"""Run the agent."""
        objective = inputs["objective"]
        first_task = inputs.get("first_task", "Make a todo list")
        self.add_task({"task_id": 1, "task_name": first_task})
        num_iters = 0
        while True:
            if self.task_list:
                self.print_task_list()
                # Step 1: Pull the first task
                task = self.task_list.popleft()
                self.print_next_task(task)
                # Step 2: Execute the task
                result = self.execute_task(objective, task["task_name"])
                this_task_id = int(task["task_id"])
                self.print_task_result(result)
                # Step 3: Store the result in Pinecone
                result_id = f"result_{task['task_id']}"
                self.vectorstore.add_texts(
                    texts=[result],
                    metadatas=[{"task": task["task_name"]}],
                    ids=[result_id],
                )
                # Step 4: Create new tasks and reprioritize task list
                new_tasks = self.get_next_task(result, task["task_name"], objective)
                for new_task in new_tasks:
                    self.task_id_counter += 1
                    new_task.update({"task_id": self.task_id_counter})
                    self.add_task(new_task)
                self.task_list = deque(self.prioritize_tasks(this_task_id, objective))
            num_iters += 1
            if self.max_iterations is not None and num_iters == self.max_iterations:
                print(
                    "\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m"
                )
                break
        return {}
[docs]    @classmethod
    def from_llm(
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/experimental/autonomous_agents/baby_agi/baby_agi.html