langfuselisted

# 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 = opena