openrouter-skill

Original：🇺🇸 English
Translated

Orchestrate Chinese LLMs (DeepSeek, Qwen, Yi, Moonshot) through OpenRouter API with LangChain. Use when: openrouter, chinese llm, deepseek, qwen, moonshot, yi model, model routing, auto router, llm orchestration.

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Sourcescientiacapital/skills
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NPX Install

npx skill4agent add scientiacapital/skills openrouter-skill

Tags

Translated version includes tags in frontmatter
llm-orchestrationopenrouter-apilangchain-integrationchinese-llmsmodel-routing

SKILL.md Content

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<objective> Enable intelligent routing to Chinese/open-source LLMs through OpenRouter's unified API. Provides model selection guidance, cost optimization, and production patterns for LangChain/LangGraph integration. </objective>
<quick_start>

1. Basic LangChain Setup

python
from langchain_openai import ChatOpenAI
import os

# Any OpenRouter model works with ChatOpenAI
llm = ChatOpenAI(
    model="deepseek/deepseek-chat",
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1",
    default_headers={
        "HTTP-Referer": "https://your-app.com",  # Optional but recommended
        "X-Title": "Your App Name"
    }
)

response = llm.invoke("Explain quantum computing in simple terms")

2. Vision Analysis (Charts, Documents)

python
from langchain_core.messages import HumanMessage
import base64

llm = ChatOpenAI(
    model="qwen/qwen-2-vl-72b-instruct",
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1"
)

# From URL
response = llm.invoke([
    HumanMessage(content=[
        {"type": "text", "text": "Analyze this chart and identify key trends"},
        {"type": "image_url", "image_url": {"url": "https://example.com/chart.png"}}
    ])
])

# From base64
with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = llm.invoke([
    HumanMessage(content=[
        {"type": "text", "text": "What does this chart show?"},
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_data}"}}
    ])
])

3. Auto-Routing (Let OpenRouter Choose)

python
# OpenRouter's Auto model selects the best model for your prompt
llm = ChatOpenAI(
    model="openrouter/auto",  # Powered by NotDiamond
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1"
)
</quick_start>
<success_criteria>
  • OpenRouter API key configured and authenticated
  • Model selection follows the decision tree (vision -> Qwen-VL, code -> DeepSeek Coder, etc.)
  • LangChain ChatOpenAI integration working with correct base URL and headers
  • Cost savings of 60-97% vs Western model equivalents for comparable quality
  • Fallback chain configured for production reliability </success_criteria>
<core_concepts>

Model Selection Decision Tree

Task Type
│
├─ Vision/Charts ──────────> qwen/qwen-2-vl-72b-instruct ($0.40/M)
├─ Code Generation ────────> deepseek/deepseek-coder ($0.14/$0.28)
├─ Deep Reasoning ─────────> qwen/qwq-32b ($0.15/$0.40)
├─ Long Documents ─────────> moonshot/moonshot-v1-128k ($0.55/M)
├─ Fast/Cheap Tasks ───────> qwen/qwen-2.5-7b-instruct ($0.09/M)
├─ General Analysis ───────> deepseek/deepseek-chat ($0.27/$1.10)
└─ Unknown/Auto ───────────> openrouter/auto

Top Chinese LLMs

ModelBest ForCost ($/1M tokens)
deepseek/deepseek-chat
General reasoning, analysis$0.27 in / $1.10 out
deepseek/deepseek-coder
Code generation$0.14 / $0.28
qwen/qwen-2-vl-72b-instruct
Vision, charts$0.40 / $0.40
qwen/qwen-2.5-7b-instruct
Fast, cheap tasks$0.09 / $0.09
qwen/qwq-32b
Deep reasoning$0.15 / $0.40
moonshot/moonshot-v1-128k
Long context (128K)$0.55 / $0.55

Cost Comparison vs Western Models

TaskWestern ModelCostChinese ModelCostSavings
ChatGPT-4o$5.00/$15.00DeepSeek Chat$0.27/$1.1095%
CodeClaude Sonnet$3.00/$15.00DeepSeek Coder$0.14/$0.2895%
VisionGPT-4o$5.00/$15.00Qwen-VL$0.40/$0.4097%
FastGPT-4o-mini$0.15/$0.60Qwen-7B$0.09/$0.0960%

LangGraph Multi-Model Factory

python
from enum import Enum
from langchain_openai import ChatOpenAI
import os

class ChineseModel(str, Enum):
    DEEPSEEK_CHAT = "deepseek/deepseek-chat"
    DEEPSEEK_CODER = "deepseek/deepseek-coder"
    QWEN_VL = "qwen/qwen-2-vl-72b-instruct"
    QWEN_FAST = "qwen/qwen-2.5-7b-instruct"
    QWQ_REASONING = "qwen/qwq-32b"
    MOONSHOT_LONG = "moonshot/moonshot-v1-128k"
    AUTO = "openrouter/auto"

def create_llm(model: ChineseModel, **kwargs) -> ChatOpenAI:
    """Factory for OpenRouter LLMs with sensible defaults."""
    return ChatOpenAI(
        model=model.value,
        openai_api_key=os.getenv("OPENROUTER_API_KEY"),
        openai_api_base="https://openrouter.ai/api/v1",
        default_headers={
            "HTTP-Referer": os.getenv("APP_URL", "http://localhost"),
            "X-Title": os.getenv("APP_NAME", "LangChain App")
        },
        **kwargs
    )

# Usage
chat_llm = create_llm(ChineseModel.DEEPSEEK_CHAT)
vision_llm = create_llm(ChineseModel.QWEN_VL)
fast_llm = create_llm(ChineseModel.QWEN_FAST, temperature=0)

Environment Setup

bash
# .env
OPENROUTER_API_KEY=sk-or-v1-...
APP_URL=https://your-app.com      # For attribution (optional)
APP_NAME=Your App Name            # For attribution (optional)
</core_concepts>
<routing> For detailed information, see: 
  • reference/models-catalog.md
    - Complete model listing with capabilities
  • reference/routing-strategies.md
    - Auto, provider, and custom routing
  • reference/langchain-integration.md
    - LangChain/LangGraph patterns
  • reference/cost-optimization.md
    - Budget management and caching
  • reference/tool-calling.md
    - Function calling patterns
  • reference/multimodal.md
    - Vision, PDF, audio support
  • reference/observability.md
    - Monitoring and tracing </routing>
<checklist> When implementing OpenRouter integration:
  • Set OPENROUTER_API_KEY environment variable
  • Choose appropriate model for task type (see decision tree)
  • Use ChatOpenAI with openai_api_base="https://openrouter.ai/api/v1"
  • Add HTTP-Referer and X-Title headers for attribution
  • Consider cost implications (Chinese models are 10-100x cheaper)
  • Enable streaming for chat interfaces
  • Implement fallback chain for production reliability
  • Set up cost tracking/budget limits </checklist>