MetaGPT: Meta Programming for Multi-Agent Collaborative Framework

By Sirui Hong, Xiawu Zheng, Jonathan P. Chen, Yuheng Cheng, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Z. Lin, Liyang Zhou, Chenyu Ran and 2 more. arXiv.org, 2023

2,148 citations recorded by the source corpus.

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:https://github.com/geekan/MetaGPT.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/703035b483c181953de1b55b5fd59cd4cd4cf211

How researchers see this paper

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In Artificial Intelligence

Citations in the corpus

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (2023, neutral): “For instance, when MetaGPT is tasked with solving a math problem, it begins developing software to address the problem, but most of the time, it does not actually solve the problem.”
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, neutral): “Overall, we can flexibly integrate these four parts in the form of natural language text.”
Why Do Multi-Agent LLM Systems Fail? (2025, positive): “To enable scalable automated evaluation, we introduce an LLM-as-a-judge pipeline (Zheng et al., 2023) using Ope-nAI’s o1.”
Large Language Model based Multi-Agents: A Survey of Progress and Challenges (2024, methodology): “For example, these environments can be like software development [Hong et al. , 2023], gaming [Mao et al. , 2023], and various other domains such as financial markets [Li et al. , 2023g] or even social behavior modeling [Park et al. , 2023].”
A Comprehensive Overview of Large Language Models (2023, positive): “Less is more for alignment (LIMA) [166] uses only 1000 carefully created demonstrations to fine-tune the model and has achieved comparable performance to GPT-4.”
A Survey on Large Language Models for Code Generation (2024, neutral): “Finally, for the model’s good and reliable performance, the paper found the following critical components: a high-quality competitive programming dataset, efficient transformer models, and large-scale sampling.”
Improving Factuality and Reasoning in Language Models through Multiagent Debate (2023, positive): “Largely concurrent to the work in this paper, a body of recent work has explored how multiagent interaction can be used with large language models (Zhang et al., 2023; Li et al., 2024; Hong et al., 2023).”
Qwen Technical Report (2023, background): “They can also function as a generalist agent (Reed et al., 2022; Bai et al., 2022a; Wang et al., 2023a; AutoGPT, 2023; Hong et al., 2023), collaborating with external systems, tools, and models to achieve the objectives set by humans.”
Executable Code Actions Elicit Better LLM Agents (2024, neutral): “…LLM evaluation tasks to assess general capability: MMLU (Hendrycks et al., 2020) for knowledge-based QA, HumanEval (Chen et al., 2021) for single-turn code-generation, GSM8K (Cobbe et al., 2021) for single-turn tool-free math reasoning, and MTBench (Zheng et al., 2023) for instruction-following.”
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024, positive): “To further reduce evaluation costs, Sun et al. (2023); Xu et al. (2023a) utilize the automatic evaluation framework based on GPT-4 proposed by Vicuna for evaluation.”
Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception (2024, methodology): “LLM-based agents Li et al. [2023], Liu et al. [2023a,b,c], Shen et al. [2023], Wu et al. [2023], Yang et al. [2023a], Shen et al. [2024], Yang et al. [2023b], Hong et al. [2023], Yang et al. [2023c], utilizing a variety of tools, have demonstrated strong capabilities in task planning and reasoning.”
Empowering Biomedical Discovery with AI Agents (2024, neutral): “An instance of this is Generative agents [62], which create interactive environments where agents mimic human behavior and interact using natural language.”
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024, positive): “As seen in the table, the GPT series significantly outperforms the LLaMA series in parameter identification, and gpt-4-0125 performs the best.”
OS-ATLAS: A Foundation Action Model for Generalist GUI Agents (2024, background): “Autonomous agents powered by LLMs, known as language agents (Weng, 2023; Sumers et al., 2023), have recently garnered significant attention due to their interactive capabilities (Wang et al., 2023; Sun et al., 2023; Hong et al., 2024a; Durante et al., 2024).”
OpenHands: An Open Platform for AI Software Developers as Generalist Agents (2024, neutral): “There are numerous recent efforts in creating open-source frameworks that facilitate the development of agents (Hong et al., 2023; Chen et al., 2024; Wu et al., 2023).”
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework (2023, background): “• MetaGPT: MetaGPT [20] is a multi-agent framework for assigning different roles to GPTs to form a collaborative software entity for complex tasks.”
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025, positive): “This emerging class of systems extends the capabilities of traditional AI Agents by enabling multiple intelligent entities to collaboratively pursue goals through structured communication [144–146], shared memory [147,148], and dynamic role assignment [22].”