langchain/experimental/plan_and_execute/executors/agent_executor.py/load_agent_executor
def load_agent_executor(
    llm: BaseLanguageModel, tools: List[BaseTool], verbose: bool = False
) -> ChainExecutor:
    agent = StructuredChatAgent.from_llm_and_tools(
        llm,
        tools,
        human_message_template=HUMAN_MESSAGE_TEMPLATE,
        input_variables=["previous_steps", "current_step", "agent_scratchpad"],
    )
    agent_executor = AgentExecutor.from_agent_and_tools(
        agent=agent, tools=tools, verbose=verbose
    )
    return ChainExecutor(chain=agent_executor)