mirror of https://github.com/openclaw/openclaw.git
1303 lines
43 KiB
TypeScript
1303 lines
43 KiB
TypeScript
import fs from "node:fs/promises";
|
|
import os from "node:os";
|
|
import path from "node:path";
|
|
import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
|
|
import type { OpenClawConfig } from "../../config/config.js";
|
|
import type { ModelDefinitionConfig } from "../../config/types.models.js";
|
|
import type {
|
|
ImageDescriptionRequest,
|
|
ImagesDescriptionRequest,
|
|
MediaUnderstandingProvider,
|
|
} from "../../plugin-sdk/media-understanding.js";
|
|
import { withFetchPreconnect } from "../../test-utils/fetch-mock.js";
|
|
import { minimaxUnderstandImage } from "../minimax-vlm.js";
|
|
import type { SandboxFsBridge } from "../sandbox/fs-bridge.js";
|
|
import { createHostSandboxFsBridge } from "../test-helpers/host-sandbox-fs-bridge.js";
|
|
import { createUnsafeMountedSandbox } from "../test-helpers/unsafe-mounted-sandbox.js";
|
|
import { makeZeroUsageSnapshot } from "../usage.js";
|
|
import { __testing, createImageTool, resolveImageModelConfigForTool } from "./image-tool.js";
|
|
|
|
type PiToolsModule = typeof import("../pi-tools.js");
|
|
type CreateOpenClawCodingToolsArgs = Parameters<PiToolsModule["createOpenClawCodingTools"]>[0];
|
|
type MockOpenClawToolsOptions = {
|
|
config?: OpenClawConfig;
|
|
agentDir?: string;
|
|
workspaceDir?: string;
|
|
sandboxRoot?: string;
|
|
sandboxFsBridge?: SandboxFsBridge;
|
|
fsPolicy?: NonNullable<Parameters<typeof createImageTool>[0]>["fsPolicy"];
|
|
modelHasVision?: boolean;
|
|
};
|
|
|
|
const piToolsHarness = vi.hoisted(() => ({
|
|
createStubTool(name: string) {
|
|
return {
|
|
name,
|
|
description: `${name} stub`,
|
|
parameters: { type: "object", properties: {} },
|
|
execute: vi.fn(),
|
|
};
|
|
},
|
|
}));
|
|
|
|
const imageProviderHarness = vi.hoisted(() => {
|
|
let providers = new Map<string, MediaUnderstandingProvider>();
|
|
return {
|
|
setProviders(next: MediaUnderstandingProvider[]) {
|
|
providers = new Map(next.map((provider) => [provider.id.toLowerCase(), provider]));
|
|
},
|
|
reset() {
|
|
providers = new Map();
|
|
},
|
|
buildProviderRegistry(overrides?: Record<string, MediaUnderstandingProvider>) {
|
|
const registry = new Map(providers);
|
|
for (const [id, provider] of Object.entries(overrides ?? {})) {
|
|
registry.set(id.toLowerCase(), provider);
|
|
}
|
|
return registry;
|
|
},
|
|
getMediaUnderstandingProvider(
|
|
id: string,
|
|
registry: Map<string, MediaUnderstandingProvider>,
|
|
): MediaUnderstandingProvider | undefined {
|
|
return registry.get(id.toLowerCase()) ?? providers.get(id.toLowerCase());
|
|
},
|
|
};
|
|
});
|
|
|
|
vi.mock("../bash-tools.js", () => ({
|
|
createExecTool: vi.fn(() => piToolsHarness.createStubTool("exec")),
|
|
createProcessTool: vi.fn(() => piToolsHarness.createStubTool("process")),
|
|
}));
|
|
|
|
vi.mock("../channel-tools.js", () => ({
|
|
copyChannelAgentToolMeta: vi.fn((_from, to) => to),
|
|
listChannelAgentTools: vi.fn(() => []),
|
|
}));
|
|
|
|
vi.mock("../apply-patch.js", () => ({
|
|
createApplyPatchTool: vi.fn(() => piToolsHarness.createStubTool("apply_patch")),
|
|
}));
|
|
|
|
vi.mock("../pi-tools.before-tool-call.js", () => ({
|
|
wrapToolWithBeforeToolCallHook: vi.fn((tool) => tool),
|
|
}));
|
|
|
|
vi.mock("../pi-tools.abort.js", () => ({
|
|
wrapToolWithAbortSignal: vi.fn((tool) => tool),
|
|
}));
|
|
|
|
vi.mock("../openclaw-tools.js", async () => {
|
|
const { createImageTool } = await import("./image-tool.js");
|
|
return {
|
|
createOpenClawTools: vi.fn((options?: MockOpenClawToolsOptions) => {
|
|
const imageTool = createImageTool({
|
|
config: options?.config,
|
|
agentDir: options?.agentDir,
|
|
workspaceDir: options?.workspaceDir,
|
|
sandbox:
|
|
options?.sandboxRoot && options?.sandboxFsBridge
|
|
? {
|
|
root: options.sandboxRoot,
|
|
bridge: options.sandboxFsBridge,
|
|
}
|
|
: undefined,
|
|
fsPolicy: options?.fsPolicy,
|
|
modelHasVision: options?.modelHasVision,
|
|
});
|
|
return imageTool ? [imageTool] : [];
|
|
}),
|
|
};
|
|
});
|
|
|
|
async function writeAuthProfiles(agentDir: string, profiles: unknown) {
|
|
await fs.mkdir(agentDir, { recursive: true });
|
|
await fs.writeFile(
|
|
path.join(agentDir, "auth-profiles.json"),
|
|
`${JSON.stringify(profiles, null, 2)}\n`,
|
|
"utf8",
|
|
);
|
|
}
|
|
|
|
async function createOpenClawCodingToolsWithFreshModules(options?: CreateOpenClawCodingToolsArgs) {
|
|
vi.resetModules();
|
|
const freshImageTool = await import("./image-tool.js");
|
|
freshImageTool.__testing.setProviderDepsForTest({
|
|
buildProviderRegistry: (overrides?: Record<string, MediaUnderstandingProvider>) =>
|
|
imageProviderHarness.buildProviderRegistry(overrides),
|
|
getMediaUnderstandingProvider: (
|
|
id: string,
|
|
registry: Map<string, MediaUnderstandingProvider>,
|
|
) => imageProviderHarness.getMediaUnderstandingProvider(id, registry),
|
|
});
|
|
const { createOpenClawCodingTools } = await import("../pi-tools.js");
|
|
return createOpenClawCodingTools(options);
|
|
}
|
|
|
|
