Monitor and Debug LLM Applications

Langfuse

Expert in Langfuse - the open-source LLM observability platform. Covers tracing,
prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex,
and OpenAI. Essential for debugging, monitoring, and improving LLM applications
in production.

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.

Expertise

• Tracing architecture
• Prompt versioning
• Evaluation strategies
• Cost optimization
• Quality monitoring

Capabilities

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

Prerequisites

• 0: LLM application basics
• 1: API integration experience
• 2: Understanding of tracing concepts
• Required skills: Python or TypeScript/JavaScript, Langfuse account (cloud or self-hosted), LLM API keys

Scope

• 0: Self-hosted requires infrastructure
• 1: High-volume may need optimization
• 2: Real-time dashboard has latency
• 3: Evaluation requires setup

Ecosystem

Primary

• Langfuse Cloud
• Langfuse Self-hosted
• Python SDK
• JS/TS SDK

Common_integrations

• LangChain
• LlamaIndex
• OpenAI SDK
• Anthropic SDK
• Vercel AI SDK

Platforms

• Any Python/JS backend
• Serverless functions
• Jupyter notebooks

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]}
)

Prompt Management

Version and deploy prompts

When to use: Managing prompts across environments

from langfuse import Langfuse

langfuse = Langfuse()

Fetch prompt from Langfuse

(Create in UI or via API first)

prompt = langfuse.get_prompt("customer-support-v2")

Get compiled prompt with variables

compiled = prompt.compile(
customer_name="John",
issue="billing question"
)

Use with OpenAI

response = openai.chat.completions.create(
model=prompt.config.get("model", "gpt-4o"),
messages=compiled,
temperature=prompt.config.get("temperature", 0.7)
)

Link generation to prompt version

trace = langfuse.trace(name="support-chat")
generation = trace.generation(
name="response",
model="gpt-4o",
prompt=prompt # Links to specific version
)

Create/update prompts via API

langfuse.create_prompt(
name="customer-support-v3",
prompt=[
{"role": "system", "content": "You are a support agent..."},
{"role": "user", "content": "{{user_message}}"}
],
config={
"model": "gpt-4o",
"temperature": 0.7
},
labels=["production"] # or ["staging", "development"]
)

Fetch specific label

prompt = langfuse.get_prompt(
"customer-support-v3",
label="production" # Gets latest with this label
)

Evaluation and Scoring

Evaluate LLM outputs systematically

When to use: Quality assurance and improvement

from langfuse import Langfuse

langfuse = Langfuse()

Manual scoring in code

trace = langfuse.trace(name="qa-flow")

After getting response

trace.score(
name="relevance",
value=0.85, # 0-1 scale
comment="Response addressed the question"
)

trace.score(
name="correctness",
value=1, # Binary: 0 or 1
data_type="BOOLEAN"
)

LLM-as-judge evaluation

def evaluate_response(question: str, response: str) -> float:
eval_prompt = f"""
Rate the response quality from 0 to 1.

Question: {question}
Response: {response}

Output only a number between 0 and 1.
"""

result = openai.chat.completions.create(
    model="gpt-4o-mini",  # Cheaper model for eval
    messages=[{"role": "user", "content": eval_prompt}]
)

return float(result.choices[0].message.content.strip())

Score asynchronously

score = evaluate_response(question, response)
trace.score(
name="quality-llm-judge",
value=score
)

Create evaluation dataset

dataset = langfuse.create_dataset(name="support-qa-v1")

Add items to dataset

langfuse.create_dataset_item(
dataset_name="support-qa-v1",
input={"question": "How do I reset my password?"},
expected_output="Go to settings > security > reset password"
)

Run evaluation on dataset

dataset = langfuse.get_dataset("support-qa-v1")

for item in dataset.items:
# Generate response
response = generate_response(item.input["question"])

# Link to dataset item
trace = langfuse.trace(name="eval-run")
trace.generation(
    name="response",
    input=item.input,
    output=response
)

# Score against expected
similarity = calculate_similarity(response, item.expected_output)
trace.score(name="similarity", value=similarity)

# Link trace to dataset item
item.link(trace, "eval-run-1")

Decorator Pattern

Clean instrumentation with decorators

When to use: Function-based applications

from langfuse.decorators import observe, langfuse_context

@observe() # Creates a trace
def chat_handler(user_id: str, message: str) -> str:
# All nested @observe calls become spans
context = get_context(message)
response = generate_response(message, context)
return response

@observe() # Becomes a span under parent trace
def get_context(message: str) -> str:
# RAG retrieval
docs = retriever.get_relevant_documents(message)
return "\n".join([d.page_content for d in docs])

@observe(as_type="generation") # LLM generation span
def generate_response(message: str, context: str) -> str:
response = openai.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": f"Context: {context}"},
{"role": "user", "content": message}
]
)
return response.choices[0].message.content

Add metadata and scores

@observe()
def main_flow(user_input: str):
# Update current trace
langfuse_context.update_current_trace(
user_id="user-123",
session_id="session-456",
tags=["production"]
)

result = process(user_input)

# Score the trace
langfuse_context.score_current_trace(
    name="success",
    value=1 if result else 0
)

return result

Works with async

@observe()
async def async_handler(message: str):
result = await async_generate(message)
return result

Collaboration

Delegation Triggers

• agent|langgraph|graph -> langgraph (Need to build agent to monitor)
• crewai|multi-agent|crew -> crewai (Need to build crew to monitor)
• structured output|extraction -> structured-output (Need to build extraction to monitor)

Observable LangGraph Agent

Skills: langfuse, langgraph

Workflow:

1. Build agent with LangGraph
2. Add Langfuse callback handler
3. Trace all LLM calls and tool uses
4. Score outputs for quality
5. Monitor and iterate

Monitored RAG Pipeline

Skills: langfuse, structured-output

Workflow:

1. Build RAG with retrieval and generation
2. Trace retrieval and LLM calls
3. Score relevance and accuracy
4. Track costs and latency
5. Optimize based on data

Evaluated Agent System

Skills: langfuse, langgraph, structured-output

Workflow:

1. Build agent with structured outputs
2. Create evaluation dataset
3. Run evaluations with traces
4. Compare prompt versions
5. Deploy best performers

Related Skills

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

When to Use

• User mentions or implies: langfuse
• User mentions or implies: llm observability
• User mentions or implies: llm tracing
• User mentions or implies: prompt management
• User mentions or implies: llm evaluation
• User mentions or implies: monitor llm
• User mentions or implies: debug llm

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.