OpenLIT Integration via OpenTelemetry

Langfuse is an OpenTelemetry backend, allowing trace ingestion from various OpenTelemetry instrumentation libraries. This guide demonstrates how to use the OpenLit instrumentation library to instrument a compatible framework or LLM provider.

Step 1: Install Dependencies

Install the necessary Python packages: openai, langfuse, and openlit. These will allow you to interact with OpenAI as well as setup the instrumentation for tracing.

    %pip install openai langfuse openlit --upgrade
    ```

## Step 2: Configure Environment Variables

Before sending any requests, you need to configure your credentials and endpoints. First, set up the Langfuse authentication by providing your public and secret keys. Then, configure the OpenTelemetry exporter endpoint and headers to point to Langfuse's backend. You should also specify your OpenAI API key.

```python
    import os

# Get keys for your project from the project settings page: https://cloud.langfuse.com
    os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...")
    os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...")
    os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com") # 🇪🇺 EU region
    # Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com

# Your openai key
    os.environ.setdefault("OPENAI_API_KEY", "sk-proj-...")
    ```

With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.

```python
    from langfuse import get_client

langfuse = get_client()

# Verify connection
    if langfuse.auth_check():
        print("Langfuse client is authenticated and ready!")
    else:
        print("Authentication failed. Please check your credentials and host.")
    ```

## Step 3: Initialize Instrumentation

With the environment set up, import the needed libraries and initialize OpenLIT instrumentation. We set `tracer=tracer` to use the tracer we created in the previous step.

```python
    import openlit

# Initialize OpenLIT instrumentation. The disable_batch flag is set to true to process traces immediately.
    openlit.init(disable_batch=True)
    ```

## Step 4: Make a Chat Completion Request

For this example, we will make a simple chat completion request to the OpenAI Chat API. This will generate trace data that you can later view in the Langfuse dashboard.

```python
    from openai import OpenAI

# Create an instance of the OpenAI client.
    openai_client = OpenAI()

# Make a sample chat completion request. This request will be traced by OpenLIT and sent to Langfuse.
    chat_completion = openai_client.chat.completions.create(
        messages=[
            {
              "role": "user",
              "content": "What is LLM Observability?",
            }
        ],
        model="gpt-4o",
    )

print(chat_completion)
    ```

## Step 5: See Traces in Langfuse

You can view the generated trace data in Langfuse. You can view this [example trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/64902f6a5b4f27738be939b7ad38eab3?timestamp=2025-02-02T22%3A09%3A53.053Z) in the Langfuse UI.