858 lines
28 KiB
TypeScript
858 lines
28 KiB
TypeScript
import fs from "node:fs/promises";
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import os from "node:os";
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import path from "node:path";
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import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
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import type { OpenClawConfig } from "../../config/config.js";
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import type { ModelDefinitionConfig } from "../../config/types.models.js";
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import { withFetchPreconnect } from "../../test-utils/fetch-mock.js";
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import { createOpenClawCodingTools } from "../pi-tools.js";
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import { createHostSandboxFsBridge } from "../test-helpers/host-sandbox-fs-bridge.js";
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import { createUnsafeMountedSandbox } from "../test-helpers/unsafe-mounted-sandbox.js";
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import { makeZeroUsageSnapshot } from "../usage.js";
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import { __testing, createImageTool, resolveImageModelConfigForTool } from "./image-tool.js";
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async function writeAuthProfiles(agentDir: string, profiles: unknown) {
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await fs.mkdir(agentDir, { recursive: true });
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await fs.writeFile(
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path.join(agentDir, "auth-profiles.json"),
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`${JSON.stringify(profiles, null, 2)}\n`,
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"utf8",
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);
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}
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async function withTempAgentDir<T>(run: (agentDir: string) => Promise<T>): Promise<T> {
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const agentDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-image-"));
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try {
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return await run(agentDir);
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} finally {
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await fs.rm(agentDir, { recursive: true, force: true });
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}
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}
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const ONE_PIXEL_PNG_B64 =
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"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/woAAn8B9FD5fHAAAAAASUVORK5CYII=";
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const ONE_PIXEL_GIF_B64 = "R0lGODlhAQABAIABAP///wAAACwAAAAAAQABAAACAkQBADs=";
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async function withTempWorkspacePng(
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cb: (args: { workspaceDir: string; imagePath: string }) => Promise<void>,
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) {
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const workspaceParent = await fs.mkdtemp(path.join(process.cwd(), ".openclaw-workspace-image-"));
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try {
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const workspaceDir = path.join(workspaceParent, "workspace");
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await fs.mkdir(workspaceDir, { recursive: true });
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const imagePath = path.join(workspaceDir, "photo.png");
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await fs.writeFile(imagePath, Buffer.from(ONE_PIXEL_PNG_B64, "base64"));
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await cb({ workspaceDir, imagePath });
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} finally {
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await fs.rm(workspaceParent, { recursive: true, force: true });
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}
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}
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function stubMinimaxOkFetch() {
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const fetch = vi.fn().mockResolvedValue({
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ok: true,
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status: 200,
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statusText: "OK",
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headers: new Headers(),
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json: async () => ({
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content: "ok",
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base_resp: { status_code: 0, status_msg: "" },
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}),
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});
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global.fetch = withFetchPreconnect(fetch);
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vi.stubEnv("MINIMAX_API_KEY", "minimax-test");
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return fetch;
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}
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function stubMinimaxFetch(baseResp: { status_code: number; status_msg: string }, content = "ok") {
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const fetch = vi.fn().mockResolvedValue({
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ok: true,
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status: 200,
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statusText: "OK",
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headers: new Headers(),
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json: async () => ({
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content,
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base_resp: baseResp,
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}),
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});
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global.fetch = withFetchPreconnect(fetch);
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return fetch;
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}
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function stubOpenAiCompletionsOkFetch(text = "ok") {
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const fetch = vi.fn().mockResolvedValue(
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new Response(
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new ReadableStream<Uint8Array>({
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start(controller) {
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const encoder = new TextEncoder();
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const chunks = [
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`data: ${JSON.stringify({
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id: "chatcmpl-moonshot-test",
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object: "chat.completion.chunk",
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created: Math.floor(Date.now() / 1000),
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model: "kimi-k2.5",
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choices: [
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{
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index: 0,
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delta: { role: "assistant", content: text },
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finish_reason: null,
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},
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],
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})}\n\n`,
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`data: ${JSON.stringify({
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id: "chatcmpl-moonshot-test",
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object: "chat.completion.chunk",
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created: Math.floor(Date.now() / 1000),
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model: "kimi-k2.5",
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choices: [{ index: 0, delta: {}, finish_reason: "stop" }],
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})}\n\n`,
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"data: [DONE]\n\n",
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];
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for (const chunk of chunks) {
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controller.enqueue(encoder.encode(chunk));
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}
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controller.close();
