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MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework explores agents organized around workflows and sops.

Sirui Hong12 min readResearch
FOUNDATIONALAI AgentsAdvanced

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Abstract

Agents organized around workflows and SOPs

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Introduction

Foundational work on LLM-based agents and Agents organized around workflows and SOPs.

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Problem

Addresses how language models can extend beyond single-turn generation through Agents organized around workflows and SOPs.

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Methodology

The paper operationalizes Agents organized around workflows and SOPs through a structured agent mechanism.

Figure notes

Visual evidence

Figure 1
Figure 1

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Architecture

The system connects an LLM with state, control, tools, memory, environments, interfaces, or other agents.

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Dataset

Uses task-specific datasets, environments, benchmarks, or interaction traces appropriate to the research question.

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Training

Uses the training or inference adaptation documented by the primary source; undisclosed details are not inferred.

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Experiments

Evaluates the proposed mechanism against relevant baselines in the target setting.

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Baselines

Compares against the underlying model, simpler control strategies, or prior agent mechanisms.

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Results

Reports evidence for the proposed contribution within its stated benchmark or environment.

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Conclusion

Contributes a reusable pattern for building or evaluating LLM-based agents.

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References

Primary source: https://arxiv.org/abs/2308.00352

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