Llm Council

Three phases: models answer independently, rank each other's anonymized responses, then a chairman synthesizes everything into a final answer. Works with OpenAI, Anthropic, Fireworks, OpenRouter, or any OpenAI-compatible endpoint you configure via env vars. The --wiki flag persists the full transcript as a searchable page, which is genuinely useful for architecture decisions you'll want to reference later. Cost scales quadratically with council size since every model ranks every other response, so stick to 3-5 models unless you have budget to burn. Best for high-stakes plan reviews or when you legitimately want multiple AI perspectives on record, not just a single model's output.