diff --git a/libs/langchain/langchain/agents/json_chat/base.py b/libs/langchain/langchain/agents/json_chat/base.py
index 70228ce273..ce41f2d296 100644
--- a/libs/langchain/langchain/agents/json_chat/base.py
+++ b/libs/langchain/langchain/agents/json_chat/base.py
@@ -17,6 +17,7 @@ def create_json_chat_agent(
     prompt: ChatPromptTemplate,
     stop_sequence: Union[bool, List[str]] = True,
     tools_renderer: ToolsRenderer = render_text_description,
+    template_tool_response: str = TEMPLATE_TOOL_RESPONSE,
 ) -> Runnable:
     """Create an agent that uses JSON to format its logic, build for Chat Models.
 
@@ -33,6 +34,8 @@ def create_json_chat_agent(
             does not support stop sequences.
         tools_renderer: This controls how the tools are converted into a string and
             then passed into the LLM. Default is `render_text_description`.
+        template_tool_response: Template prompt that uses the tool response (observation)
+            to make the LLM generate the next action to take.
 
     Returns:
         A Runnable sequence representing an agent. It takes as input all the same input
@@ -157,6 +160,11 @@ def create_json_chat_agent(
     if missing_vars:
         raise ValueError(f"Prompt missing required variables: {missing_vars}")
 
+    if "{observation}" not in template_tool_response:
+        raise ValueError(
+            "Template tool response missing required variable 'observation'"
+        )
+
     prompt = prompt.partial(
         tools=tools_renderer(list(tools)),
         tool_names=", ".join([t.name for t in tools]),
@@ -170,7 +178,7 @@ def create_json_chat_agent(
     agent = (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_log_to_messages(
-                x["intermediate_steps"], template_tool_response=TEMPLATE_TOOL_RESPONSE
+                x["intermediate_steps"], template_tool_response=template_tool_response
             )
         )
         | prompt