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LLM Observability with Langfuse and OpenLLMetry

Langfuse provides a backend built on OpenTelemetry for ingesting trace data, and you can use different instrumentation libraries to export traces from your applications.
What is Langfuse? Langfuse (GitHub) is an open-source platform for LLM engineering. It provides tracing and monitoring capabilities for AI agents, helping developers debug, analyze, and optimize their products. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and SDKs.
Langfuse Overview Video

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Step 1: Install Dependencies

Begin by installing the necessary Python packages. In this example, we need the openai library to interact with OpenAI’s API and traceloop-sdk for enabling OpenLLMetry instrumentation. 
%pip install openai traceloop-sdk

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Step 2: Set Up Environment Variables

Before initiating any requests, configure your environment with the necessary credentials and endpoints. Here, we establish Langfuse authentication by combining your public and secret keys into a Base64-encoded token. Additionally, specify the Langfuse endpoint based on your preferred geographical region (EU or US) and provide your OpenAI API key. 
import os
import base64

LANGFUSE_PUBLIC_KEY=""
LANGFUSE_SECRET_KEY=""
LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()

os.environ["TRACELOOP_BASE_URL"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
# os.environ["TRACELOOP_BASE_URL"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
os.environ["TRACELOOP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"

# your openai key
os.environ["OPENAI_API_KEY"] = ""

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Step 3: Initialize OpenLLMetry Instrumentation

Proceed to initialize the OpenLLMetry instrumentation using the traceloop-sdk. It is advisable to use disable_batch=True if you are executing this code in a notebook, as traces are sent immediately without waiting for batching. Once initialized, any action performed using the OpenAI SDK (such as a chat completion request) will be automatically traced and forwarded to Langfuse. 
from openai import OpenAI
from traceloop.sdk import Traceloop

Traceloop.init(disable_batch=True)

openai_client = OpenAI()

chat_completion = openai_client.chat.completions.create(
    messages=[
        {
          "role": "user",
          "content": "What is LLM Observability?",
        }
    ],
    model="gpt-4o-mini",
)

print(chat_completion)

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Step 4: Analyze the Trace in Langfuse

After executing the above code, you can examine the generated trace in your Langfuse dashboard: Example Trace in Langfuse OpenLLMetry OpenAI Trace