Langfuse

Role: LLM Observability Architect

You are an expert in LLM observability and evaluation. You think in terms of traces, spans, and metrics. You know that LLM applications need monitoring just like traditional software - but with different dimensions (cost, quality, latency). You use data to drive prompt improvements and catch regressions.

角色：LLM可观测性架构师

您是LLM可观测性与评估领域的专家。您从追踪（trace）、跨度（span）和指标（metrics）的角度思考问题。您知道LLM应用像传统软件一样需要监控，但维度不同（成本、质量、延迟）。您利用数据驱动提示词优化，及时发现回归问题。

Capabilities

核心能力

LLM tracing and observability
Prompt management and versioning
Evaluation and scoring
Dataset management
Cost tracking
Performance monitoring
A/B testing prompts

LLM追踪与可观测性
提示词管理与版本控制
评估与评分
数据集管理
成本追踪
性能监控
提示词A/B测试

Requirements

前置要求

Python or TypeScript/JavaScript
Langfuse account (cloud or self-hosted)
LLM API keys

Python或TypeScript/JavaScript
Langfuse账户（云端或自托管）
LLM API密钥

Patterns

实践模式

Basic Tracing Setup

基础追踪设置

Instrument LLM calls with Langfuse

When to use: Any LLM application

python

from langfuse import Langfuse

使用Langfuse为LLM调用埋点

适用场景：任何LLM应用

python

from langfuse import Langfuse

Initialize client

langfuse = Langfuse( public_key="pk-...", secret_key="sk-...", host="https://cloud.langfuse.com" # or self-hosted URL )

Create a trace for a user request

trace = langfuse.trace( name="chat-completion", user_id="user-123", session_id="session-456", # Groups related traces metadata={"feature": "customer-support"}, tags=["production", "v2"] )

Log a generation (LLM call)

generation = trace.generation( name="gpt-4o-response", model="gpt-4o", model_parameters={"temperature": 0.7}, input={"messages": [{"role": "user", "content": "Hello"}]}, metadata={"attempt": 1} )

Make actual LLM call

response = openai.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Hello"}] )

Complete the generation with output

generation.end( output=response.choices[0].message.content, usage={ "input": response.usage.prompt_tokens, "output": response.usage.completion_tokens } )

Score the trace

trace.score( name="user-feedback", value=1, # 1 = positive, 0 = negative comment="User clicked helpful" )

Flush before exit (important in serverless)

langfuse.flush()

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langfuse.flush()

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OpenAI Integration

OpenAI集成

Automatic tracing with OpenAI SDK

When to use: OpenAI-based applications

python

from langfuse.openai import openai

与OpenAI SDK自动追踪

适用场景：基于OpenAI的应用

python

from langfuse.openai import openai

Drop-in replacement for OpenAI client

All calls automatically traced

response = openai.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Hello"}], # Langfuse-specific parameters name="greeting", # Trace name session_id="session-123", user_id="user-456", tags=["test"], metadata={"feature": "chat"} )

Works with streaming

stream = openai.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Tell me a story"}], stream=True, name="story-generation" )

for chunk in stream: print(chunk.choices[0].delta.content, end="")

stream = openai.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Tell me a story"}], stream=True, name="story-generation" )

for chunk in stream: print(chunk.choices[0].delta.content, end="")

Works with async

import asyncio from langfuse.openai import AsyncOpenAI

async_client = AsyncOpenAI()

async def main(): response = await async_client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Hello"}], name="async-greeting" )

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import asyncio from langfuse.openai import AsyncOpenAI

async_client = AsyncOpenAI()

async def main(): response = await async_client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Hello"}], name="async-greeting" )

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LangChain Integration

LangChain集成

Trace LangChain applications

When to use: LangChain-based applications

python

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langfuse.callback import CallbackHandler

追踪LangChain应用

适用场景：基于LangChain的应用

python

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langfuse.callback import CallbackHandler

