Venue: NAACL 2025

# EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms

 Siyu Yuan\({}^{1}\), Kaitao Song\({}^{2}\)+,

Jiangjie Chen\({}^{1}\), Xu Tan\({}^{2}\), Dongsheng Li\({}^{2}\), Deqing Yang\({}^{1}\)

Fudan University\({}^{1}\), Microsoft Research Asia\({}^{2}\)

syyuan21@m.fudan.edu.cn, {kaitaosong, xuta, dongsli}@microsoft.com

{jjchen19,yangdeqing}@fudan.edu.cn

###### Abstract

The rise of powerful large language models (LLMs) has spurred a new trend in building LLM-based autonomous agents for solving complex tasks, especially multi-agent systems. Despite the remarkable progress, we notice that existing works are heavily dependent on human-designed frameworks, which greatly limits the functional scope and scalability of agent systems. How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability still remains a significant challenge. In this paper, we introduce EvoAgent, a generic method to automatically extend specialized agents to multi-agent systems via the evolutionary algorithm, thereby improving the effectiveness of LLM-based agents in solving tasks. Specifically, we consider the existing agent frameworks as the initial individual and then apply a series of evolutionary operators (e.g., mutation, crossover, selection, etc.) to generate multiple agents with diverse settings. Experimental results across various tasks show that EvoAgent can significantly enhance the task-solving capability of LLM-based agents, and can be generalized to any LLM-based agent framework to extend them into multi-agent systems. Resources are available at [https://evo-agent.github.io/](https://evo-agent.github.io/).

105C03C07C06C04C05C0

agent. To address this point, we notice that human society is composed of vast individuals, each possessing their unique characteristics. By selecting, orchestrating, and cooperating with different individuals, humans can form an efficient team group to handle complicated missions in the real world. Therefore, there has been an increasing trend to develop multi-agent collaboration frameworks (Park et al., 2023; Li et al., 2023; Wu et al., 2023; Hong et al., 2024) to simulate human behaviors for solving complex tasks. By developing a series of specialized agents with diverse settings, multi-agent systems enable us to reveal emergent abilities among multiple agents and synergize their specialized expertise to achieve superior performance, akin to simulating human populations. Nevertheless, it is worthy noting that, in most of (multi)-agent frameworks, their designs heavily depend on handcrafted settings, including character roles, task scopes, skills, and prompt settings. Although we admit that meticulous human design is quite useful for instructing LLM-based agents to understand tasks, it also limits scaling up the number of agents to further improve performance due to expensive human labor. Considering the increasing popularity of LLM-based autonomous agents, how to create a generic agent generation paradigm to automatically build multi-agent systems is a critical challenge.

In this paper, we introduce a novel method, EvoAgent, that formulates agent generation as the evolutionary processing (Back and Schwefel, 1993) in human society. Specifically, to align human society, each agent can be considered as individuals that can procreate its population across successive generations. Motivated by this mechanism, we can simulate such a human behavior to automatically generate multiple agents based on any pre-defined agents. Therefore, as shown in Figure 1, EvoAgent can be considered as a one-shot agent generation method that starts from a specialized agent as the initial agent, and then considers its settings (e.g., role, skills, prompts, and so on) as the variables to be evolved. With a series operation of EAs (e.g., selection, crossover, mutation), EvoAgent can automatically create multiple evolutionary agents based on the initial specialized agent. Moreover, EvoAgent is not limited to the infrastructure of agent frameworks, as it is a generic multi-agent generation method. Thus, it can be applied to any agent framework and expanded to multi-agent systems without any extra human effort.

We conduct experiments on multiple datasets, including knowledge-based question answering and multi-modal reasoning (SS 4.1), interactive scientific solving (SS 4.2) and real-world complex planning (SS 4.3). Experimental results indicate that EvoAgent can generate multiple agents with diverse skills and harness their capabilities to consistently improve the performance of the model in different scenarios. Besides, to validate the scalability of EvoAgent in creating massive agents, we also apply our method to some conversational scenarios (e.g., debate), and the results also indicate the potential of EvoAgent in generating multiple diverse agents. Overall, the contributions of this paper can be summarized as below:

* We introduce EvoAgent, a simple and generic multi-agent generation method to improve the effectiveness of LLM-based agents in solving tasks. EvoAgent can automatically generate new specialized agents and is applicable to any agent framework.
* We formulate the agent generation processing as an evolutionary pipeline, that encompasses multiple operators (e.g., selection, crossover, mutation) to generate agent population without additional human supervision.
* We conduct extensive experiments on various tasks and demonstrate the effectiveness, scalability, and generality of our EvoAgent. Particularly, EvoAgent can significantly enhance the performance of LLM-based agents in both challenging open-world scenarios and complex real-world planning by generating more specialized agents.

## 2 Related Work

LLM-based Autonomous AgentsWith the emergence of powerful large language models (OpenAI, 2023; Team, 2023; Touvron et al., 2023; Anthropic, 2024), many researchers have endeavored to develop advanced autonomous agents (Gravitas, 2023; Shen et al., 2023a; Nakajima, 2023) empowered by multiple high-level LLM skills (e.g., personas (Park et al., 2023b; Wang et al., 2024; Chen et al., 2024a), planning (Wei et al., 2022b; Chen et al., 2023c; Zhang et al., 2024; Yuan et al., 2023), tool (Schick et al., 2023; Shen et al., 2023a, b; Yuan et al., 2024) and 

memory (Weston et al., 2015; Shinn et al., 2023)). Some of them also extend agent frameworks to multi-agent collaboration (e.g., MetaGPT (Li et al., 2023b), Generative Agents (Park et al., 2023b), AutoGen (Wu et al., 2023), Camel (Li et al., 2023a), AgentVerse (Chen et al., 2024b) and so on), by designing multiple specific roles. These systems also demonstrate satisfactory performance in addressing massive, challenging tasks. However, it is worth noting that most of the popular agent frameworks heavily relied on handcrafted designs. The abundant human efforts necessitated by these systems also limit the adaptability and flexibility of agents to handle unexpected challenges (Qian et al., 2023; He et al., 2023; Chen et al., 2024b; Hong et al., 2024). In this paper, we propose EvoAgent, a method that can be applied to any LLM-based agent framework and easily extend to multi-agent systems. By using EA, our method allows us to iteratively generate and optimize multiple agents with diverse settings.

Agent GenerationRecent studies have shown that assigning personas or roles to LLM-based autonomous agents can influence their behavior and performance in generation tasks (Xu et al., 2023; Deshpande et al., 2023; Park et al., 2023a; Li et al., 2023a). Current methods primarily involve manually assigning these personas and limit multi-agent collaboration to single or fixed roles, which requires significant human effort and hinders generalization (Li et al., 2023a; Wu et al., 2023; Li et al., 2023b; Hong et al., 2024). To address this, some frameworks like AgentVerse (Chen et al., 2024b) and AutoAgents (Chen et al., 2023b) have been proposed to automatically generate unlimited agents for collaborative task completion. However, these methods still heavily depend on human-designed interventions, which limits their scalability and functionality. For example, AutoAgents requires agent settings to satisfy a "Planner - Agent Observer" framework. Meanwhile, AgentVerse formulates a pipeline of "Expert Recruitment - Collaborative Decision Making - Action Execution - Evaluation" to build agents. These architectures also limit the task scope of designing agents. In contrast, EvoAgent can automatically formulate the current agent frameworks to multi-agent systems with high-quality generated specialized agents by using EAs, which is flexible and adaptable to various agent frameworks.

## 3 Method

Generally, human society comprises a broad spectrum of individuals from diverse cultures, encompassing multiple generations. To solve specific tasks, human society usually involves a lot of expert individuals and aggregates their specialized expertise to achieve better answer. Thus, it can be considered as the foundation to facilitate multi-agent collaborations. To fulfill this point, how to automatically create multiple agents would be very critical. Inspired by evolutionism, we formulate agent generation as an evolutionary process to generate multiple agents without any human labor.

### Preliminary

Evolutionary algorithm (EA) (Bartz-Beielstein et al., 2014; Eiben et al., 2015), is a general algorithm to simulate the biological behaviors in evolution, including reproduction, mutation, recombination, and selection. By introducing genetic algorithm (Sampson, 1976; Holland, 1992; Mitchell, 1998; Schmitt, 2001; Mirjalili et al., 2020) of the "survival of the fittest" mechanism, it can also be considered as an optimization method to improve individuals. Therefore, EAs also belong to the non-parametric learning method, which can be applied to any framework. All we need to do is define which parts should be evolved and the corresponding evolutionary operators. We also note some recent works (Guo et al., 2023; Chen et al., 2023a) indicate the potential of EAs that can be applied to optimize discrete prompts. So, in this paper, we explore how to formulate the agent generation problem as an evolutionary task.

### EvoAgent

By assigning various settings to specific skills (e.g., role-playing, planning, tools and so on), agents could exhibit diverse task-solving capabilities. Therefore, our objective is to produce a population of agents with distinct skills, to establish effective multi-agent systems. To fulfill this point, we treat each specialized agent as an unique individual and denote each skill as the part to be evolved, akin to humans. So, we consider the procedure of agent generation to be evolutionary processing. Specifically, existing frameworks usually describe agent skills as the language. Thus, we can employ LLM to simulate evolutionary operators to update the system settings of agents and create new agents. As shown in Figure 1, we formulate the procedure 

of EvoAgent as a four-stage pipeline:

**STEP 1: Initialization** To conduct EAs, we first need to confirm our initial agents. Here, we enable EvoAgent to start from a pre-defined agent framework (e.g., MetaGPT Hong et al. (2024) and AutoGen Wu et al. (2023)), which serves as the initial (parent) agents. Moreover, we also define which parts of this agent should be upgraded. Generally, since EAs is a generic algorithm, EvoAgent is applicable to any agent frameworks and extends them as multi-agent frameworks. We will then explore how to generate new agents in the next steps.

