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Integrate Langfuse with Agno

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

What is Agno? Agno is a platform for building and managing AI agents.

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 API/SDKs.

Get Started

We’ll walk through examples of using Agno and integrating it with Langfuse.

Step 1: Install Dependencies

%pip install agno openai langfuse yfinance openlit

Step 2: Set Up Environment Variables

Get your Langfuse API keys by signing up for Langfuse Cloud or self-hosting Langfuse. You’ll also need your OpenAI API key.

import os
 
# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..." 
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..." 
os.environ["LANGFUSE_BASE_URL"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_BASE_URL"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
 
# your openai key
os.environ["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.

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: Sending Traces to Langfuse

This example demonstrates how to use the OpenLit instrumentation library to ingfe

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
 
# Initialize OpenLIT instrumentation
import openlit
openlit.init(tracer=langfuse._otel_tracer, disable_batch=True)
 
# Create and configure the agent
agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[DuckDuckGoTools()],
    markdown=True,
    debug_mode=True,
)
 
# Use the agent
agent.print_response("What is currently trending on Twitter?")

Step 4: See Traces in Langfuse

After running the agent examples above, you can view the traces generated by your Agno agent in Langfuse.

Agno Agents OpenLit Instrumentation

Example trace in Langfuse

Interoperability with the Python SDK

You can use this integration together with the Langfuse SDKs to add additional attributes to the trace.

The @observe() decorator provides a convenient way to automatically wrap your instrumented code and add additional attributes to the trace.

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-trace"],
        metadata={"email": "user@langfuse.com"},
        version="1.0.0"
    ):
 
        # YOUR APPLICATION CODE HERE
        result = call_llm(input)
 
        # Update the trace input and output
        langfuse.update_current_trace(
            input=input,
            output=result,
        )
 
        return result

Learn more about using the Decorator in the Langfuse SDK instrumentation docs.

Troubleshooting

No traces appearing

First, enable debug mode in the Python SDK:

export LANGFUSE_DEBUG="True"

Then run your application and check the debug logs:

OTel spans appear in the logs: Your application is instrumented correctly but traces are not reaching Langfuse. To resolve this:
1. Call langfuse.flush() at the end of your application to ensure all traces 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, so you should filter them out. See Unwanted spans in Langfuse 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.

Next Steps

Once you have instrumented your code, you can manage, evaluate and debug your application:

Manage prompts in Langfuse Add evaluation scores Run LLM-as-a-judge Evaluators Create datasets Create custom dashboards Test queries in the Playground

OpenTelemetry Amazon Bedrock AgentCore

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