- name
- model-council
- description
- Multi-model consensus system — send a query to 3+ different LLMs via OpenRouter simultaneously, then a judge model evaluates all responses and produces a winner, reasoning, and synthesized best answer. Like having a board of AI advisors. Use for important decisions, code review, research verification.
- homepage
- https://www.agxntsix.ai
- license
- MIT
- compatibility
- Python 3.10+, OpenRouter API key
- metadata
- {"openclaw": {"emoji": "\�\�\️", "requires": {"env": ["OPENROUTER_API_KEY"]}, "primaryEnv": "OPENROUTER_API_KEY", "homepage": "https://www.agxntsix.ai"}}
Model Council 🏛️
Get consensus from multiple AI models on any question.
Send your query to 3+ different LLMs simultaneously via OpenRouter. A judge model evaluates all responses and produces a winner, reasoning, and synthesized best answer.
When to Use
- Important decisions — Don't trust one model's opinion
- Code review — Get multiple perspectives on architecture choices
- Research verification — Cross-check facts across models
- Creative work — Compare writing styles and pick the best
- Debugging — When one model is stuck, others might see the issue
How It Works
Your Question
├──→ Claude Sonnet 4 ──→ Response A
├──→ GPT-4o ──→ Response B
└──→ Gemini 2.0 Flash ──→ Response C
│
Judge (Opus) evaluates all
│
├── Winner + Reasoning
├── Synthesized Best Answer
└── Cost BreakdownQuick Start
# Basic usage
python3 {baseDir}/scripts/model_council.py "What's the best database for a real-time analytics dashboard?"
# Custom models
python3 {baseDir}/scripts/model_council.py --models "anthropic/claude-sonnet-4,openai/gpt-4o,google/gemini-2.5-pro" "Your question"
# Custom judge
python3 {baseDir}/scripts/model_council.py --judge "openai/gpt-4o" "Your question"
# JSON output
python3 {baseDir}/scripts/model_council.py --json "Your question"
# Set max tokens per response
python3 {baseDir}/scripts/model_council.py --max-tokens 2000 "Your question"Configuration
| Flag | Default | Description |
|---|---|---|
--models | claude-sonnet-4, gpt-4o, gemini-2.0-flash | Comma-separated model list |
--judge | anthropic/claude-opus-4-6 | Judge model |
--max-tokens | 1024 | Max tokens per council member |
--json | false | Output as JSON |
--timeout | 60 | Timeout per model (seconds) |
Environment
Requires OPENROUTER_API_KEY environment variable.
Output Example
═══ MODEL COUNCIL RESULTS ═══
Question: What's the best way to handle auth in a microservices architecture?
── Council Member Responses ──
🤖 anthropic/claude-sonnet-4 ($0.0043)
Use a centralized auth service with JWT tokens...
🤖 openai/gpt-4o ($0.0038)
Implement OAuth 2.0 with an API gateway...
🤖 google/gemini-2.0-flash-001 ($0.0012)
Consider using service mesh with mTLS...
── Judge Verdict (anthropic/claude-opus-4-6, $0.0125) ──
🏆 Winner: anthropic/claude-sonnet-4
Reasoning: Most comprehensive and practical approach...
📝 Synthesized Answer:
The best approach combines elements from all three...
💰 Total Cost: $0.0218Credits
Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.
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