Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization (2404.02183v1)

Abstract: Recent advancements in automatic code generation using LLM agent have brought us closer to the future of automated software development. However, existing single-agent approaches face limitations in generating and improving large-scale, complex codebases due to constraints in context length. To tackle this challenge, we propose Self-Organized multi-Agent framework (SoA), a novel multi-agent framework that enables the scalable and efficient generation and optimization of large-scale code. In SoA, self-organized agents operate independently to generate and modify code components while seamlessly collaborating to construct the overall codebase. A key feature of our framework is the automatic multiplication of agents based on problem complexity, allowing for dynamic scalability. This enables the overall code volume to be increased indefinitely according to the number of agents, while the amount of code managed by each agent remains constant. We evaluate SoA on the HumanEval benchmark and demonstrate that, compared to a single-agent system, each agent in SoA handles significantly less code, yet the overall generated code is substantially greater. Moreover, SoA surpasses the powerful single-agent baseline by 5% in terms of Pass@1 accuracy.

• The paper introduces a multi-agent framework that scales code generation using distributed agents, achieving a 5% improvement in Pass@1 accuracy.
• It employs a hierarchical structure where a Mother agent delegates tasks to Child agents, enabling parallel, efficient handling of complex codebases.
• Experimental results show that the SoA framework outperforms single-agent systems, addressing context length limitations and enhancing automated code optimization.

Self-Organized Agents: A Framework for Large-Scale Code Generation and Optimization

Introduction

The recent progression in LLMs has substantially advanced automated code generation, yet significant limitations remain when dealing with voluminous, complex codebases using single-agent frameworks. This paper proposes the Self-Organized Agent (SoA) framework, a multi-agent approach designed to address these limitations by enabling scalable and efficient code generation and optimization.

In single-agent models, context length constraints introduce challenges in managing large codebases, ultimately impeding the ability to generate and optimize complex code efficiently. The SoA framework distributes tasks across multiple self-organized agents, each independently handling code generation and modification, thus allowing dynamic scalability based on problem complexity.

The authors evaluate the SoA framework using the HumanEval benchmark, showcasing its ability to produce significantly more code than a single-agent system with a 5% improvement in Pass@1 accuracy.

SoA Framework Overview

The SoA framework, illustrated in (Figure 1), addresses the limitations of single-agent systems by employing self-organization principles, which allow agents to operate independently yet coalesce their contributions efficiently.

Figure 1: Comparative overview showing a single-agent system versus the distributed SoA multi-agent framework.

In contrast to a single-agent approach that is overwhelmed by increasing responsibilities as the codebase grows, the SoA paradigm optimally distributes tasks among agents. Each agent manages its portion of the codebase, facilitating efficient code generation and modification, even as complexity escalates. Additionally, agents can autonomously multiply based on the complexity of the problem.

Code Generation and Modification Methodology

The code generation process within SoA is depicted in (Figure 2). Child agents are responsible for implementing specific functions from docstrings provided by a central Mother agent, which coordinates higher-level task delegation.

Figure 2: Code generation coordination between Mother and Child agents within the SoA framework.

In this hierarchical structure, the Mother agent generates a skeleton function and allocates subtasks to newly spawned agents, allowing parallel processing (Figure 3). This methodology enhances scalability by ensuring no single agent bears the burden of the entire codebase.

Figure 3: Detail of state propagation and collaboration for code modification among agents.

Experimentation and Results

The evaluation against competitive models like Reflexion, AlphaCode, and Codex demonstrates SoA's superior scalability and efficiency. As outlined in (Figure 4), SoA achieves a 5% higher Pass@1 accuracy rate compared to the Reflexion agent, emphasizing its robust performance in generating functional and extensive code.

Figure 4: Comparative analysis of code generation volumes between SoA and Reflexion frameworks.

Implications and Future Work

The implications of the SoA framework are significant for advancing automated software development. Its multi-agent strategy could ease the complexity in developing large-scale applications and handling intricate code dependencies.

Looking ahead, further optimization of communication and synergy among agents can enhance SoA's adaptability for even larger and more complex projects. Additionally, integration of even more advanced LLMs could provide further improvements in code quality and development speed.

Conclusion

This paper presents the SoA framework as a robust alternative to single-agent systems for large-scale code generation. By leveraging the collective capability of self-organized agents, the framework overcomes existing scalability barriers, achieving higher performance and efficiency, making it a compelling approach for evolving automated programming tasks.

The potential of SoA lies in its dynamic scalability and efficient task allocation among agents, presenting opportunities for future research to further push the boundaries of automated software development.