Install
openclaw skills install model-router-waaiModel Router - Parallel multi-LLM invocation with result merging. Use when: need better answers, compare model outputs, or get best result from multiple LLMs. / 并行调用多模型 - 并行调用多个大模型,结果智能合并。
openclaw skills install model-router-waaiParallel multi-LLM invocation with intelligent result merging. Get the best from multiple models.
| EN | CN |
|---|---|
| Need better answers than single model | 需要比单一模型更好的答案 |
| Compare outputs from different LLMs | 对比不同模型输出 |
| Get best result through ensemble | 通过集成获取最佳结果 |
| Critical tasks requiring reliability | 需要可靠性的关键任务 |
User Task / 用户任务
│
▼
┌──────────────────┐
│ Parallel Invoke │ / 并行调用
│ ┌────┬────┬────┐│
│ │GPT4│Claude│Kimi││
│ └────┴────┴────┘│
└──────────────────┘
│
▼
┌──────────────────┐
│ Target Merge │ / 目标模型合并
│ Target LLM │
└──────────────────┘
│
▼
Final Result / 最终结果
| Feature | EN | CN |
|---|---|---|
| Parallel invocation | 并行调用多模型 | |
| Auto-routing | 自动路由 | |
| Result merging | 结果智能合并 | |
| Quality assessment | 质量评估 |
| Model | Provider |
|---|---|
| gpt4 | OpenAI |
| claude | Anthropic |
| kimi | 月之暗面 |
| deepseek | 深度求索 |
| qwen | 阿里 |
| ernie | 百度 |
| gemini |
from model_router import Router
# Simple / 简单
result = await Router().run(
task="写一首关于春天的诗",
models=["gpt4", "claude", "kimi"],
merge_model="gpt4"
)
# Advanced / 高级
result = await Router().run(
task="分析这段代码",
models=["cursor", "windsurf", "codeium"],
merge_model="claude",
timeout=30,
merge_prompt="合并以下代码审查结果,给出最佳建议"
)
{
"final": "...", # 合并后的最终结果
"sources": [...], # 各模型原始结果
"merge_model": "claude", # 合并用的模型
"total_time": 2.5 # 总耗时(秒)
}
npx clawhub install model-router-waai