This is a Jupyter notebook

Integrate Langfuse with Google’s Agent Development Kit

This notebook demonstrates how to capture detailed traces from a Google Agent Development Kit (ADK) application with Langfuse using the OpenTelemetry (OTel) protocol.

Why Agent Development Kit?
Google’s Agent Development Kit streamlines building, orchestrating, and tracing generative-AI agents out of the box, letting you move from prototype to production far faster than wiring everything yourself.

Why Langfuse?
Langfuse gives you a detailed dashboard and rich analytics for every prompt, model response, and function call in your agent, making it easy to debug, evaluate, and iterate on LLM apps.

Step 1: Install dependencies

%pip install google-adk opentelemetry-sdk opentelemetry-exporter-otlp -q

Step 2: Set up environment variables

Fill in the Langfuse and OpenTelemetry credentials for your project. Also set your Gemini API key.

import os
import base64
 
LANGFUSE_PUBLIC_KEY = "pk-lf-..."
LANGFUSE_SECRET_KEY = "sk-lf-..."
LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()
 
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
 
# Gemini API Key (Get from Google AI Studio: https://aistudio.google.com/app/apikey)
os.environ["GOOGLE_API_KEY"] = "..."

Step 3: Initialise OTel

We configure an OTLPSpanExporter so every span generated in this notebook is pushed straight to Langfuse.

from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
 
provider = TracerProvider(resource=Resource.create({"service.name": "hello_agent"}))
exporter = OTLPSpanExporter()
provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("hello_app")

Step 4: Build a hello world agent

Every tool call and model completion is captured as an OpenTelemetry span and forwarded to Langfuse.

from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
 
# 1. ‘say_hello’ tool
def say_hello():
    return {"greeting": "Hello Langfuse 👋"}
 
agent = Agent(
    name="hello_agent",
    model="gemini-2.0-flash",
    instruction="Always greet using the say_hello tool.",
    tools=[say_hello],
)
 
# 2. session service + runner 
session_service = InMemorySessionService()
 
APP_NAME  = "hello_app"
USER_ID   = "demo-user"
SESSION_ID = "demo-session"  # any string; UUIDs work too
 
# create the session once
session_service.create_session(
    app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID
)
 
runner = Runner(agent=agent, app_name=APP_NAME, session_service=session_service)
 
# 3.  single‑turn run 
user_msg = types.Content(role="user", parts=[types.Part(text="hi")])
 
for event in runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=user_msg):
    if event.is_final_response():
        print(event.content.parts[0].text)