Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
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 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.
We’ll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.
Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the Langfuse OpenTelemetry Docs for more information on the Langfuse OpenTelemetry endpoint /api/public/otel
and authentication.
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.
Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.
We’ll create a simple CrewAI application where multiple agents collaborate to answer a user’s question.
After running the agent, you can view the traces generated by your CrewAI application in Langfuse. You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.
Public example trace in Langfuse