M ega A gent: A Large-Scale Autonomous LLM -based Multi-Agent System Without Predefined SOP s (2025)

Abstract: LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent , a large-scale autonomous LLM-based multi-agent system. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication, and comprehensive system monitoring of agents. In evaluations, MegaAgent demonstrates exceptional performance, successfully developing a Gobang game within 800 seconds and scaling up to 590 agents in a national policy simulation to generate multi-domain policies. It significantly outperforms existing systems, such as MetaGPT, in both task completion efficiency and scalability. By eliminating the need for predefined SOPs, MegaAgent demonstrates exceptional scalability and autonomy, setting a foundation for advancing true autonomy in MAS.

## introduction the remarkable planning and cognitive capabilities of large language models (llms) @xcite have spurred significant interest in llm-based multi-agent systems (mas) @xcite @xcite , which coordinate multiple llm agents to address complex tasks. for example, metagpt introduces a metaprogramming framework to simulate the software development process @xcite , while simulacra @xcite models social interactions among 25 llm-powered agents in a simulated town, showcasing the potential of these systems to replicate real-world dynamics. the demand for large-scale social simulation applications, such as social media and war simulations @xcite @xcite , is driving the development of mas capable of simulating complex real-world scenarios. however, existing mas have two limitations. (1) they fail to achieve adaptive task coordination when the task is big and complex e.g. generating hundreds of agents for a social simulation; and do not consider the coordination between a large scale of agents. (2) most systems heavily depend on user-defined configurations, including predefined agent roles, standard operating procedures (sops), and static communication graphs @xcite @xcite . this approach limits flexibility and requires significant human effort when deploying numerous agents to complete a task. addressing the above limitations presents the following key challenges: (1) facilitating adaptive and effective communication among agents and with external file systems. as tasks grow in complexity and scale, managing communication becomes increasingly difficult, especially when incorporating parallelism and coordinating multiple agents across different rounds of communication @xcite . (2) ensuring that each agent completes its task accurately without relying on predefined sops. llm agents often generate hallucinated outputs @xcite or fail to complete tasks correctly within a single round @xcite , necessitating robust mechanisms to ensure reliability and correctness. this is particularly critical in multi-agent systems, where hallucinations can propagate and compromise the entire system's performance @xcite . drawing inspiration from operating systems (os), where processes and threads efficiently manage tasks through: (1) generating multiple threads within a process to complete a task, (2) enabling different processes to operate in parallel, and (3) operating a producerconsumer-based message queue, we propose megaagent to address the aforementioned limitations. megaagent decomposes large tasks into multiple hierarchical subtasks (analogous to processes), with each subtask completed by a dedicated group of agents (similar to threads). communication occurs either within agent groups or between them as needed, resembling interprocess communication in an os. users simply need to provide a meta prompt to boss agent, after which the task is autonomously completed. the novelty comparison between megaagent and popular baselines is in ## experiments we evaluate megaagent's capabilities through a series of experiments. while we first establish its performance on standard benchmarks, including reasoning and math problems ( §subsection 3.1), our primary focus is on two scenarios demanding extensive multi-agent coordination: software development and social simulation. these latter tasks are chosen because they provide a more realistic representation of coordination challenges in human societies, often exceeding the capacity of a single llm agent @xcite . we focus on the following three research questions: rq1: can megaagent perform well on standard benchmarks compared with other baselines? ( §subsection 3.1) rq2: can megaagent complete a task requiring extensive coordination without a predefined sop? how do other baselines compare? ( §subsection 3.2) rq3: can megaagent be effectively scaled to handle more complex tasks that involve a significantly larger number of agents, showcasing its scalability? how does it compare to other baselines?( §subsection 3.3) ## related work we discuss the most related work here and leave more details in appendix c. ## conclusion we present megaagent, a large-scale autonomous llmbased multi-agent system enabling dynamic and autonomous coordination, where users only need to provide a meta prompt at the start of the process. megaagent demonstrates strong foundational capabilities by outperforming existing baselines on standard benchmarks. furthermore, through a gobang game software development experiment, we demonstrate megaagent's superior autonomy and coordination compared to baseline models. our social simulation on national policy generation highlights megaagent's scalability to hundreds of agents while ensuring effective coordination. with its hierarchical and adaptive design, megaagent has the potential to serve as the foundational os for future mas. we encourage the research community to further explore enhancing agent coordination to address the increasing demands of large-scale mas.