**STEP 2: Crossover & Mutation** In the first iteration, we directly use the initial agents as the parents. And then, we design two kinds of evolutionary operators, named _Crossover_ and _Mutation_. For _Crossover_, we first enable the parent agents to generate results based on user requests. Then, based on the generated results, we ask LLMs to check which skills should be improved and then update them. This mechanism allows us to generate child agents in new settings without requiring any human labor. Moreover, we also need to guarantee the diversity between the child agents and parents. To this end, we design a _Mutation_ operation that requires LLMs to compare the child agents and parent agents and then modify the child agents to make them distinct from their parents while maintaining their task-solving capability. Based on these evolutionary operators, we can generate effective and diverse agents during one iteration. Besides, as we also need to conduct multiple iterations, we will append all agents generated in the previous generation into the next iteration.

**STEP 3: Selection** Based on the above steps, we can obtain multiple candidate agents with diverse settings. To guarantee the quality of agents, we also introduce a selection mechanism like EAs. Here, we conduct a _quality-check module_ with an LLM to detect whether the generated agents can satisfy it has inherited the characteristics and maintained differences from parent agents. We will select N child agents as the evolved agents in each iteration.

**STEP 4: Results Update** Based on the above steps, we obtain many new agents that evolved from parent agents, but with diverse settings. To improve task-solving capabilities, we ask each child agent to generate candidate results and then use LLMs to integrate these candidates with the result from the previous iteration into a new result, akin to a natural selection processing stage. Moreover, 

we can automatically generate more agents by repeating the operations from step 2 to step 4 until the number of agents has fulfilled our targets.

By introducing EA, EvoAgent enables us to automatically extend the existing agent framework to a multi-agent system without any extra human designs. The mechanism also makes EvoAgent can be applied to any agent framework without any prerequisites. We also present the details of EvoAgent in Algorithm 1.

## 4 Experiment

In this section, we adopt EvoAgent to multiple applications to illustrate that EvoAgent can help LLM-based agents better accomplish tasks with multi-agent generation.1 We also demonstrate that EvoAgent can be applicable in supporting currently widely used multi-agent frameworks, such as MetaGPT, AutoGen, and Camel in Appendix D.

Footnote 1: The data examples of EvoAgent on these tasks are provided in Appendix C.

### NLP and Multi-Modal Tasks

BenchmarksTo align previous experiences, e.g., Self-Refine Madaan et al. (2023) and Solo Performance Prompting Wang et al. (2023), we select three NLP knowledge-intensive and reasoning-intensive tasks from Wang et al. (2023) and one multi-modal task:

* **Logic Grid Puzzle** is a reasoning task with 200 puzzles featuring 2 to 5 unique occupants in different houses. The aim is to identify house numbers for one occupant with provided clues.
* **Trivia Creative Writing** is a knowledge-intensive task consisting of 100 instances. This task requires a model to write a coherent story while incorporating answers to N trivia questions.
* **Codenames Collaborative** is a reasoning-intensive task with 50 instances. It involves a model identifying target words based on a given hint and a complete list of words.
* **MMMU**Yue et al. (2023) is a comprehensive and general benchmark for multi-discipline multimodal understanding and reasoning. MMMU has three levels of difficulty: easy, medium, and hard. We evaluate EvoAgent against baselines using the multiple-choice questions in the validation set of MMMU, which includes 847 questions spanning 30 different domains.

BaselinesFor NLP tasks, we select LLama2-13B-Chat Touvron et al. (2023), GPT-3.5 OpenAI (2022) and GPT-4 OpenAI (2023) as our backbone networks. We compare EvoAgent with 0-shot learning Direct, Chain-of-thought CoT prompting Wei et al. (2022) and Self-Refine Madaan et al. (2023) and Solo Performance Prompting SPP Wang et al. (2023). For Self-Refine, we follow Madaan et al. (2023) to design feedback and refine prompts with three iterations. SPP is a multi-agent collaboration prompting strategy that asks a single LLM to identify and discuss with multiple personas with few-shot learning. For SPP, we follow the original setting Wang et al. (2023). We also compare EvoAgent with some pre-defined multi-agent agent generation frameworks, i.e., AgentVerse Chen et al. (2024) and AutoAgent Chen et al. (2023). For MMMU, we select GPT-4V Yang et al. (2023) and Gemini-Pro as the backbone and compare EvoAgent with CoT prompting, Self-Refine (SR), and SPP.2

\begin{table}
\begin{tabular}{l l r r r} \hline \hline
**Model** & **Method** & **Logic** & **Writing** & **Code** \\ \hline  & Direct & 4.00 & 28.00 & 0.00 \\  & CoT & 26.00 & 46.00 & 18.00 \\ LLama2-13B & SPP & 0.00 & 4.00 & 1.00 \\  & Self-Refine\({}_{3}\) & 33.50 & 31.20 & 12.37 \\  & AgentVerse & 10.00 & 12.00 & 15.36 \\  & AutoAgents & 16.00 & 18.00 & 13.50 \\  & EvoAgent\({}_{(1,3)}\) & **35.50** & **49.60** & **27.83** \\ \hline  & Direct & 48.00 & 56.20 & 76.29 \\ GPT-3.5 & CoT & 47.50 & 51.00 & 71.13 \\  & SPP & 56.00 & 54.40 & 61.86 \\  & Self-Refine\({}_{3}\) & 47.50 & 59.19 & 46.39 \\  & AgentVerse & 66.50 & 56.20 & 50.48 \\  & AutoAgents & 68.00 & 55.35 & 52.16 \\  & EvoAgent\({}_{(1,3)}\) & **71.50** & **60.80** & **79.38** \\ \hline  & Direct & 60.50 & 75.40 & 79.38 \\  & CoT & 65.50 & 74.00 & 80.41 \\  & SPP & 64.50 & 79.20 & 78.35 \\ GPT-4 & Self-Refine\({}_{3}\) & 64.50 & 74.60 & 79.38 \\  & AgentVerse & 66.50 & 78.00 & 80.41 \\  & AutoAgents & 69.00 & 82.00 & 83.56 \\  & EvoAgent\({}_{(1,3)}\) & **77.00** & **84.40** & **84.53** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of LLMs with different methods on Logic Grid Puzzle (**Logic**), Trivia Creative Writing (**Writing**) and Codenames Collaborative (**Code**). The best results are **bolded**, and the second best ones are underlined.

 

Evaluation MetricsWe adhere to the evaluation metrics specified in the original setting. Specifically, for Logic Grid Puzzle and MMMU tasks, we report the accuracy of all questions. For Trivia Creative Writing, we measure the ratio of correctly mentioned answers in the trivia questions. For Codenames Collaborative, we calculate the overlapping ratio between the predicted words from the Guesser and the target words as the metric.

Result & AnalysisIn our experiments, we adopt the agent settings of Wang et al. (2023) (for NLP tasks) and Yue et al. (2023) (for MMMU) as the initial agent. For our method, we denote it as EvoAgent\({}_{(N,T)}\), where N is the population size generated in each iteration, and T is the number of iterations. Here, to align with Self-Refine, we set N as 1 and T as 3, which means we conduct three iterations, each of which generates a new specialized agent. Thus, for each data sample, EvoAgent extends 3 different specialized agents to collaborate with the initial agent. Our results are reported in Table 1, and we can observe:

1. By utilizing multiple generated agents, EvoAgent can greatly improve LLM performances in both NLP knowledge and reasoning tasks. Moreover, EvoAgent outperforms both Agent-Verse and AutoAgent, highlighting the effectiveness and generality of EvoAgent.
2. When using weaker LLMs, SPP usually produces poor performances, consistent with the findings in Wang et al. (2023). This suggests the limited effectiveness of SPP in smaller and less capable models. However, EvoAgent can provide consistent improvements among each LLM, proving its strong generalization by using diverse generated agents.

In addition, Figure 2 shows that Self-Refine (SR) and SPP degrade performance compared to CoT prompting in MMMU task. However, EvoAgent can generate multiple domain-specific agents and thus improve multi-modal models in addressing scientific questions across various difficulty levels. More analysis about the time efficiency of EvoAgent is shown in Appendix B.1.

Interactive Scientific Solving Simulation BenchmarkCompared with traditional NLP or multi-modal tasks, autonomous agents usually need to perform problem-solving abilities akin to humans in interactive and open-world environments. We choose ScienceWorld Wang et al. (2022), a complex interactive environment requiring skills in long-term memory, sub-task decomposition, and scientific and commonsense knowledge. We evaluate 30 scientific tasks in ScienceWorld to demonstrate the capability of EvoAgent in solving tasks in more challenging open-world environments.

Baseline and Evaluation MetricsFollowing Lin et al. (2023), we require LLMs to perform an action at each step by using in-context learning 3. For evaluation, each task in ScienceWorld includes some sub-tasks, and we report the results by calculating the completed sub-tasks for the whole task.

Footnote 3: The introduction of the settings of LLMs are shown in Appendix A.4.

Result & AnalysisFor EvoAgent, we adopt the agent framework with original settings in Lin et al. (2023) as the initial agent. Since each step in ScienceWorld requires using EA, we set the population size N as 1 and the iterations T as 1 for efficiency, denoted as EvoAgent\({}_{(1,1)}\). Thus, for each task in ScienceWorld, EvoAgent can extend

Figure 2: Overall results of GPT-4V and Gemini-Pro with different methods on the MMMU validation set. We also compare the performance of GPT-4V and Gemini-Pro across three difficulty levels.

