Langfuse — 开源 LLM 工程平台

Quick Start — Python SDK with OpenAI

The @observe() decorator creates a span for any function. langfuse.openai wraps the OpenAI SDK so every call becomes an automatic child span with prompt, response, usage, and cost. For non-OpenAI providers, use Langfuse’s generic SDK or the OpenTelemetry instrumentation.

# pip install langfuse openai
import os
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com"  # or self-host URL

# The drop-in: langfuse-wrapped OpenAI client → every call auto-traced
from langfuse.openai import openai  # instead of: from openai import OpenAI

client = openai.OpenAI()

def greet(name: str) -> str:
    resp = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": f"Greet {name} in one sentence."}],
        name="greet",  # span name shown in Langfuse UI
    )
    return resp.choices[0].message.content

# Trace a whole agent turn with nested spans
from langfuse.decorators import observe

@observe()
def agent(user_msg: str) -> str:
    greeting = greet("William")           # nested LLM span
    return f"{greeting}\nYou said: {user_msg}"

print(agent("Hello from Langfuse"))
# Langfuse UI now shows a tree: agent → greet (LLM call, prompt/response, cost, latency).

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