此示例演示了如何通过 OpenLIT 使用 Langfuse 来追踪模型调用。
import base64
import os
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
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}"
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
trace_provider = TracerProvider()
trace_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))
# Sets the global default tracer provider
from opentelemetry import trace
trace.set_tracer_provider(trace_provider)
# Creates a tracer from the global tracer provider
tracer = trace.get_tracer(__name__)
import openlit
# Initialize OpenLIT instrumentation. The disable_batch flag is set to true to process traces immediately.
openlit.init(tracer=tracer, disable_batch=True)
agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
tools=[DuckDuckGoTools()],
markdown=True,
debug_mode=True,
)
agent.print_response("What is currently trending on Twitter?")
安装依赖
pip install agno openai langfuse openlit opentelemetry-sdk opentelemetry-exporter-otlp
设置环境变量
export LANGFUSE_PUBLIC_KEY=<你的公共密钥>
export LANGFUSE_SECRET_KEY=<你的密钥>
运行 Agent
python cookbook/observability/langfuse_via_openlit.py
OTEL_EXPORTER_OTLP_ENDPOINT
以匹配您的数据区域或本地部署:
https://us.cloud.langfuse.com/api/public/otel
适用于美国区域https://cloud.langfuse.com/api/public/otel
适用于欧盟区域http://localhost:3000/api/public/otel
适用于本地部署