diff --git a/libs/partners/pinecone/langchain_pinecone/vectorstores.py b/libs/partners/pinecone/langchain_pinecone/vectorstores.py
index 34b3f85211..1c21179f64 100644
--- a/libs/partners/pinecone/langchain_pinecone/vectorstores.py
+++ b/libs/partners/pinecone/langchain_pinecone/vectorstores.py
@@ -161,19 +161,26 @@ class PineconeVectorStore(VectorStore):
             chunk_ids = ids[i : i + embedding_chunk_size]
             chunk_metadatas = metadatas[i : i + embedding_chunk_size]
             embeddings = self._embedding.embed_documents(chunk_texts)
-            async_res = [
+            vector_tuples = zip(chunk_ids, embeddings, chunk_metadatas)
+            if async_req:
+                # Runs the pinecone upsert asynchronously.
+                async_res = [
+                    self._index.upsert(
+                        vectors=batch_vector_tuples,
+                        namespace=namespace,
+                        async_req=async_req,
+                        **kwargs,
+                    )
+                    for batch_vector_tuples in batch_iterate(batch_size, vector_tuples)
+                ]
+                [res.get() for res in async_res]
+            else:
                 self._index.upsert(
-                    vectors=batch,
+                    vectors=vector_tuples,
                     namespace=namespace,
                     async_req=async_req,
                     **kwargs,
                 )
-                for batch in batch_iterate(
-                    batch_size, zip(chunk_ids, embeddings, chunk_metadatas)
-                )
-            ]
-            if async_req:
-                [res.get() for res in async_res]
 
         return ids
 
@@ -412,6 +419,7 @@ class PineconeVectorStore(VectorStore):
         upsert_kwargs: Optional[dict] = None,
         pool_threads: int = 4,
         embeddings_chunk_size: int = 1000,
+        async_req: bool = True,
         *,
         id_prefix: Optional[str] = None,
         **kwargs: Any,
@@ -453,6 +461,7 @@ class PineconeVectorStore(VectorStore):
             namespace=namespace,
             batch_size=batch_size,
             embedding_chunk_size=embeddings_chunk_size,
+            async_req=async_req,
             id_prefix=id_prefix,
             **(upsert_kwargs or {}),
         )
diff --git a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py
index 3d64cec29d..292d29b63c 100644
--- a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py
+++ b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py
@@ -9,6 +9,7 @@ import pytest
 from langchain_core.documents import Document
 from langchain_openai import OpenAIEmbeddings
 from pinecone import PodSpec
+from pytest_mock import MockerFixture
 
 from langchain_pinecone import PineconeVectorStore
 
@@ -290,3 +291,41 @@ class TestPinecone:
 
         query = "What did the president say about Ketanji Brown Jackson"
         _ = docsearch.similarity_search(query, k=1, namespace=NAMESPACE_NAME)
+
+    @pytest.fixture
+    def mock_pool_not_supported(self, mocker: MockerFixture) -> None:
+        """
+        This is the error thrown when multiprocessing is not supported.
+        See https://github.com/langchain-ai/langchain/issues/11168
+        """
+        mocker.patch(
+            "multiprocessing.synchronize.SemLock.__init__",
+            side_effect=OSError(
+                "FileNotFoundError: [Errno 2] No such file or directory"
+            ),
+        )
+
+    @pytest.mark.usefixtures("mock_pool_not_supported")
+    def test_that_async_freq_uses_multiprocessing(
+        self, texts: List[str], embedding_openai: OpenAIEmbeddings
+    ) -> None:
+        with pytest.raises(OSError):
+            PineconeVectorStore.from_texts(
+                texts=texts,
+                embedding=embedding_openai,
+                index_name=INDEX_NAME,
+                namespace=NAMESPACE_NAME,
+                async_req=True,
+            )
+
+    @pytest.mark.usefixtures("mock_pool_not_supported")
+    def test_that_async_freq_false_enabled_singlethreading(
+        self, texts: List[str], embedding_openai: OpenAIEmbeddings
+    ) -> None:
+        PineconeVectorStore.from_texts(
+            texts=texts,
+            embedding=embedding_openai,
+            index_name=INDEX_NAME,
+            namespace=NAMESPACE_NAME,
+            async_req=False,
+        )