This is a Jupyter notebook

Integrate Langfuse with Agno

This notebook demonstrates how to integrate Langfuse with Agno using OpenTelemetry via OpenInference or the OpenLIT SDK. 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 opentelemetry-sdk opentelemetry-exporter-otlp openinference-instrumentation-agno yfinance openlit

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 for more information on the Langfuse OpenTelemetry endpoint /api/public/otel and authentication.

You’ll also need your OpenAI API key.

import os
import base64
 
# 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_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
 
LANGFUSE_AUTH = base64.b64encode(
    f"{os.environ.get('LANGFUSE_PUBLIC_KEY')}:{os.environ.get('LANGFUSE_SECRET_KEY')}".encode()
).decode()
 
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = os.environ.get("LANGFUSE_HOST") + "/api/public/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
 
# your openai key
os.environ["OPENAI_API_KEY"] = "sk-proj-..."

Step 3: Sending Traces to Langfuse

Example 1: Using Langfuse with OpenInference

This example demonstrates how to instrument your Agno agent with OpenInference and send traces to Langfuse.

import base64
import os
 
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.yfinance import YFinanceTools
from openinference.instrumentation.agno import AgnoInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
 
# Configure the tracer provider
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))
trace_api.set_tracer_provider(tracer_provider=tracer_provider)
 
# Start instrumenting agno
AgnoInstrumentor().instrument()
 
# Create and configure the agent
agent = Agent(
    name="Stock Price Agent",
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[YFinanceTools()],
    instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
    debug_mode=True,
)
 
# Use the agent
agent.print_response("What is the current price of Tesla?")

Example 2: Using Langfuse with OpenLIT

This example demonstrates how to use Langfuse via OpenLIT to trace model calls.

import base64
import os
 
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry import trace
 
# Configure the tracer provider
trace_provider = TracerProvider()
trace_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))
trace.set_tracer_provider(trace_provider)
 
# Initialize OpenLIT instrumentation
import openlit
openlit.init(tracer=trace.get_tracer(__name__), 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?")