Langfuse Via OpenInference With Response Model

"""
Langfuse Via OpenInference With Response Model
==============================================

Demonstrates Langfuse tracing for an Agno agent that returns structured output.
"""

import base64
import os
from enum import Enum

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.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from pydantic import BaseModel, Field

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
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"  # US data region
)
# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel"  # EU data region
# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "http://localhost:3000/api/public/otel"  # Local deployment (>= v3.22.0)

os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"

tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))

# Start instrumenting agno
AgnoInstrumentor().instrument(tracer_provider=tracer_provider)


class MarketArea(Enum):
    USA = "USA"
    UK = "UK"
    EU = "EU"
    ASIA = "ASIA"


class StockPrice(BaseModel):
    price: str = Field(description="The price of the stock")
    symbol: str = Field(description="The symbol of the stock")
    date: str = Field(description="Current day")
    area: MarketArea


# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
    name="Stock Price Agent",
    model=OpenAIChat(id="gpt-5.2"),
    tools=[YFinanceTools()],
    instructions="You are a stock price agent. You check and return the current price of a stock.",
    debug_mode=True,
    output_schema=StockPrice,
)


# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    agent.print_response("What is the current price of Tesla?")