Google Gemini SDK

npm install @google/genai

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({
  apiKey: process.env.APISALE_API_KEY,
  httpOptions: {
    baseUrl: "https://api.apisale.ai",
  },
});

const pager = await ai.models.list();
for await (const model of pager) {
  console.log(model.name);
}

curl "https://api.apisale.ai/v1beta/models" \
  -H "Authorization: Bearer $APISALE_API_KEY"

const response = await ai.models.generateContent({
  model: "gemini-2.5-flash-image",
  contents: "A watercolor illustration of a fox in autumn leaves",
});

// Image bytes are in response.candidates[0].content.parts

const op = await ai.models.generateContent({
  model: "veo-3.1",
  contents: "Slow motion coffee pour, macro lens",
});

// When the response includes a run/job id, poll until done
// (exact shape matches Gemini video preview APIs — see response for status field)

curl -X POST "https://api.apisale.ai/v1beta/models/gemini-2.5-flash-image:generateContent" \
  -H "Authorization: Bearer $APISALE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{
      "role": "user",
      "parts": [{ "text": "A minimalist logo for a coffee brand" }]
    }]
  }'

from google import genai
import os

client = genai.Client(
    api_key=os.environ["APISALE_API_KEY"],
    http_options={"base_url": "https://api.apisale.ai"},
)

response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents="A neon cityscape at night",
)
print(response)