Langchain Openrouter

LangChain OpenRouter Skill

Expert assistance for langchain-openrouter: one class (ChatOpenRouter) that routes to hundreds of models from OpenAI, Anthropic, Google, Meta, and more through a single API and API key.

Install: pip install -U langchain-openrouter
Setup: export OPENROUTER_API_KEY=your_api_key
Models: https://openrouter.ai/models

Reference: references/api.md (500 KB — full API reference).

When to Use This Skill

Activate when:

• Switching between model providers — using ChatOpenRouter with different model strings
• Routing to a specific provider — using openrouter_provider={"order": [...]} to prefer one backend
• Using reasoning models — setting reasoning param for models like deepseek/deepseek-r1
• Tool calling — using .bind_tools() with any OpenRouter model
• Structured output — using .with_structured_output() with a Pydantic schema
• Adding app attribution — setting app_url, app_title, app_categories for marketplace
• Grouping requests — using session_id for observability
• Tracing broadcast metadata — using trace param
• Using OpenRouter plugins — setting plugins param

Quick Reference

ChatOpenRouter — basic usage

from langchain_openrouter import ChatOpenRouter

# Use any model from openrouter.ai/models
model = ChatOpenRouter(
    model="anthropic/claude-sonnet-4-6",
    temperature=0.7,
    max_tokens=1024,
    # api_key="...",  # or set OPENROUTER_API_KEY env var
)

response = model.invoke("Explain quantum entanglement in plain English.")
print(response.content)

# Stream
for chunk in model.stream("Write a haiku about code:"):
    print(chunk.content, end="", flush=True)

Switch models with one variable

from langchain_openrouter import ChatOpenRouter

# GPT-4o
model = ChatOpenRouter(model="openai/gpt-4o", temperature=0)

# Gemini Flash (fast, cheap)
model = ChatOpenRouter(model="google/gemini-2.0-flash", temperature=0)

# Llama 3.1 (open source, free tier)
model = ChatOpenRouter(model="meta-llama/llama-3.1-8b-instruct", temperature=0.5)

# DeepSeek R1 (reasoning)
model = ChatOpenRouter(model="deepseek/deepseek-r1", temperature=0)

Force routing to a specific provider

from langchain_openrouter import ChatOpenRouter

# Force anthropic/claude-sonnet-4-5 to route through Anthropic (not a proxy)
model = ChatOpenRouter(
    model="anthropic/claude-sonnet-4-6",
    openrouter_provider={
        "order": ["Anthropic"],          # try Anthropic first
        # "allow_fallbacks": False,      # fail if Anthropic unavailable
        # "require_parameters": True,    # only use providers that support all params
    },
)

# Route through Azure OpenAI
model = ChatOpenRouter(
    model="openai/gpt-4o",
    openrouter_provider={"order": ["Azure"]},
)

Reasoning models

from langchain_openrouter import ChatOpenRouter

model = ChatOpenRouter(
    model="deepseek/deepseek-r1",
    reasoning={
        "effort": "high",       # low | medium | high
        # "exclude": False,     # True to hide reasoning from response
    },
    temperature=0,
)

response = model.invoke("What is the 100th Fibonacci number?")
print(response.content)
# response.additional_kwargs may contain reasoning trace

Tool calling

from langchain_openrouter import ChatOpenRouter
from langchain_core.tools import tool

@tool
def get_stock_price(ticker: str) -> float:
    """Get the current stock price for a ticker symbol."""
    return 150.25  # mock

model = ChatOpenRouter(model="openai/gpt-4o-mini")
model_with_tools = model.bind_tools([get_stock_price])

response = model_with_tools.invoke("What's the current AAPL price?")
print(response.tool_calls)

Structured output

from langchain_openrouter import ChatOpenRouter
from pydantic import BaseModel, Field

class MovieReview(BaseModel):
    title: str = Field(description="Movie title")
    rating: float = Field(description="Rating from 0 to 10")
    summary: str = Field(description="Brief review summary")

model = ChatOpenRouter(model="anthropic/claude-sonnet-4-6")
structured = model.with_structured_output(MovieReview)

review = structured.invoke("Review the movie Inception.")
print(f"{review.title}: {review.rating}/10")

Attribution and observability

from langchain_openrouter import ChatOpenRouter

model = ChatOpenRouter(
    model="openai/gpt-4o-mini",
    app_url="https://myapp.example.com",    # shows in OpenRouter dashboard
    app_title="My LLM App",
    app_categories=["productivity", "coding"],
    session_id="user-session-abc123",        # group related requests
    trace={                                   # metadata for broadcast
        "user_id": "user-123",
        "experiment": "v2-prompts",
    },
)

API Reference

ChatOpenRouter key parameters

openrouter_provider options

{
    "order": ["Anthropic", "AWS Bedrock"],  # provider preference order
    "allow_fallbacks": True,                 # fall back to other providers
    "require_parameters": True,              # only providers supporting all params
    "data_collection": "deny",               # opt out of training data use
    "only": ["Anthropic"],                   # restrict to specific providers
    "ignore": ["Azure"],                     # exclude specific providers
}

Popular Model IDs

Full list: https://openrouter.ai/models

Reference Files

Source: https://reference.langchain.com/python/langchain-openrouter
Platform docs: https://openrouter.ai/docs
API key: https://openrouter.ai/keys