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# Source code for langchain.agents.conversational.output_parser

    import re
    from typing import Union

    from langchain_core.agents import AgentAction, AgentFinish
    from langchain_core.exceptions import OutputParserException

    from langchain.agents.agent import AgentOutputParser
    from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS

    class ConvoOutputParser(AgentOutputParser):
        """Output parser for the conversational agent."""

        ai_prefix: str = "AI"
        """Prefix to use before AI output."""

        format_instructions: str = FORMAT_INSTRUCTIONS
        """Default formatting instructions"""

        def get_format_instructions(self) -> str:
            """Returns formatting instructions for the given output parser."""
            return self.format_instructions

        def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
            if f"{self.ai_prefix}:" in text:
                return AgentFinish(
                    {"output": text.split(f"{self.ai_prefix}:")[-1].strip()}, text
                )
            regex = r"Action: (.*?)[\n]*Action Input: ([\s\S]*)"
            match = re.search(regex, text, re.DOTALL)
            if not match:
                raise OutputParserException(f"Could not parse LLM output: `{text}`")
            action = match.group(1)
            action_input = match.group(2)
            return AgentAction(action.strip(), action_input.strip(" ").strip('"'), text)

        @property
        def _type(self) -> str:
            return "conversational"