---
title: Langfuse Integration
description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
icon: vials
---

# Integrate Langfuse with CrewAI

This notebook demonstrates how to integrate **Langfuse** with **CrewAI** using OpenTelemetry via the **OpenLit** SDK. By the end of this notebook, you will be able to trace your CrewAI applications with Langfuse for improved observability and debugging.

> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source LLM engineering platform. It provides tracing and monitoring capabilities for LLM applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and APIs/SDKs.

[![Langfuse Overview Video](https://github.com/user-attachments/assets/3926b288-ff61-4b95-8aa1-45d041c70866)](https://langfuse.com/watch-demo)

## Get Started

We'll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.

### Step 1: Install Dependencies


```python
%pip install langfuse openlit crewai crewai_tools
```

### Step 2: Set Up Environment Variables

Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started) for more information on the Langfuse OpenTelemetry endpoint `/api/public/otel` and authentication.


```python
import os
import base64

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

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

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

### Step 3: Initialize OpenLit

Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.


```python
import openlit

openlit.init()
```

### Step 4: Create a Simple CrewAI Application

We'll create a simple CrewAI application where multiple agents collaborate to answer a user's question.


```python
from crewai import Agent, Task, Crew

from crewai_tools import (
    WebsiteSearchTool
)

web_rag_tool = WebsiteSearchTool()

writer = Agent(
        role="Writer",
        goal="You make math engaging and understandable for young children through poetry",
        backstory="You're an expert in writing haikus but you know nothing of math.",
        tools=[web_rag_tool],  
    )

task = Task(description=("What is {multiplication}?"),
            expected_output=("Compose a haiku that includes the answer."),
            agent=writer)

crew = Crew(
  agents=[writer],
  tasks=[task],
  share_crew=False
)
```

### Step 5: See Traces in Langfuse

After running the agent, you can view the traces generated by your CrewAI application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.

![CrewAI example trace in Langfuse](https://langfuse.com/images/cookbook/integration_crewai/crewai-example-trace.png)

_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e2cf380ffc8d47d28da98f136140642b?timestamp=2025-02-05T15%3A12%3A02.717Z&observation=3b32338ee6a5d9af)_

## References

- [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started)
