AI live translation

wuchale can use LLMs to translate messages on-the-fly, during development or during the CLI invokation. When coupled with the HMR support, you can get a seamless experience where you edit the source code in one language and see the updates in the browser in another language live.

Gemini

Gemini was chosen as the default particularly because it has free (limited) access to use it with an API key. You can get your own free API key from Google AI Studio. Once you have that, you can set it to the environment variable:

export GEMINI_API_KEY=your_api_key

And then you can use wuchale as normal: start the dev server or use the CLI command. It will pick up the untranslated messages and translate them using Gemini.

To customize how Gemini is used, you can import it and customize it in the config:

wuchale.config.js

import { gemini } from "wuchale";

export default {
  //...
  ai: gemini({
    model: 'gemini-3-flash-preview',
    batchSize: 40,
    parallel: 5,
    think: true, // default: false
  }),
  //...
};

Other LLMs

To use another LLM, you can create your own object that conforms to the AI config type.

wuchale.config.js

export default {
  //...
  ai: {
    name: "ChatGPT", // e.g.
    batchSize: 50,
    parallel: 10,
    translate: (content, instruction) => {
      // logic
      return translatedContent;
    },
  },
  //...
};

Which model you use, whether it’s local or remote, or how you connect to it is completely up to you. You can implement your own logic, or use a pre-made package.

For example, you can choose to use Vercel’s AI SDK package to get a uniform API you can work with accross different providers. Taking GPT 5 as an example, you can configure wuchale to use it like so:

wuchale.config.js

import { defineConfig } from "wuchale"

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

export default defineConfig({
    locales: ['en', 'es'],
    adapters: {
        // ...
    },
    ai: {
        name: 'GPT-5',
        batchSize: 50,
        parallel: 1,
        translate: async (messages, instruction) => {
            const { text } = await generateText({
                model: openai('gpt-5.1'),
                system: instruction,
                prompt: messages,
            })
            return text
        }
    }
})

You can then set the environment variable OPENAI_API_KEY to the API key and start the dev server.

Now since the SDK provides a uniform API, you can use any model from any provider that it supports.

Mistake handling

If the LLM makes mistakes (which it often does) and doesn’t translate some of the messages, or makes mistakes on the message keys etc, wuchale collects the untranslated messages and the messages whose keys (untranslated) were not properly returned and gives it to the LLM again.

For example, let’s say we have this:

msgid "Hello!"
msgstr ""

msgid "Welcome"
msgstr ""

msgid "Welcome {0}!"
msgstr ""

And the LLM returns this:

msgid "Hello"
msgstr "Hola"

msgid "Welcom-"
msgstr "Bienven-"

msgid "Welcome {0}!"
msgstr ""

Then wuchale retries for the following:

msgid "Welcome"
msgstr ""

msgid "Welcome {0}!"
msgstr ""

And if it still makes mistakes, the process continues until all messages are translated.

Usage limit optimization

wuchale is designed to keep the number of requests as low as possible in order not to exceed the limit. How:

Requests

You can use the batchSize and parallel options to control how big and how many the requests are. Reasonable defaults have been chosen for the default Gemini, but you can customize them.

No re-translation

Once a message is translated, it is stored in the catalog, just like a human translated it. And wuchale never makes another request to translate that same message.