async function withTempAgentDir<T>(run: (agentDir: string) => Promise<T>): Promise<T> {
|
|
const agentDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-image-"));
|
|
try {
|
|
return await run(agentDir);
|
|
} finally {
|
|
await fs.rm(agentDir, { recursive: true, force: true });
|
|
}
|
|
}
|
|
|
|
const ONE_PIXEL_PNG_B64 =
|
|
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/woAAn8B9FD5fHAAAAAASUVORK5CYII=";
|
|
const ONE_PIXEL_GIF_B64 = "R0lGODlhAQABAIABAP///wAAACwAAAAAAQABAAACAkQBADs=";
|
|
const ONE_PIXEL_JPEG_B64 = "QUJDRA==";
|
|
|
|
async function withTempWorkspacePng(
|
|
cb: (args: { workspaceDir: string; imagePath: string }) => Promise<void>,
|
|
options?: { parentDir?: string },
|
|
) {
|
|
const parentDir = options?.parentDir ?? os.tmpdir();
|
|
const workspaceParent = await fs.mkdtemp(path.join(parentDir, "openclaw-workspace-image-"));
|
|
try {
|
|
const workspaceDir = path.join(workspaceParent, "workspace");
|
|
await fs.mkdir(workspaceDir, { recursive: true });
|
|
const imagePath = path.join(workspaceDir, "photo.png");
|
|
await fs.writeFile(imagePath, Buffer.from(ONE_PIXEL_PNG_B64, "base64"));
|
|
await cb({ workspaceDir, imagePath });
|
|
} finally {
|
|
await fs.rm(workspaceParent, { recursive: true, force: true });
|
|
}
|
|
}
|
|
|
|
function registerImageToolEnvReset(priorFetch: typeof global.fetch, keys: string[]) {
|
|
beforeEach(() => {
|
|
for (const key of keys) {
|
|
vi.stubEnv(key, "");
|
|
}
|
|
});
|
|
|
|
afterEach(() => {
|
|
vi.unstubAllEnvs();
|
|
global.fetch = priorFetch;
|
|
});
|
|
}
|
|
|
|
function stubMinimaxOkFetch() {
|
|
const fetch = vi.fn().mockResolvedValue({
|
|
ok: true,
|
|
status: 200,
|
|
statusText: "OK",
|
|
headers: new Headers(),
|
|
json: async () => ({
|
|
content: "ok",
|
|
base_resp: { status_code: 0, status_msg: "" },
|
|
}),
|
|
});
|
|
global.fetch = withFetchPreconnect(fetch);
|
|
vi.stubEnv("MINIMAX_API_KEY", "minimax-test");
|
|
return fetch;
|
|
}
|
|
|
|
function stubMinimaxFetch(baseResp: { status_code: number; status_msg: string }, content = "ok") {
|
|
const fetch = vi.fn().mockResolvedValue({
|
|
ok: true,
|
|
status: 200,
|
|
statusText: "OK",
|
|
headers: new Headers(),
|
|
json: async () => ({
|
|
content,
|
|
base_resp: baseResp,
|
|
}),
|
|
});
|
|
global.fetch = withFetchPreconnect(fetch);
|
|
return fetch;
|
|
}
|
|
|
|
function stubOpenAiCompletionsOkFetch(text = "ok") {
|
|
const fetch = vi.fn().mockImplementation(
|
|
async () =>
|
|
new Response(
|
|
new ReadableStream<Uint8Array>({
|
|
start(controller) {
|
|
const encoder = new TextEncoder();
|
|
const chunks = [
|
|
`data: ${JSON.stringify({
|
|
id: "chatcmpl-moonshot-test",
|
|
object: "chat.completion.chunk",
|
|
created: Math.floor(Date.now() / 1000),
|
|
model: "kimi-k2.5",
|
|
choices: [
|
|
{
|
|
index: 0,
|
|
delta: { role: "assistant", content: text },
|
|
finish_reason: null,
|
|
},
|
|
],
|
|
})}\n\n`,
|
|
`data: ${JSON.stringify({
|
|
id: "chatcmpl-moonshot-test",
|
|
object: "chat.completion.chunk",
|
|
created: Math.floor(Date.now() / 1000),
|
|
model: "kimi-k2.5",
|
|
choices: [{ index: 0, delta: {}, finish_reason: "stop" }],
|
|
})}\n\n`,
|
|
"data: [DONE]\n\n",
|
|
];
|
|
for (const chunk of chunks) {
|
|
controller.enqueue(encoder.encode(chunk));
|
|
}
|
|
controller.close();
|
|
},
|
|
}),
|
|
{
|
|
status: 200,
|
|
headers: { "content-type": "text/event-stream" },
|
|
},
|
|
),
|
|
);
|
|
global.fetch = withFetchPreconnect(fetch);
|
|
return fetch;
|
|
}
|
|
|
|
function createMinimaxImageConfig(): OpenClawConfig {
|
|
return {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "minimax/MiniMax-M2.7" },
|
|
imageModel: { primary: "minimax/MiniMax-VL-01" },
|
|
},
|
|
},
|
|
plugins: {
|
|
entries: {
|
|
minimax: { enabled: true },
|
|
},
|
|
},
|
|
};
|
|
}
|
|
|
|
function createDefaultImageFallbackExpectation(primary: string) {
|
|
return {
|
|
primary,
|
|
fallbacks: ["openai/gpt-5-mini", "anthropic/claude-opus-4-5"],
|
|
};
|
|
}
|
|
|
|
const minimaxProvider = {
|
|
id: "minimax",
|
|
capabilities: ["image"],
|
|
describeImage: async (params: ImageDescriptionRequest) => ({
|
|
text: await minimaxUnderstandImage({
|
|
apiKey: process.env.MINIMAX_API_KEY ?? "",
|
|
prompt: params.prompt ?? "Describe the image.",
|
|
imageDataUrl: `data:${params.mime ?? "image/jpeg"};base64,${params.buffer.toString("base64")}`,
|
|
}),
|
|
model: "MiniMax-VL-01",
|
|
}),
|
|
describeImages: async (params: ImagesDescriptionRequest) => {
|
|
const parts: string[] = [];
|
|
for (const [index, image] of params.images.entries()) {
|
|
const text = await minimaxUnderstandImage({
|
|
apiKey: process.env.MINIMAX_API_KEY ?? "",
|
|
prompt:
|
|
params.images.length > 1
|
|
? `${params.prompt ?? "Describe the image."}\n\nDescribe image ${index + 1} of ${params.images.length} independently.`
|
|
: (params.prompt ?? "Describe the image."),
|
|
imageDataUrl: `data:${image.mime ?? "image/jpeg"};base64,${image.buffer.toString("base64")}`,
|
|
});
|
|
parts.push(params.images.length > 1 ? `Image ${index + 1}:\n${text.trim()}` : text.trim());
|
|
}
|
|
return {
|
|
text: parts.join("\n\n").trim(),
