The LLM Council
$ council run drafter --mode arch "Design a mass hallucination prevention system"
╔══════════════════════════════════════════════════════════╗
║ ⚖️ THE LLM COUNCIL CONVENES ⚖️ ║
╚══════════════════════════════════════════════════════════╝
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ ┌───────────┐ │ │ ┌───────────┐ │ │ ┌───────────┐ │
│ │ ╭───────╮ │ │ │ │ ╭───────╮ │ │ │ │ ╭───────╮ │ │
│ │ │GPT5.4│ │ │ │ │ │ CLAUDE│ │ │ │ │ │GEMINI │ │ │
│ │ ╰───────╯ │ │ │ │ ╰───────╯ │ │ │ │ ╰───────╯ │ │
│ │ ◉ ◉ │ │ │ │ ◉ ◉ │ │ │ │ ◉ ◉ │ │
│ │ ⌣ │ │ │ │ ▽ │ │ │ │ ○ │ │
│ └───────────┘ │ │ └───────────┘ │ │ └───────────┘ │
│ JUDGE #1 │ │ JUDGE #2 │ │ JUDGE #3 │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
│ "I propose we use │ "Actually, I must │ "Interesting, but
│ a vector database..." │ respectfully disagree" │ what about...?"
│ │ │
└────────────────────────┼────────────────────────┘
▼
┌──────────────────────────────┐
│ 🔥 ADVERSARIAL DEBATE 🔥 │
│ │
│ GPT5.4: "Your approach has │
│ a cold start issue" │
│ │
│ CLAUDE: "Fair, but yours │
│ doesn't scale" │
│ │
│ GEMINI: "Both valid. What │
│ if we combine..." │
└──────────────┬───────────────┘
▼
┌──────────────────────────────┐
│ ✅ VERDICT REACHED ✅ │
│ │
│ Synthesized best ideas │
│ Schema-validated output │
│ Confidence: 94% │
└──────────────────────────────┘
[Council] Task completed in 45.2s | 3 judges | 2 debate rounds | Cost: $0.12
A multi-model orchestration package that runs adversarial council workflows across OpenAI, Anthropic, Google, Vertex, OpenRouter, and local CLIs.
This is not a Claude-only framework. Claude Code is one supported client and one supported provider path among several.
This release also includes a mode-aware execution path with runtime profiles,
routed handoff, capability planning, and deterministic eval tooling. Those
capabilities materially extend the package runtime and make the public surface
more explicit for planning, review, security, and research workflows.
Why Use a Council?
Single-model outputs have blind spots. By running multiple models in parallel and having them critique each other, the council:
- Catches errors that any single model might miss
- Reduces hallucination through cross-validation
- Produces higher-quality outputs via adversarial refinement
- Validates structure with JSON schema enforcement and retry logic
Features