---
name: langfuse
description: "สกิลสำหรับ Langfuse ใช้เมื่อต้องการแนวทาง ขั้นตอน Template หรือคำแนะนำเชิงปฏิบัติสำหรับงานวิเคราะห์ข้อมูล วิจัย AI ฐานข้อมูล วิทยาศาสตร์ข้อมูล และรายงานเชิงลึก ในหัวข้อนี้"
source: vibeship-spawner-skills (Apache 2.0)
---

# 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

```python
from langfuse import Langfuse

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

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

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

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

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

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

# Langfuse
langfuse.flush()
```

### OpenAI Integration

Automatic tracing with OpenAI SDK

**When to use**: OpenAI-based applications

```python
from langfuse.openAI import openAI

# Langfuse
# Langfuse

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"}
)

# Langfuse
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="")

# Langfuse
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

```python
from langchain_openAI import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langfuse.callback import CallbackHandler

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

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

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

chain = prompt | llm

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

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

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

# Langfuse
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`