|
|
model: "MiniMax-VL-01",
|
|
};
|
|
},
|
|
} satisfies MediaUnderstandingProvider;
|
|
|
|
async function describeMoonshotImage(
|
|
params: ImageDescriptionRequest,
|
|
): Promise<{ text: string; model: string }> {
|
|
const baseUrl =
|
|
params.cfg.models?.providers?.moonshot?.baseUrl?.trim() ?? "https://api.moonshot.ai/v1";
|
|
await fetch(`${baseUrl.replace(/\/$/, "")}/chat/completions`, {
|
|
method: "POST",
|
|
headers: {
|
|
"content-type": "application/json",
|
|
authorization: `Bearer ${process.env.MOONSHOT_API_KEY ?? ""}`,
|
|
},
|
|
body: JSON.stringify({
|
|
model: params.model,
|
|
messages: [
|
|
{
|
|
role: "user",
|
|
content: [
|
|
{ type: "text", text: params.prompt ?? "Describe the image." },
|
|
{
|
|
type: "image_url",
|
|
image_url: {
|
|
url: `data:${params.mime ?? "image/jpeg"};base64,${params.buffer.toString("base64")}`,
|
|
},
|
|
},
|
|
],
|
|
},
|
|
],
|
|
}),
|
|
});
|
|
return { text: "ok moonshot", model: params.model };
|
|
}
|
|
|
|
async function describeMoonshotImages(
|
|
params: ImagesDescriptionRequest,
|
|
): Promise<{ text: string; model: string }> {
|
|
const [first] = params.images;
|
|
if (!first) {
|
|
return { text: "", model: params.model };
|
|
}
|
|
return await describeMoonshotImage({
|
|
...params,
|
|
buffer: first.buffer,
|
|
fileName: first.fileName,
|
|
mime: first.mime,
|
|
});
|
|
}
|
|
|
|
const moonshotProvider = {
|
|
id: "moonshot",
|
|
capabilities: ["image"],
|
|
describeImage: describeMoonshotImage,
|
|
describeImages: describeMoonshotImages,
|
|
} satisfies MediaUnderstandingProvider;
|
|
|
|
function installImageUnderstandingProviderStubs(...providers: MediaUnderstandingProvider[]) {
|
|
imageProviderHarness.setProviders(providers);
|
|
__testing.setProviderDepsForTest({
|
|
buildProviderRegistry: (overrides?: Record<string, MediaUnderstandingProvider>) =>
|
|
imageProviderHarness.buildProviderRegistry(overrides),
|
|
getMediaUnderstandingProvider: (
|
|
id: string,
|
|
registry: Map<string, MediaUnderstandingProvider>,
|
|
) => imageProviderHarness.getMediaUnderstandingProvider(id, registry),
|
|
});
|
|
}
|
|
|
|
function makeModelDefinition(id: string, input: Array<"text" | "image">): ModelDefinitionConfig {
|
|
return {
|
|
id,
|
|
name: id,
|
|
reasoning: false,
|
|
input,
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
contextWindow: 128_000,
|
|
maxTokens: 8_192,
|
|
};
|
|
}
|
|
|
|
async function expectImageToolExecOk(
|
|
tool: {
|
|
execute: (toolCallId: string, input: { prompt: string; image: string }) => Promise<unknown>;
|
|
},
|
|
image: string,
|
|
) {
|
|
await expect(
|
|
tool.execute("t1", {
|
|
prompt: "Describe the image.",
|
|
image,
|
|
}),
|
|
).resolves.toMatchObject({
|
|
content: [{ type: "text", text: "ok" }],
|
|
});
|
|
}
|
|
|
|
function requireImageTool<T>(tool: T | null | undefined): T {
|
|
expect(tool).not.toBeNull();
|
|
if (!tool) {
|
|
throw new Error("expected image tool");
|
|
}
|
|
return tool;
|
|
}
|
|
|
|
function createRequiredImageTool(args: Parameters<typeof createImageTool>[0]) {
|
|
return requireImageTool(createImageTool(args));
|
|
}
|
|
|
|
type ImageToolInstance = ReturnType<typeof createRequiredImageTool>;
|
|
|
|
async function withTempSandboxState(
|
|
run: (ctx: { stateDir: string; agentDir: string; sandboxRoot: string }) => Promise<void>,
|
|
) {
|
|
const stateDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-image-sandbox-"));
|
|
const agentDir = path.join(stateDir, "agent");
|
|
const sandboxRoot = path.join(stateDir, "sandbox");
|
|
await fs.mkdir(agentDir, { recursive: true });
|
|
await fs.mkdir(sandboxRoot, { recursive: true });
|
|
try {
|
|
await run({ stateDir, agentDir, sandboxRoot });
|
|
} finally {
|
|
await fs.rm(stateDir, { recursive: true, force: true });
|
|
}
|
|
}
|
|
|
|
async function withMinimaxImageToolFromTempAgentDir(
|
|
run: (tool: ImageToolInstance) => Promise<void>,
|
|
) {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg = createMinimaxImageConfig();
|
|
await run(createRequiredImageTool({ config: cfg, agentDir }));
|
|
});
|
|
}
|
|
|
|
function findSchemaUnionKeywords(schema: unknown, path = "root"): string[] {
|
|
if (!schema || typeof schema !== "object") {
|
|
return [];
|
|
}
|
|
if (Array.isArray(schema)) {
|
|
return schema.flatMap((item, index) => findSchemaUnionKeywords(item, `${path}[${index}]`));
|
|
}
|
|
const record = schema as Record<string, unknown>;
|
|
const out: string[] = [];
|
|
for (const [key, value] of Object.entries(record)) {
|
|
const nextPath = `${path}.${key}`;
|
|
if (key === "anyOf" || key === "oneOf" || key === "allOf") {
|
|
out.push(nextPath);
|
|
}
|
|
out.push(...findSchemaUnionKeywords(value, nextPath));
|
|
}
|
|
return out;
|
|
}
|
|
|
|
describe("image tool implicit imageModel config", () => {
|
|
const priorFetch = global.fetch;
|
|
registerImageToolEnvReset(priorFetch, [
|
|
"OPENAI_API_KEY",
|
|
"ANTHROPIC_API_KEY",
|
|
"ANTHROPIC_OAUTH_TOKEN",
|
|
"MINIMAX_API_KEY",
|
|
"ZAI_API_KEY",
|
|
"Z_AI_API_KEY",
|
|
// Avoid implicit Copilot provider discovery hitting the network in tests.
