LangFuse

LangFuse is an LLM engineering platform that helps teams collaboratively develop, monitor, evaluate, and debug AI applications. This guide demonstrates how to integrate Vercel AI Gateway with LangFuse to access various AI models and providers.

1. Create a new project

   First, create a new directory for your project and initialize it:

   terminal

   mkdir langfuse-ai-gateway
   cd langfuse-ai-gateway
   pnpm dlx init -y

2. Install dependencies

   Install the required LangFuse packages along with the dotenv and @types/node packages:

   pnpmyarnnpmbun

   Terminal

   pnpm i langfuse openai dotenv @types/node

   Terminal

   yarn add langfuse openai dotenv @types/node

   Terminal

   npm i langfuse openai dotenv @types/node

   Terminal

   bun add langfuse openai dotenv @types/node

3. Configure environment variables

   Create a .env file with your Vercel AI Gateway API key and LangFuse API keys:

   .env

   AI_GATEWAY_API_KEY=your-api-key-here
    
   LANGFUSE_PUBLIC_KEY=your_langfuse_public_key
   LANGFUSE_SECRET_KEY=your_langfuse_secret_key
   LANGFUSE_HOST=https://cloud.langfuse.com

   If you're using the AI Gateway from within a Vercel deployment, you can also use the VERCEL_OIDC_TOKEN environment variable which will be automatically provided.

4. Create your LangFuse application

   Create a new file called index.ts with the following code:

   index.ts

   import { observeOpenAI } from 'langfuse';
   import OpenAI from 'openai';
    
   const openaiClient = new OpenAI({
     apiKey: process.env.AI_GATEWAY_API_KEY,
     baseURL: 'https://ai-gateway.vercel.sh/v1',
   });
    
   const client = observeOpenAI(openaiClient, {
     generationName: 'fun-fact-request', // Optional: Name of the generation in Langfuse
   });
    
   const response = await client.chat.completions.create({
     model: 'moonshotai/kimi-k2',
     messages: [
       { role: 'system', content: 'You are a helpful assistant.' },
       { role: 'user', content: 'Tell me about the food scene in San Francisco.' },
     ],
   });
    
   console.log(response.choices[0].message.content);

   The following code:

   • Creates an OpenAI client configured to use the Vercel AI Gateway
   • Uses observeOpenAI to wrap the client for automatic tracing and logging
   • Makes a chat completion request through the AI Gateway
   • Automatically captures request/response data, token usage, and metrics

5. Running the application

   Run your application using Node.js:

   pnpmyarnnpmbun

   Terminal

   pnpm dlx tsx index.ts 

   Terminal

   yarn dlx tsx index.ts 

   Terminal

   npx tsx index.ts 

   Terminal

   bunx tsx index.ts 

   You should see a response from the AI model in your console.