Llmcouncil Router

LLM Council Router

Route any prompt to the best-performing LLM. The API finds the top model for a given query based on thousands of peer-reviewed council deliberations — then you call that model directly.

Base URL

https://clawbot.llmcouncil.ai

Authentication

Use the X-API-Key header with your LLM Council API key.

X-API-Key: clwb_YOUR_KEY_HERE

Get a free key at llmcouncil.ai/developers.

Endpoint: POST /v1/route

Find the best-performing model for a query.

Request

{
  "query": "Explain quantum entanglement simply",
  "k": 20
}

Response

{
  "query": "Explain quantum entanglement simply",
  "nearest_councils": 20,
  "model": "anthropic/claude-sonnet-4",
  "relevance": 0.8234,
  "confidence": 0.65,
  "model_rankings": [
    { "rank": 1, "model": "anthropic/claude-sonnet-4", "nearby_wins": 13, "nearby_appearances": 20 },
    { "rank": 2, "model": "openai/gpt-4.1", "nearby_wins": 5, "nearby_appearances": 18 }
  ]
}

How to use this skill

When the user asks you to find the best model for a task, or when you need to decide which LLM to use:

1. Call the routing API with the user's query:

curl -X POST https://clawbot.llmcouncil.ai/v1/route \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $LLMCOUNCIL_API_KEY" \
  -d '{"query": "USER_QUERY_HERE"}'

2. Read the response — the model field is the best-performing model for that query type.

3. Chain with OpenRouter — model IDs match the OpenRouter catalogue directly, no mapping needed:

import requests, os

# Step 1: Get the best model from LLM Council
route = requests.post(
    "https://clawbot.llmcouncil.ai/v1/route",
    headers={"X-API-Key": os.environ["LLMCOUNCIL_API_KEY"]},
    json={"query": "Write a Python web scraper"},
).json()

best_model = route["model"]       # e.g. "anthropic/claude-sonnet-4"
confidence = route["confidence"]   # e.g. 0.85

# Step 2: Call that model via OpenRouter
answer = requests.post(
    "https://openrouter.ai/api/v1/chat/completions",
    headers={"Authorization": f"Bearer {os.environ['OPENROUTER_API_KEY']}"},
    json={
        "model": best_model,
        "messages": [{"role": "user", "content": "Write a Python web scraper"}],
    },
).json()

print(answer["choices"][0]["message"]["content"])

Rate Limits

When to use this

• User asks "which model is best for X?"
• You need to pick the optimal model for a specific task type
• You want data-driven model selection instead of guessing
• You want to chain model routing with OpenRouter for automatic best-model dispatch