diff --git a/mem0/utils/scoring.py b/mem0/utils/scoring.py
index 2076c42dd..e85a9cb8e 100644
--- a/mem0/utils/scoring.py
+++ b/mem0/utils/scoring.py
@@ -107,7 +107,7 @@ def score_and_rank(
         if mem_id is None:
             continue
 
-        semantic_score = result.get("score", 0.0)
+        semantic_score = result.get("score") or 0.0
         if semantic_score < threshold:
             continue
 
diff --git a/mem0/vector_stores/langchain.py b/mem0/vector_stores/langchain.py
index 451c21283..e616a45b1 100644
--- a/mem0/vector_stores/langchain.py
+++ b/mem0/vector_stores/langchain.py
@@ -21,6 +21,16 @@ class OutputData(BaseModel):
     payload: Optional[Dict]  # metadata
 
 
+# Methods that accept a pre-computed embedding and return (Document, float) pairs.
+# Tried in order; first match wins. Not part of the base VectorStore contract,
+# but exposed by several concrete implementations.
+_SCORED_BY_VECTOR_METHODS = [
+    "similarity_search_by_vector_with_relevance_scores",  # Chroma
+    "similarity_search_with_score_by_vector",             # FAISS, Qdrant
+    "similarity_search_by_vector_with_score",             # Pinecone, YDB
+]
+
+
 class Langchain(VectorStoreBase):
     def __init__(self, client: VectorStore, collection_name: str = "mem0"):
         self.client = client
@@ -95,14 +105,40 @@ class Langchain(VectorStoreBase):
         """
         Search for similar vectors in LangChain.
         """
-        # For each vector, perform a similarity search
+        kwargs = {"embedding": vectors, "k": top_k}
         if filters:
-            results = self.client.similarity_search_by_vector(embedding=vectors, k=top_k, filter=filters)
-        else:
-            results = self.client.similarity_search_by_vector(embedding=vectors, k=top_k)
+            kwargs["filter"] = filters
 
-        final_results = self._parse_output(results)
-        return final_results
+        # Try methods that return (Document, float) pairs — not in the base contract
+        # but available on several concrete implementations.
+        for method_name in _SCORED_BY_VECTOR_METHODS:
+            method = getattr(self.client, method_name, None)
+            if method is None:
+                continue
+            try:
+                results = method(**kwargs)
+                return [
+                    OutputData(
+                        id=getattr(doc, "id", None),
+                        score=float(score),
+                        payload=getattr(doc, "metadata", {}),
+                    )
+                    for doc, score in results
+                ]
+            except (NotImplementedError, TypeError):
+                continue
+
+        # Fallback: similarity_search_by_vector returns List[Document] with no scores.
+        # Assign 1.0 so score_and_rank never receives None (None < threshold crashes).
+        docs = self.client.similarity_search_by_vector(**kwargs)
+        return [
+            OutputData(
+                id=getattr(doc, "id", None),
+                score=1.0,
+                payload=getattr(doc, "metadata", {}),
+            )
+            for doc in docs
+        ]
 
     def delete(self, vector_id):
         """
diff --git a/tests/utils/test_scoring.py b/tests/utils/test_scoring.py
index 51a1480c6..1163ed526 100644
--- a/tests/utils/test_scoring.py
+++ b/tests/utils/test_scoring.py
@@ -119,6 +119,13 @@ class TestScoreAndRank:
         scored = score_and_rank([], {}, {}, threshold=0.1, top_k=10)
         assert scored == []
 
+    def test_none_score_treated_as_zero(self):
+        """Defensive: score=None must not crash on None < threshold comparison."""
+        results = [{"id": "a", "score": None, "payload": {"data": "mem a"}}]
+        # Should not raise TypeError; None score is treated as 0.0 and filtered out
+        scored = score_and_rank(results, {}, {}, threshold=0.1, top_k=10)
+        assert scored == []
+
     def test_score_clamped_to_1(self):
         results = [{"id": "a", "score": 1.0, "payload": {}}]
         bm25 = {"a": 1.0}
diff --git a/tests/vector_stores/test_langchain_vector_store.py b/tests/vector_stores/test_langchain_vector_store.py
index fc32adc58..8a1e7b973 100644
--- a/tests/vector_stores/test_langchain_vector_store.py
+++ b/tests/vector_stores/test_langchain_vector_store.py
@@ -57,6 +57,9 @@ def test_search_vectors(langchain_instance):
     assert results[0].payload == {"name": "vector1"}
     assert results[1].id == "id2"
     assert results[1].payload == {"name": "vector2"}
+    # scores must never be None — score_and_rank crashes on None < threshold
+    assert results[0].score == 1.0
+    assert results[1].score == 1.0
 
     # Test search with filters
     filters = {"name": "vector1"}
@@ -226,6 +229,56 @@ def test_list_with_exception(langchain_instance):
     assert results == []
 
 
+def test_search_score_is_never_none(langchain_instance):
+    """Regression: similarity_search_by_vector returns Documents with no scores.
+    search() must not propagate None — score_and_rank crashes on None < threshold."""
+    from mem0.utils.scoring import score_and_rank
+
+    mock_docs = [Mock(metadata={"data": "mem A"}, id="id1"), Mock(metadata={"data": "mem B"}, id="id2")]
+    langchain_instance.client.similarity_search_by_vector.return_value = mock_docs
+
+    results = langchain_instance.search(query="test", vectors=[[0.1, 0.2]], top_k=5)
+
+    assert all(r.score is not None for r in results), "score must never be None"
+    # Fallback path: no scored method available, so 1.0 is assigned.
+    assert all(r.score == 1.0 for r in results)
+
+    # Verify the full pipeline does not raise TypeError
+    candidates = [{"id": r.id, "score": r.score, "payload": r.payload} for r in results]
+    ranked = score_and_rank(candidates, {}, {}, threshold=0.1, top_k=5)
+    assert len(ranked) == 2
+
+
+def test_search_uses_scored_method_when_available(langchain_instance):
+    """When a scored-by-vector method exists on the client, use it to get real scores."""
+    mock_docs = [Mock(metadata={"data": "mem A"}, id="id1"), Mock(metadata={"data": "mem B"}, id="id2")]
+    # Inject a non-spec method that returns (Document, float) pairs
+    langchain_instance.client.similarity_search_by_vector_with_relevance_scores = Mock(
+        return_value=[(mock_docs[0], 0.95), (mock_docs[1], 0.42)]
+    )
+
+    results = langchain_instance.search(query="test", vectors=[[0.1, 0.2]], top_k=5)
+
+    langchain_instance.client.similarity_search_by_vector_with_relevance_scores.assert_called_once()
+    langchain_instance.client.similarity_search_by_vector.assert_not_called()
+    assert results[0].score == pytest.approx(0.95)
+    assert results[1].score == pytest.approx(0.42)
+
+
+def test_search_falls_back_when_scored_method_raises_not_implemented(langchain_instance):
+    """If the scored method raises NotImplementedError, fall back to score=1.0."""
+    mock_docs = [Mock(metadata={"data": "mem A"}, id="id1")]
+    langchain_instance.client.similarity_search_by_vector_with_relevance_scores = Mock(
+        side_effect=NotImplementedError
+    )
+    langchain_instance.client.similarity_search_by_vector.return_value = mock_docs
+
+    results = langchain_instance.search(query="test", vectors=[[0.1, 0.2]], top_k=5)
+
+    langchain_instance.client.similarity_search_by_vector.assert_called_once()
+    assert results[0].score == 1.0
+
+
 def test_update_wraps_vector_and_payload_in_lists(langchain_instance):
     """Regression test for Langchain update() type mismatch.