 

M different specialized agents to collaborate with the initial agent, where M is the number of steps in this task. Results in Table 2 show that:

1. EvoAgent can also extend interactive agents to multi-agent systems in solving complete scientific tasks in dynamic, open-world environments and consistently improve the performance of LLMs.
2. Our method exhibits the most substantial improvement in short-trajectory tasks, with less significant gains in medium and long-trajectory tasks. We argue that the capability of multi-agent systems will also be affected by a longer context. Future work can investigate the effect of long context on multi-agent systems.

Generally, these results also demonstrate the generalization of EvoAgent, which can also be used for solving interactive tasks in an open-world environment.

### Real-World Scenarios

BenchmarkPlanning in complex and realistic environments is also a crucial skill for building autonomous agents. Thus, we also select TravelPlanner Xie et al. (2024), a benchmark designed to evaluate language agents in real-world complex planning with multiple constraints.

Baseline and Evaluation MetricsFollowing Xie et al. (2024), we select Mistral-7B Jiang et al. (2023), GPT-3.5, Gemini-Pro Team (2023) and GPT-4 as our backbone models. We compare EvoAgent with 0-shot learning Direct, CoT prompting, ReAct Yao et al. (2023), SPP, and Self-Refine within each backbone model. For evaluation, we adhere to the original metrics from TravelPlanner, reporting the delivery rate, commonsense constraint pass rate, hard constraint pass rate, and final pass rate for all methods 4.

Footnote 4: Detailed introduction of experiment settings is provided in Appendix A.5.

Result & AnalysisFor EvoAgent, we adopt the original settings in TravelPlanner as the initial agent. In our main experiment, to fairly compare with Self-Refine, we set the population size N as 1 and the iterations T as 3 for efficiency, denoted as EvoAgent\({}_{(1,3)}\). Thus, for each user query in TravelPlanner, EvoAgent can extend 3 different specialized agents to collaborate with the initial agent. Results in Table 3 show that:

1. Although existing paradigms (e.g., Self-Refine and SPP) have demonstrated decent results in

\begin{table}
\begin{tabular}{l l c r r r r r} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Delivery**} & \multicolumn{2}{c}{**Commonsense**} & \multicolumn{2}{c}{**Hard Constraint**} & \multirow{2}{*}{**Final**} \\  & & **Rate** & & **Micro** & **Macro** & & **Micro** & **Macro** \\ \hline \multirow{6}{*}{Mistral-7B} & Direct & 100.0 & **64.7** & **2.2** & 3.1 & 0.0 & 0.0 \\  & CoT & 100.0 & 60.5 & 1.1 & 1.0 & 0.0 & 0.0 \\  & SPP & 100.0 & 55.1 & 0.0 & 0.7 & **0.6** & 0.0 \\  & Self-Refine\({}_{3}\) & 100.0 & 58.3 & 0.0 & 0.7 & 0.0 & 0.0 \\  & EvoAgent\({}_{(1,3)}\) & 100.0 & 60.1 & **2.2** & **4.5** & **0.6** & 0.0 \\ \hline \multirow{6}{*}{GPT-3.5} & Direct & 100.0 & 57.3 & 3.9 & 11.0 & 3.3 & 0.0 \\  & CoT & 100.0 & 61.0 & 2.8 & 10.0 & 3.3 & 0.0 \\  & ReAct & 82.2 & 42.3 & 0.6 & **11.9** & **4.6** & 0.0 \\  & SPP & 99.4 & 54.6 & 1.7 & 3.8 & 1.1 & 0.0 \\  & Self-Refine\({}_{3}\) & 100.0 & 56.0 & 1.7 & 3.1 & 1.1 & 0.0 \\  & EvoAgent\({}_{(1,3)}\) & 100.0 & **64.2** & **7.8** & 11.0 & 4.4 & **1.1** \\ \hline \multirow{6}{*}{Gemini-Pro} & Direct & 90.0 & 61.7 & 7.8 & 16.4 & **7.8** & 0.6 \\  & CoT & 90.0 & 61.4 & 7.2 & 10.0 & 6.1 & **1.7** \\ \cline{1-1}  & SPP & 100.0 & 67.6 & 7.8 & 10.2 & 3.9 & 1.1 \\ \cline{1-1}  & Self-Refine\({}_{3}\) & 95.6 & 65.8 & 6.1 & 15.0 & 4.4 & 0.6 \\ \cline{1-1}  & EvoAgent\({}_{(1,3)}\) & 100.0 & **73.5** & **12.8** & **16.9** & 7.2 & **1.7** \\ \hline \multirow{6}{*}{GPT-4} & Direct & 100.0 & 79.4 & 15.8 & 27.5 & 16.1 & 2.2 \\ \cline{1-1}  & CoT & 100.0 & 76.7 & 11.7 & 22.4 & 12.8 & 2.2 \\ \cline{1-1}  & SPP & 96.7 & 70.6 & 5.6 & 11.4 & 7.8 & 0.6 \\ \cline{1-1}  & Self-Refine\({}_{3}\) & 98.9 & 75.3 & 7.2 & 12.4 & 7.2 & 1.1 \\ \cline{1-1}  & EvoAgent\({}_{(1,3)}\) & 100.0 & **81.5** & **21.1** & **31.4** & **18.9** & **7.2** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main results of different LLMs and planning strategies on the TravelPlanner validation set. EvoAgent\({}_{(N,T)}\) indicates that the population size per iteration is N and the number of iterations is T. The best results are **bolded**, and the second best ones are underlined.

 

some conventional NLP tasks, they still lack capability in handling complex planning tasks. These results also demonstrate that only using human-design prompting strategies is insufficient to handle complex planning tasks.
2. EvoAgent can automatically generate multiple agents, such as those focused on culinary experiences and transportation, and forming a multi-agent collaboration paradigm. Therefore, the generated travel plans are more aligned with user preferences and commonsense rules.
3. By using EvoAgent to automatically generate multiple agents and forming a multi-agent collaboration paradigm, we can develop higher-quality plans that better meet user preferences. That also indicates the significance of multi-agent systems for complex planning tasks.

### Ablation Studies

To better understand the value of EvoAgent, we conduct detailed analyses on TravelPlanner, focusing on the impact of population size and iteration number and the effectiveness of the quality-check module in the selection stage.

Experiment SettingsWe evaluate the performance of different LLMs at varying population sizes N and iteration number T. For each query, EvoAgent can generate N\(\times\)T different specialized agents to collaborate with the initial agent. We employ an LLM that shares the same backbone as the initial agent for updates. To select results from candidates for this LLM to update, we adopt three different selection strategies: 1) _Random_: one result is selected randomly from the pool of candidates; 2) _PK_: we ask an agent with the same backbone as the initial agent to identify the optimal results from the pool of candidates; 3) _All-in_: Rather than selecting a single result, we update using all candidates.

Moreover, we also attempt Suggest\({}_{3}\), Overgen\({}_{3}\) and PromptRefine\({}_{3}\) as variants to prove the effectiveness of our method. For Suggest\({}_{3}\), instead of generating new results, we ask new generated agents to only give suggestions for initial agents to revise their results. For Overgen\({}_{3}\), we first ask initial agents to generate 3 different results at one time, and then these agents can output the final results based on these multiple candidates. For PromptRefine\({}_{3}\), instead of generating agents, we ask the initial agent to refine its prompts three times to better answer the query. 5 Footnote 5: The full prompts of different ablation settings are shown in Appendix A.1.

Result & AnalysisTo obtain stable findings, we first obtain results from GPT-3.5 and GeminiPro across different population sizes and selection strategies. We then average their results over various metrics to clearly compare the strengths and weaknesses of these variants.

The results are shown in Table 4.6 We find that EvoAgent significantly outperforms the Overgen, demonstrating the effectiveness of generating specialized agents to assist with complex planning. Although obtaining suggestions from new generated agents can improve the performance on common-sense constraints, these methods greatly harm the agents to meet the user preference. Modifying the prompt can improve the performance of agents, yet it remains less effective than EvoAgent.

Footnote 6: The complete results with further analysis are shown in Appendix B.2

When the population size exceeds one (N\(>1\)), agents may generate similar agents. Thus, lacking a quality-check module leads to reduced travel plan quality. Furthermore, when population size and iteration number increase, the model aligns travel plans more closely with user preferences but diminishing adherence to commonsense rules, consistent with the findings in Table 3. We hypothesize that this variability stems from the initial agent's bias in adjusting its outputs based on the results generated by new agents, notably prioritizing user preferences over commonsense rules. Future work

\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{w/o QC} & \multicolumn{2}{c}{w/ QC} \\  & **Com.** & **Hard** & **Com.** & **Hard** \\ \hline Direct & - & - & 59.5 & 13.7 \\ Suggest\({}_{3}\) & - & - & 61.7 & 8.4 \\ Overgen\({}_{3}\) & - & - & 61.4 & 10.7 \\ PromptRefine\({}_{3}\) & - & - & 63.0 & 13.8 \\ \hline \multicolumn{5}{c}{Different Population Size} \\ EvoAgent\({}_{(1,3)}\) & **68.9** & 14.0 & **68.9** & 14.0 \\ EvoAgent\({}_{(1,5)}\) & 67.5 & **16.9** & 67.5 & **16.9** \\ EvoAgent\({}_{(2,3)}\) & 62.8 & 12.7 & 67.0 & 15.2 \\ EvoAgent\({}_{(3,3)}\) & 62.7 & 13.7 & 66.8 & 15.8 \\ \hline \multicolumn{5}{c}{Different Selection Strategies} \\ Random & 62.9 & 12.7 & **67.1** & 15.0 \\ PK & **63.5** & **13.6** & 66.4 & 14.5 \\ All-in & 61.9 & 13.2 & **67.1** & **17.0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average commonsense constraint pass rate (**Com.**) and hard constraint pass rate (**Hard**) of ablated variants on TravelPlanner.

