## 1 Introduction
To alleviate these challenges, it is often necessary for team members to cooperate and negotiate with one another, whereas a single agent as an individual lacks the ability to synergize their resources in executing the task [19]. Consequently, a multi-agent framework involves two or more agents, each of which has different roles and responsibilities. The communication and negotiation mechanisms among multiple agents can effectively enhance the overall performance of task solutions [20]. For example, COPPER [21] enhances the collaborative performance of multi-agent systems through a self-reflective mechanism that enables the intelligence agents to learn from environmental feedback and iteratively improve their behavior. In addition to the aforementioned research, there are also studies dedicated to improving LLMs for tool learning to leverage external resources, such as API calls for intermediate steps [22; 23]. By utilizing tools, LLMs can significantly improve an agent's performance on certain tasks, such as improving their accuracy in mathematical reasoning problems [24] and web searching for relevant news [25]. Several studies have improved the ability of LLMs to use tools through fine-tuning. For example, Toolformer [26] employs a self-supervised approach to fine-tune LLMs so that they can acquire the ability to call APIs. These methods necessitate training with specialized datasets and toolsets, limiting their application to proprietary LLMs and their adaptation to various complex tasks. In contrast, there are also methods for using external tools through searching tool documentation or tool descriptions [27]. The main challenge with these approaches is that as the number of tools increases, LLMs tend to get disillusioned and confused when selecting tools [28]. In this regard, exploiting the rich knowledge and powerful comprehension of LLMs for the semantic enhancement of tool descriptions can improve LLMs' accuracy in retrieving tools and strengthen their ability to utilize external tools. To sum up, this paper proposes CoReaAgents, a collaboration and reasoning framework based on LLM-powered agents, which benefit from group intelligence [29]. The CoReaAgents framework enhances task processing accuracy and quality by simulating social division and collaboration among specialized agents, including the Plan Agent, the Tool Agent and the Reflect Agent. The Plan Agent functions as a precise task planner, responsible for task planning and decision making. It employs prospective reasoning to decompose tasks into multiple subtasks with specific dependencies and execution sequences. The Tool Agent functions as a proficient tool user, performing tool augmentation for each tool in the relevant toolset and executing each subtask as a single-step reasoning task. The Reflect Agent functions as an objective task evaluator, reflecting on the results of each subtask and determining the next planning step based on the current results. This ensures dynamic adjustments in task planning and facilitates reflection throughout the process. Moreover, our framework accomplishes overall planning and intermediate steps through the collaboration of multiple team members for continuous adjustment and updating over the course of the tasks. With the assistance of these agents and synergistic mechanisms, the CoReaAgents framework can significantly improve the ability and effectiveness of LLMs in solving complex reasoning tasks. To validate our framework, we conduct extensive experiments on three distinct complex reasoning tasks. The datasets for these tasks include API-Bankand ToolBench for tool learning, FuncQA and SVAMP for math reasoning and HotpotQA for multi-hop QA. The results show that the CoReaAgents framework outperforms various comparative methods in both quantity and quality. In summary, our contributions can be summarized as follows:  
* We propose CoReaAgents, a collaboration and reasoning framework based on LLM-powered agents.