langfuse

# 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 # 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 ```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="") # 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 ```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 # 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` --- > Converted and distributed by [TomeVault](https://tomevault.io) | [Claim this content](https://tomevault.io/claim/davila7/claude-code-templates) <!-- tomevault:2.0:skill_md:2026-04-05 -->