Vercel AI SDK

The Vercel AI SDK runs model calls and tool loops in your TypeScript application. Its OpenAI-compatible provider sends each request to the exchange through Chat Completions.

Print the setup with the Liquid CLI

The Liquid CLI prints the setup for the Vercel AI SDK with your model filled in. To run your own program with the exchange in its environment, use liquid exec. It sets OPENAI_BASE_URL, ANTHROPIC_BASE_URL and the keys for that process only.

curl -fsSL https://inference.ai.exchange/cli/install.sh | bash
liquid login
liquid connect vercel-ai
liquid exec -- node app.mjs

To set up the SDK yourself, follow the steps below.

Before you start

• Use Node 22 or later. The SDK does not accept an older release.

• Use a workspace with a balance and a key with the spend scope. See API keys.

• Export the key in the shell that starts your application:

  export LIQUID_API_KEY=sk-liquid-...
  

1. Install the SDK

# Node 22 or later
npm install ai @ai-sdk/openai-compatible

See the official provider guide.

2. Add the exchange as a provider

Save this file as liquid.ts.

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

const liquid = createOpenAICompatible({
  name: 'liquid',
  baseURL: 'https://router.inference.ai.exchange/v1',
  apiKey: process.env.LIQUID_API_KEY,
  headers: { 'x-liquid-cap-usd': '1.00' },
})

export const model = liquid('liquid.auto')

The base URL ends in /v1, because the provider adds /chat/completions to it. Choose a model, router model, or preset from GET /v1/models. See Market data.

The x-liquid-cap-usd header sets a $1.00 cap for each request.

3. Send a request

import { generateText } from 'ai'

import { model } from './liquid.ts'

const { text } = await generateText({ model, prompt: 'Say hello.' })
console.log(text)

Purchasing rules

Put the exchange object in providerOptions, below the name of the provider. The provider copies the object into the request body. See Purchasing rules for the fields.

const { text } = await generateText({
  model,
  prompt: 'Say hello.',
  providerOptions: { liquid: { exchange: { cap_usd: 0.05 } } },
})

The key liquid is the name that createOpenAICompatible received. A different key has no effect.

A cap in the body replaces the cap header. To change only the header for one call, set headers on that call:

const { text } = await generateText({
  model,
  prompt: 'Say hello.',
  headers: { 'x-liquid-cap-usd': '0.05' },
})

If no offer fits the cap, the exchange refuses the request. It does not reduce the output limit.

To limit the total spend, use a separate key with a spend limit. The exchange refuses more requests after that limit.

Local tools

A tool made with tool() runs in your application. The exchange sends its definition to the provider and returns the model's tool calls. The SDK runs each function and sends the result in the next request.

import { readFileSync } from 'node:fs'

import { generateText, isStepCount, jsonSchema, tool } from 'ai'

import { model } from './liquid.ts'

const { text } = await generateText({
  model,
  prompt: 'Summarize notes.txt.',
  tools: {
    read_notes: tool({
      description: 'Read notes.txt from the current directory.',
      inputSchema: jsonSchema({ type: 'object', properties: {} }),
      execute: async () => readFileSync('notes.txt', 'utf8'),
    }),
  },
  stopWhen: isStepCount(6),
})

Without stopWhen, the SDK stops after one request and does not send the tool result. isStepCount(6) permits six requests. Each request is a separate job with its own cap.

See the official tool guide for schemas made with Zod.

Stream a response

streamText uses the same model:

import { streamText } from 'ai'

import { model } from './liquid.ts'

const result = streamText({ model, prompt: 'Say hello.' })
for await (const part of result.textStream) {
  process.stdout.write(part)
}

To get token counts from a stream, add includeUsage: true to the options of createOpenAICompatible.

Read the job and its cap

The exchange returns the x-liquid-job-id and x-liquid-cap-usd headers before any token. x-liquid-cap-usd is the maximum charge for the job.

const result = await generateText({ model, prompt: 'Say hello.' })
const headers = result.response.headers
console.log(headers?.['x-liquid-job-id'], headers?.['x-liquid-cap-usd'])

With streamText, use (await result.response).headers.

Verify the connection

Check the receipts on the Credits page. Requests show Vercel AI SDK as the client. Use the receipt for the settled charge. The SDK's token usage does not state the exchange's charge.

Troubleshooting

Next steps

• Purchasing rules: caps, models, strategy, and service limits.
• API keys: total spend limits for each application.
• OpenAI SDK: call the endpoints directly.