council-of-llms

name	council-of-llms
emoji	🏛️
description	Multi-model deliberation for high-stakes decisions. Don't take one model's word for it.
details	**Council of LLMs** orchestrates structured multi-model debate — routing a single question to multiple LLMs simultaneously, collecting their answers, and surfacing agreements/disagreements. ## Best For - Security audits - Architecture decisions - Policy analysis - LLM output evaluation ## Pre-requisites - OpenClaw with 2+ LLM providers configured - Recommended: Ollama Cloud for parallel execution ## Quick Start ```bash # Run with demo question council # Run with your question council "Should we use JWT or session cookies?" # Interactive model selection council --select-models "Architecture decision" ``` ## Usage ### Model Selection ```bash # List available models council --list-models # Explicit model list council "Security audit" --models "ollama/kimi-k2.5,openai/gpt-4o" # Use preset council "Code review" --preset security ``` ### Configuration ```bash # Sequential mode (limited hardware) council "Question" --sequential # Extended timeout council "Question" --timeout 180 # Export results council "Question" --output report.md ``` ## Safeguards - Timeout per model: 120s (configurable) - Cost cap: 50K tokens - Max rounds: 2 - Model diversity required - Rate limiting ## When NOT to Use - Quick factual lookups - Real-time applications - Cost-sensitive products - Tasks requiring consistent answers
install	["npm install -g clawhub","clawhub install wahajahmed010/council-of-llms"]

Council of LLMs

Multi-model deliberation for high-stakes decisions. Don't take one model's word for it.

Version: 1.0.0
License: MIT
Author: Wahaj Ahmed

Overview

The Council of LLMs orchestrates structured multi-model debate — routing a single question to multiple LLMs simultaneously, collecting their answers, and surfacing agreements/disagreements. Built for decisions where being wrong costs more than the overhead of multiple perspectives.

Best for: Security audits, architecture decisions, policy analysis, LLM output evaluation
Not for: Quick lookups, casual chat, first drafts

Pre-requisites

• OpenClaw with multiple LLM providers configured (required)

  • Verify: openclaw status shows 2+ providers
  • Examples: ollama/kimi-k2.5, openai/gpt-4o, anthropic/claude-3-opus

• Multi-model access (recommended)

  • Local Ollama: Can run 1 model at a time (sequential mode)
  • Recommended: Ollama Cloud — parallel multi-model execution
    • Sign up: https://ollama.com/cloud
    • Configure: openclaw config set ollama.cloud.token=YOUR_TOKEN

Installation

Via ClawHub (Recommended)

clawhub install wahajahmed010/council-of-llms

Manual

cd ~/.openclaw/skills
git clone https://github.com/wahajahmed010/council-of-llms.git

Usage

Quick Start (Zero Config)

# Run with built-in sample question
council

# Run with your own question
council "Should we use JWT or session cookies for auth?"

# Security audit example
council --review "Analyze this Python function for security issues" --input ./auth.py

Model Selection

# List available models
council --list-models

# Interactive model selection
council "Architecture decision" --select-models

# Explicit model list
council "Security audit" --models "ollama/kimi-k2.5,openai/gpt-4o,anthropic/claude-3-opus"

# Use specific council preset
council "Code review" --preset security

Configuration

# Sequential mode (for limited hardware)
council "Question" --sequential

# Extended timeout for complex analysis
council "Question" --timeout 180

# Export results
council "Question" --output report.md

How It Works

Architecture

User Question
      ↓
[Pre-flight Check] → Verify 2+ models available
      ↓
[Agent Spawning] → Spawn 2-3 agents with different models
      ↓
[Round 1: Opening] → Each agent provides initial analysis
      ↓
[Round 2: Rebuttal] → Agents respond to each other's points
      ↓
[Synthesis] → Compare positions, find agreements/disagreements
      ↓
[Report] → Structured output with verdict

Fallback Mode

If sessions_spawn is unavailable, the skill automatically switches to single-prompt multi-persona simulation — all "agents" represented as sections in one prompt. Slightly less authentic but works everywhere.

Output Format

# Council Report: [Question]

## Participants
- Strategist (ollama/kimi-k2.5)
- Security Expert (openai/gpt-4o)
- Pragmatist (anthropic/claude-3-opus)

## Individual Positions

### Strategist
**Stance:** JWT with short expiry
**Key Points:**
- Stateless authentication scales horizontally
- Reduces database lookups
- Industry standard for microservices

### Security Expert
**Stance:** Session cookies with httpOnly
**Key Points:**
- XSS protection via httpOnly flag
- Easier revocation on compromise
- No token storage complexity

### Pragmatist
**Stance:** Hybrid approach
**Key Points:**
- Sessions for web, JWT for API
- Best of both worlds
- Implementation overhead worth it

## Agreement Matrix

| Point | Strategist | Security | Pragmatist |
|-------|------------|----------|------------|
| Stateless scaling | ✅ | ⚠️ | ✅ |
| XSS protection | ⚠️ | ✅ | ✅ |
| Revocation ease | ⚠️ | ✅ | ✅ |
| Implementation | ✅ | ✅ | ⚠️ |

## Key Disagreements

1. **Security vs Scalability**: Security Expert prioritizes safety over performance
2. **Complexity**: Strategist sees JWT as simpler; Security Expert sees sessions as simpler

## Synthesis

**Consensus:** Hybrid approach recommended for most teams
**Dissent:** Security Expert maintains pure sessions for high-security contexts
**Confidence:** Medium (genuine disagreement on trade-offs)

## Recommendation

Start with session cookies. Migrate to JWT only if:
- Horizontal scaling becomes bottleneck
- Stateless requirement is critical
- Team has JWT expertise

---
*Generated by Council of LLMs v1.0.0*
*Models: kimik2.5, gpt-4o, claude-3-opus*
*Time: 45s | Tokens: 12,847*

Safeguards

The skill includes automatic protections:

Safeguard	Default	Description
Timeout per model	120s	Kills slow models, proceeds with others
Cost cap	50K tokens	Hard stop if projection exceeds limit
Max rounds	2	Prevents infinite deliberation
Model diversity	Required	Rejects if all models same provider
Rate limiting	10/min	Prevents accidental spam
Partial failure	Continue	Works even if 1 model fails
Context budget	70% window	Fails fast before overflow
User opt-in	Required	Shows cost estimate before run

Configuration

~/.openclaw/council-config.json:

{
  "default_models": [
    "ollama/kimi-k2.5",
    "openai/gpt-4o",
    "anthropic/claude-3-opus"
  ],
  "timeout": 120,
  "max_tokens_per_model": 8192,
  "cost_warning_threshold": 25000,
  "sequential_fallback": true,
  "output_format": "markdown",
  "presets": {
    "security": {
      "models": ["openai/gpt-4o", "anthropic/claude-3-opus"],
      "system_prompt": "security-expert"
    },
    "architecture": {
      "models": ["ollama/kimi-k2.5", "anthropic/claude-3-opus"],
      "system_prompt": "systems-architect"
    }
  }
}

Limitations

• Speed: 2-3x slower than single model (parallel helps)
• Cost: Multiplies by number of models
• Not for: Simple facts, casual chat, first drafts
• Diversity is limited: Most models share training data biases

When NOT to Use

• Simple factual queries (weather, definitions)
• Real-time applications (chat, support bots) where latency matters
• Cost-sensitive products with limited API budgets
• Tasks requiring authoritative, consistent answers (legal, medical — a council of conflicting advice is dangerous)

License

MIT © 2026 Wahaj Ahmed