|
|
"COPILOT_GITHUB_TOKEN",
|
|
"GH_TOKEN",
|
|
"GITHUB_TOKEN",
|
|
]);
|
|
|
|
beforeEach(() => {
|
|
installImageUnderstandingProviderStubs(minimaxProvider, moonshotProvider);
|
|
});
|
|
|
|
afterEach(() => {
|
|
imageProviderHarness.reset();
|
|
__testing.setProviderDepsForTest();
|
|
});
|
|
|
|
it("stays disabled without auth when no pairing is possible", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "openai/gpt-5.2" } } },
|
|
};
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toBeNull();
|
|
expect(createImageTool({ config: cfg, agentDir })).toBeNull();
|
|
});
|
|
});
|
|
|
|
it("pairs minimax primary with MiniMax-VL-01 (and fallbacks) when auth exists", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
vi.stubEnv("MINIMAX_API_KEY", "minimax-test");
|
|
vi.stubEnv("OPENAI_API_KEY", "openai-test");
|
|
vi.stubEnv("ANTHROPIC_API_KEY", "anthropic-test");
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "minimax/MiniMax-M2.7" } } },
|
|
};
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual(
|
|
createDefaultImageFallbackExpectation("minimax/MiniMax-VL-01"),
|
|
);
|
|
expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
|
|
});
|
|
});
|
|
|
|
it("pairs minimax-portal primary with MiniMax-VL-01 (and fallbacks) when auth exists", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
await writeAuthProfiles(agentDir, {
|
|
version: 1,
|
|
profiles: {
|
|
"minimax-portal:default": {
|
|
type: "oauth",
|
|
provider: "minimax-portal",
|
|
access: "oauth-test",
|
|
refresh: "refresh-test",
|
|
expires: Date.now() + 60_000,
|
|
},
|
|
},
|
|
});
|
|
vi.stubEnv("OPENAI_API_KEY", "openai-test");
|
|
vi.stubEnv("ANTHROPIC_API_KEY", "anthropic-test");
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "minimax-portal/MiniMax-M2.7" } } },
|
|
};
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual(
|
|
createDefaultImageFallbackExpectation("minimax-portal/MiniMax-VL-01"),
|
|
);
|
|
expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
|
|
});
|
|
});
|
|
|
|
it("pairs zai primary with glm-4.6v (and fallbacks) when auth exists", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
vi.stubEnv("ZAI_API_KEY", "zai-test");
|
|
vi.stubEnv("OPENAI_API_KEY", "openai-test");
|
|
vi.stubEnv("ANTHROPIC_API_KEY", "anthropic-test");
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "zai/glm-4.7" } } },
|
|
};
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual(
|
|
createDefaultImageFallbackExpectation("zai/glm-4.6v"),
|
|
);
|
|
expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
|
|
});
|
|
});
|
|
|
|
it("pairs a custom provider when it declares an image-capable model", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
await writeAuthProfiles(agentDir, {
|
|
version: 1,
|
|
profiles: {
|
|
"acme:default": { type: "api_key", provider: "acme", key: "sk-test" },
|
|
},
|
|
});
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "acme/text-1" } } },
|
|
models: {
|
|
providers: {
|
|
acme: {
|
|
baseUrl: "https://example.com",
|
|
models: [
|
|
makeModelDefinition("text-1", ["text"]),
|
|
makeModelDefinition("vision-1", ["text", "image"]),
|
|
],
|
|
},
|
|
},
|
|
},
|
|
};
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual({
|
|
primary: "acme/vision-1",
|
|
});
|
|
expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
|
|
});
|
|
});
|
|
|
|
it("pairs a provider when config uses an alias key", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
await writeAuthProfiles(agentDir, {
|
|
version: 1,
|
|
profiles: {
|
|
"amazon-bedrock:default": {
|
|
type: "api_key",
|
|
provider: "amazon-bedrock",
|
|
key: "sk-test",
|
|
},
|
|
},
|
|
});
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "aws-bedrock/text-1" } } },
|
|
models: {
|
|
providers: {
|
|
"amazon-bedrock": {
|
|
baseUrl: "https://example.com",
|
|
models: [
|
|
makeModelDefinition("text-1", ["text"]),
|
|
makeModelDefinition("vision-1", ["text", "image"]),
|
|
],
|
|
},
|
|
},
|
|
},
|
|
};
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual({
|
|
primary: "amazon-bedrock/vision-1",
|
|
});
|
|
expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
|
|
});
|
|
});
|
|
|
|
it("prefers explicit agents.defaults.imageModel", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg: OpenClawConfig = {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "minimax/MiniMax-M2.7" },
|
|
imageModel: { primary: "openai/gpt-5-mini" },
|
|
},
|
|
},
|
|
};
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual({
|
|
primary: "openai/gpt-5-mini",
|
|
});
|
|
});
|
|
});
|
|
|
|
it("keeps image tool available when primary model supports images (for explicit requests)", async () => {
|
|
// When the primary model supports images, we still keep the tool available
|
|
// because images are auto-injected into prompts. The tool description is
|
|
// adjusted via modelHasVision to discourage redundant usage.