 

can explore and alleviate this bias.

Remarkably, the PK strategy initially yields superior results without the quality-check module, but this trend reverses once quality checks are implemented. We speculate that, without the quality-check module, PK partially fulfills this role, aiding in selecting better candidates. However, with the quality-check module, PK introduces bias by favoring specific fields of expertise while neglecting others, resulting in a less effective than random strategy. Meanwhile, the All-in strategy performs optimally when a quality-check module is included. Future research can leverage long-context LLMs to expand more agents with EvoAgent to better solve complex real-world tasks.

## 5 Conclusion

In this paper, we propose EvoAgent, an automatic multi-agent generation system by leveraging evolutionary algorithms. EvoAgent is suitable to any existing agent framework and extends it to multi-agent systems with diverse and effective agents by using a series of evolutionary operations, including mutation, crossover, and selection. Experiments on multiple tasks show that EvoAgent can significantly improve the capabilities of LLM-based agents in solving complex tasks.

### Limitations

First, EvoAgent requires the model to generate multiple specialized agents, which brings more token cost than a single agent. Besides, except for AgentVerse and AutoAgents in Table 1, we do not conduct extensive comparisons with existing multi-agent systems frameworks. This is because these frameworks require additional design efforts, and designing a suitable framework for each benchmark used in our experiment is beyond the scope of this paper. Finally, we do not manually evaluate the quality of the generated agents, but the results show that EvoAgent significantly improves the performance of LLM-based agent on complex tasks.

### Ethics Statement

We acknowledge that all authors are informed about and adhere to the ACL ARR Code of Ethics and the Code of Conduct.

#### Risks

The benchmarks in our experiment are sourced from publicly available sources. However, we cannot guarantee that they are devoid of socially harmful or toxic language. We use ChatGPT (OpenAI, 2022) to correct grammatical errors in this paper.

## Acknowledgement

We thank the anonymous reviewers for their valuable comments. This work is partially supported by the Chinese NSF Major Research Plan (No.92270121).

## References

* [Anthropic2024] Anthropic. 2024. The claude 3 model family: Opus, sonnet, haiku.
* Back and Schwefel (1993) Thomas Back and Hans-Paul Schwefel. 1993. An overview of evolutionary algorithms for parameter optimization. _Evol. Comput._, 1(1):1-23.
* Bartz-Beielstein et al. (2014) Thomas Bartz-Beielstein, Jurgen Branke, Jorn Mehnen, and Olaf Mersmann. 2014. Evolutionary algorithms. _Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery_, 4(3):178-195.
* 16, 2023_.
* Chen et al. (2023b) Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Borje F Karlsson, Jie Fu, and Yemin Shi. 2023b. AutoaGents: A framework for automatic agent generation. _arXiv preprint arXiv:2309.17288_.
* Chen et al. (2024a) Jiangjie Chen, Xintao Wang, Rui Xu, Siyu Yuan, Yixai Zhang, Wei Shi, Jian Xie, Shuang Li, Ruihan Yang, Tinghui Zhu, et al. 2024a. From persona to personalization: A survey on role-playing language agents. _arXiv preprint arXiv:2404.18231_.
* Chen et al. (2023c) Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder, and Kyle Richardson. 2023c. Put your money where your mouth is: Evaluating strategic planning and execution of llm agents in an auction arena. _arXiv preprint arXiv:2310.05746_.
* Chen et al. (2024b) Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu, Yi-Hsin Hung, Chen Qian, Yujia Qin, Xin Cong, Ruobing Xie, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2024b. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors. In _The Twelfth International Conference on Learning Representations_.
* Deshpande et al. (2023) Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan. 2023 

Toxicity in chatgpt: Analyzing persona-assigned language models. In _Findings of the Association for Computational Linguistics: EMNLP 2023_, pages 1236-1270, Singapore. Association for Computational Linguistics.
* Dubey et al. (2024) Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. _arXiv preprint arXiv:2407.21783_.
* Eiben et al. (2015) Agoston E Eiben, James E Smith, AE Eiben, and JE Smith. 2015. What is an evolutionary algorithm? _Introduction to evolutionary computing_, pages 25-48.
* Gravitas (2023) Significant Gravitas. 2023. Auto-gpt: An autonomous gpt-4 experiment. [https://github.com/Significant-Gravitas/Auto-GPT](https://github.com/Significant-Gravitas/Auto-GPT).
* Guo et al. (2023) Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. 2023. Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. _CoRR_, abs/2309.08532.
* He et al. (2023) Zhitao He, Pengfei Cao, Yubo Chen, Kang Liu, Ruopeng Li, Mengshu Sun, and Jun Zhao. 2023. LEGO: A multi-agent collaborative framework with role-playing and iterative feedback for causality explanation generation. In _Findings of the Association for Computational Linguistics: EMNLP 2023_, pages 9142-9163, Singapore. Association for Computational Linguistics.
* Holland (1992) John H Holland. 1992. _Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence_. MIT press.
* Hong et al. (2024) Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Livyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jurgen Schmidhuber. 2024. MetaGPIT: Meta programming for a multi-agent collaborative framework. In _The Twelfth International Conference on Learning Representations_.
* Jiang et al. (2023) Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lelio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothee Lacroix, and William El Sayed. 2023. Mistral 7b.
* Li et al. (2023a)Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbuullin, and Bernard Ghanem. 2023a. CAMEL: Communicative agents for "mind" exploration of large language model society. In _Thirty-seventh Conference on Neural Information Processing Systems_.
* Li et al. (2023b) Yuan Li, Yixuan Zhang, and Lichao Sun. 2023b. Metagents: Simulating interactions of human behaviors for llvm-based task-oriented coordination via collaborative generative agents. _arXiv preprint arXiv:2310.06500_.
* Lin et al. (2023) Bill Yuchen Lin, Yicheng Fu, Karina Yang, Faeez Brahman, Shiyu Huang, Chandra Bhagaratula, Prithviraj Ammanaroblu, Yejin Choi, and Xiang Ren. 2023. Swifstage: A generative agent with fast and slow thinking for complex interactive tasks. In _Thirty-seventh Conference on Neural Information Processing Systems_.
* Maadan et al. (2023)Aman Maadan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyun Gao, Sarah Wiegrefke, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhish, and Peter Clark. 2023. Self-refine: Iterative refinement with self-feedback. In _Thirty-seventh Conference on Neural Information Processing Systems_.
* Mirjalili et al. (2020) Seyedal Mirjalili, Jin Song Dong, Ali Safa Sadiq, and Hossam Faris. 2020. Genetic algorithm: Theory, literature review, and application in image reconstruction. _Nature-inspired optimizers: Theories, literature reviews and applications_, pages 69-85.
* Mitchell (1998) Melanie Mitchell. 1998. _An introduction to genetic algorithms_. MIT press.
* Nakajima (2023) Yohei Nakajima. 2023. Babyagi. [https://github.com/yoheinakajima/babyagi](https://github.com/yoheinakajima/babyagi).
* OpenAI (2022)OpenAI. 2022. Chatgtpct.
* OpenAI (2023)OpenAI. 2023. GPT-4 technical report. _CoRR_, abs/2303.08774.
* Park et al. (2023a)Joon Sung Park, Joseph O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023a. Generative agents: Interactive simulacra of human behavior. In _Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology_, UIST '23, New York, NY, USA. Association for Computing Machinery.
* Park et al. (2023b)Joon Sung Park, Joseph C. O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023b. Generative agents: Interactive simulators of human behavior. In _Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST 2023, San Francisco, CA, USA, 29 October 2023- 1 November 2023_, pages 2:1-2:22. ACM.
* Qian et al. (2023) Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. 2023. Communicative agents for software development. _arXiv preprint arXiv:2307.07924_.
* Sampson (1976) Jeffrey R Sampson. 1976. Adaptation in natural and artificial systems (john h. holland).

 

Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can reach themselves to use tools. _CoRR_, abs/2302.04761.
* Schmitt (2001) Lothar M Schmitt. 2001. Theory of genetic algorithms. _Theoretical Computer Science_, 259(1-2):1-61.
* Shen et al. (2023a) Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023a. Hugginggt: Solving AI tasks with chatgpt and its friends in huggingface. _CoRR_, abs/2303.17580.
* Shen et al. (2023b) Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, and Yueting Zhuang. 2023b. Taskbench: Benchmarking large language models for task automation. _arXiv preprint arXiv:2311.18760_.
* Shinn et al. (2023) Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023. Reflexion: an autonomous agent with dynamic memory and self-reflection. _arXiv preprint arXiv:2303.11366_.
* Team (2023) Gemini Team. 2023. Gemini: A family of highly capable multimodal models.
* Touvron et al. (2021) Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiboueh, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Ooyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcia Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Kouara, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihalyo, Pushkar Mishra, Igor Molbyog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashil Runga, Kayal Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binn Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Purxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharnan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023. Llama 2: Open foundation and fine-tuned chat models. _CoRR_, abs/2307.09288.
* Wang et al. (2022) Ruoyao Wang, Peter Janssen, Marc-Alexandre Cote, and Prithviraj Ammanabrolu. 2022. ScienceWorld: Is your a great smart arena a 'tgh grader? In _Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing_, pages 11279-11298, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
* Wang et al. (2024) Xintao Wang, Yunze Xiao, Jen tes Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, and Yanghua Xiao. 2024. Incharacter: Evaluating personality fidelity in role-playing agents through psychological interviews.
* Wang et al. (2023) Zhenhailong Wang, Shaeguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. 2023. Unleashing cognitive synergy in large language models: A task-solving agent through multi-person self-collaboration. _arXiv preprint arXiv:2307.05300_.
* Wei et al. (2022a) Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022a. Chain of thought prompting elicits reasoning in large language models. In _Advances in Neural Information Processing Systems_.
* Wei et al. (2022b) Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022b. Chain-of-thought prompting elicits reasoning in large language models. In _NeurIPS_.
* Weston et al. (2015) Jason Weston, Sumit Chopra, and Antoine Bordes. 2015. Memory networks. In _3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings_.
* Wu et al. (2023) Qingwu Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. Autogen: Enabling next-gen llm applications via multiagent conversation framework. _arXiv preprint arXiv:2308.08155_.
* Xie et al. (2024) Jian Xie, Kai Zhang, Jiangjiie Chen, Tinghui Zhu, Remez Lou, Yuandong Tian, Yanghua Xiao, and Yu Su. 2024. Travelplanner: A benchmark for real-world planning with language agents. _arXiv preprint arXiv:2402.01622_.
* Xu et al. (2023) Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Yongdong Zhang, and Zhendong Mao. 2023. ExpertPrompting: INructuring large language models to be distinguished experts. _arXiv preprint arXiv:2305.14688_.
* Yang et al. (2023) Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijun Wang. 2023. The dawn of lmms: Preliminary explorations with gpt-4v ( vision). _arXiv preprint arXiv:2309.17421_, 9(1):1.
* Yao et al. (2023) Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2023. React: Synergizing reasoning and acting in language models. In _The Eleventh International Conference on Learning Representations_.
* Yuan et al. (2023) Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Robert Jankowski, Yanghua Xiao, and Deqing Yang. 2023. Distilling script knowledge from large language models for constrained language planning. _arXiv preprint arXiv:2305.05252_.
* Yuan et al. (2024) Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Ren Kan, Dongsheng Li, and Deqing Yang. 2024. Easytool: Enhancing llm-based agents with concise tool instruction. _arXiv preprint arXiv:2401.06201_ 

Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. 2023. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. _arXiv preprint arXiv:2311.16502_.
* Zhang et al. (2024) Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, and Jiangjie Chen. 2024. Timearena: Shaping efficient multifasking language agents in a time-aware simulation. _arXiv preprint arXiv:2402.05733_.

 

Experiment Settings

### Prompt for Baselines and EvoAgent

Listing 1 and 2 shows the full prompt for 0-shot learning (Direct), Chain-of-thought (CoT) prompting Wei et al. (2022) and Self-Refine Madaan et al. (2023) and Solo Performance Promping, i.e., SPP Wang et al. (2023). Listing 3 and 4 show the prompt of EvoAgent and different ablation settings.

### Model Selection

For OpenAI models, we use gpt-35-turbo and gpt-4-32k with the version of 2024-02-15-preview in Azure.7 For Gemini-pro, we use Google Gemini-Pro APIs to obtain results. We set the temperature to 0 for all models.

Footnote 7: [https://azure.microsoft.com/en-us/products/ai-services/openai-service](https://azure.microsoft.com/en-us/products/ai-services/openai-service)

### Human-designed Agent Framework

AgentVerse Chen et al. (2024) and AutoAgent Chen et al. (2023) are frameworks designed to generate an unlimited number of agents for collaborative tasks automatically. Despite this automation, they still rely on human-designed interventions. AutoAgents requires agent settings to satisfy a "Planner - Agent Observer - Plan Observer" framework, while AgentVerses formulates a pipeline of "Expert Recruitment - Collaborative Decision Making - Action Execution - Evaluation" to build agents. We argue that these human-designed architectures limit their scalability and functionality. We follow their experimental settings and compared them with our method.

### Experimental Details of ScienceWorld

Following Lin et al. (2023), we adopt the REACT Yao et al. (2023) method for each LLM, which introduces a virtual 'think' action. This action allows LLMs to generate subgoals during the action planning process.

### Evaluation Details of TravelPlanner

Grounding to travel planning, a real-world usecase that inherently involves various constraints like user preferences and commonsense rules, TravelPlanner evaluates whether agents can formulate flexible travel plans using gathered information to meet these constraints. We test EvoAgent and all baselines on the TravelPlanner validation set, which consists of 180 user queries with the collected information. To evaluate the travel plans generated by agents, TravelPlanner adopts the following evaluation metrics:

* Delivery Rate: Assesses if agents can complete a plan within a limited number of steps (30 in our experimental setting). Failures are due to dead loops, numerous failed attempts, or exceeding the step limit.
* Commonsense Constraint Pass Rate: Evaluates if an agent can incorporate commonsense into their plan.
* Hard Constraint Pass Rate: Measures if a plan meets all explicit hard constraints in the query, testing the agent's ability to adapt to diverse user preferences.
* Final Pass Rate: Indicates the proportion of viable plans that meet all criteria, reflecting the agent's proficiency in creating practical plans. Furthermore, TravelPlanner uses micro and macro strategies to assess the Commonsense and Hard Constraint Pass Rates. The micro strategy calculates the ratio of met constraints to the total. The macro strategy measures the proportion of plans that meet all commonsense or hard constraints. Together, these strategies assess an agent's ability to satisfy individual constraints and all constraints comprehensively.

## Appendix B More Analysis of EvoAgent

### Time Efficiency of EvoAgent

We conduct experiments on Logic Grid Puzzle (Logic), Trivia Creative Writing (Writing), and Codenames Collaborative (Code) by using Llama-3.1-70B-Instruct Dubey et al. (2024) with SGLang 8. SGLang is a fast-serving framework that allows us to track the time and computational resources required for model improvements. Specifically, we deploy Llama-3.1-70B-Instruct on an 8-GPU setup using RTX 3090 (bf16). We compare EvoAgent with a single-agent approach and other multi-agent systems. The results are presented in Table 5. Our findings show that, compared to a single-agent system, EvoAgent requires more time but delivers better performance. When compared to multi-agent systems such as AgentVerse and AuGo Agents, EvoAgent achieves similar time costs 

while producing the best performance. These results highlight the effectiveness and versatility of EvoAgent.

### More Analysis of Ablation Studies

The complete results of ablation studies on TravelPlanner are shown in Table 6. This result indicates that the absence of the quality-check module significantly lowers the delivery pass rate when the All-in strategy is applied. To explore the reasons, we revisit the results and discover that sometimes unsuitable agents create overly lengthy travel plans that fail to meet the criteria. For example, the model might erroneously assign a nutritionist to devise travel plans, resulting in excessively detailed meal arrangements and nutritional breakdowns. Therefore, the input length surpasses the context window of LLMs, preventing the final result generation.

Moreover, we also conduct experiments on the Trivia Creative Writing task to investigate the impact of the number of iterations on model performance in traditional NLP tasks. As shown in Figure 7, model performance improves with increasing iterations. However, the improvement plateaus when the iteration count exceeds three. We suggest that traditional NLP tasks are relatively simple, and beyond a certain iteration number, even with a quality-check module in place, the generated agents tend to be similar and thus converge.

## Appendix C Examples of EvoAgent

### Evoagent Examples of NLP reasoning and knowledge tasks

Listing 5, 6 and7 presents some multi-agent generation examples generated by GPT-4 based EvoAgent in Logic Grid Puzzle, Trivia Creative Writing and Codenames Collaborative for a better understanding.

### EvoAgent Examples of MMMU

Listing 8 presents some multi-agent generation examples generated by GPT-4 based EvoAgent in MMMU dataset for a better understanding.

### EvoAgent Examples of ScienceWorld

Listing 9 presents some multi-agent generation examples generated by GPT-4 based EvoAgent in ScienceWorld for a better understanding.

### EvoAgent Examples of TravelPlanner

Listing 10 presents some multi-agent generation examples generated by GPT-4 based EvoAgent in TravelPlanner for a better understanding.

### Examples of EvoAgent's Adaption to Multi-agent Collaboration Frameworks

Previous experiments have demonstrated that our method can automatically extend existing agent frameworks to multi-agent systems, thus greatly improving LLM-based agents in various scenarios. We also attempt to extend our work to real-world multi-agent applications, to verify it can scale up the number of agents in building multi-agent scenarios.

### EvoAgent for MetaGPT

MetaGPT Hong et al. (2024) is a meta-programming framework that enhances LLM-based multi-agent collaborations by integrating efficient human workflows. It employs an assembly line approach to assign diverse roles to agents, effectively simplifying complex tasks into manageable subtasks that multiple agents can execute collaboratively. As shown in Figure 3, we choose the debate scenario used in MetaGPT, which includes two debaters with different opinions, leading to dull and repetitive content generation. Here, instead of manually assigning new roles, we applied EvoAgent to extend each debate team to more

\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Logic** & **Logic Time (s)** & **Writing** & **Writing Time (s)** & **Code** & **Code Time (s)** \\ \hline Direct & 50.00 & 318.00 & 56.40 & 441.00 & 64.32 & 168.00 \\ CoT & 53.00 & 443.40 & 63.20 & 628.80 & 62.74 & 225.00 \\ SPP & 55.50 & 648.00 & 58.00 & 822.00 & 59.26 & 445.20 \\ Self-Refine & 53.00 & 942.00 & 60.80 & 1098.00 & 59.26 & 628.20 \\ AgentVerse & 60.00 & 1056.00 & 70.40 & 1518.00 & 68.90 & 824.40 \\ AutoAgents & 61.50 & 988.80 & 69.60 & 1303.80 & 68.35 & 770.40 \\ \hline EvoAgent & **67.00** & 1060.80 & **74.50** & 1209.60 & **74.62** & 859.20 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time cost of different methods for Llama-3.1-7OB-Instruct.

 

agents with diverse settings, increasing the variety of opinions and the quality of the debate.

