diff --git a/samples/langchain_on_vertexai/prebuilt_langchain_agent_template.py b/samples/langchain_on_vertexai/prebuilt_langchain_agent_template.py
index f4a14675..6dbffc33 100644
--- a/samples/langchain_on_vertexai/prebuilt_langchain_agent_template.py
+++ b/samples/langchain_on_vertexai/prebuilt_langchain_agent_template.py
@@ -92,7 +92,7 @@ def similarity_search(query: str) -> list[Document]:
 DISPLAY_NAME = os.getenv("DISPLAY_NAME") or "PrebuiltAgent"
 
 remote_app = reasoning_engines.ReasoningEngine.create(
-    reasoning_engines.LangchainAgent(
+    reasoning_engines.LangchainAgent(  # type: ignore[arg-type]
         model="gemini-2.0-flash-001",
         tools=[similarity_search],  # type: ignore[list-item]
         model_kwargs={
@@ -103,6 +103,6 @@ def similarity_search(query: str) -> list[Document]:
     display_name="PrebuiltAgent",
     sys_version="3.11",
     extra_packages=["config.py"],
-)  # type: ignore[arg-type]
+)
 
 print(remote_app.query(input="movies about engineers"))  # type: ignore[attr-defined]
diff --git a/samples/langchain_on_vertexai/retriever_agent_with_history_template.py b/samples/langchain_on_vertexai/retriever_agent_with_history_template.py
index e2eea8d6..ff4de53b 100644
--- a/samples/langchain_on_vertexai/retriever_agent_with_history_template.py
+++ b/samples/langchain_on_vertexai/retriever_agent_with_history_template.py
@@ -27,11 +27,11 @@
     TABLE_NAME,
     USER,
 )
-from langchain import hub
 from langchain.agents import AgentExecutor, create_react_agent
 from langchain.tools.retriever import create_retriever_tool
 from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings
+from langsmith import Client
 from vertexai.preview import reasoning_engines  # type: ignore
 
 from langchain_google_alloydb_pg import (
@@ -109,7 +109,12 @@ def set_up(self):
 
         # Initialize the LLM and prompt
         llm = ChatVertexAI(model_name=self.model_name, project=self.project)
-        base_prompt = hub.pull("langchain-ai/react-agent-template")
+        # ``react-agent-template`` is an official, trusted LangChain prompt.
+        # langsmith disables pulling public prompts by default, so explicitly
+        # acknowledge the source with ``dangerously_pull_public_prompt``.
+        base_prompt = Client().pull_prompt(
+            "langchain-ai/react-agent-template", dangerously_pull_public_prompt=True
+        )
         instructions = (
             "You are an assistant for question-answering tasks. "
             "Use the following pieces of retrieved context to answer "
@@ -135,19 +140,20 @@ def set_up(self):
             history_messages_key="chat_history",
         )
 
-    def query(self, input: str, session_id: str, **kwargs: Any) -> str:
+    def query(self, **kwargs: Any) -> Any:
         """Query the application.
 
         Args:
-            input: The user query.
-            session_id: The user's session id.
+            **kwargs: Keyword arguments forwarded from the reasoning engine.
+                Expects ``input`` (the user query) and ``session_id``
+                (the user's session id).
 
         Returns:
-            The LLM response dictionary.
+            The LLM response.
         """
         response = self.agent.invoke(
-            {"input": input},
-            config={"configurable": {"session_id": session_id}},
+            {"input": kwargs["input"]},
+            config={"configurable": {"session_id": kwargs["session_id"]}},
         )
         return response["output"]
 
diff --git a/samples/langchain_on_vertexai/retriever_chain_template.py b/samples/langchain_on_vertexai/retriever_chain_template.py
index 905c41dd..55854736 100644
--- a/samples/langchain_on_vertexai/retriever_chain_template.py
+++ b/samples/langchain_on_vertexai/retriever_chain_template.py
@@ -109,18 +109,18 @@ def set_up(self):
         # an LLM to generate a response
         self.chain = create_retrieval_chain(retriever, combine_docs_chain)
 
-    def query(self, input: str, **kwargs: Any) -> str:
+    def query(self, **kwargs: Any) -> Any:
         """Query the application.
 
         Args:
-            input: The user query.
-            **kwargs: Additional arguments for Protocol compliance.
+            **kwargs: Keyword arguments forwarded from the reasoning engine.
+                Expects an ``input`` key containing the user query.
 
         Returns:
-            The LLM response dictionary.
+            The LLM response.
         """
         # Define the runtime logic that serves user queries
-        response = self.chain.invoke({"input": input})
+        response = self.chain.invoke({"input": kwargs["input"]})
         return response["answer"]
 
 
diff --git a/samples/requirements.txt b/samples/requirements.txt
index 55ca38fb..a7feb5db 100644
--- a/samples/requirements.txt
+++ b/samples/requirements.txt
@@ -1,4 +1,4 @@
-google-cloud-aiplatform[reasoningengine,langchain]==1.97.0
+google-cloud-aiplatform[reasoningengine,langchain]==1.133.0
 google-cloud-resource-manager==1.15.0
 langchain-community==0.3.31
 langchain-google-alloydb-pg==0.13.0