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# Source code for langchain_experimental.agents.agent_toolkits.python.base

    """Python agent."""

    from typing import Any, Dict, Optional

    from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
    from langchain.agents.mrkl.base import ZeroShotAgent
    from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
    from langchain.agents.types import AgentType
    from langchain.chains.llm import LLMChain
    from langchain_core.callbacks.base import BaseCallbackManager
    from langchain_core.language_models import BaseLanguageModel
    from langchain_core.messages import SystemMessage

    from langchain_experimental.agents.agent_toolkits.python.prompt import PREFIX
    from langchain_experimental.tools.python.tool import PythonREPLTool

    def create_python_agent(
        llm: BaseLanguageModel,
        tool: PythonREPLTool,
        agent_type: AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION,
        callback_manager: Optional[BaseCallbackManager] = None,
        verbose: bool = False,
        prefix: str = PREFIX,
        agent_executor_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs: Dict[str, Any],
    ) -> AgentExecutor:
        """Construct a python agent from an LLM and tool."""
        tools = [tool]
        agent: BaseSingleActionAgent

        if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
            prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
            llm_chain = LLMChain(
                llm=llm,
                prompt=prompt,
                callback_manager=callback_manager,
            )
            tool_names = [tool.name for tool in tools]
            agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
        elif agent_type == AgentType.OPENAI_FUNCTIONS:
            system_message = SystemMessage(content=prefix)
            _prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
            agent = OpenAIFunctionsAgent(
                llm=llm,
                prompt=_prompt,
                tools=tools,
                callback_manager=callback_manager,
                **kwargs,
            )
        else:
            raise ValueError(f"Agent type {agent_type} not supported at the moment.")
        return AgentExecutor.from_agent_and_tools(
            agent=agent,
            tools=tools,
            callback_manager=callback_manager,
            verbose=verbose,
            **(agent_executor_kwargs or {}),
        )