Study Finds 68% of 'Multi-Agent' AI Repos Actually Run a Single Model

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A new empirical census of 86 highly-starred GitHub repositories that self-describe as 'multi-agent' systems found that 68.2% are actually single-model or non-agent implementations. The research applied a three-axis taxonomy covering model-instance structure, topology, and judge/critic presence to establish a full-population ground truth. Among the 27 genuinely multi-agent systems identified, orchestrator-worker was the most common architecture, accounting for 48% of cases, while judge or critic agents appeared in just 3.3% of annotated repos. The study also found a reverse gap: 44 repositories used multi-agent frameworks without claiming the label in their descriptions. Published as part of the peer-reviewed journal Silicon Science, the full dataset and a reproducible pipeline are publicly available on GitHub.

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