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Integrating Agno with Langfuse

Langfuse provides a robust platform for tracing and monitoring AI model calls. By integrating Agno with Langfuse, you can utilize OpenInference and OpenLIT to send traces and gain insights into your agent’s performance.

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Prerequisites

  1. Install Dependencies Ensure you have the necessary packages installed: 
    pip install agno openai langfuse opentelemetry-sdk opentelemetry-exporter-otlp openinference-instrumentation-agno
  2. Setup Langfuse Account
    • Either self-host or sign up for an account at Langfuse.
    • Obtain your public and secret API keys from the Langfuse dashboard.
  3. Set Environment Variables Configure your environment with the Langfuse API keys: 
    export LANGFUSE_PUBLIC_KEY=<your-public-key>
export LANGFUSE_SECRET_KEY=<your-secret-key>

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Sending Traces to Langfuse

  • ​
    Example: 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

# Set environment variables for Langfuse
LANGFUSE_AUTH = base64.b64encode(
    f"{os.getenv('LANGFUSE_PUBLIC_KEY')}:{os.getenv('LANGFUSE_SECRET_KEY')}".encode()
).decode()
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"

# 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: 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

# Set environment variables for Langfuse
LANGFUSE_AUTH = base64.b64encode(
    f"{os.getenv('LANGFUSE_PUBLIC_KEY')}:{os.getenv('LANGFUSE_SECRET_KEY')}".encode()
).decode()
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"

# 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?")

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Notes

  • Environment Variables: Ensure your environment variables are correctly set for the API keys and OTLP endpoint.
  • Data Regions: Adjust the OTEL_EXPORTER_OTLP_ENDPOINT for your data region or local deployment as needed. Available regions include:
    • https://us.cloud.langfuse.com/api/public/otel for the US region
    • https://eu.cloud.langfuse.com/api/public/otel for the EU region
    • http://localhost:3000/api/public/otel for local deployment
By following these steps, you can effectively integrate Agno with Langfuse, enabling comprehensive observability and monitoring of your AI agents.