LLM Council Router

Route any prompt to the best-performing LLM using peer-reviewed council rankings from LLM Council

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

text

https://clawbot.llmcouncil.ai

Authentication

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

text

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

json

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

Field	Type	Required	Description
query	string	Yes	The prompt or question to route
k	integer	No	Number of past evaluations to consider (default: 20)

Response

json

{
  "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 }
  ]
}

Field	Type	Description
model	string	Top recommended model ID (matches OpenRouter catalogue)
relevance	float (0–1)	How closely matched evaluations relate to your query. Above 0.75 is strong.
confidence	float (0–1)	How decisively the top model outperforms alternatives. Higher = clearer winner.
nearest_councils	integer	Number of relevant past evaluations used
model_rankings	array	All models ranked by performance across matched evaluations

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:

bash

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:

python

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

Tier	Daily Limit	Attribution
Free	100 requests/day	Required
Pro	10,000 requests/day	None

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