diff --git a/libs/langchain/langchain/embeddings/cache.py b/libs/langchain/langchain/embeddings/cache.py
index 382827991ff..51d3969e539 100644
--- a/libs/langchain/langchain/embeddings/cache.py
+++ b/libs/langchain/langchain/embeddings/cache.py
@@ -128,6 +128,41 @@ class CacheBackedEmbeddings(Embeddings):
             List[List[float]], vectors
         )  # Nones should have been resolved by now
 
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of texts.
+
+        The method first checks the cache for the embeddings.
+        If the embeddings are not found, the method uses the underlying embedder
+        to embed the documents and stores the results in the cache.
+
+        Args:
+            texts: A list of texts to embed.
+
+        Returns:
+            A list of embeddings for the given texts.
+        """
+        vectors: List[
+            Union[List[float], None]
+        ] = await self.document_embedding_store.amget(texts)
+        missing_indices: List[int] = [
+            i for i, vector in enumerate(vectors) if vector is None
+        ]
+        missing_texts = [texts[i] for i in missing_indices]
+
+        if missing_texts:
+            missing_vectors = await self.underlying_embeddings.aembed_documents(
+                missing_texts
+            )
+            await self.document_embedding_store.amset(
+                list(zip(missing_texts, missing_vectors))
+            )
+            for index, updated_vector in zip(missing_indices, missing_vectors):
+                vectors[index] = updated_vector
+
+        return cast(
+            List[List[float]], vectors
+        )  # Nones should have been resolved by now
+
     def embed_query(self, text: str) -> List[float]:
         """Embed query text.
 
@@ -148,6 +183,26 @@ class CacheBackedEmbeddings(Embeddings):
         """
         return self.underlying_embeddings.embed_query(text)
 
+    async def aembed_query(self, text: str) -> List[float]:
+        """Embed query text.
+
+        This method does not support caching at the moment.
+
+        Support for caching queries is easily to implement, but might make
+        sense to hold off to see the most common patterns.
+
+        If the cache has an eviction policy, we may need to be a bit more careful
+        about sharing the cache between documents and queries. Generally,
+        one is OK evicting query caches, but document caches should be kept.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            The embedding for the given text.
+        """
+        return await self.underlying_embeddings.aembed_query(text)
+
     @classmethod
     def from_bytes_store(
         cls,
diff --git a/libs/langchain/tests/unit_tests/embeddings/test_caching.py b/libs/langchain/tests/unit_tests/embeddings/test_caching.py
index 48c6adbab66..8c24e73b95b 100644
--- a/libs/langchain/tests/unit_tests/embeddings/test_caching.py
+++ b/libs/langchain/tests/unit_tests/embeddings/test_caching.py
@@ -47,3 +47,23 @@ def test_embed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
     vector = cache_embeddings.embed_query(text)
     expected_vector = [5.0, 6.0]
     assert vector == expected_vector
+
+
+async def test_aembed_documents(cache_embeddings: CacheBackedEmbeddings) -> None:
+    texts = ["1", "22", "a", "333"]
+    vectors = await cache_embeddings.aembed_documents(texts)
+    expected_vectors: List[List[float]] = [[1, 2.0], [2.0, 3.0], [1.0, 2.0], [3.0, 4.0]]
+    assert vectors == expected_vectors
+    keys = [
+        key async for key in cache_embeddings.document_embedding_store.ayield_keys()
+    ]
+    assert len(keys) == 4
+    # UUID is expected to be the same for the same text
+    assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12"
+
+
+async def test_aembed_query(cache_embeddings: CacheBackedEmbeddings) -> None:
+    text = "query_text"
+    vector = await cache_embeddings.aembed_query(text)
+    expected_vector = [5.0, 6.0]
+    assert vector == expected_vector