Vercel AI SDK

The Vercel AI SDK is a TypeScript toolkit for building AI-powered applications. Connect it to TensorX using the OpenAI-compatible provider.

Prerequisites

• Node.js 18+

• TensorX API key from app.tensorx.ai

Installation

npm install ai @ai-sdk/openai-compatible
# or
pnpm add ai @ai-sdk/openai-compatible
# or
yarn add ai @ai-sdk/openai-compatible

Configuration

Create Provider Instance

import { createOpenAICompatible } from '@ai-sdk/openai-compatible';

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,
  baseURL: 'https://api.tensorx.ai/v1',
});

Environment Variables

Create a .env.local file:

TENSORX_API_KEY=your-tensorx-api-key

Usage Examples

Text Generation

import { generateText } from 'ai';
import { createOpenAICompatible } from '@ai-sdk/openai-compatible';

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,
  baseURL: 'https://api.tensorx.ai/v1',
});

const { text } = await generateText({
  model: tensorx('z-ai/glm-5.3'),
  prompt: 'Write a haiku about programming.',
});

console.log(text);

Streaming Text

import { streamText } from 'ai';
import { createOpenAICompatible } from '@ai-sdk/openai-compatible';

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,
  baseURL: 'https://api.tensorx.ai/v1',
});

const { textStream } = await streamText({
  model: tensorx('z-ai/glm-5.3-flash'),
  prompt: 'Explain the theory of relativity.',
});

for await (const chunk of textStream) {
  process.stdout.write(chunk);
}

Chat Completions

import { generateText } from 'ai';
import { createOpenAICompatible } from '@ai-sdk/openai-compatible';

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,
  baseURL: 'https://api.tensorx.ai/v1',
});

const { text } = await generateText({
  model: tensorx('z-ai/glm-5.3'),
  messages: [
    { role: 'system', content: 'You are a helpful coding assistant.' },
    { role: 'user', content: 'How do I create a REST API in Node.js?' },
  ],
});

console.log(text);

Structured Output

import { generateObject } from 'ai';
import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { z } from 'zod';

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,
  baseURL: 'https://api.tensorx.ai/v1',
});

const { object } = await generateObject({
  model: tensorx('z-ai/glm-5.3-flash'),
  schema: z.object({
    recipe: z.object({
      name: z.string(),
      ingredients: z.array(z.string()),
      steps: z.array(z.string()),
    }),
  }),
  prompt: 'Generate a recipe for chocolate chip cookies.',
});

console.log(object.recipe);

Next.js Integration

API Route (App Router)

// app/api/chat/route.ts
import { streamText } from 'ai';
import { createOpenAICompatible } from '@ai-sdk/openai-compatible';

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,
  baseURL: 'https://api.tensorx.ai/v1',
});

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

  const result = await streamText({
    model: tensorx('z-ai/glm-5.3'),
    messages,
  });

  return result.toDataStreamResponse();
}

React Component

// components/Chat.tsx
'use client';

import { useChat } from 'ai/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat({
    api: '/api/chat',
  });

  return (
    <div>
      {messages.map((m) => (
        <div key={m.id}>
          <strong>{m.role}:</strong> {m.content}
        </div>
      ))}
      
      <form onSubmit={handleSubmit}>
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Say something..."
        />
        <button type="submit">Send</button>
      </form>
    </div>
  );
}

Tool Calling

import { generateText, tool } from 'ai';
import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
import { z } from 'zod';

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,
  baseURL: 'https://api.tensorx.ai/v1',
});

const result = await generateText({
  model: tensorx('z-ai/glm-5.3-flash'),
  tools: {
    weather: tool({
      description: 'Get the weather in a location',
      parameters: z.object({
        location: z.string().describe('The location to get weather for'),
      }),
      execute: async ({ location }) => {
        // Implement weather lookup
        return { temperature: 72, condition: 'sunny' };
      },
    }),
  },
  prompt: 'What is the weather in San Francisco?',
});

console.log(result);

Model selection

Call GET /v1/models with your TensorX API key and use one of the returned IDs. If the integration depends on a capability or token limit, query GET /v1/model/info before selecting the model. See Models for examples.

---

Troubleshooting

API connection errors

Verify your configuration:

const tensorx = createOpenAICompatible({
  name: 'tensorx',
  apiKey: process.env.TENSORX_API_KEY,  // Must be set
  baseURL: 'https://api.tensorx.ai/v1',  // Include /v1
});

Streaming not working

Ensure you're using streamText instead of generateText and returning toDataStreamResponse().

Resources

• Vercel AI SDK Documentation

• Vercel AI SDK GitHub

• TensorX API Reference