OpenRouter Integration

In this guide, we’ll show you how to integrate Langfuse with OpenRouter.

What is OpenRouter? OpenRouter provides an OpenAI-compatible completion API to +280 language models and providers that you can call directly or using the OpenAI SDK. This allows developers to access a variety of LLMs through a single, unified interface.

What is Langfuse? Langfuse is an open source LLM engineering platform that helps teams trace LLM calls, monitor performance, and debug issues in their AI applications.

Since OpenRouter uses the OpenAI API schema, we can utilize Langfuse’s native integration with the OpenAI SDK, available in both Python and TypeScript.

Get started

pip install langfuse openai

import os
 
# Set your Langfuse API keys
LANGFUSE_SECRET_KEY="sk-lf-..."
LANGFUSE_PUBLIC_KEY="pk-lf-..."
# 🇪🇺 EU region
LANGFUSE_HOST="https://cloud.langfuse.com"
# 🇺🇸 US region
# LANGFUSE_HOST="https://us.cloud.langfuse.com"
 
# Set your OpenRouter API key (OpenRouter uses the 'OPENAI_API_KEY' environment variable)
os.environ["OPENAI_API_KEY"] = "<YOUR_OPENROUTER_API_KEY>"

Example 1: Simple LLM Call

Since OpenRouter provides an OpenAI-compatible API, we can use the Langfuse OpenAI SDK wrapper to automatically log OpenRouter calls as generations in Langfuse.

The base_url is set to OpenRouter’s API endpoint.
You can replace "qwen/qwen-plus" with any model available on OpenRouter.
The default_headers can include optional headers as per OpenRouter’s documentation.
The extra_body={"usage": {"include": True}} includes the costs that OpenRouter returns.

# Import the Langfuse OpenAI SDK wrapper
from langfuse.openai import openai
 
# Create an OpenAI client with OpenRouter's base URL
client = openai.OpenAI(
    base_url="https://openrouter.ai/api/v1",
    default_headers={
        "HTTP-Referer": "<YOUR_SITE_URL>",  # Optional: Your site URL
        "X-Title": "<YOUR_SITE_NAME>",      # Optional: Your site name
    }
)
 
# Make a chat completion request
response = client.chat.completions.create(
    model="qwen/qwen-plus",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a fun fact about space."}
    ],
    extra_body={"usage": {"include": True}},
    name="fun-fact-request"  # Optional: Name of the generation in Langfuse
)
 
# Print the assistant's reply
print(response.choices[0].message.content)

Example 2: Nested LLM Calls

By using the @observe() decorator, we can capture execution details of any Python function, including nested LLM calls, inputs, outputs, and execution times. This provides in-depth observability with minimal code changes.

The @observe() decorator captures inputs, outputs, and execution details of the functions.
Nested functions summarize_text and analyze_sentiment are also decorated, creating a hierarchy of traces.
Each LLM call within the functions is logged, providing a detailed trace of the execution flow.

from langfuse import observe
from langfuse.openai import openai
 
# Create an OpenAI client with OpenRouter's base URL
client = openai.OpenAI(
    base_url="https://openrouter.ai/api/v1",
)
 
@observe()  # This decorator enables tracing of the function
def analyze_text(text: str):
    # First LLM call: Summarize the text
    summary_response = summarize_text(text)
    summary = summary_response.choices[0].message.content
 
    # Second LLM call: Analyze the sentiment of the summary
    sentiment_response = analyze_sentiment(summary)
    sentiment = sentiment_response.choices[0].message.content
 
    return {
        "summary": summary,
        "sentiment": sentiment
    }
 
@observe()  # Nested function to be traced
def summarize_text(text: str):
    return client.chat.completions.create(
        model="openai/gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You summarize texts in a concise manner."},
            {"role": "user", "content": f"Summarize the following text:\n{text}"}
        ],
        extra_body={"usage": {"include": True}},
        name="summarize-text"
    )
 
@observe()  # Nested function to be traced
def analyze_sentiment(summary: str):
    return client.chat.completions.create(
        model="openai/gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You analyze the sentiment of texts."},
            {"role": "user", "content": f"Analyze the sentiment of the following summary:\n{summary}"}
        ],
        extra_body={"usage": {"include": True}},
        name="analyze-sentiment"
    )
 
# Example usage
text_to_analyze = "OpenAI's GPT-4 model has significantly advanced the field of AI, setting new standards for language generation."
analyze_text(text_to_analyze)

Example trace in Langfuse

Public link to example trace in Langfuse

Interoperability with the Python SDK

You can use this integration together with the Langfuse Python SDK to add additional attributes to the trace.

The @observe() decorator provides a convenient way to automatically wrap your instrumented code and add additional attributes to the trace.

from langfuse import observe, get_client
 
langfuse = get_client()
 
@observe()
def my_instrumented_function(input):
    output = my_llm_call(input)
 
    langfuse.update_current_trace(
        input=input,
        output=output,
        user_id="user_123",
        session_id="session_abc",
        tags=["agent", "my-trace"],
        metadata={"email": "[email protected]"},
        version="1.0.0"
    )
 
    return output

Learn more about using the Decorator in the Python SDK docs.

Next Steps

Once you have instrumented your code, you can manage, evaluate and debug your application:

Manage prompts in Langfuse Add evaluation scores Run LLM-as-a-judge Evaluators Create datasets Create custom dashboards Test queries in the Playground

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