### EvoAgent for Camel

Camel (Li et al., 2023a) is recognized for its framework that supports communicative role-playing agents. Initially, humans establish this framework by conceptualizing an idea and designing specific roles, such as the AI assistant role and the AI user role. These roles are then assigned to the assistant and user agents, respectively, enabling them to fulfill the task. As illustrated in Figure 4, EvoAgent can be utilized to automatically produce agents from AI assistants for interaction with AI users, bypassing the need for manual role design.

### EvoAgent for AutoGen

AutoGen Wu et al. (2023) offers a framework that enables the creation of customizable and oversable agents by integrating various LLMs. Initially, humans configure the assistant agents along with a user proxy agent. Then, a group chat manager is responsible for selecting a speaker, gathering responses, and disseminating the message. As depicted in Figure 4, EvoAgent facilitates the creation of multiple expert roles from a single assistant agent, thereby increasing the agent number in group chats without the need for manual design.

\begin{table}
\begin{tabular}{l l c r r r r r} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**w/o Quality Check**} & \multicolumn{2}{c}{**w/ Quality Check**} \\ \cline{5-8}  & & & **Delivery** & **Com. Hard** & **Delivery** & **Com. Hard** \\ \hline \multirow{9}{*}{GPT-3.5} & Direct & - & - & - & 100.0 & 57.3 & 11.0 \\  & & Suggest\({}_{3}\) & - & - & - & 100.0 & 57.5 & 5.7 \\  & & Overgen\({}_{3}\) & - & - & - & 98.3 & 56.3 & 9.0 \\  & & PromptRefine\({}_{3}\) & - & - & - & 100.0 & 61.2 & 11.0 \\ \cline{2-8}  & & EvoAgent\({}_{(1,3)}\) & 100.0 & **64.2** & 11.0 & 100.0 & 64.2 & 11.0 \\  & & EvoAgent\({}_{(1,5)}\) & 100.0 & 61.0 & **12.6** & 100.0 & 61.0 & 12.6 \\ \cline{2-8}  & Random & EvoAgent\({}_{(2,3)}\) & 100.0 & 59.4 & 10.2 & 100.0 & 65.4 & 13.8 \\  & & EvoAgent\({}_{(3,3)}\) & 98.9 & 59.2 & 11.4 & 100.0 & 65.8 & 14.0 \\ \cline{2-8}  & PK & EvoAgent\({}_{(2,3)}\) & 99.4 & 59.4 & 7.1 & 100.0 & **66.0** & 11.7 \\  & & EvoAgent\({}_{(3,3)}\) & 98.9 & 58.5 & 11.2 & 100.0 & 61.3 & 12.4 \\ \cline{2-8}  & All-in & EvoAgent\({}_{(2,3)}\) & 97.2 & 59.4 & 10.0 & 100.0 & 64.2 & **15.5** \\  & & EvoAgent\({}_{(3,3)}\) & 93.3 & 56.0 & 8.3 & 100.0 & 65.2 & 12.6 \\ \hline \multirow{9}{*}{Gemini-Pro} & Direct & - & - & - & 90.0 & 61.7 & 16.4 \\  & Suggests\({}_{3}\) & - & - & - & 100.0 & 65.8 & 11.0 \\  & & Overgen\({}_{3}\) & - & - & - & 100.0 & 66.5 & 12.4 \\  & & PromptRefine\({}_{3}\) & - & - & - & 96.7 & 64.9 & 16.7 \\ \cline{2-8}  & & EvoAgent\({}_{(1,3)}\) & 100.0 & 73.5 & 16.9 & 100.0 & 73.5 & 16.9 \\  & & EvoAgent\({}_{(1,5)}\) & 100.0 & **74.0** & **21.2** & 100.0 & **74.0** & **21.2** \\ \cline{2-8}  & Random & EvoAgent\({}_{(2,3)}\) & 96.7 & 65.9 & 13.1 & 99.4 & 67.3 & 14.0 \\  & & EvoAgent\({}_{(3,3)}\) & 97.2 & 67.0 & 16.0 & 100.0 & 70.0 & 18.1 \\ \cline{2-8}  & PK & EvoAgent\({}_{(2,3)}\) & 97.2 & 67.4 & 19.0 & 99.4 & 69.8 & 17.1 \\  & & EvoAgent\({}_{(3,3)}\) & 97.2 & 68.5 & 17.1 & 99.4 & 68.4 & 16.7 \\ \cline{2-8}  & & EvoAgent\({}_{(3,3)}\) & 95.0 & 65.1 & 16.7 & 99.4 & 69.0 & 19.0 \\ \cline{2-8}  & All-in & EvoAgent\({}_{(2,3)}\) & 95.0 & 66.9 & 17.9 & 100.0 & 70.1 & 20.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different popularity selection strategies for LLMs on TravelPlanner. The best results are **bolded**, and the second best ones are underlined.

\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**w/o Quality Check**} & \multicolumn{2}{c}{**w/ Quality Check**} \\ \cline{3-8}  & & & **Delivery** & **Com. Hard** & **Delivery** & **Com. Hard** \\ \hline \multirow{9}{*}{GPT-3.5} & Direct & - & - & - & 100.0 & 57.3 & 11.0 \\  & & Suggest\({}_{3}\) & - & - & - & 100.0 & 57.5 & 5.7 \\  & & Overgen\({}_{3}\) & - & - & - & 98.3 & 56.3 & 9.0 \\  & & PromptRefine\({}_{3}\) & - & - & - & 100.0 & 61.2 & 11.0 \\ \cline{2-8}  & & EvoAgent\({}_{(1,3)}\) & 100.0 & **64.2** & 11.0 & 100.0 & 64.2 & 11.0 \\  & & EvoAgent\({}_{(1,5)}\) & 100.0 & 61.0 & **12.6** & 100.0 & 61.0 & 12.6 \\ \cline{2-8}  & Random & EvoAgent\({}_{(2,3)}\) & 100.0 & 59.4 & 10.2 & 100.0 & 65.4 & 13.8 \\  & & EvoAgent\({}_{(3,3)}\) & 98.9 & 59.2 & 11.4 & 100.0 & 65.8 & 14.0 \\ \cline{2-8}  & & EvoAgent\({}_{(2,3)}\) & 99.4 & 59.4 & 7.1 & 100.0 & **66.0** & 11.7 \\  & & evOAgent\({}_{(3,3)}\) & 98.9 & 58.5 & 11.2 & 100.0 & 61.3 & 12.4 \\ \cline{2-8}  & & All-in & EvoAgent\({}_{(2,3)}\) & 97.2 & 59.4 & 10.0 & 100.0 & 64.2 & **15.5** \\  & & evOAgent\({}_{(3,3)}\) & 93.3 & 56.0 & 8.3 & 100.0 & 65.2 & 12.6 \\ \hline \multirow{9}{*}{Gemii-Pro} & Direct & - & - & - & 90.0 & 61.7 & 16.4 \\  & & Suggests\({}_{3}\) & - & - & - & 100.0 & 65.8 & 11.0 \\  & & Opergen\({}_{3}\) & - & - & - & 100.0 & 66.5 & 12.4 \\ \cline{2-8}  & & PromptRefine\({}_{3}\) & - & - & - & 96.7 & 64.9 & 16.7 \\ \cline{2-8}  & & EvoAgent\({}_{(1,3)}\) & 100.0 & 73.5 & 16.9 & 100.0 & 73.5 & 16.9 \\ \cline{2-8}  & & EvoAgent\({}_{(1,5)}\) & 100.0 & **74.0** & **21.2** & 100.0 & **74.0** & **21.2** \\ \cline{2-8}  & & EvoAgent\({}_{(2,3)}\) & 96.7 & 65.9 & 13.1 & 99.4 & 67.3 & 1 

* [14] M. C. Bader, A. K. Goyal, and J. R. Lafferty. The role of the action-based action 

Direct Method: {question} Answer:

CoT Method: {question} Youneed to give reasons first and then give the answer. Answer:

Self-Refine Method:

Step One: Feedback Generation: Youare a helpful assistant that provides feedback on {task}

{question} This is theanswer from a student: {answer}.

Please do not refine the answer but gives some insightful suggestions for the student to help him better answer the question. Suggestion:

Step Two: Result Refine: {question} This is your answer: {answer} There is the suggestion from an assistant: Suggestion: {feedback} Now you can refine your answers with his suggestion to better answer the question. Keep in mind that his suggestion may not be correct, so critically decide whether to accept his response or stick with your original one. You need to give reasons first and then give the answer. Revised Answer: 

When faced with a task, begin by identifying the participants who will contribute to solving the task. Then, initiate a multi-round collaboration process until a final solution is reached. The participants will give critical comments and detailed suggestions whenever necessary.

