Council: A Structured Deliberation Protocol Across Diverse AI Models

Council introduces a structured deliberation protocol that allows multiple AI models to engage in thoughtful discussion and reach consensus on complex problems. Rather than relying on a single model's output, this framework orchestrates multiple models—whether running locally or in hybrid setups—to collaborate and reason through problems systematically.

For those running local LLM infrastructure, Council addresses a critical challenge: how to improve inference quality and reliability without scaling to larger models. By combining multiple smaller models through structured deliberation, practitioners can achieve better reasoning and fewer hallucinations while keeping compute requirements manageable. This approach aligns well with local deployment constraints where maximizing quality per compute unit is essential.

The framework is particularly valuable for self-hosted setups where you have full control over which models participate in deliberation. Organizations can run specialized smaller models locally—one for reasoning, one for fact-checking, one for summarization—and use Council to coordinate their outputs, creating an intelligent multi-model system that operates entirely on-premises.

Source: Hacker News · Relevance: 8/10