that supports using `functions`.
        tools: The tools this agent has access to.
        prompt: The prompt for this agent, should support agent_scratchpad as one
            of the variables. For an easy way to construct this prompt, use
            `OpenAIMultiFunctionsAgent.create_prompt(...)`
    """
    llm: BaseLanguageModel
    tools: Sequence[BaseTool]
    prompt: BasePromptTemplate
[docs]    def get_allowed_tools(self) -> List[str]:
        """Get allowed tools."""
        return [t.name for t in self.tools]
    @root_validator
    def validate_llm(cls, values: dict) -> dict:
        if not isinstance(values["llm"], ChatOpenAI):
            raise ValueError("Only supported with ChatOpenAI models.")
        return values
    @root_validator
    def validate_prompt(cls, values: dict) -> dict:
        prompt: BasePromptTemplate = values["prompt"]
        if "agent_scratchpad" not in prompt.input_variables:
            raise ValueError(
                "`agent_scratchpad` should be one of the variables in the prompt, "
                f"got {prompt.input_variables}"
            )
        return values
    @property
    def input_keys(self) -> List[str]:
        """Get input keys. Input refers to user input here."""
        return ["input"]
    @property
    def functions(self) -> List[dict]:
        enum_vals = [t.name for t in self.tools]
        tool_selection = {
            # OpenAI functions returns a single tool invocation
            # Here we force the single tool invocation it returns to
            # itself be a list of tool invocations. We do this by constructing
            # a new tool that has one argument which is a list of tools
            # to use.