Hereares some examples: --- Example Task 1: Usenumbersandbasicarithmeticoperations(+ - * /) to obtain 24. Youneedtouseallnumbers,andeachnumbercanonlybeusedonce. Input:61211 Participants:AIAssistant(you);MathExpert Startcollaboration! MathExpert:Let'sanalyzethetaskindetail.Youneedtomakesurethatyoumeettherequirement,thatyouneedtouseexactlythefournumbers(61211)toconstruct24.Toreach24,youcanthinkofthecommondivisorsof24suchas4,6,8,3andtrytotconstructthesefirst.Alsoyounedothinkoftpotentialadditions thatcanreach24,suchas12+12. AIAssistant(you):Thanksforthehnits!Here'soneinitialsolution:(12/(1+1))*6=24 MathExpert:Let'scheckthemanswerstepbystep.(1+1)=2,(12/2)=6,6*6=36whichisnot24!Theanswerisnotcorrect.Canyofixthisbyconsideringother combinations?Pleasedonontmakesimilarmistakes. AIAssistant(you):Thanksforpointingouthemistake.Hereisarevisedsolution considering24canalsohereachedbys3*8:(6+1+1)*(12/4)=24. MathExpert:Let'sfirstcheckifthecalculationiscorrect.(6+1+1)=8,12/4=3,8*3=24.Thecalculationiscorrect,butyoused611124whichisnotthesameastheniput61211.Canyouavoidusinganumberhatismotopartotheinput? AIAssistant(you):Youreright,hereisalarevisedsolutionconsidering24canbecacheby12+12andwithoutusinganyadditionalnumbers:6*(1-1)+12=24.MathExpert:Let'scheckthensoveragain.1-1=0,6*0=0,0+12=12.Ibelieveyouareveryclose,hereisalahint:trytocchangethe"1-1"to"1+1".AIAssistant(you):Sure,hereislencorrectedanswer:6*(1+1)+12=24 MathExpert:Let'sverifythesolution.1+1=2,6*2=12,12+12=12.Youused11612whichisidenticaltotheinput61211.Everythinglooksgood! Finishcollaboration! Finalanswer:6*(1+1)+12=24 --- {question}

 

CrossoverandMutation: {question} Thisisyourresult: {answer} Now,yoanccreateandcollaboratewithmultipleexpertstoimproveyourresult. Therefore,pleasedecribeinsamuchdetailaspossiblethedifferentskillsandfocusesyouneedfrommultipleexpertsindividually.Wewillprovideeachexpertwiththemsameinformationandquery.However,pleasenotethateachprofessionhasitsownspecialization,soyoucanassigneachexperttotjustonesub-tasktonsureamorefefinedresponse.Wewillrelaytheirresponsestoyouinturn,allowingyoungtomeroorganizethemintoabetteranser.Pleasenotethatthedescriptionshouldbenarratedinthesecondperson,forexample:YouareaxXX. Thesearethedescriptionsoftheexpertsyouhavecreatedbeforeforthistask: {description} Therefore,youncedotfollowtwoprinciples:1.Crossover:Youneedtocheckwhichskillsshouldbeimprovedinthereviousagentsandthenupdathem.2.Mutation:Youencedtomakenewagentsdistinctfrompreviousagentswhilemaintainingtheristask-solvingcapability. Now,youcangivethedescriptionforanewexpert(Pleanedethatonlybecone,doontgivemultipleaton 

PK: {question} We invite {n} experts. They give the results based on their ownprofessional knowledge: Here are second-person descriptions of these experts with theiranswers: {select} Now you can should help us select the best result which can meet the query. You need to give reasons first and then give the answer with the format: "Final Answer: Expert #XX"

All-in: {question} This is youranswer: {old_answer}. Furthermore, you also invite {n} experts. They alsogive answers based on their own professional knowledge: Here are second person descriptions of these experts with their answers: {description_ls} Now you can refine your answer with these answers to better meet the query. Suggest: {specialized_Agent_description} {question} This is the result from an AI assistant: {answer}. Please do not refine the plan but gives some insightful suggestions for the AI assistant to helpful better meet the user's query. Suggestion: OverGen: {question} Please generate three different results at one time for user to choose from. The format can be: Result #1: Result #2: Result #3: Three Different Candidate Results: PromptRefine: {question} This is theresult from an AI assistant, which description is "{original_description}": {answer}. Please do not refine the result but refinet the description of the AI assistant to help it better answer the user's query. Please note that the description should be narrated in the second person, for example: You area xxx. Description: 

Question: There are 5 houses in a row, numbered 1 on the left to 5 on the right. There is one person living in each house. The people int thesehouses have different characteristics:

- Each person has a different kind of car: oneowns an SUV, one owns asedan, one drives a convertible, onedrives a minivan, and onedrives a truck

- Each person has a favorite color: one likes red, one likes yellow, one likes green , one likes white, and one likes blue

- Each person is wearing different shoes: one is wearing running shoes, one is wearing high heels, one is wearing sandals, and one is wearing loafers

Clue(s):

1. Thereis onehouse between where theperson who is wearing loafers lives and

where the person whodrives a minivan lives. 2. The person whodrives a truck does not live in the fourth house. 3. The person who drives a truck lives directly left

of the person who is wearing sandals. 4. The person who likes redlives somewhere to

the right of the person who likes yellow. 5. The person who is wearing loafers

lives somewhere to the left of the person who drives a truck. 6. The person who likes blue lives in the fifth house. 7. There are two houses between where the person who owns a sedan lives and where the person who is wearing runnings shoes

lives. 8. The person who is wearing boots lives somewhere to the left of the person

who drives a truck. 9. The person who likes white lives in the fourth house. 10. There is one house between where the person who likes green lives and where the

person who drives a truck lives. 11. The person who owns an SUV does not live in the first house.

What is the number of the house where the person who is wearing boosts lives? choice:

2, choice: 5, choice: 3, choice: 1, choice: 4.

EvoAgent:

Specialized Agent #! Description:

Youare a logician, specializing in logical reasoning and problem-solving. Your task

ist oanalyze the given clues and use deductive reasoning to determine the correct sequence of houses, their occupants, and their characteristics. You will need to consider each clue carefully, understanding how it relates to the others and how it narrows down the possibilities. Your focus should be onensuring the logical consistency of the solution, making sure that all cluesares satisfied without contradiction.

Reason: From clue 6 and 9, we know that the person who likes blue lives in the fifth

house and the person who likeswhite lives in the fourth house. From clue 4, the

person who likesred lives somewhere to the right of the person who likes yellow. Since the fourth and fifth houses are occupied by the people who like white and blue

, the person who likes red must live in the third house and the person who likes

yellow must live in the first or second house.From clue 10, there is one house

between where the person who likes green lives and where the person who drives a

truck lives. Since the third, fourth, and fifth houses are occupied by the people

who like red, white, and blue, the person who likes green must live in the first or

second house. Therefore, the person who likes yellow must live in the first house

and the person who likes green must live in the second house.from clue 11, the

person who owns an SUV does not live in the first house. So, the person won's an

SUV must live in the second, third, or fourth house.From clue 2, the person who

drives a truck does not live in the fourth house. So, the person who drives a truck

must live in the second or third house.From clue 3, the person who drives a truck

lives directly left of the person who is wearing sandals. So, the person who drives

a truck must live in the second house and the person who is wearing sandals must

live in the third house.From clue 8, the person who is wearing boots

lives somewhere

to the left of the person who drives a truck. So, the person who is wearing boots

must live in the first house.

Answer: choice: 1 

Write a short and coherent story about Zootopia that incorporates the answers to the following 5 questions:
1. Following success at the 1979 election whose party conference speecech included the lines 'you turn if you wantto, the lady's not for turning'?
2. Which snooker player was simply known as 'The Grinder'?
3. Which former West Indian fast bowler wasknown as 'Whispering Death'?
4. Which Eastenders character had adog called Willy?
5. In which year didthe first episode of Minder take place?

EvoAgent: Specialized Agent #1 Description: You are aports historian with a deep understanding of snooker and cricket from the 1970s and 1980s. Youare familiar with the careers and playing styles of Cliff Thorburn, known as 'TheGrinder', and Michael Holding, known as 'Whispering Death'. Your role in this task will be to provide context and insight intothesports figuresand their significance in thestory about Zootopia.

Specialized Agent #2 Description: You are apolitical historian with a deep understanding of the political climate and key figuresof the 1970s and 1980s, particularly in the United Kingdom. You have extensive knowledge about Margaret Thatcher, her policies, and her impact on society . Your role in this task will be to provide context and insight into the significance of the reference to Thatcher's famous speechin thestoryabout Zootopia.

Story: Inte vibrant city of Zootopia, they year was 1979, a time of significant change and excitement. Thecity had just elected its first female mayor, a lioness named Margaret Thatcher, known for her unwavering determination and strong leadership. Her victory speech at the party conference echoed the famous line from the human world's Margaret Thatcher, "You turn if you want to, the lady's not for turning." This phrase resonated with the citizens of Zootopia, who admired her steadfastness and determination. In the heart of the city, a meticulous badger known as 'The Grinder' ran a popular snooker club. His nickname was a tribute to the legendary human snooker player Cliff Thorburn, known for his relentless and meticulous style of play. The Grinder's snooker club was a hub of social activity, where animals from all walks of life came to unwind and engage in friendly competition. Meanwhile, a new recruit had joined the Zootopia Police Department, a cheetah named Michael Holding. Known as 'Whispering Death' during his cricketing days, a moniker borrowed from the former West Indian fast bowler, hewas now a respected figure in the community, using his speed and stealth to uphold the law. In another part of the city, a popular soap opera was being filmed. Theshow was called "Eastenders of Zootopia," and one of the beloved characters was a rabbit named Ethel Skinner, who had a dog named Willy. This was a charming reference to the human Eastenders character of the same name, symbolizing the enduring friendships that were a cornerstone of Zootopian society. The year 1979 was also significant as it marked the airing of the first episode of the Zootopian version of the humans show "Minder". The show was a reflection of the city's vibrant and diverse culture, much like the original Britishseries. It was a testament to the city's ability to adapt and evolve, while still holding onto its rich history and traditions. Inclusion , Zootopia was a melting pot of cultures and histories, much like the television shows and characters it referenced. These references served as a bridge between the past and the present, reminding the Citizens of their roots while encouraging them to embrace the future.

Evaluation: correct_count=5/5 

Try to identify the 3 words best associated with the word "fruit" from the following word list:

["bowl", "einstein", "banana", "brain", "judge", "room", "crusader", "stamp", "peach", "halloween", "troll", "slipper", "earthquake", "tip", "sun", "quarter", "pad ", "nude", "helmet", "laundry", "wedding", "mirror", "sleep", "minotaur", "wish"].

