tomevault-io OpenRouter
Expert guidance for multi-provider AI model access.
Triggers
Use this skill when:
- Accessing multiple AI model providers through one API
- Building multi-model AI applications
- Implementing cost optimization or model fallbacks
- Working with OpenRouter for LLM provider abstraction
- Routing requests between different AI providers
- Keywords: openrouter, multi-model, model routing, fallback, provider abstraction
Installation
pip install openai # Uses OpenAI-compatible API
Quick Start
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="your-openrouter-key"
)
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
Available Models
# Anthropic
model = "anthropic/claude-3.5-sonnet"
model = "anthropic/claude-3-opus"
model = "anthropic/claude-3-haiku"
# OpenAI
model = "openai/gpt-4o"
model = "openai/gpt-4-turbo"
model = "openai/gpt-3.5-turbo"
# Google
model = "google/gemini-pro-1.5"
model = "google/gemini-flash-1.5"
# Meta
model = "meta-llama/llama-3.1-405b-instruct"
model = "meta-llama/llama-3.1-70b-instruct"
# Mistral
model = "mistralai/mistral-large"
model = "mistralai/mixtral-8x7b-instruct"
# Free models (rate limited)
model = "meta-llama/llama-3.1-8b-instruct:free"
Request Headers
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello!"}],
extra_headers={
"HTTP-Referer": "https://your-app.com", # For rankings
"X-Title": "Your App Name" # For identification
}
)
Streaming
stream = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
Function Calling
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather for a city",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string"}
},
"required": ["city"]
}
}
}]
response = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Weather in Paris?"}],
tools=tools,
tool_choice="auto"
)
if response.choices[0].message.tool_calls:
for call in response.choices[0].message.tool_calls:
print(f"Function: {call.function.name}")
print(f"Args: {call.function.arguments}")
Provider Routing
# Route to specific provider
response = client.chat.completions.create(
model="openai/gpt-4o", # Explicit provider
messages=[{"role": "user", "content": "Hello"}]
)
# Let OpenRouter choose best provider
response = client.chat.completions.create(
model="gpt-4o", # OpenRouter routes automatically
messages=[{"role": "user", "content": "Hello"}],
extra_body={
"provider": {
"order": ["Azure", "OpenAI"], # Preferred order
"allow_fallbacks": True
}
}
)
Cost Control
# Set spending limit per request
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello"}],
extra_body={
"max_price": {
"prompt": 0.01, # Max $/1K prompt tokens
"completion": 0.03 # Max $/1K completion tokens
}
}
)
# Check usage
print(f"Prompt tokens: {response.usage.prompt_tokens}")
print(f"Completion tokens: {response.usage.completion_tokens}")
Fallbacks
# Define fallback models
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello"}],
extra_body={
"route": "fallback",
"models": [
"anthropic/claude-3.5-sonnet",
"openai/gpt-4o",
"google/gemini-pro-1.5"
]
}
)
Transforms
# Enable prompt transforms for better compatibility
response = client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello"}],
extra_body={
"transforms": ["middle-out"] # Compress long prompts
}
)
Get Model Info
import requests
# List all models
response = requests.get(
"https://openrouter.ai/api/v1/models",
headers={"Authorization": f"Bearer {api_key}"}
)
models = response.json()["data"]
for model in models[:5]:
print(f"{model['id']}: ${model['pricing']['prompt']}/1K tokens")
Check Credits
import requests
response = requests.get(
"https://openrouter.ai/api/v1/auth/key",
headers={"Authorization": f"Bearer {api_key}"}
)
info = response.json()
print(f"Credits remaining: ${info['data']['limit'] - info['data']['usage']}")
Async Usage
import asyncio
from openai import AsyncOpenAI
async def main():
client = AsyncOpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="your-key"
)
response = await client.chat.completions.create(
model="anthropic/claude-3.5-sonnet",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
asyncio.run(main())
LangChain Integration
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="anthropic/claude-3.5-sonnet",
openai_api_base="https://openrouter.ai/api/v1",
openai_api_key="your-openrouter-key",
model_kwargs={
"extra_headers": {
"HTTP-Referer": "https://your-app.com"
}
}
)
response = llm.invoke("Hello!")
Resources
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