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Computer Science > Artificial Intelligence

arXiv:2308.00352 (cs)
[Submitted on 1 Aug 2023 (v1), last revised 7 Aug 2023 (this version,
v3)]

Title:MetaGPT: Meta Programming for Multi-Agent Collaborative
Framework

Authors:Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin
Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang
Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu
Download a PDF of the paper titled MetaGPT: Meta Programming for
Multi-Agent Collaborative Framework, by Sirui Hong and 12 other
authors
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    Abstract: Recently, remarkable progress has been made in
    automated task-solving through the use of multi-agent driven by
    large language models (LLMs). However, existing LLM-based
    multi-agent works primarily focus on solving simple dialogue
    tasks, and complex tasks are rarely studied, mainly due to the
    LLM hallucination problem. This type of hallucination becomes
    cascading when naively chaining multiple intelligent agents,
    resulting in a failure to effectively address complex problems.
    Therefore, we introduce MetaGPT, an innovative framework that
    incorporates efficient human workflows as a meta programming
    approach into LLM-based multi-agent collaboration. Specifically,
    MetaGPT encodes Standardized Operating Procedures (SOPs) into
    prompts to enhance structured coordination. Subsequently, it
    mandates modular outputs, empowering agents with domain expertise
    comparable to human professionals, to validate outputs and
    minimize compounded errors. In this way, MetaGPT leverages the
    assembly line paradigm to assign diverse roles to various agents,
    thereby establishing a framework that can effectively and
    cohesively deconstruct complex multi-agent collaborative
    problems. Our experiments on collaborative software engineering
    benchmarks demonstrate that MetaGPT generates more coherent and
    correct solutions compared to existing chat-based multi-agent
    systems. This highlights the potential of integrating human
    domain knowledge into multi-agent systems, thereby creating new
    opportunities to tackle complex real-world challenges. The GitHub
    repository of this project is publicly available on:this https
    URL.

Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as:  arXiv:2308.00352 [cs.AI]
          (or arXiv:2308.00352v3 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2308.00352
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Sirui Hong [view email]
[v1] Tue, 1 Aug 2023 07:49:10 UTC (7,361 KB)
[v2] Wed, 2 Aug 2023 04:11:02 UTC (7,362 KB)
[v3] Mon, 7 Aug 2023 19:20:19 UTC (12,029 KB)
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