model="gpt-4-1106-preview",
                as_agent=True
            )
            agent_executor = AgentExecutor(agent=agent, tools=tools)
            agent_executor.invoke({"content": "What's 10 - 4 raised to the 2.7"})
    Example using custom tools and custom execution:
        .. code-block:: python
            from langchain_experimental.openai_assistant import OpenAIAssistantRunnable
            from langchain.agents import AgentExecutor
            from langchain_core.agents import AgentFinish
            from langchain.tools import E2BDataAnalysisTool
            tools = [E2BDataAnalysisTool(api_key="...")]
            agent = OpenAIAssistantRunnable.create_assistant(
                name="langchain assistant e2b tool",
                instructions="You are a personal math tutor. Write and run code to answer math questions.",
                tools=tools,
                model="gpt-4-1106-preview",
                as_agent=True
            )
            def execute_agent(agent, tools, input):
                tool_map = {tool.name: tool for tool in tools}
                response = agent.invoke(input)
                while not isinstance(response, AgentFinish):
                    tool_outputs = []
                    for action in response:
                        tool_output = tool_map[action.tool].invoke(action.tool_input)
                        tool_outputs.append({"output": tool_output, "tool_call_id": action.tool_call_id})
                    response = agent.invoke(
                        {
                            "tool_outputs": tool_outputs,
                            "run_id": action.run_id,
                            "thread_id": action.thread_id
                        }
                    )
                return response
            response = execute_agent(agent, tools, {"content": "What's 10 - 4 raised to the 2.7"})