diff --git a/libs/community/langchain_community/tools/vectorstore/tool.py b/libs/community/langchain_community/tools/vectorstore/tool.py
index e51deaeaa1d..c0cbdb67062 100644
--- a/libs/community/langchain_community/tools/vectorstore/tool.py
+++ b/libs/community/langchain_community/tools/vectorstore/tool.py
@@ -3,7 +3,10 @@
 import json
 from typing import Any, Dict, Optional
 
-from langchain_core.callbacks import CallbackManagerForToolRun
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.tools import BaseTool
@@ -51,9 +54,30 @@ class VectorStoreQATool(BaseVectorStoreTool, BaseTool):
         chain = RetrievalQA.from_chain_type(
             self.llm, retriever=self.vectorstore.as_retriever()
         )
-        return chain.run(
-            query, callbacks=run_manager.get_child() if run_manager else None
+        return chain.invoke(
+            {chain.input_key: query},
+            config={"callbacks": [run_manager.get_child() if run_manager else None]},
+        )[chain.output_key]
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        from langchain.chains.retrieval_qa.base import RetrievalQA
+
+        chain = RetrievalQA.from_chain_type(
+            self.llm, retriever=self.vectorstore.as_retriever()
         )
+        return (
+            await chain.ainvoke(
+                {chain.input_key: query},
+                config={
+                    "callbacks": [run_manager.get_child() if run_manager else None]
+                },
+            )
+        )[chain.output_key]
 
 
 class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool):
@@ -87,7 +111,28 @@ class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool):
             self.llm, retriever=self.vectorstore.as_retriever()
         )
         return json.dumps(
-            chain(
+            chain.invoke(
+                {chain.question_key: query},
+                return_only_outputs=True,
+                callbacks=run_manager.get_child() if run_manager else None,
+            )
+        )
+
+    async def _arun(
+        self,
+        query: str,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the tool asynchronously."""
+        from langchain.chains.qa_with_sources.retrieval import (
+            RetrievalQAWithSourcesChain,
+        )
+
+        chain = RetrievalQAWithSourcesChain.from_chain_type(
+            self.llm, retriever=self.vectorstore.as_retriever()
+        )
+        return json.dumps(
+            await chain.ainvoke(
                 {chain.question_key: query},
                 return_only_outputs=True,
                 callbacks=run_manager.get_child() if run_manager else None,