indicies for which points are neighbots
+        # distances = how far away
+        # ELLPACk (similar)
+        indices = np.empty((n_samples_transform, n_neighbors), dtype=int)
+        distances = np.empty((n_samples_transform, n_neighbors))
+
+        for i, x in enumerate(X):
+            # hannoy for each row by_vec
+            results = self.hannoy_reader_.by_vec(
+                # returning in a list form because Rust requires it
+                x.tolist(),
+                n=n_neighbors,
+                ef_search=self.ef_search,
+            )
+            # unpacking into pre-allocated arrays
+            for j, (idx, dist) in enumerate(results):
+                indices[i, j] = idx
+                distances[i, j] = dist
+        # distance correction
+        if self.metric == "euclidean":
+            np.sqrt(distances, out=distances)
+
+        # going from ELLPACK-like structure into CSR matrix
+        indptr = np.arange(0, n_samples_transform * n_neighbors + 1, n_neighbors)
+        kneighbors_graph = csr_matrix(
+            (distances.ravel(), indices.ravel(), indptr),
+            shape=(n_samples_transform, self.n_samples_fit_),
+        )
+
+        return kneighbors_graph
+
+    def fit_transform(self, X, y=None):
+        self.fit(X)
+        result = self._transform(self.fit_X)
+        # don't need those vectors anymore, so delete it
+        del self.fit_X
+        return result
+
+    def __sklearn_tags__(self) -> Tags:
+        # metadata
+        return Tags(
+            estimator_type="transformer",
+            target_tags=TargetTags(required=False),
+            transformer_tags=TransformerTags(),
+        )