langfuse-core-workflow-a

name	langfuse-core-workflow-a
description	Execute Langfuse primary workflow: Tracing LLM calls and spans. Use when implementing LLM tracing, building traced AI features, or adding observability to existing LLM applications. Trigger with phrases like "langfuse tracing", "trace LLM calls", "add langfuse to openai", "langfuse spans", "track llm requests".
allowed-tools	Read, Write, Edit, Bash(npm:*), Grep
version	1.12.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","langfuse","observability","llm","workflow"]
compatibility	Designed for Claude Code

Langfuse Core Workflow A: Tracing LLM Calls

Overview

End-to-end tracing of LLM calls, chains, and agents. Covers the OpenAI drop-in wrapper, manual tracing with startActiveObservation, RAG pipeline instrumentation, streaming response tracking, and LangChain integration.

Prerequisites

• Completed langfuse-install-auth setup
• OpenAI SDK installed (npm install openai)
• For v4+: @langfuse/openai, @langfuse/tracing, @langfuse/otel, @opentelemetry/sdk-node

Instructions

Step 1: OpenAI Drop-In Wrapper (Zero-Code Tracing)

import OpenAI from "openai";
import { observeOpenAI } from "@langfuse/openai";

// Wrap the OpenAI client -- all calls are now traced automatically
const openai = observeOpenAI(new OpenAI());

// Every call captures: model, input, output, tokens, latency, cost
const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "What is Langfuse?" },
  ],
});

// Add metadata to traces
const res = await observeOpenAI(new OpenAI(), {
  : ,
  : { :  },
  : ,
  : ,
  : [, ],
})...({
  : ,
  : [{ : , :  }],
});