diff --git a/libs/community/langchain_community/graph_vectorstores/cassandra.py b/libs/community/langchain_community/graph_vectorstores/cassandra.py
index 6fb04c60a13..33fc5ba8f91 100644
--- a/libs/community/langchain_community/graph_vectorstores/cassandra.py
+++ b/libs/community/langchain_community/graph_vectorstores/cassandra.py
@@ -120,18 +120,31 @@ class CassandraGraphVectorStore(GraphVectorStore):
         return store
 
     def similarity_search(
-        self, query: str, k: int = 4, **kwargs: Any
+        self,
+        query: str,
+        k: int = 4,
+        metadata_filter: dict[str, Any] = {},
+        **kwargs: Any,
     ) -> List[Document]:
         embedding_vector = self._embedding.embed_query(query)
         return self.similarity_search_by_vector(
             embedding_vector,
             k=k,
+            metadata_filter=metadata_filter,
         )
 
     def similarity_search_by_vector(
-        self, embedding: List[float], k: int = 4, **kwargs: Any
+        self,
+        embedding: List[float],
+        k: int = 4,
+        metadata_filter: dict[str, Any] = {},
+        **kwargs: Any,
     ) -> List[Document]:
-        nodes = self.store.similarity_search(embedding, k=k)
+        nodes = self.store.similarity_search(
+            embedding,
+            k=k,
+            metadata_filter=metadata_filter,
+        )
         return list(nodes_to_documents(nodes))
 
     def traversal_search(
@@ -140,9 +153,15 @@ class CassandraGraphVectorStore(GraphVectorStore):
         *,
         k: int = 4,
         depth: int = 1,
+        metadata_filter: dict[str, Any] = {},
         **kwargs: Any,
     ) -> Iterable[Document]:
-        nodes = self.store.traversal_search(query, k=k, depth=depth)
+        nodes = self.store.traversal_search(
+            query,
+            k=k,
+            depth=depth,
+            metadata_filter=metadata_filter,
+        )
         return nodes_to_documents(nodes)
 
     def mmr_traversal_search(
@@ -155,6 +174,7 @@ class CassandraGraphVectorStore(GraphVectorStore):
         adjacent_k: int = 10,
         lambda_mult: float = 0.5,
         score_threshold: float = float("-inf"),
+        metadata_filter: dict[str, Any] = {},
         **kwargs: Any,
     ) -> Iterable[Document]:
         nodes = self.store.mmr_traversal_search(
@@ -165,5 +185,6 @@ class CassandraGraphVectorStore(GraphVectorStore):
             adjacent_k=adjacent_k,
             lambda_mult=lambda_mult,
             score_threshold=score_threshold,
+            metadata_filter=metadata_filter,
         )
         return nodes_to_documents(nodes)