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.

Capabilities

• LLM tracing and observability

• Prompt management and versioning

• Evaluation and scoring

• Dataset management

• Cost tracking

• Performance monitoring

• A/B testing prompts

Requirements

• Python or TypeScript/JavaScript

• Langfuse account (cloud or self-hosted)

• LLM API keys

Patterns

Basic Tracing Setup

Instrument LLM calls with Langfuse

When to use: Any LLM application

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()

OpenAI Integration

Automatic tracing with OpenAI SDK

When to use: OpenAI-based applications

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="")

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" )

LangChain Integration

Trace LangChain applications

When to use: LangChain-based applications

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

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]} )

Anti-Patterns

❌ Not Flushing in Serverless

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.

❌ Tracing Everything

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.

❌ No User/Session IDs

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.

Limitations

• Self-hosted requires infrastructure

• High-volume may need optimization

• Real-time dashboard has latency

• Evaluation requires setup

Related Skills

Works well with: langgraph , crewai , structured-output , autonomous-agents