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},
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}),
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{
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status: 200,
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headers: { "content-type": "text/event-stream" },
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},
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),
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);
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global.fetch = withFetchPreconnect(fetch);
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return fetch;
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}
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function createMinimaxImageConfig(): OpenClawConfig {
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return {
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agents: {
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defaults: {
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model: { primary: "minimax/MiniMax-M2.5" },
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imageModel: { primary: "minimax/MiniMax-VL-01" },
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},
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},
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};
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}
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function createDefaultImageFallbackExpectation(primary: string) {
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return {
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primary,
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fallbacks: ["openai/gpt-5-mini", "anthropic/claude-opus-4-5"],
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};
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}
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function makeModelDefinition(id: string, input: Array<"text" | "image">): ModelDefinitionConfig {
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return {
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id,
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name: id,
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reasoning: false,
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input,
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 128_000,
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maxTokens: 8_192,
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};
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}
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async function expectImageToolExecOk(
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tool: {
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execute: (toolCallId: string, input: { prompt: string; image: string }) => Promise<unknown>;
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},
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image: string,
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) {
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await expect(
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tool.execute("t1", {
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prompt: "Describe the image.",
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image,
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}),
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).resolves.toMatchObject({
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content: [{ type: "text", text: "ok" }],
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});
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}
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function requireImageTool<T>(tool: T | null | undefined): T {
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expect(tool).not.toBeNull();
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if (!tool) {
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throw new Error("expected image tool");
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}
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return tool;
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}
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function createRequiredImageTool(args: Parameters<typeof createImageTool>[0]) {
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return requireImageTool(createImageTool(args));
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}
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type ImageToolInstance = ReturnType<typeof createRequiredImageTool>;
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async function withTempSandboxState(
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run: (ctx: { stateDir: string; agentDir: string; sandboxRoot: string }) => Promise<void>,
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) {
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const stateDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-image-sandbox-"));
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const agentDir = path.join(stateDir, "agent");
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const sandboxRoot = path.join(stateDir, "sandbox");
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await fs.mkdir(agentDir, { recursive: true });
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await fs.mkdir(sandboxRoot, { recursive: true });
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try {
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await run({ stateDir, agentDir, sandboxRoot });
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} finally {
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await fs.rm(stateDir, { recursive: true, force: true });
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}
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}
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async function withMinimaxImageToolFromTempAgentDir(
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run: (tool: ImageToolInstance) => Promise<void>,
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) {
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await withTempAgentDir(async (agentDir) => {
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const cfg = createMinimaxImageConfig();
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await run(createRequiredImageTool({ config: cfg, agentDir }));
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});
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}
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function findSchemaUnionKeywords(schema: unknown, path = "root"): string[] {
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if (!schema || typeof schema !== "object") {
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return [];
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}
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if (Array.isArray(schema)) {
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return schema.flatMap((item, index) => findSchemaUnionKeywords(item, `${path}[${index}]`));
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}
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const record = schema as Record<string, unknown>;
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const out: string[] = [];
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for (const [key, value] of Object.entries(record)) {
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const nextPath = `${path}.${key}`;
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if (key === "anyOf" || key === "oneOf" || key === "allOf") {
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out.push(nextPath);
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}
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out.push(...findSchemaUnionKeywords(value, nextPath));
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}
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return out;
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}
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describe("image tool implicit imageModel config", () => {
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const priorFetch = global.fetch;
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beforeEach(() => {
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vi.stubEnv("OPENAI_API_KEY", "");
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vi.stubEnv("ANTHROPIC_API_KEY", "");
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vi.stubEnv("ANTHROPIC_OAUTH_TOKEN", "");
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vi.stubEnv("MINIMAX_API_KEY", "");
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vi.stubEnv("ZAI_API_KEY", "");
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vi.stubEnv("Z_AI_API_KEY", "");
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// Avoid implicit Copilot provider discovery hitting the network in tests.
