Example "plan-and-execute" not work

[zrg-team]

Issues went implement "plan-and-execute"

i followed
https://github.com/langchain-ai/langgraphjs/blob/main/examples/plan-and-execute/plan-and-execute.ipynb

Input

const createAgentRunnable = async (
  llm: ChatOpenAI,
  tools: Awaited<ReturnType<typeof createAgentTools>>,
) => {
  // Get the prompt to use - you can modify this!
  const prompt = new ChatPromptTemplate({
    inputVariables: ['input', 'chat_history', 'agent_scratchpad'],
    promptMessages: [
      new SystemMessage(AGENT_PROMPT_SYSTEM_MESSAGE),
      new MessagesPlaceholder('chat_history'),
      HumanMessagePromptTemplate.fromTemplate('{input}'),
      HumanMessagePromptTemplate.fromTemplate(RESPONSE_INTRODUCT_MESSAGE_TEMPLATE),
      new MessagesPlaceholder('agent_scratchpad'),
    ],
  })
  // Construct the OpenAI Functions agent
  return await createOpenAIFunctionsAgent({
    llm,
    tools,
    prompt,
  })
}

export const createOGDeveloperAgentExecutor = async (
  data: AgentDataInput,
  services: AgentServiceInput,
) => {
  const tools = await createAgentTools(data, services)
  const agent = await createAgentRunnable(services.llm, tools)

  const agentExecutor = createAgentExecutor({
    agentRunnable: agent,
    tools: [],
  })

  return agentExecutor
}

ExecuteStep

executeStep = async (state: PlanExecuteState): Promise<Partial<PlanExecuteState>> => {
    const task = state.input
    const agentResponse = await this.agentExecutor.invoke({
      input: task,
      chat_history: state.chatHistory,
    })
    console.log('agentResponse', agentResponse)
    return { pastSteps: [task, agentResponse.agentOutcome.returnValues.output] }
  }

Error

err TypeError: Cannot read properties of undefined (reading 'returnValues')

The agentExcutor invoke response is not including "agentOutcome"

agentResponse {
  input: 'hi',
  chat_history: [
    AIMessage {
      lc_serializable: true,
      lc_kwargs: [Object],
      lc_namespace: [Array],
      content: '(Backend Developer) Sorry can not help you now. Please try again later.',
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      tool_calls: [],
      invalid_tool_calls: []
    },
    AIMessage {
      lc_serializable: true,
      lc_kwargs: [Object],
      lc_namespace: [Array],
      content: '(Backend Developer) AI thinking ...',
      name: undefined,
      additional_kwargs: {},
      response_metadata: {},
      tool_calls: [],
      invalid_tool_calls: []
    }
  ],
  output: "I'm ready to assist you. How can I help you today?",
  intermediateSteps: []
}