LangChain gives you tools for every step of the agent development lifecycle. This guide demonstrates how to integrate Vercel AI Gateway with LangChain 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 langchain-ai-gateway
   cd langchain-ai-gateway
   pnpm dlx init -y

2. Install dependencies

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

   pnpmyarnnpmbun

   pnpm i langchain @langchain/core @langchain/openai dotenv @types/node

3. Configure environment variables

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

   .env

   AI_GATEWAY_API_KEY=your-api-key-here

   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 LangChain application

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

   index.ts

   import 'dotenv/config';
   import { ChatOpenAI } from '@langchain/openai';
   import { HumanMessage } from '@langchain/core/messages';
    
   async function main() {
     console.log('=== LangChain Chat Completion with AI Gateway ===');
    
     const apiKey =
       process.env.AI_GATEWAY_API_KEY || process.env.VERCEL_OIDC_TOKEN;
    
     const chat = new ChatOpenAI({
       apiKey: apiKey,
       modelName: 'openai/gpt-4o-mini',
       temperature: 0.7,
       configuration: {
         baseURL: 'https://ai-gateway.vercel.sh/v1',
       },
     });
    
     try {
       const response = await chat.invoke([
         new HumanMessage('Write a one-sentence bedtime story about a unicorn.'),
       ]);
    
       console.log('Response:', response.content);
     } catch (error) {
       console.error('Error:', error);
     }
   }
    
   main().catch(console.error);

   The following code:

   • Initializes a ChatOpenAI instance configured to use the AI Gateway
   • Sets the model temperature to 0.7
   • Makes a chat completion request
   • Handles any potential errors

5. Running the application

   Run your application using Node.js:

   pnpmyarnnpmbun

   pnpm dlx tsx index.ts 

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