diff --git a/libs/langchain/langchain/agents/conversational_chat/output_parser.py b/libs/langchain/langchain/agents/conversational_chat/output_parser.py
index 5fa52f1703..6a200af31c 100644
--- a/libs/langchain/langchain/agents/conversational_chat/output_parser.py
+++ b/libs/langchain/langchain/agents/conversational_chat/output_parser.py
@@ -8,21 +8,45 @@ from langchain.output_parsers.json import parse_json_markdown
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 
+# Define a class that parses output for conversational agents
 class ConvoOutputParser(AgentOutputParser):
     """Output parser for the conversational agent."""
 
     def get_format_instructions(self) -> str:
+        """Returns formatting instructions for the given output parser."""
         return FORMAT_INSTRUCTIONS
 
     def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        """Attempts to parse the given text into an AgentAction or AgentFinish.
+
+        Raises:
+             OutputParserException if parsing fails.
+        """
         try:
+            # Attempt to parse the text into a structured format (assumed to be JSON
+            # stored as markdown)
             response = parse_json_markdown(text)
-            action, action_input = response["action"], response["action_input"]
-            if action == "Final Answer":
-                return AgentFinish({"output": action_input}, text)
+
+            # If the response contains an 'action' and 'action_input'
+            if "action" in response and "action_input" in response:
+                action, action_input = response["action"], response["action_input"]
+
+                # If the action indicates a final answer, return an AgentFinish
+                if action == "Final Answer":
+                    return AgentFinish({"output": action_input}, text)
+                else:
+                    # Otherwise, return an AgentAction with the specified action and
+                    # input
+                    return AgentAction(action, action_input, text)
             else:
-                return AgentAction(action, action_input, text)
+                # If the necessary keys aren't present in the response, raise an
+                # exception
+                raise OutputParserException(
+                    f"Missing 'action' or 'action_input' in LLM output: {text}"
+                )
         except Exception as e:
+            # If any other exception is raised during parsing, also raise an
+            # OutputParserException
             raise OutputParserException(f"Could not parse LLM output: {text}") from e
 
     @property