|
|
vi.stubEnv("OPENAI_API_KEY", "test-key");
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg: OpenClawConfig = {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "acme/vision-1" },
|
|
imageModel: { primary: "openai/gpt-5-mini" },
|
|
},
|
|
},
|
|
models: {
|
|
providers: {
|
|
acme: {
|
|
baseUrl: "https://example.com",
|
|
models: [makeModelDefinition("vision-1", ["text", "image"])],
|
|
},
|
|
},
|
|
},
|
|
};
|
|
// Tool should still be available for explicit image analysis requests
|
|
expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual({
|
|
primary: "openai/gpt-5-mini",
|
|
});
|
|
const tool = createImageTool({ config: cfg, agentDir, modelHasVision: true });
|
|
expect(tool).not.toBeNull();
|
|
expect(tool?.description).toContain(
|
|
"Only use this tool when images were NOT already provided",
|
|
);
|
|
});
|
|
});
|
|
|
|
it("sends moonshot image requests with user+image payloads only", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
vi.stubEnv("MOONSHOT_API_KEY", "moonshot-test");
|
|
const fetch = stubOpenAiCompletionsOkFetch("ok moonshot");
|
|
const cfg: OpenClawConfig = {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "moonshot/kimi-k2.5" },
|
|
imageModel: { primary: "moonshot/kimi-k2.5" },
|
|
},
|
|
},
|
|
models: {
|
|
providers: {
|
|
moonshot: {
|
|
api: "openai-completions",
|
|
baseUrl: "https://api.moonshot.ai/v1",
|
|
models: [makeModelDefinition("kimi-k2.5", ["text", "image"])],
|
|
},
|
|
},
|
|
},
|
|
};
|
|
|
|
const tool = requireImageTool(createImageTool({ config: cfg, agentDir }));
|
|
const result = await tool.execute("t1", {
|
|
prompt: "Describe this image in one word.",
|
|
image: `data:image/png;base64,${ONE_PIXEL_PNG_B64}`,
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
const [url, init] = fetch.mock.calls[0] as [unknown, { body?: unknown }];
|
|
expect(String(url)).toBe("https://api.moonshot.ai/v1/chat/completions");
|
|
expect(typeof init?.body).toBe("string");
|
|
const bodyRaw = typeof init?.body === "string" ? init.body : "";
|
|
const payload = JSON.parse(bodyRaw) as {
|
|
messages?: Array<{
|
|
role?: string;
|
|
content?: Array<{
|
|
type?: string;
|
|
text?: string;
|
|
image_url?: { url?: string };
|
|
}>;
|
|
}>;
|
|
};
|
|
|
|
expect(payload.messages?.map((message) => message.role)).toEqual(["user"]);
|
|
const userContent = payload.messages?.[0]?.content ?? [];
|
|
expect(userContent).toEqual(
|
|
expect.arrayContaining([
|
|
expect.objectContaining({
|
|
type: "text",
|
|
text: "Describe this image in one word.",
|
|
}),
|
|
expect.objectContaining({ type: "image_url" }),
|
|
]),
|
|
);
|
|
expect(userContent.find((block) => block.type === "image_url")?.image_url?.url).toContain(
|
|
"data:image/png;base64,",
|
|
);
|
|
expect(bodyRaw).not.toContain('"role":"developer"');
|
|
expect(result.content).toEqual(
|
|
expect.arrayContaining([expect.objectContaining({ type: "text", text: "ok moonshot" })]),
|
|
);
|
|
});
|
|
});
|
|
|
|
it("falls back to the generic image runtime when openrouter has no media provider registration", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const fetch = stubOpenAiCompletionsOkFetch("ok openrouter");
|
|
const cfg: OpenClawConfig = {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "openrouter/google/gemini-2.5-flash-lite" },
|
|
imageModel: { primary: "openrouter/google/gemini-2.5-flash-lite" },
|
|
},
|
|
},
|
|
models: {
|
|
providers: {
|
|
openrouter: {
|
|
api: "openai-completions",
|
|
baseUrl: "https://openrouter.ai/api/v1",
|
|
apiKey: "openrouter-test",
|
|
models: [makeModelDefinition("google/gemini-2.5-flash-lite", ["text", "image"])],
|
|
},
|
|
},
|
|
},
|
|
};
|
|
|
|
const tool = requireImageTool(createImageTool({ config: cfg, agentDir }));
|
|
const result = await tool.execute("t1", {
|
|
prompt: "Describe the image.",
|
|
image: `data:image/png;base64,${ONE_PIXEL_PNG_B64}`,
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
expect(result.content).toEqual(
|
|
expect.arrayContaining([expect.objectContaining({ type: "text", text: "ok openrouter" })]),
|
|
);
|
|
});
|
|
});
|
|
|
|
it("falls back to the generic multi-image runtime when openrouter has no media provider registration", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const fetch = stubOpenAiCompletionsOkFetch("ok multi");
|
|
const cfg: OpenClawConfig = {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "openrouter/google/gemini-2.5-flash-lite" },
|
|
imageModel: { primary: "openrouter/google/gemini-2.5-flash-lite" },
|
|
},
|
|
},
|
|
models: {
|
|
providers: {
|
|
openrouter: {
|
|
api: "openai-completions",
|
|
baseUrl: "https://openrouter.ai/api/v1",
|
|
apiKey: "openrouter-test",
|
|
models: [makeModelDefinition("google/gemini-2.5-flash-lite", ["text", "image"])],
|
|
},
|
|
},
|
|
},
|
|
};
|
|
|
|
const tool = requireImageTool(createImageTool({ config: cfg, agentDir }));
|
|
const result = await tool.execute("t1", {
|
|
prompt: "Describe the images.",
|
|
images: [
|
|
`data:image/png;base64,${ONE_PIXEL_PNG_B64}`,
|
|
`data:image/png;base64,${ONE_PIXEL_PNG_B64}`,
|
|
],
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
expect(result.content).toEqual(
|
|
expect.arrayContaining([expect.objectContaining({ type: "text", text: "ok multi" })]),
|
|
);
|
|
});
|
|
});
|
|
|
|
it("falls back to the generic image runtime when minimax-portal has no media provider registration", async () => {
|
|
await withTempAgentDir(async (agentDir) => {
|
|
installImageUnderstandingProviderStubs();
|
|
