---
source: Jupyter Notebook
title: Observability for CrewAI with Langfuse Integration
sidebarTitle: CrewAI
logo: /images/integrations/crewai_icon.svg
description: Discover how to integrate Langfuse with CrewAI for enhanced LLM application monitoring, debugging, and tracing. Improve your AI development workflow today.
category: Integrations
---
# Integrate Langfuse with CrewAI
This notebook provides a step-by-step guide on integrating **Langfuse** with **CrewAI** to achieve observability and debugging for your LLM applications.
> **What is CrewAI?** [CrewAI](https://github.com/crewAIInc/crewAI) ([GitHub](https://github.com/crewAIInc/crewAI)) is a framework for orchestrating autonomous AI agents. CrewAI enables you to create AI teams where each agent has specific roles, tools, and goals, working together to accomplish complex tasks. Each member (agent) brings unique skills and expertise, collaborating seamlessly to achieve your objectives.
> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source AI engineering platform. It offers tracing and monitoring capabilities for LLM applications. Langfuse helps developers debug, analyze, and optimize their AI systems by providing detailed insights and integrating with a wide array of tools and frameworks through native integrations, OpenTelemetry, and dedicated SDKs.
## Getting Started
Let's walk through a practical example of using CrewAI and integrating it with Langfuse for comprehensive tracing.
### Step 1: Install Dependencies
```python
%pip install langfuse crewai openinference-instrumentation-crewai -q
```
### Step 2: Configure Langfuse SDK
Next, set up your Langfuse API keys. You can get these keys by signing up for a free [Langfuse Cloud](https://langfuse.com/cloud) account or by [self-hosting Langfuse](https://langfuse.com/self-hosting). These environment variables are essential for the Langfuse client to authenticate and send data to your Langfuse project.
```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"); # πͺπΊ EU region
# Other Langfuse data regions include πΊπΈ US: https://us.cloud.langfuse.com, π―π΅ Japan: https://jp.cloud.langfuse.com and βοΈ 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 CrewAI Instrumentation
Now, we initialize the [OpenInference instrumentation SDK](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-crewai) to automatically capture CrewAI operations and export OpenTelemetry (OTel) spans to Langfuse.
```python
from openinference.instrumentation.crewai import CrewAIInstrumentor
CrewAIInstrumentor().instrument(skip_dep_check=True)
```
### Step 4: Basic CrewAI Application
Let's create a straightforward CrewAI application. In this example, we'll create a simple crew with agents that can collaborate to complete tasks. This will serve as the foundation for demonstrating Langfuse tracing.
```python
from crewai import Agent, Task, Crew
# Define your agents with roles and goals
coder = Agent(
role='Software developer',
goal='Write clear, concise code on demand',
backstory='An expert coder with a keen eye for software trends.',
)
# Create tasks for your agents
task1 = Task(
description="Define the HTML for making a simple website with heading- Hello World! Langfuse monitors your CrewAI agent!",
expected_output="A clear and concise HTML code",
agent=coder
)
# Instantiate your crew
crew = Crew(
agents=[coder],
tasks=[task1],
)
with langfuse.start_as_current_observation(as_type="span", name="crewai-index-trace"):
result = crew.kickoff()
print(result)
langfuse.flush()
```
### Step 5: View Traces in Langfuse
After executing the application, navigate to your Langfuse Trace Table. You will find detailed traces of the application's execution, providing insights into the LLM calls, agent operations, inputs, outputs, and performance metrics. The trace will show the complete flow from task processing through agent collaboration to response generation.
[Example Trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/a287bb31e317433610d8827617471140?timestamp=2025-07-11T07:45:13.601Z&display=details)
## Interoperability with the Python SDK
You can use this integration together with the Langfuse [SDKs](/docs/observability/sdk/overview) to add additional attributes to the observation.
The [`@observe()` decorator](/docs/observability/sdk/instrumentation#custom-instrumentation) provides a convenient way to automatically wrap your instrumented code and add additional attributes to the observation.
```python
from langfuse import observe, propagate_attributes, get_client
langfuse = get_client()
@observe()
def my_llm_pipeline(input):
# Add additional attributes (user_id, session_id, metadata, version, tags) to all spans created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
tags=["agent", "my-observation"],
metadata={"email": "user@langfuse.com"},
version="1.0.0"
):
# YOUR APPLICATION CODE HERE
result = call_llm(input)
return result
# Run the function
my_llm_pipeline("Hi")
```
Learn more about using the Decorator in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
The [Context Manager](/docs/observability/sdk/instrumentation#custom-instrumentation) allows you to wrap your instrumented code using context managers (with `with` statements), which allows you to add additional attributes to the observation.
```python
from langfuse import get_client, propagate_attributes
langfuse = get_client()
with langfuse.start_as_current_observation(
as_type="span",
name="my-observation",
trace_context={"trace_id": "abcdef1234567890abcdef1234567890"}, # Must be 32 hex chars
) as observation:
# Add additional attributes (user_id, session_id, metadata, version, tags)
# to all observations created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
metadata={"experiment": "variant_a", "env": "prod"},
version="1.0",
):
# YOUR APPLICATION CODE HERE
result = call_llm("some input")
# Flush events in short-lived applications
langfuse.flush()
```
Learn more about using the Context Manager in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
## Troubleshooting
No observations appearing
First, enable [debug mode](/docs/observability/sdk/advanced-features#logging--debugging) in the Python SDK:
```bash
export LANGFUSE_DEBUG="True"
```
Then run your application and check the debug logs:
- **OTel observations appear in the logs:** Your application is instrumented correctly but observations are not reaching Langfuse. To resolve this:
1. Call [`langfuse.flush()`](/docs/observability/sdk/instrumentation#client-lifecycle--flushing) at the end of your application to ensure all observations are exported.
2. Verify that you are using the correct API keys and base URL.
- **No OTel spans in the logs:** Your application is not instrumented correctly. Make sure the instrumentation runs before your application code.
Unwanted observations in Langfuse
The Langfuse SDK is based on OpenTelemetry. Other libraries in your application may emit OTel spans that are not relevant to you. These still count toward your [billable units](/docs/administration/billable-units), so you should filter them out. See [Unwanted spans in Langfuse](/faq/all/unwanted-http-database-spans) for details.
Missing attributes
Some attributes may be stored in the metadata object of the observation rather than being mapped to the Langfuse data model. If a mapping or integration does not work as expected, please [raise an issue on GitHub](/issues).
## Next Steps
Once you have instrumented your code, you can manage, evaluate and debug your application:
- [Manage prompts in Langfuse](/docs/prompts/get-started)
- [Add evaluation scores](/docs/evaluation/features/evaluation-methods/custom-scores)
- [Run LLM-as-a-judge Evaluators](/docs/scores/model-based-evals)
- [Create datasets](/docs/datasets/overview)
- [Create custom dashboards](/docs/analytics/custom-dashboards)
- [Test queries in the Playground](/docs/playground)
---
## 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/frameworks/crewai.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**: , 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 `) to read or write traces, prompts, datasets, and scores from the terminal.
Found an error in these docs? Please open an issue at .