# Langfuse Hello World

> 'Create a minimal working Langfuse trace example. Use when starting a new Langfuse integration, testing your setup, or learning basic Langfuse tracing patterns. Trigger with phrases like "langfuse hello world", "langfuse example", "langfuse quick start", "first langfuse trace", "simple langfuse code". '

- Skill: `thedixitjain/langfuse-hello-world` (Agent Skill)
- Install (CLI): `npx skillmds add thedixitjain/langfuse-hello-world`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/langfuse-hello-world/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: thedixitjain (https://skillmd.com/u/thedixitjain)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/thedixitjain/langfuse-hello-world

---


# Langfuse Hello World

## Overview

Create your first Langfuse trace with real SDK calls. Demonstrates the trace/span/generation hierarchy, the `observe` wrapper, and the OpenAI drop-in integration.

## Prerequisites

- Completed `langfuse-install-auth` setup
- Valid API credentials in environment variables
- OpenAI API key (for the OpenAI integration example)

## Instructions

### Step 1: Hello World with v4+ Modular SDK

```typescript
// hello-langfuse.ts
import { startActiveObservation, observe, updateActiveObservation } from "@langfuse/tracing";
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

// Register OpenTelemetry processor (once at startup)
const sdk = new NodeSDK({
  spanProcessors: [new LangfuseSpanProcessor()],
});
sdk.start();

async function main() {
  // Create a top-level trace with startActiveObservation
  await startActiveObservation("hello-world", async (span) => {
    span.update({
      input: { message: "Hello, Langfuse!" },
      metadata: { source: "hello-world-example" },
    });

    // Nested span -- automatically linked to parent
    await startActiveObservation("process-input", async (child) => {
      child.update({ input: { text: "processing..." } });
      await new Promise((r) => setTimeout(r, 100));
      child.update({ output: { result: "done" } });
    });

    // Nested generation (LLM call tracking)
    await startActiveObservation(
      { name: "llm-response", asType: "generation" },
      async (gen) => {
        gen.update({
          model: "gpt-4o",
          input: [{ role: "user", content: "Say hello" }],
          output: { content: "Hello! How can I help you today?" },
          usage: { promptTokens: 5, completionTokens: 10, totalTokens: 15 },
        });
      }
    );

    span.update({ output: { status: "completed" } });
  });

  // Allow time for the span processor to flush
  await sdk.shutdown();
  console.log("Trace created! Check your Langfuse dashboard.");
}

main().catch(console.error);
```

### Step 2: Hello World with `observe` Wrapper

The `observe` wrapper traces existing functions without modifying internals:

```typescript
import { observe, updateActiveObservation } from "@langfuse/tracing";

// Wrap any async function -- it becomes a traced span
const processQuery = observe(async (query: string) => {
  updateActiveObservation({ input: { query } });

  // Simulate processing
  const result = `Processed: ${query}`;

  updateActiveObservation({ output: { result } });
  return result;
});

// Wrap an LLM call as a generation
const generateAnswer = observe(
  { name: "generate-answer", asType: "generation" },
  async (prompt: string) => {
    updateActiveObservation({
      model: "gpt-4o",
      input: [{ role: "user", content: prompt }],
    });

    const answer = "Langfuse is an open-source LLM observability platform.";

    updateActiveObservation({
      output: answer,
      usage: { promptTokens: 10, completionTokens: 20 },
    });
    return answer;
  }
);

// Both functions auto-nest when called within an observed context
const pipeline = observe(async () => {
  await processQuery("What is Langfuse?");
  await generateAnswer("Explain Langfuse in one sentence.");
});

await pipeline();
```

### Step 3: Hello World with Legacy v3 SDK

```typescript
import { Langfuse } from "langfuse";

const langfuse = new Langfuse();

async function helloLangfuse() {
  const trace = langfuse.trace({
    name: "hello-world",
    userId: "demo-user",
    metadata: { source: "hello-world-example" },
    tags: ["demo", "getting-started"],
  });

  // Span: child operation
  const span = trace.span({
    name: "process-input",
    input: { message: "Hello, Langfuse!" },
  });
  await new Promise((r) => setTimeout(r, 100));
  span.end({ output: { result: "Processed successfully!" } });

  // Generation: LLM call tracking
  trace.generation({
    name: "llm-response",
    model: "gpt-4o",
    input: [{ role: "user", content: "Say hello" }],
    output: { content: "Hello! How can I help you today?" },
    usage: { promptTokens: 5, completionTokens: 10, totalTokens: 15 },
  });

  await langfuse.flushAsync();
  console.log("Trace URL:", trace.getTraceUrl());
}

helloLangfuse();
```

### Step 4: Python Hello World

```python
from langfuse.decorators import observe, langfuse_context

@observe()
def process_query(query: str) -> str:
    return f"Processed: {query}"

@observe(as_type="generation")
def generate_response(prompt: str) -> str:
    langfuse_context.update_current_observation(
        model="gpt-4o",
        usage={"prompt_tokens": 10, "completion_tokens": 20},
    )
    return "Hello from Langfuse!"

@observe()
def main():
    result = process_query("Hello!")
    response = generate_response("Say hello")
    return response

main()
```

## Trace Hierarchy

```
Trace: hello-world
  ├── Span: process-input
  │     input: { message: "Hello, Langfuse!" }
  │     output: { result: "Processed successfully!" }
  └── Generation: llm-response
        model: gpt-4o
        input: [{ role: "user", content: "Say hello" }]
        output: "Hello! How can I help you today?"
        usage: { promptTokens: 5, completionTokens: 10 }
```

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Import error | SDK not installed | `npm install @langfuse/tracing @langfuse/otel @opentelemetry/sdk-node` |
| Auth error (401) | Invalid credentials | Verify `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` |
| Trace not appearing | Data not flushed | Call `sdk.shutdown()` (v4+) or `langfuse.flushAsync()` (v3) |
| Network error | Host unreachable | Check `LANGFUSE_BASE_URL` value |
| No auto-nesting | Missing OTel setup | Register `LangfuseSpanProcessor` with `NodeSDK` |

## Resources

- [Langfuse JS/TS SDK Cookbook](https://langfuse.com/guides/cookbook/js_langfuse_sdk)
- [TypeScript SDK Instrumentation](https://langfuse.com/docs/observability/sdk/typescript/instrumentation)
- [Python Decorators Guide](https://langfuse.com/docs/sdk/python/decorators)
- [Observation Types](https://langfuse.com/docs/observability/features/observation-types)

## Next Steps

Proceed to `langfuse-core-workflow-a` for real OpenAI/Anthropic tracing, or `langfuse-local-dev-loop` for development workflow setup.

---

**Source:** [`jeremylongshore/claude-code-plugins-plus-skills`](https://github.com/jeremylongshore/claude-code-plugins-plus-skills) → `plugins/saas-packs/langfuse-pack/skills/langfuse-hello-world/SKILL.md`