await writeAuthProfiles(agentDir, {
|
|
version: 1,
|
|
profiles: {
|
|
"minimax-portal:default": {
|
|
type: "oauth",
|
|
provider: "minimax-portal",
|
|
access: "oauth-test",
|
|
refresh: "refresh-test",
|
|
expires: Date.now() + 60_000,
|
|
},
|
|
},
|
|
});
|
|
const fetch = stubMinimaxOkFetch();
|
|
const cfg: OpenClawConfig = {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "minimax-portal/MiniMax-M2.7" },
|
|
imageModel: { primary: "minimax-portal/MiniMax-VL-01" },
|
|
},
|
|
},
|
|
};
|
|
|
|
const tool = requireImageTool(createImageTool({ config: cfg, agentDir }));
|
|
await expectImageToolExecOk(tool, `data:image/png;base64,${ONE_PIXEL_PNG_B64}`);
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
});
|
|
});
|
|
|
|
it("exposes an Anthropic-safe image schema without union keywords", async () => {
|
|
await withMinimaxImageToolFromTempAgentDir(async (tool) => {
|
|
const violations = findSchemaUnionKeywords(tool.parameters, "image.parameters");
|
|
expect(violations).toEqual([]);
|
|
|
|
const schema = tool.parameters as {
|
|
properties?: Record<string, unknown>;
|
|
};
|
|
const imageSchema = schema.properties?.image as { type?: unknown } | undefined;
|
|
const imagesSchema = schema.properties?.images as
|
|
| { type?: unknown; items?: unknown }
|
|
| undefined;
|
|
const imageItems = imagesSchema?.items as { type?: unknown } | undefined;
|
|
|
|
expect(imageSchema?.type).toBe("string");
|
|
expect(imagesSchema?.type).toBe("array");
|
|
expect(imageItems?.type).toBe("string");
|
|
});
|
|
});
|
|
|
|
it("keeps an Anthropic-safe image schema snapshot", async () => {
|
|
await withMinimaxImageToolFromTempAgentDir(async (tool) => {
|
|
expect(JSON.parse(JSON.stringify(tool.parameters))).toEqual({
|
|
type: "object",
|
|
properties: {
|
|
prompt: { type: "string" },
|
|
image: { description: "Single image path or URL.", type: "string" },
|
|
images: {
|
|
description: "Multiple image paths or URLs (up to maxImages, default 20).",
|
|
type: "array",
|
|
items: { type: "string" },
|
|
},
|
|
model: { type: "string" },
|
|
maxBytesMb: { type: "number" },
|
|
maxImages: { type: "number" },
|
|
},
|
|
});
|
|
});
|
|
});
|
|
|
|
it("still rejects temp workspace paths outside allowed local roots when workspaceOnly is off", async () => {
|
|
await withTempWorkspacePng(async ({ workspaceDir, imagePath }) => {
|
|
const fetch = stubMinimaxOkFetch();
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg = createMinimaxImageConfig();
|
|
|
|
const withoutWorkspace = createRequiredImageTool({ config: cfg, agentDir });
|
|
await expect(
|
|
withoutWorkspace.execute("t1", { prompt: "Describe.", image: imagePath }),
|
|
).rejects.toThrow(/not under an allowed directory/i);
|
|
|
|
const withWorkspace = createRequiredImageTool({ config: cfg, agentDir, workspaceDir });
|
|
|
|
await expectImageToolExecOk(withWorkspace, imagePath);
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
});
|
|
});
|
|
});
|
|
|
|
it("respects fsPolicy.workspaceOnly for non-sandbox image paths", async () => {
|
|
await withTempWorkspacePng(async ({ workspaceDir, imagePath }) => {
|
|
const fetch = stubMinimaxOkFetch();
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg = createMinimaxImageConfig();
|
|
|
|
const tool = createRequiredImageTool({
|
|
config: cfg,
|
|
agentDir,
|
|
workspaceDir,
|
|
fsPolicy: { workspaceOnly: true },
|
|
});
|
|
|
|
// File inside workspace is allowed.
|
|
await expectImageToolExecOk(tool, imagePath);
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
|
|
// File outside workspace is rejected even without sandbox.
|
|
const outsideDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-outside-"));
|
|
const outsideImage = path.join(outsideDir, "secret.png");
|
|
await fs.writeFile(outsideImage, Buffer.from(ONE_PIXEL_PNG_B64, "base64"));
|
|
try {
|
|
await expect(
|
|
tool.execute("t2", { prompt: "Describe.", image: outsideImage }),
|
|
).rejects.toThrow(/not under an allowed directory/i);
|
|
} finally {
|
|
await fs.rm(outsideDir, { recursive: true, force: true });
|
|
}
|
|
});
|
|
});
|
|
});
|
|
|
|
it("still rejects non-workspace local image paths when workspaceOnly is disabled", async () => {
|
|
const fetch = stubMinimaxOkFetch();
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg = createMinimaxImageConfig();
|
|
const outsideDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-image-outside-"));
|
|
const outsideImage = path.join(outsideDir, "secret.png");
|
|
await fs.writeFile(outsideImage, Buffer.from(ONE_PIXEL_PNG_B64, "base64"));
|
|
try {
|
|
const tool = createRequiredImageTool({
|
|
config: cfg,
|
|
agentDir,
|
|
fsPolicy: { workspaceOnly: false },
|
|
});
|
|
|
|
await expect(
|
|
tool.execute("t1", { prompt: "Describe.", image: outsideImage }),
|
|
).rejects.toThrow(/not under an allowed directory/i);
|
|
expect(fetch).not.toHaveBeenCalled();
|
|
} finally {
|
|
await fs.rm(outsideDir, { recursive: true, force: true });
|
|
}
|
|
});
|
|
});
|
|
|
|
it("allows workspace images via createOpenClawCodingTools when workspace root is explicit", async () => {
|
|
await withTempWorkspacePng(async ({ workspaceDir, imagePath }) => {
|
|
const fetch = stubMinimaxOkFetch();
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg = createMinimaxImageConfig();
|
|
|
|
const tools = await createOpenClawCodingToolsWithFreshModules({
|
|
config: cfg,
|
|
agentDir,
|
|
workspaceDir,
|
|
});
|
|
const tool = requireImageTool(tools.find((candidate) => candidate.name === "image"));
|
