Langfuse Observability Patterns

Quick Guide: Use the Langfuse TypeScript SDK (built on OpenTelemetry) to add observability to LLM applications. Install @langfuse/tracing, @langfuse/otel, and @opentelemetry/sdk-node for core tracing. Use startActiveObservation() for automatic context propagation or observe() to wrap functions. Use @langfuse/openai with observeOpenAI() for zero-config OpenAI tracing. Use LangfuseClient from @langfuse/client for prompt management, scores, and datasets. Always call forceFlush() or sdk.shutdown() in short-lived processes.

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CRITICAL: Before Using This Skill

(You MUST import and register instrumentation.ts at the top of your entry point BEFORE any other imports -- OpenTelemetry must instrument modules before they are loaded)

(You MUST call forceFlush() or sdk.shutdown() in short-lived processes (serverless, scripts, CLI tools) -- events are batched and will be lost without explicit flushing)

(You MUST use @langfuse/openai with observeOpenAI() for OpenAI SDK tracing -- do NOT manually create generation observations for OpenAI calls when the wrapper handles it automatically)

(You MUST set LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY, and LANGFUSE_BASE_URL via environment variables -- never hardcode credentials)

(You MUST use startActiveObservation() or observe() for nested tracing -- manual startObservation() requires explicit .end() calls and does NOT propagate context automatically)