EvoAgent: Specialized Agent#! Description: You are a cultural anthropologist with afocus on symbolism and rituals. Your expertise in understanding how different objects, including fruits, are used and symbolized in various cultures will be beneficial in this task. You can provide insights into how certain words might be associated with "fruit" from a cultural or symbolic perspective. Your focus will be on the cultural and symbolic aspects of the task.

Reason: 1.'Banana': This is a type of fruit, rich in potassium and dietary fiber. Itis commonly consumed raw and is used in a variety of dishes like desserts, smoothies, and baked goods.
2.'Peach': This is also a type of fruit, known for its sweet and juicy nature. Peaches arerich in vitamins A and C and are often eaten far owed in dishes like pies, jams, and salads.
3.'Bowl': This word is associated with fruit in the context of a fruit bowl, which is a common way to store and display various types of fruit. A bowl of fruit can also be a healthy snack or dessert option. Answer: Banana, Peach, Bowl

Specialized Agent#2 Description: You are a nutritionist with a deep understanding of different types of food, including fruits. Your knowledge about the nutritional value, common uses, and cultural significance of various fruits will be helpful in this task. You can provide insights into how certain words might be associated with "fruit" from a dietary or nutritional perspective. Your focus will be on the nutritional and dietary aspects of the task.

Reason: 1.'Banana': This is a direct association as a banana is a type of fruit. In many cultures, bananas symbolize fertility and prosperity. In Hinduism, for example, bananas are considered a sacred fruit and areused in religious rituals.
2.'Peach': Similar to banana, a peach is also a type of fruit. In Chinese culture, peachesaresymbols of immortality and unity. They are often used in traditional rituals and ceremonies.
3.'Bowl': A bowl is often used to hold fruits. In many cultures, a bowl of fruit is a symbol of abundance, fertility, and prosperity. It is often used in rituals and ceremonies as an offering or acenterpiece. Answer: banana, peach, bowl

FinalAnswer: After considering the expert's input, I find that their reasoning aligns with my initial thoughts. The words 'banana' and 'peak' are indeed types of fruit, and their nutritional value and common uses further strengthen their association with the word 'fruit'. The word 'bowl' is also associated with fruit in the context of a fruit bowl, which is a common way to store and display various types of fruit. Therefore, I agree with the expert's choices. Final Answer: banana, peach, bowl.

 

Question: <image1> The elliptical orbit of a planet around the Sun is shown on the diagram. Which of the following statements is true? A. the eccentricity of the orbit is less than zero B. the eccentricity of the orbit is greater than 1 C. the sun might be atpoint C D. thesun might be at point D E. the sun might be atpoint B (Correct)

CoT: Reason: In an elliptical orbit, the Sun is located at one of the two foci of the ellipse. The eccentricity of an orbit is a measure of how much the orbit deviates from a perfect circle, with @ being a perfect circle and 1 being a parabola. Since the orbits shown is an ellipse, the eccentricity must be between 0 and 1. Points A and E are the farthest points from the center of the ellipse, so the Sun cannot be located at either of those points. Point B is at the center of the ellipse, so the Sun cannot be located there either. This leaves points C and D as possible locations for the Sun. However, since the Sun must be atone of the foci and point D is closer to the center of the ellipse than point C, it is more likely that the Sun is located at point C. Answer: C. the sun might be at point C

EvoAgent: Specialized Agent #1 Description: Youare a mathematician with a specialization ingeometry, particularly in the study of conics sections which include ellipses. Your expertise allows you to understand the properties and definitions of ellipses, including the concept of foci. You can provide mathematical perspective on why the Sun must be located at one of the foci of the elliptical orbit and not at any other point. Your knowledge will help explain the geometric reasons behind the positioning of the Sun in relation to the planet's orbit, and why the given options in the statements do not align with the mathematical definition of an ellipse. Reason: An ellipse is defined as the set of all points for which the sum of the distances to two fixed points, called foci, is constant. The Sun, being one of the massive bodies in the solar system, must be located at one of the foci of the elliptical orbit of a planet due to the gravitational forces that govern the motion of the planets. Option E is correct because the Sun might be at point B, which is one of the foci of the ellipse. The other focus would be at a point symmetrically opposite to B along the major axis. Answer: E. the sun might be at point B.

Specialized Agent #2 Description: Youare anastronomer with a specialization in observational astronomy. Your expertis lies in the practical observation and data collection of celestial bodies, including the use of telescopes and other instruments to study the positions and movements of planets and stars. You have a deep understanding of how the apparent motion of celestial bodies can be used to infer their actual motion in space. You can provide insight into how observations of planetary orbits, such as the one depicted in the diagram, have historically been used to deduce the laws of planetary motion and the positions of stars like the Sun. Your knowledge will help explain how practical observations support the theoretical understanding that the Sun must be located at one of the foci of the elliptical orbit. Reason: The eccentricity of an orbit is a measure of how much it deviates from being a perfect circle. It ranges from @ (a perfect circle) to 1 (a parabola). An eccentricity less than zero or greater than 1 is not possible for an elliptical orbit. Therefore, options A and B are incorrect. According to Kepler's laws of planetary motion, the Sun must be located at one of the foci of the elliptical orbit . Point B is located at aposition that could be one of the foci of the ellipse. Answer: E. the sun might be at point B.

 

Task 1: Your task is to determine if a metal fork is electrically conductive. The metal fork is located around the kitchen. First, focus on themetal fork. If conductive, place it inthe blue box. If nonconductive, place it inthe green box. EvoAgent: Specialized Agent Description: You are at thermodynamics expert. Your expertise in heat transfer and temperature measurement is crucial for this task. You understand how to properly use thermometer and interpret its readings. You also have knowledge about the properties of various substances, including their typicaltemperature ranges. Your task is to guide the assistant in using the thermometer to accurately measure the temperature of the unknown substance B.

Task 2: Your task is to boil gallium. For compounds without a boiling point, combusting the substance is also acceptable. First, focus on the substance. Then, take actions that will cause it to change its state of matter. EvoAgent: Specialized Agent Description: You are a metallurgist with a deep understanding of the properties and behaviors of metals, including gallium. Your expertise includes knowledge of the melting and boiling points of various metals, as well as the processes and equipment needed to heat them to these temperatures. You area also familiar with safety procedures and precautions when handling and heating metals. Your task is to provide detailed instructions on how to safely and effectively boil gallium in the foundry.

Task 3: Your task is to change the state of matter of gallium. First, focus on the substance. Then, take actions that will cause it to change its state of matter. EvoAgent: Specialized Agent Description: You are a chemist with a specialization in the properties and behaviors of substances. You have a deep understanding of the state changes of matter, specifically how substances like gallium can transition from solid to liquid or gas. You are familiar with the conditions required for these changes to occur, such as temperature and pressure. You also have knowledge of the equipment and procedures used in a foundry, which will be useful in this task. Your role is to provide guidance on how to manipulate the conditions in the foundry to achieve the desired state changing gallium.

 

Question: Can you help me with a travel plan departing from Buffalo to Atlanta for a duration of 3 days, specifically from March 2nd to March 4th, 2022? I plant to travel alone and my planned budget fort the trip is around $1,100.

EvoAgent: Specialized Agent #1 Description: You are a culinary expert with a deep understanding of food culture in Atlanta. Your expertise includes knowledge of the best local restaurants, popular dishes, and dietary restrictions. You can provide recommendations for breakFast, lunch, and dinner spots that offer a unique taste of Atlanta's food scene. Your suggestions should consider the traveler's budget and the proximity of the restaurants to the attractions and accommodation listed in the plan.

Specialized Agent #2 Description: You are a travel logistics expert with extensive knowledge of transportation options and costs between Buffalo and Atlanta. Your expertise includes understanding flight schedules, costs, and durations, as well as alternative transportation methods such as self-driving and taxi services. You can provide the most cost-effective and time -efficient transportation options for the traveler, considering their budget and the travel dates provided in the plan.

Specialized Agent #3 Description: You are a local tourism expert with comprehensive knowledge of Atlanta's attractions . Your expertise includes understanding the city's popular tourist spots, historical sites, museums, parks, and unique local experiences. You can provide recommendations for attractions that the traveler can visit during their stay, considering their budget and the proximity of these attractions to the traveler's accommodation and meals spots. Your suggestions should also take into account the operating hours of these attractions to ensure they align with the traveler's schedule.

Answer: Day 1: Current City: from Buffalo to Atlanta Transportation: Flight Number: F3502691, from Buffalo to Atlanta, Departure Time: 18:48, Arrival Time: 20:51 Breakfast: Not applicable asthe flights in the evening. Attraction: Not applicable as theflight is inthe evening. Lunch: Not applicable as the flight is in the evening. Dinner: Saffron, Atlanta ccommodation: Spacious private room close St. Barnabas Hospital, Atlanta Day 2: Current City: Atlanta Transportation: - Breakfast: Adda, Atlanta Attraction: Atlanta Botanical Garden, Atlanta; World of Coca-Cola, Atlanta. Lunch: Baba Au Rhum, Atlanta Dinner: Asian Bistro, Atlanta Accommodation: Spacious private room close St. Barnabas Hospital, Atlanta Day 3: Current City: from Atlanta to Buffalo Transportation: Flight Number: F350@648, from Atlanta to Buffalo, DepartureTime: 21:24, Arrival Time: 23:26 Breakfast: Chef Style, Atlanta Attraction: Georgia Aquarium, Atlanta; Martin Luther King, Jr. National Historical Park, Atlanta. Lunch: Pizza Central, Atlanta Dinner: Daawat-e-Kashmir, Atlanta Accommodation: -.