|
|
|
await expectImageToolExecOk(tool, imagePath);
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
});
|
|
});
|
|
});
|
|
|
|
it("resolves relative image paths against workspaceDir", async () => {
|
|
await withTempWorkspacePng(async ({ workspaceDir }) => {
|
|
// Place image in a subdirectory of the workspace
|
|
const subdir = path.join(workspaceDir, "inbox");
|
|
await fs.mkdir(subdir, { recursive: true });
|
|
const imagePath = path.join(subdir, "receipt.png");
|
|
await fs.writeFile(imagePath, Buffer.from(ONE_PIXEL_PNG_B64, "base64"));
|
|
|
|
const fetch = stubMinimaxOkFetch();
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg = createMinimaxImageConfig();
|
|
const tool = createRequiredImageTool({ config: cfg, agentDir, workspaceDir });
|
|
|
|
// Relative path should be resolved against workspaceDir
|
|
await expectImageToolExecOk(tool, "inbox/receipt.png");
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
});
|
|
});
|
|
});
|
|
|
|
it("sandboxes image paths like the read tool", async () => {
|
|
await withTempSandboxState(async ({ agentDir, sandboxRoot }) => {
|
|
await fs.writeFile(path.join(sandboxRoot, "img.png"), "fake", "utf8");
|
|
const sandbox = { root: sandboxRoot, bridge: createHostSandboxFsBridge(sandboxRoot) };
|
|
|
|
vi.stubEnv("OPENAI_API_KEY", "openai-test");
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "minimax/MiniMax-M2.7" } } },
|
|
};
|
|
const tool = createRequiredImageTool({ config: cfg, agentDir, sandbox });
|
|
|
|
await expect(tool.execute("t1", { image: "https://example.com/a.png" })).rejects.toThrow(
|
|
/Sandboxed image tool does not allow remote URLs/i,
|
|
);
|
|
|
|
await expect(tool.execute("t2", { image: "../escape.png" })).rejects.toThrow(
|
|
/escapes sandbox root/i,
|
|
);
|
|
});
|
|
});
|
|
|
|
it("applies tools.fs.workspaceOnly to image paths in sandbox mode", async () => {
|
|
await withTempSandboxState(async ({ agentDir, sandboxRoot }) => {
|
|
await fs.writeFile(
|
|
path.join(agentDir, "secret.png"),
|
|
Buffer.from(ONE_PIXEL_PNG_B64, "base64"),
|
|
);
|
|
const sandbox = createUnsafeMountedSandbox({ sandboxRoot, agentRoot: agentDir });
|
|
const fetch = stubMinimaxOkFetch();
|
|
const cfg: OpenClawConfig = {
|
|
...createMinimaxImageConfig(),
|
|
tools: { fs: { workspaceOnly: true } },
|
|
};
|
|
|
|
const tools = await createOpenClawCodingToolsWithFreshModules({
|
|
config: cfg,
|
|
agentDir,
|
|
sandbox,
|
|
workspaceDir: sandboxRoot,
|
|
});
|
|
const readTool = tools.find((candidate) => candidate.name === "read");
|
|
if (!readTool) {
|
|
throw new Error("expected read tool");
|
|
}
|
|
const imageTool = requireImageTool(tools.find((candidate) => candidate.name === "image"));
|
|
|
|
await expect(readTool.execute("t1", { path: "/agent/secret.png" })).rejects.toThrow(
|
|
/Path escapes sandbox root/i,
|
|
);
|
|
await expect(
|
|
imageTool.execute("t2", {
|
|
prompt: "Describe the image.",
|
|
image: "/agent/secret.png",
|
|
}),
|
|
).rejects.toThrow(/Path escapes sandbox root/i);
|
|
expect(fetch).not.toHaveBeenCalled();
|
|
});
|
|
});
|
|
|
|
it("rewrites inbound absolute paths into sandbox media/inbound", async () => {
|
|
await withTempSandboxState(async ({ agentDir, sandboxRoot }) => {
|
|
await fs.mkdir(path.join(sandboxRoot, "media", "inbound"), {
|
|
recursive: true,
|
|
});
|
|
await fs.writeFile(
|
|
path.join(sandboxRoot, "media", "inbound", "photo.png"),
|
|
Buffer.from(ONE_PIXEL_PNG_B64, "base64"),
|
|
);
|
|
|
|
const fetch = stubMinimaxOkFetch();
|
|
|
|
const cfg: OpenClawConfig = {
|
|
agents: {
|
|
defaults: {
|
|
model: { primary: "minimax/MiniMax-M2.7" },
|
|
imageModel: { primary: "minimax/MiniMax-VL-01" },
|
|
},
|
|
},
|
|
};
|
|
const sandbox = { root: sandboxRoot, bridge: createHostSandboxFsBridge(sandboxRoot) };
|
|
const tool = createRequiredImageTool({ config: cfg, agentDir, sandbox });
|
|
|
|
const res = await tool.execute("t1", {
|
|
prompt: "Describe the image.",
|
|
image: "@/Users/steipete/.openclaw/media/inbound/photo.png",
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
expect((res.details as { rewrittenFrom?: string }).rewrittenFrom).toContain("photo.png");
|
|
});
|
|
});
|
|
});
|
|
|
|
describe("image tool data URL support", () => {
|
|
it("decodes base64 image data URLs", () => {
|
|
const pngB64 =
|
|
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/woAAn8B9FD5fHAAAAAASUVORK5CYII=";
|
|
const out = __testing.decodeDataUrl(`data:image/png;base64,${pngB64}`);
|
|
expect(out.kind).toBe("image");
|
|
expect(out.mimeType).toBe("image/png");
|
|
expect(out.buffer.length).toBeGreaterThan(0);
|
|
});
|
|
|
|
it("rejects non-image data URLs", () => {
|
|
expect(() => __testing.decodeDataUrl("data:text/plain;base64,SGVsbG8=")).toThrow(
|
|
/Unsupported data URL type/i,
|
|
);
|
|
});
|
|
});
|
|
|
|
describe("image tool MiniMax VLM routing", () => {
|
|
const pngB64 =
|
|
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/woAAn8B9FD5fHAAAAAASUVORK5CYII=";
|
|
const priorFetch = global.fetch;
|
|
registerImageToolEnvReset(priorFetch, [
|
|
"MINIMAX_API_KEY",
|
|
"COPILOT_GITHUB_TOKEN",
|
|
"GH_TOKEN",
|
|
"GITHUB_TOKEN",
|
|
]);
|
|
|
|
beforeEach(() => {
|
|
installImageUnderstandingProviderStubs(minimaxProvider);
|
|
});
|
|
|
|
afterEach(() => {
|
|
imageProviderHarness.reset();
|
|
__testing.setProviderDepsForTest();
|
|
});
|
|
|
|
async function createMinimaxVlmFixture(baseResp: { status_code: number; status_msg: string }) {
|
|
const fetch = stubMinimaxFetch(baseResp, baseResp.status_code === 0 ? "ok" : "");