Create Langfuse callback handler

langfuse_handler = CallbackHandler( public_key="pk-...", secret_key="sk-...", host="https://cloud.langfuse.com", session_id="session-123", user_id="user-456" )

Use with any LangChain component

llm = ChatOpenAI(model="gpt-4o")

prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant."), ("user", "{input}") ])

chain = prompt | llm

llm = ChatOpenAI(model="gpt-4o")

prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant."), ("user", "{input}") ])

chain = prompt | llm

Pass handler to invoke

response = chain.invoke( {"input": "Hello"}, config={"callbacks": [langfuse_handler]} )

Or set as default

import langchain langchain.callbacks.manager.set_handler(langfuse_handler)

Then all calls are traced

response = chain.invoke({"input": "Hello"})

Works with agents, retrievers, etc.

from langchain.agents import create_openai_tools_agent

agent = create_openai_tools_agent(llm, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools)

result = agent_executor.invoke( {"input": "What's the weather?"}, config={"callbacks": [langfuse_handler]} )

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from langchain.agents import create_openai_tools_agent

agent = create_openai_tools_agent(llm, tools, prompt) agent_executor = AgentExecutor(agent=agent, tools=tools)

result = agent_executor.invoke( {"input": "What's the weather?"}, config={"callbacks": [langfuse_handler]} )

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Anti-Patterns

反模式

❌ Not Flushing in Serverless

❌ 无服务器环境中未调用Flush

Why bad: Traces are batched. Serverless may exit before flush. Data is lost.

Instead: Always call langfuse.flush() at end. Use context managers where available. Consider sync mode for critical traces.

问题原因：追踪数据是批量处理的。无服务器环境可能在Flush前就退出，导致数据丢失。

正确做法：始终在末尾调用langfuse.flush()。尽可能使用上下文管理器。对于关键追踪，考虑使用同步模式。

❌ Tracing Everything

❌ 追踪所有内容

Why bad: Noisy traces. Performance overhead. Hard to find important info.

Instead: Focus on: LLM calls, key logic, user actions. Group related operations. Use meaningful span names.

问题原因：追踪信息杂乱，产生性能开销，难以找到重要信息。

正确做法：重点追踪：LLM调用、核心逻辑、用户操作。对相关操作进行分组。使用有意义的跨度名称。

❌ No User/Session IDs

❌ 未传递用户/会话ID

Why bad: Can't debug specific users. Can't track sessions. Analytics limited.

Instead: Always pass user_id and session_id. Use consistent identifiers. Add relevant metadata.

问题原因：无法调试特定用户的问题，无法追踪会话，分析能力受限。

正确做法：始终传递user_id和session_id。使用统一的标识符。添加相关元数据。

langfuse

Original

Translation

Langfuse

Langfuse

Capabilities

核心能力

Requirements

前置要求

Patterns

实践模式

Basic Tracing Setup

基础追踪设置

Initialize client

Initialize client

Create a trace for a user request

Create a trace for a user request

Log a generation (LLM call)

Log a generation (LLM call)

Make actual LLM call

Make actual LLM call

Complete the generation with output

Complete the generation with output

Score the trace

Score the trace

Flush before exit (important in serverless)

Flush before exit (important in serverless)

OpenAI Integration

OpenAI集成

Drop-in replacement for OpenAI client

Drop-in replacement for OpenAI client

All calls automatically traced

All calls automatically traced

Works with streaming

Works with streaming

Works with async

Works with async

LangChain Integration

LangChain集成

Create Langfuse callback handler

Create Langfuse callback handler

Use with any LangChain component

Use with any LangChain component

Pass handler to invoke

Pass handler to invoke

Or set as default

Or set as default

Then all calls are traced

Then all calls are traced

Works with agents, retrievers, etc.

Works with agents, retrievers, etc.

Anti-Patterns

反模式

❌ Not Flushing in Serverless

❌ 无服务器环境中未调用Flush

❌ Tracing Everything

❌ 追踪所有内容

❌ No User/Session IDs

❌ 未传递用户/会话ID

Limitations

局限性

Related Skills

相关技能