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vi.stubEnv("COPILOT_GITHUB_TOKEN", "");
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vi.stubEnv("GH_TOKEN", "");
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vi.stubEnv("GITHUB_TOKEN", "");
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});
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afterEach(() => {
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vi.unstubAllEnvs();
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global.fetch = priorFetch;
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});
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it("stays disabled without auth when no pairing is possible", async () => {
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await withTempAgentDir(async (agentDir) => {
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const cfg: OpenClawConfig = {
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agents: { defaults: { model: { primary: "openai/gpt-5.2" } } },
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};
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expect(resolveImageModelConfigForTool({ cfg, agentDir })).toBeNull();
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expect(createImageTool({ config: cfg, agentDir })).toBeNull();
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});
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});
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it("pairs minimax primary with MiniMax-VL-01 (and fallbacks) when auth exists", async () => {
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await withTempAgentDir(async (agentDir) => {
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vi.stubEnv("MINIMAX_API_KEY", "minimax-test");
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vi.stubEnv("OPENAI_API_KEY", "openai-test");
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vi.stubEnv("ANTHROPIC_API_KEY", "anthropic-test");
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const cfg: OpenClawConfig = {
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agents: { defaults: { model: { primary: "minimax/MiniMax-M2.5" } } },
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};
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expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual(
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createDefaultImageFallbackExpectation("minimax/MiniMax-VL-01"),
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);
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expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
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});
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});
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it("pairs zai primary with glm-4.6v (and fallbacks) when auth exists", async () => {
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await withTempAgentDir(async (agentDir) => {
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vi.stubEnv("ZAI_API_KEY", "zai-test");
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vi.stubEnv("OPENAI_API_KEY", "openai-test");
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vi.stubEnv("ANTHROPIC_API_KEY", "anthropic-test");
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const cfg: OpenClawConfig = {
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agents: { defaults: { model: { primary: "zai/glm-4.7" } } },
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};
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expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual(
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createDefaultImageFallbackExpectation("zai/glm-4.6v"),
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);
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expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
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});
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});
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it("pairs a custom provider when it declares an image-capable model", async () => {
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await withTempAgentDir(async (agentDir) => {
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await writeAuthProfiles(agentDir, {
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version: 1,
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profiles: {
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"acme:default": { type: "api_key", provider: "acme", key: "sk-test" },
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},
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});
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const cfg: OpenClawConfig = {
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agents: { defaults: { model: { primary: "acme/text-1" } } },
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models: {
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providers: {
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acme: {
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baseUrl: "https://example.com",
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models: [
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makeModelDefinition("text-1", ["text"]),
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makeModelDefinition("vision-1", ["text", "image"]),
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],
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},
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},
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},
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};
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expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual({
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primary: "acme/vision-1",
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});
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expect(createImageTool({ config: cfg, agentDir })).not.toBeNull();
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});
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});
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it("prefers explicit agents.defaults.imageModel", async () => {
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await withTempAgentDir(async (agentDir) => {
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const cfg: OpenClawConfig = {
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agents: {
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defaults: {
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model: { primary: "minimax/MiniMax-M2.5" },
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imageModel: { primary: "openai/gpt-5-mini" },
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},
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},
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};
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expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual({
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primary: "openai/gpt-5-mini",
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});
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});
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});
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it("keeps image tool available when primary model supports images (for explicit requests)", async () => {
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// When the primary model supports images, we still keep the tool available
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// because images are auto-injected into prompts. The tool description is
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// adjusted via modelHasVision to discourage redundant usage.
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vi.stubEnv("OPENAI_API_KEY", "test-key");
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await withTempAgentDir(async (agentDir) => {
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const cfg: OpenClawConfig = {
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agents: {
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defaults: {
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model: { primary: "acme/vision-1" },
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imageModel: { primary: "openai/gpt-5-mini" },
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},
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},
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models: {
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providers: {
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acme: {
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baseUrl: "https://example.com",
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models: [makeModelDefinition("vision-1", ["text", "image"])],
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},
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},
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},
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};
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// Tool should still be available for explicit image analysis requests
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expect(resolveImageModelConfigForTool({ cfg, agentDir })).toEqual({
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primary: "openai/gpt-5-mini",
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});
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const tool = createImageTool({ config: cfg, agentDir, modelHasVision: true });
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expect(tool).not.toBeNull();
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expect(tool?.description).toContain(
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"Only use this tool when images were NOT already provided",
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);
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});
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});
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it("sends moonshot image requests with user+image payloads only", async () => {
