diff --git a/python/python/lancedb/rerankers/__init__.py b/python/python/lancedb/rerankers/__init__.py
index c7e34d57..0b767a67 100644
--- a/python/python/lancedb/rerankers/__init__.py
+++ b/python/python/lancedb/rerankers/__init__.py
@@ -5,6 +5,7 @@ from .cross_encoder import CrossEncoderReranker
 from .linear_combination import LinearCombinationReranker
 from .openai import OpenaiReranker
 from .jinaai import JinaReranker
+from .rrf import RRFReranker
 
 __all__ = [
     "Reranker",
@@ -14,4 +15,5 @@ __all__ = [
     "OpenaiReranker",
     "ColbertReranker",
     "JinaReranker",
+    "RRFReranker",
 ]
diff --git a/python/python/lancedb/rerankers/rrf.py b/python/python/lancedb/rerankers/rrf.py
new file mode 100644
index 00000000..a4d95a39
--- /dev/null
+++ b/python/python/lancedb/rerankers/rrf.py
@@ -0,0 +1,60 @@
+import pyarrow as pa
+
+from collections import defaultdict
+from .base import Reranker
+
+
+class RRFReranker(Reranker):
+    """
+    Reranks the results using Reciprocal Rank Fusion(RRF) algorithm based
+    on the scores of vector and FTS search.
+    Parameters
+    ----------
+    K : int, default 60
+        A constant used in the RRF formula (default is 60). Experiments
+        indicate that k = 60 was near-optimal, but that the choice is
+        not critical. See paper:
+        https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf
+    return_score : str, default "relevance"
+        opntions are "relevance" or "all"
+        The type of score to return. If "relevance", will return only the relevance
+        score. If "all", will return all scores from the vector and FTS search along
+        with the relevance score.
+    """
+
+    def __init__(self, K: int = 60, return_score="relevance"):
+        if K <= 0:
+            raise ValueError("K must be greater than 0")
+        super().__init__(return_score)
+        self.K = K
+
+    def rerank_hybrid(
+        self,
+        query: str,  # noqa: F821
+        vector_results: pa.Table,
+        fts_results: pa.Table,
+    ):
+        vector_ids = vector_results["_rowid"].to_pylist() if vector_results else []
+        fts_ids = fts_results["_rowid"].to_pylist() if fts_results else []
+        rrf_score_map = defaultdict(float)
+
+        # Calculate RRF score of each result
+        for ids in [vector_ids, fts_ids]:
+            for i, result_id in enumerate(ids, 1):
+                rrf_score_map[result_id] += 1 / (i + self.K)
+
+        # Sort the results based on RRF score
+        combined_results = self.merge_results(vector_results, fts_results)
+        combined_row_ids = combined_results["_rowid"].to_pylist()
+        relevance_scores = [rrf_score_map[row_id] for row_id in combined_row_ids]
+        combined_results = combined_results.append_column(
+            "_relevance_score", pa.array(relevance_scores, type=pa.float32())
+        )
+        combined_results = combined_results.sort_by(
+            [("_relevance_score", "descending")]
+        )
+
+        if self.score == "relevance":
+            combined_results = combined_results.drop_columns(["score", "_distance"])
+
+        return combined_results
diff --git a/python/python/tests/test_rerankers.py b/python/python/tests/test_rerankers.py
index 1e303905..b7ffc10c 100644
--- a/python/python/tests/test_rerankers.py
+++ b/python/python/tests/test_rerankers.py
@@ -7,6 +7,8 @@ from lancedb.conftest import MockTextEmbeddingFunction  # noqa
 from lancedb.embeddings import EmbeddingFunctionRegistry
 from lancedb.pydantic import LanceModel, Vector
 from lancedb.rerankers import (
+    LinearCombinationReranker,
+    RRFReranker,
     CohereReranker,
     ColbertReranker,
     CrossEncoderReranker,
@@ -140,7 +142,7 @@ def _run_test_reranker(reranker, table, query, query_vector, schema):
     assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
 
 
-def test_linear_combination(tmp_path):
+def _run_test_hybrid_reranker(reranker, tmp_path):
     table, schema = get_test_table(tmp_path)
     # The default reranker
     result1 = (
@@ -177,6 +179,16 @@ def test_linear_combination(tmp_path):
     )
 
 
+def test_linear_combination(tmp_path):
+    reranker = LinearCombinationReranker()
+    _run_test_hybrid_reranker(reranker, tmp_path)
+
+
+def test_rrf_reranker(tmp_path):
+    reranker = RRFReranker()
+    _run_test_hybrid_reranker(reranker, tmp_path)
+
+
 @pytest.mark.skipif(
     os.environ.get("COHERE_API_KEY") is None, reason="COHERE_API_KEY not set"
 )