LLM Council (Fireworks AI)

When to Use

Use when this workflow matches the user request: Use this skill for its documented workflow.

_Source: dair-ai/dair-academy-plugins (MIT)._

This skill implements Karpathy's LLM Council concept where multiple open-weight LLMs deliberate on a query, powered entirely by Fireworks AI:

1. Phase 1: All models respond to the query independently (parallel)
2. Phase 2: Models rank each other's anonymized responses
3. Phase 3: A Chairman LLM synthesizes the final answer

All inference runs through Fireworks AI using open-weight models. The speed and pricing of Fireworks makes it practical to run multi-model deliberation that would be slow or expensive on other providers.

CRITICAL RULES

1. ALWAYS use AskUserQuestion to let the user select council models (multiselect) and the Chairman model
2. ALWAYS save raw responses to files - never summarize or truncate API outputs
3. ALWAYS show full transparency - display all individual responses, all rankings, AND the final synthesis
4. NEVER skip the ranking phase - it is essential to the council deliberation process
5. Read from files for display - ensures content is shown unmodified
6. ALWAYS display the final output to the user after Phase 3 completes

Pre-flight Check

Before running any phase, verify the Fireworks API key is set:

if [ -z "$FIREWORKS_API_KEY" ]; then
  echo "ERROR: FIREWORKS_API_KEY is not set."
  echo "Create a Fireworks AI account at: https://fireworks.ai/"
  echo "Then export it in your shell profile (~/.zshrc or ~/.bashrc):"
  echo '  export FIREWORKS_API_KEY="your_api_key_here"'
  exit 1
fi
echo "FIREWORKS_API_KEY is set."

Available Models

Present these options to the user via AskUserQuestion (multiselect):

Workflow

Step 1: Gather User Input

Use AskUserQuestion to get:

1. The query/question for the council (or accept it from the conversation)
2. Which models to include (multiselect, recommend 3-5 models)
3. Which model should be the Chairman (single select)

Note: AskUserQuestion supports max 4 options per question. Since there are 7 models, split model selection across two questions, or show the most popular 4 and let the user type "Other" for the rest. A good default is to show 4 models in the first question and note the others are available via "Other". Rotate which models are shown based on variety.

Example AskUserQuestion for model selection (show 4, mention others):

question: "Which models should participate in the LLM Council? (Also available via Other: Llama 4 Maverick, Qwen3 235B, GLM 5)"
header: "Models"
multiSelect: true
options:
  - label: "DeepSeek V3.2"
    description: "DeepSeek's newest and most capable model"
  - label: "MiniMax M2.1"
    description: "MiniMax's strong open-weight model"
  - label: "Kimi K2.5"
    description: "Moonshot's strong open-weight model"
  - label: "DeepSeek V3.1"
    description: "DeepSeek's proven reasoning model"

Example AskUserQuestion for chairman:

question: "Which model should be the Chairman (synthesizes the final answer)?"
header: "Chairman"
multiSelect: false
options:
  - label: "DeepSeek V3.2 (Recommended)"
    description: "Newest DeepSeek, strong at comprehensive analysis"
  - label: "GLM 5"
    description: "Strong reasoning for synthesis"
  - label: "Kimi K2.5"
    description: "Strong at structured synthesis"
  - label: "MiniMax M2.1"
    description: "Strong open-weight model for synthesis"

Model Name to ID Mapping

Use this mapping to convert user selections to Fireworks model IDs:

MODEL_MAP = {
    "GLM 5": "accounts/fireworks/models/glm-5",
    "DeepSeek V3.1": "accounts/fireworks/models/deepseek-v3p1",
    "DeepSeek V3.2": "accounts/fireworks/models/deepseek-v3p2",
    "MiniMax M2.1": "accounts/fireworks/models/minimax-m2p1",
    "Kimi K2.5": "accounts/fireworks/models/kimi-k2p5",
    "Qwen3 235B": "accounts/fireworks/models/qwen3-235b-a22b",
    "Llama 4 Maverick": "accounts/fireworks/models/llama4-maverick-instruct-basic",
}

Step 2: Run Phase 1 - Individual Responses

After gathering input, run this script to get responses from all selected models in parallel:

QUERY="USER_QUERY_HERE"
MODELS='["accounts/fireworks/models/glm-5", "accounts/fireworks/models/deepseek-v3p1"]'

python3 << 'PYEOF'
import os
import json
import req