---
title: OpenLIT Integration via OpenTelemetry
sidebarTitle: OpenLIT
logo: /images/integrations/opentelemetry_icon.svg
description: Example cookbook for the OpenLIT Langfuse integration using OpenTelemetry.
category: Integrations
---

# OpenLIT Integration via OpenTelemetry

Langfuse is an [OpenTelemetry backend](https://langfuse.com/docs/opentelemetry/get-started), allowing trace ingestion from various OpenTelemetry instrumentation libraries. This guide demonstrates how to use the [OpenLit](https://docs.openlit.io/latest/features/tracing) 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.

```python
%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.

![OpenLIT OpenAI Trace](https://langfuse.com/images/cookbook/otel-integration-openlit/openlit-openai-trace.png)

<!-- agent-instructions -->

---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/integrations/other/openlit.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.