|
|
|
|
const agentDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-minimax-vlm-"));
|
|
vi.stubEnv("MINIMAX_API_KEY", "minimax-test");
|
|
const cfg = createMinimaxImageConfig();
|
|
const tool = createRequiredImageTool({ config: cfg, agentDir });
|
|
return { fetch, tool };
|
|
}
|
|
|
|
it("accepts image for single-image requests and calls /v1/coding_plan/vlm", async () => {
|
|
const { fetch, tool } = await createMinimaxVlmFixture({ status_code: 0, status_msg: "" });
|
|
|
|
const res = await tool.execute("t1", {
|
|
prompt: "Describe the image.",
|
|
image: `data:image/png;base64,${pngB64}`,
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
const [url, init] = fetch.mock.calls[0];
|
|
expect(String(url)).toBe("https://api.minimax.io/v1/coding_plan/vlm");
|
|
expect(init?.method).toBe("POST");
|
|
expect(String((init?.headers as Record<string, string>)?.Authorization)).toBe(
|
|
"Bearer minimax-test",
|
|
);
|
|
expect(String(init?.body)).toContain('"prompt":"Describe the image."');
|
|
expect(String(init?.body)).toContain('"image_url":"data:image/png;base64,');
|
|
|
|
const text = res.content?.find((b) => b.type === "text")?.text ?? "";
|
|
expect(text).toBe("ok");
|
|
});
|
|
|
|
it("accepts images[] for multi-image requests", async () => {
|
|
const { fetch, tool } = await createMinimaxVlmFixture({ status_code: 0, status_msg: "" });
|
|
|
|
const res = await tool.execute("t1", {
|
|
prompt: "Compare these images.",
|
|
images: [`data:image/png;base64,${pngB64}`, `data:image/jpeg;base64,${ONE_PIXEL_JPEG_B64}`],
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(2);
|
|
const details = res.details as
|
|
| {
|
|
images?: Array<{ image: string }>;
|
|
}
|
|
| undefined;
|
|
expect(details?.images).toHaveLength(2);
|
|
});
|
|
|
|
it("combines image + images with dedupe and enforces maxImages", async () => {
|
|
const { fetch, tool } = await createMinimaxVlmFixture({ status_code: 0, status_msg: "" });
|
|
|
|
const deduped = await tool.execute("t1", {
|
|
prompt: "Compare these images.",
|
|
image: `data:image/png;base64,${pngB64}`,
|
|
images: [
|
|
`data:image/png;base64,${pngB64}`,
|
|
`data:image/jpeg;base64,${ONE_PIXEL_JPEG_B64}`,
|
|
`data:image/jpeg;base64,${ONE_PIXEL_JPEG_B64}`,
|
|
],
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(2);
|
|
const dedupedDetails = deduped.details as
|
|
| {
|
|
images?: Array<{ image: string }>;
|
|
}
|
|
| undefined;
|
|
expect(dedupedDetails?.images).toHaveLength(2);
|
|
|
|
const tooMany = await tool.execute("t2", {
|
|
prompt: "Compare these images.",
|
|
image: `data:image/png;base64,${pngB64}`,
|
|
images: [`data:image/gif;base64,${ONE_PIXEL_GIF_B64}`],
|
|
maxImages: 1,
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(2);
|
|
expect(tooMany.details).toMatchObject({
|
|
error: "too_many_images",
|
|
count: 2,
|
|
max: 1,
|
|
});
|
|
});
|
|
|
|
it("surfaces MiniMax API errors from /v1/coding_plan/vlm", async () => {
|
|
const { tool } = await createMinimaxVlmFixture({ status_code: 1004, status_msg: "bad key" });
|
|
|
|
await expect(
|
|
tool.execute("t1", {
|
|
prompt: "Describe the image.",
|
|
image: `data:image/png;base64,${pngB64}`,
|
|
}),
|
|
).rejects.toThrow(/MiniMax VLM API error/i);
|
|
});
|
|
});
|
|
|
|
describe("image tool response validation", () => {
|
|
function createAssistantMessage(
|
|
overrides: Partial<{
|
|
api: string;
|
|
provider: string;
|
|
model: string;
|
|
stopReason: string;
|
|
errorMessage: string;
|
|
content: unknown[];
|
|
}>,
|
|
) {
|
|
return {
|
|
role: "assistant",
|
|
api: "openai-responses",
|
|
provider: "openai",
|
|
model: "gpt-5-mini",
|
|
stopReason: "stop",
|
|
timestamp: Date.now(),
|
|
usage: makeZeroUsageSnapshot(),
|
|
content: [] as unknown[],
|
|
...overrides,
|
|
};
|
|
}
|
|
|
|
it.each([
|
|
{
|
|
name: "caps image-tool max tokens by model capability",
|
|
maxOutputTokens: 4000,
|
|
expected: 4000,
|
|
},
|
|
{
|
|
name: "keeps requested image-tool max tokens when model capability is higher",
|
|
maxOutputTokens: 8192,
|
|
expected: 4096,
|
|
},
|
|
{
|
|
name: "falls back to requested image-tool max tokens when model capability is missing",
|
|
maxOutputTokens: undefined,
|
|
expected: 4096,
|
|
},
|
|
])("$name", ({ maxOutputTokens, expected }) => {
|
|
expect(__testing.resolveImageToolMaxTokens(maxOutputTokens)).toBe(expected);
|
|
});
|
|
|
|
it.each([
|
|
{
|
|
name: "rejects image-model responses with no final text",
|
|
message: createAssistantMessage({
|
|
content: [{ type: "thinking", thinking: "hmm" }],
|
|
}) as never,
|
|
expectedError: /returned no text/i,
|
|
},
|
|
{
|
|
name: "surfaces provider errors from image-model responses",
|
|
message: createAssistantMessage({
|
|
stopReason: "error",
|
|
errorMessage: "boom",
|
|
}) as never,
|
|
expectedError: /boom/i,
|
|
},
|
|
])("$name", ({ message, expectedError }) => {
|
|
expect(() =>
|
|
__testing.coerceImageAssistantText({
|
|
provider: "openai",
|
|
model: "gpt-5-mini",
|
|
message,
|
|
}),
|
|
).toThrow(expectedError);
|
|
});
|
|
|
|
it("returns trimmed text from image-model responses", () => {
|
|
const text = __testing.coerceImageAssistantText({
|
|
provider: "anthropic",
|
|
model: "claude-opus-4-5",
|
|
message: {
|
|
...createAssistantMessage({
|
|
api: "anthropic-messages",
|
|
provider: "anthropic",
|
|
model: "claude-opus-4-5",
|
|
}),
|
|
content: [{ type: "text", text: " hello " }],
|
|
} as never,
|
|
});
|
|
expect(text).toBe("hello");
|
|
});
|
|
});
|