LLM Council Router

调用 LLM Council 路由 API，根据用户查询从历史评估中检索表现最好的模型，返回相关性、置信度和模型排名；还可串联 OpenRouter 直接调用推荐模型生成回答。运行需要相应 API Key，查询文本会发送到外部服务。

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热度 67.47

最近更新 2026-05-17 12:13:19

分类：搜索依赖：第三方 API / 代码执行 / 内部调用大模型应用场景：推荐 / API 集成 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add llmcouncil-router

Metadata

name: llmcouncil-router
description: Route any prompt to the best-performing LLM using peer-reviewed council rankings from LLM Council
homepage: https://llmcouncil.ai
user-invocable: true
metadata: {"openclaw":{"requires":{"env":["LLMCOUNCIL_API_KEY"]},"emoji":"🧠"}}

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
}

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

{
  "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:

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"}'

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

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