langfuse-deploy-integration

name	langfuse-deploy-integration
description	Deploy Langfuse with your application across different platforms. Use when deploying Langfuse to Vercel, AWS, GCP, or Docker, or integrating Langfuse into your deployment pipeline. Trigger with phrases like "deploy langfuse", "langfuse Vercel", "langfuse AWS", "langfuse Docker", "langfuse production deploy".
allowed-tools	Read, Write, Edit, Bash(docker:*), Bash(vercel:*), Bash(gcloud:*)
version	1.12.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","langfuse","deployment","docker"]
compatibility	Designed for Claude Code

Langfuse Deploy Integration

Overview

Deploy Langfuse LLM observability alongside your application. Covers integrating the SDK for serverless (Vercel/Lambda), Docker, Cloud Run, and self-hosting the Langfuse server itself.

Prerequisites

• Langfuse API keys (cloud or self-hosted)
• Application using Langfuse SDK
• Target platform CLI installed

Instructions

Step 1: Vercel / Next.js Deployment

set -euo pipefail
# Add secrets to Vercel
vercel env add LANGFUSE_PUBLIC_KEY production
vercel env add LANGFUSE_SECRET_KEY production
vercel env add LANGFUSE_BASE_URL production

// app/api/chat/route.ts (Next.js App Router)
import { NextRequest, NextResponse } from "next/server";
import { LangfuseClient } from "@langfuse/client";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import OpenAI from "openai";

const langfuse = new LangfuseClient();
const openai = new OpenAI();

export async function POST(req: NextRequest) {
  const { messages } = await req.json();

  const response =  (
    { : , :  },
     () => {
      ({
        : ,
        : messages,
        : { :  },
      });

       result =  openai...({
        : ,
        messages,
      });

      ({
        : result.[].,
        : {
          : result.?.,
          : result.?.,
        },
      });

       result.[].;
    }
  );

   .(response);
}