Image Generation
+
+ The image generation endpoints support both OpenAI
+ POST /v1/images/generations and Gemini Imagen
+ POST /v1beta/models/{model}:predict formats. Return single or multiple images
+ as URLs or base64-encoded data.
+
Endpoints
+| Method | +Path | +Format | +
|---|---|---|
| POST | +/v1/images/generations | +JSON (OpenAI) | +
| POST | +/v1beta/models/{model}:predict | +JSON (Gemini Imagen) | +
Unit Test: Single Image URL
++ Using the programmatic API with vitest, register a fixture and assert on the response. +
+ +import { LLMock } from "@copilotkit/aimock";
+import { describe, it, expect, beforeAll, afterAll } from "vitest";
+
+let mock: LLMock;
+
+beforeAll(async () => {
+ mock = new LLMock();
+ await mock.start();
+});
+
+afterAll(async () => {
+ await mock.stop();
+});
+
+it("returns a single image URL", async () => {
+ mock.onImage("a sunset over mountains", {
+ image: { url: "https://example.com/sunset.png" },
+ });
+
+ const res = await fetch(`${mock.url}/v1/images/generations`, {
+ method: "POST",
+ headers: { "Content-Type": "application/json" },
+ body: JSON.stringify({
+ model: "dall-e-3",
+ prompt: "a sunset over mountains",
+ n: 1,
+ size: "1024x1024",
+ }),
+ });
+
+ const body = await res.json();
+ expect(body.data[0].url).toBe("https://example.com/sunset.png");
+});Unit Test: Multiple Images
+ +it("returns multiple images", async () => {
+ mock.onImage("cats", {
+ images: [
+ { url: "https://example.com/cat1.png" },
+ { url: "https://example.com/cat2.png" },
+ ],
+ });
+
+ const res = await fetch(`${mock.url}/v1/images/generations`, {
+ method: "POST",
+ headers: { "Content-Type": "application/json" },
+ body: JSON.stringify({
+ model: "dall-e-3",
+ prompt: "cats playing",
+ n: 2,
+ }),
+ });
+
+ const body = await res.json();
+ expect(body.data).toHaveLength(2);
+ expect(body.data[0].url).toBe("https://example.com/cat1.png");
+ expect(body.data[1].url).toBe("https://example.com/cat2.png");
+});Unit Test: Base64 Response
+ +it("returns base64-encoded image", async () => {
+ mock.onImage("logo", {
+ image: { b64_json: "iVBORw0KGgoAAAANSUhEUg..." },
+ });
+
+ const res = await fetch(`${mock.url}/v1/images/generations`, {
+ method: "POST",
+ headers: { "Content-Type": "application/json" },
+ body: JSON.stringify({
+ model: "dall-e-3",
+ prompt: "a company logo",
+ response_format: "b64_json",
+ }),
+ });
+
+ const body = await res.json();
+ expect(body.data[0].b64_json).toBeDefined();
+});Unit Test: Gemini Imagen Format
+ +it("handles Gemini Imagen predict endpoint", async () => {
+ mock.onImage("landscape", {
+ image: { url: "https://example.com/landscape.png" },
+ });
+
+ const res = await fetch(
+ `${mock.url}/v1beta/models/imagen-3.0-generate-002:predict`,
+ {
+ method: "POST",
+ headers: { "Content-Type": "application/json" },
+ body: JSON.stringify({
+ instances: [{ prompt: "a beautiful landscape" }],
+ parameters: { sampleCount: 1 },
+ }),
+ }
+ );
+
+ const body = await res.json();
+ expect(body.predictions).toBeDefined();
+});JSON Fixture
+ +{
+ "fixtures": [
+ {
+ "match": { "userMessage": "sunset" },
+ "response": {
+ "image": { "url": "https://example.com/sunset.png" }
+ }
+ },
+ {
+ "match": { "userMessage": "cats" },
+ "response": {
+ "images": [
+ { "url": "https://example.com/cat1.png" },
+ { "url": "https://example.com/cat2.png" }
+ ]
+ }
+ }
+ ]
+}Response Format
+Matches the OpenAI /v1/images/generations response format:
-
+
created— Unix timestamp
+ -
+
data[].url— URL of the generated image (when using URL format) +
+ -
+
data[].b64_json— base64-encoded image data (when using b64_json + format) +
+ -
+
data[].revised_prompt— the prompt as revised by the model (optional) +
+
+ Image fixtures use match.userMessage which maps to the
+ prompt field in the request body. The prompt matcher checks
+ for substring matches.
+
Record & Replay
+
+ When no fixture matches an incoming request, aimock can proxy it to the real API and
+ record the response as a fixture for future replays. Enable recording with the
+ --record flag or via RecordConfig in the programmatic API.
+ Recorded image fixtures capture the url or b64_json from the
+ provider response and save them to disk, so subsequent runs replay instantly without
+ hitting the real API.
+
npx aimock --record --provider-openai https://api.openai.com