Langfuse tracing

Skill Ampli-Group/agentic-mobile-blueprint/.agents/skills/langfuse-tracing

Add Langfuse observability and tracing to AI operations in Supabase Edge Functions. Use when creating functions with LLM calls that need monitoring, cost tracking, or debugging.From its SKILL.md

Install

npx -y skills add Ampli-Group/agentic-mobile-blueprint --skill langfuse-tracing

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reads credentialsReads from 3 credential sources: `LANGFUSE_PUBLIC_KEY` and 2 more.
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SKILL.md

3.1 KB, 822 tokens by cl100k_base, as published. Nobody here has run it

Langfuse Tracing for AI Operations

Available Modules

_shared/langfuse/
├── shared.ts      # Base: Tracer, withTrace()
├── gemini.ts      # Gemini: text, tools, images
├── anthropic.ts   # Anthropic: Claude text, tools
└── openai.ts      # OpenAI: text, tools, images

Basic Usage

Wrap Function with Tracing

import { withTrace, traceLLMGemini } from '../_shared/langfuse/gemini.ts';

Deno.serve(async (req: Request) => {
  return await withTrace(
    {
      name: 'my-function',
      userId: user.id,
      sessionId: item_id,
      input: { prompt: 'analyze this' },
      metadata: { function: 'my-function' },
      tags: ['ai', 'analysis'],
    },
    async (tracer) => {
      // Your AI calls here
      const { text } = await traceLLMGemini(tracer, {
        model: 'gemini-2.5-flash',
        messages: [{ role: 'user', content: 'Hello' }],
      });
      
      return new Response(JSON.stringify({ result: text }));
    }
  );
});

Gemini: Text Completion

const { text, usage } = await traceLLMGemini(tracer, {
  model: 'gemini-2.5-flash',
  messages: [{ role: 'user', content: 'Summarize this' }],
  temperature: 0.7,
  maxTokens: 500,
});

Gemini: Structured Output

const { data } = await traceLLMGemini<{ title: string; price: number }>(tracer, {
  model: 'gemini-2.5-flash',
  messages: [{ role: 'user', content: 'Extract product info' }],
  schema: {
    type: 'object',
    properties: {
      title: { type: 'string' },
      price: { type: 'number' },
    },
    required: ['title', 'price'],
  },
});
// data.title, data.price are typed!

Gemini: Function Calling

const result = await traceLLMWithToolsGemini(tracer, 'agent-name', {
  model: 'gemini-2.5-flash',
  messages: [{ role: 'user', content: 'Get weather in SF' }],
  tools: [{
    name: 'get_weather',
    description: 'Get current weather',
    parameters: {
      type: 'object',
      properties: { location: { type: 'string' } },
      required: ['location'],
    },
  }],
});

if (result.message.tool_calls) {
  // Execute tools
}

What Gets Tracked

✅ Traces: Function execution with nested operations
✅ Generations: LLM calls with prompts and responses
✅ Usage: Token counts and costs
✅ Latency: Timing for each operation
✅ Metadata: Custom tags and context
✅ Errors: Automatic error tracking

Setup

Set environment variables in .env.local:

LANGFUSE_PUBLIC_KEY=pk-lf-xxxxx
LANGFUSE_SECRET_KEY=sk-lf-xxxxx
LANGFUSE_HOST=https://cloud.langfuse.com

Available Functions

Gemini: traceLLMGemini, streamLLMGemini, traceLLMWithToolsGemini, traceImageEditGemini
Anthropic: traceLLMAnthropic, streamLLMAnthropic, traceLLMWithToolsAnthropic
OpenAI: traceLLMOpenAI, streamLLMOpenAI, traceLLMWithToolsOpenAI, traceImageEditOpenAI

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.