> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mutual-dissent.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# Introduction
> Cross-vendor multi-model debate and consensus engine for AI response distillation
Mutual Dissent sends a query to multiple AI models simultaneously, shares competing responses back to each model for reflection and critique, then synthesizes a final answer through a user-selected model. Works with cross-vendor panels (Claude, GPT, Gemini, Grok) or single-model multi-agent panels where context isolation drives independent evaluation.
## Why Multi-Model Debate?
Single-vendor multi-agent systems share training data, safety postures, and blind spots. Cross-vendor debate surfaces disagreements that correlated architectures can't. Mutual Dissent supports both approaches: cross-vendor panels for maximum diversity, or single-model panels (e.g., `--panel claude,claude,claude`) where each agent evaluates independently through context isolation.
## The Debate Loop
1. **Fan out** — the query goes to all panel agents in parallel
2. **Reflect** — each agent sees the others' responses and argues back (1–3 rounds)
3. **Synthesize** — a designated model distills the full debate into a final answer
4. **Log** — the complete transcript is saved as structured JSON with cost and token data
## Transcript Logging
Every debate produces a structured JSON transcript with cost, token, latency, and routing data. Browse and export transcripts via the web dashboard or CLI. The `--ground-truth` flag scores synthesis against a reference answer using a judge model.
## Get Started
Install and run your first debate in under 5 minutes
Panels, rounds, synthesis, and transcripts explained
Full command and flag reference
API keys, routing, and defaults