Enable customizing the output parser of `OpenAIFunctionsAgent` (#15827)

- **Description:** This PR defines the output parser of
OpenAIFunctionsAgent as an attribute, enabling customization and
subclassing of the parser logic.
- **Issue:** Subclassing is currently impossible as the
`OpenAIFunctionsAgentOutputParser` class is hard coded into the `plan`
and `aplan` methods
  - **Dependencies:** None

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---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/langchain/langchain/agents/openai_functions_agent/base.py

@@ -1,5 +1,5 @@
 """Module implements an agent that uses OpenAI's APIs function enabled API."""
-from typing import Any, List, Optional, Sequence, Tuple, Union
+from typing import Any, List, Optional, Sequence, Tuple, Type, Union
 
 from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
 from langchain_core._api import deprecated
@@ -47,6 +47,9 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
     llm: BaseLanguageModel
     tools: Sequence[BaseTool]
     prompt: BasePromptTemplate
+    output_parser: Type[
+        OpenAIFunctionsAgentOutputParser
+    ] = OpenAIFunctionsAgentOutputParser
 
     def get_allowed_tools(self) -> List[str]:
         """Get allowed tools."""
@@ -105,9 +108,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
                 messages,
                 callbacks=callbacks,
             )
-        agent_decision = OpenAIFunctionsAgentOutputParser._parse_ai_message(
-            predicted_message
-        )
+        agent_decision = self.output_parser._parse_ai_message(predicted_message)
         return agent_decision
 
     async def aplan(
@@ -136,9 +137,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         predicted_message = await self.llm.apredict_messages(
             messages, functions=self.functions, callbacks=callbacks
         )
-        agent_decision = OpenAIFunctionsAgentOutputParser._parse_ai_message(
-            predicted_message
-        )
+        agent_decision = self.output_parser._parse_ai_message(predicted_message)
         return agent_decision
 
     def return_stopped_response(