BaseAgent (抽象基類)
  └─ ReActAgent (抽象基類)
      └─ ToolCallAgent
          ├─ PlanningAgent
          ├─ SWEAgent
          └─ Manus

async run(request):
  if state != IDLE:
    raise RuntimeError


  if request:
    update_memory(USER, request)


  async with state_context(RUNNING):
    while current_step < max_steps and state != FINISHED:
      current_step += 1
      step_result = await step()  # 調用子類的 step() 方法
      if is_stuck():
         handle_stuck_state()


      results.append(step_result)


    if current_step >= max_steps:
      state = IDLE
      results.append("Terminated: Reached max steps")


  return "\n".join(results)

async step():
  should_act = await think()  # 調用子類的 think()
  if should_act:
    return await act()  # 調用子類的 act()
  else:
    return "Thinking complete - no action needed"

async think():
  # ... (準備消息和工具信息) ...
  response = await llm.ask_tool(...) # 讓LLM決定是否調用工具，以及調用哪個工具
  if response.tool_calls:
     self.tool_calls = response.tool_calls
     return True # 需要執行工具
  else:
     return False


async act():
   if self.tool_calls:
      tool_call = self.tool_calls.pop(0) # 取出第一個工具調用
      result = await execute_tool(tool_call)
      await _handle_special_tool(tool_call.function.name,result) # 如果是特殊工具，則處理。
      update_memory(TOOL, result, tool_call_id=tool_call.id)
      return result
   else:
       return "No tool to execute"

async run(request):
  if request:
     await create_initial_plan(request)
  return await super().run()  # 調用 ToolCallAgent.run() -> ReActAgent.run() -> BaseAgent.run()




async create_initial_plan(request):
  # 1. 構造消息，讓 LLM 創建計劃
  messages = [...]
  response = await llm.ask_tool(..., tool_choice=ToolChoice.AUTO)


  # 2. 處理 LLM 的響應，提取工具調用（應該是 planning 工具的調用）
  for tool_call in response.tool_calls:
    if tool_call.function.name == "planning":
      result = await execute_tool(tool_call) # 執行 planning 工具
      # 3. 將工具執行結果（計劃）存入內存
      update_memory(TOOL, result, tool_call_id=tool_call.id)




async think():
    prompt = f"CURRENT PLAN STATUS:\n{await self.get_plan()}\n\n{self.next_step_prompt}"
    self.messages.append(Message.user_message(prompt))
    self.current_step_index = await self._get_current_step_index()
    result = await super().think() # 調用 ToolCallAgent 的 think()


    if result and self.tool_calls:
       # 記錄工具和步驟的關聯
       latest_tool_call = self.tool_calls[0]
       if latest_tool_call 不是 planning tool 且 不是 special tool:
           self.step_execution_tracker[latest_tool_call.id] = {
               "step_index": self.current_step_index,
               "tool_name": latest_tool_call.function.name,
                "status": "pending"
           }
    return result


async act():
   result = await super().act() # 調用 ToolCallAgent 的 act()
   if self.tool_calls:
       latest_tool_call = self.tool_calls[0]
       if latest_tool_call.id in self.step_execution_tracker:
          self.step_execution_tracker[latest_tool_call.id]["status"] = "completed"
          self.step_execution_tracker[latest_tool_call.id]["result"] = result


          if latest_tool_call 不是 planning tool 且 不是 special tool:
              await self.update_plan_status(latest_tool_call.id)
   return result




async update_plan_status(tool_call_id):
  # 1. 檢查 tool_call_id 是否在 tracker 中，以及狀態是否為 completed
  # 2. 調用 planning 工具的 mark_step 命令，將對應步驟標記為 completed


async _get_current_step_index():
   # 1. 獲取當前計劃 (文本)
   # 2. 解析計劃文本，找到第一個 [ ] 或 [→] 的步驟
   # 3. 調用 planning 工具的 mark_step 命令，將當前步驟設置為 in_progress
   # 4. 返回步驟索引

async think():
  self.working_dir = await self.bash.execute("pwd")  # 獲取當前工作目錄
  self.next_step_prompt = self.next_step_prompt.format(current_dir=self.working_dir) # 更新提示
  return await super().think()  # 調用 ToolCallAgent 的 think()