<quick_start>

1. Basic LangChain Setup

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)

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)

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

Model Best For Cost ($/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

Task Western Model Cost Chinese Model Cost Savings

Chat GPT-4o $5.00/$15.00 DeepSeek Chat $0.27/$1.10 95%

Code Claude Sonnet $3.00/$15.00 DeepSeek Coder $0.14/$0.28 95%

Vision GPT-4o $5.00/$15.00 Qwen-VL $0.40/$0.40 97%

Fast GPT-4o-mini $0.15/$0.60 Qwen-7B $0.09/$0.09 60%

LangGraph Multi-Model Factory

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

.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>

• 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

• 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