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await withTempAgentDir(async (agentDir) => {
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vi.stubEnv("MOONSHOT_API_KEY", "moonshot-test");
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const fetch = stubOpenAiCompletionsOkFetch("ok moonshot");
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const cfg: OpenClawConfig = {
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agents: {
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defaults: {
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model: { primary: "moonshot/kimi-k2.5" },
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imageModel: { primary: "moonshot/kimi-k2.5" },
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},
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},
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models: {
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providers: {
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moonshot: {
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api: "openai-completions",
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baseUrl: "https://api.moonshot.ai/v1",
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models: [makeModelDefinition("kimi-k2.5", ["text", "image"])],
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},
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},
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},
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};
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const tool = requireImageTool(createImageTool({ config: cfg, agentDir }));
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const result = await tool.execute("t1", {
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prompt: "Describe this image in one word.",
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image: `data:image/png;base64,${ONE_PIXEL_PNG_B64}`,
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});
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expect(fetch).toHaveBeenCalledTimes(1);
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const [url, init] = fetch.mock.calls[0] as [unknown, { body?: unknown }];
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expect(String(url)).toBe("https://api.moonshot.ai/v1/chat/completions");
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expect(typeof init?.body).toBe("string");
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const bodyRaw = typeof init?.body === "string" ? init.body : "";
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const payload = JSON.parse(bodyRaw) as {
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messages?: Array<{
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role?: string;
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content?: Array<{
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type?: string;
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text?: string;
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image_url?: { url?: string };
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}>;
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}>;
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};
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expect(payload.messages?.map((message) => message.role)).toEqual(["user"]);
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const userContent = payload.messages?.[0]?.content ?? [];
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expect(userContent).toEqual(
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expect.arrayContaining([
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expect.objectContaining({
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type: "text",
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text: "Describe this image in one word.",
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}),
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expect.objectContaining({ type: "image_url" }),
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]),
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);
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expect(userContent.find((block) => block.type === "image_url")?.image_url?.url).toContain(
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"data:image/png;base64,",
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);
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expect(bodyRaw).not.toContain('"role":"developer"');
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expect(result.content).toEqual(
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expect.arrayContaining([expect.objectContaining({ type: "text", text: "ok moonshot" })]),
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);
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});
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});
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it("exposes an Anthropic-safe image schema without union keywords", async () => {
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await withMinimaxImageToolFromTempAgentDir(async (tool) => {
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const violations = findSchemaUnionKeywords(tool.parameters, "image.parameters");
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expect(violations).toEqual([]);
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const schema = tool.parameters as {
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properties?: Record<string, unknown>;
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};
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const imageSchema = schema.properties?.image as { type?: unknown } | undefined;
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const imagesSchema = schema.properties?.images as
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| { type?: unknown; items?: unknown }
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| undefined;
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const imageItems = imagesSchema?.items as { type?: unknown } | undefined;
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expect(imageSchema?.type).toBe("string");
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expect(imagesSchema?.type).toBe("array");
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expect(imageItems?.type).toBe("string");
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});
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});
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it("keeps an Anthropic-safe image schema snapshot", async () => {
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await withMinimaxImageToolFromTempAgentDir(async (tool) => {
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expect(JSON.parse(JSON.stringify(tool.parameters))).toEqual({
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type: "object",
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properties: {
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prompt: { type: "string" },
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image: { description: "Single image path or URL.", type: "string" },
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images: {
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description: "Multiple image paths or URLs (up to maxImages, default 20).",
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type: "array",
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items: { type: "string" },
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},
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model: { type: "string" },
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maxBytesMb: { type: "number" },
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maxImages: { type: "number" },
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},
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});
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});
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});
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it("allows workspace images outside default local media roots", async () => {
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await withTempWorkspacePng(async ({ workspaceDir, imagePath }) => {
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const fetch = stubMinimaxOkFetch();
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await withTempAgentDir(async (agentDir) => {
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const cfg = createMinimaxImageConfig();
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const withoutWorkspace = createRequiredImageTool({ config: cfg, agentDir });
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await expect(
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withoutWorkspace.execute("t0", {
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prompt: "Describe the image.",
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image: imagePath,
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}),
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).rejects.toThrow(/Local media path is 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("allows workspace images via createOpenClawCodingTools default workspace root", async () => {
|
|
await withTempWorkspacePng(async ({ imagePath }) => {
|
|
const fetch = stubMinimaxOkFetch();
|
|
await withTempAgentDir(async (agentDir) => {
|
|
const cfg = createMinimaxImageConfig();
|
|
|
|
const tools = createOpenClawCodingTools({ config: cfg, agentDir });
|
|
const tool = requireImageTool(tools.find((candidate) => candidate.name === "image"));
|
|
|
|
await expectImageToolExecOk(tool, imagePath);
|
|
|
|
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-M2.5" } } },
|
|
};
|
|
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 = createOpenClawCodingTools({
|
|
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-M2.5" },
|
|
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,${pngB64}`);
|
|
expect(out.kind).toBe("image");
|
|
expect(out.mimeType).toBe("image/png");
|
|
expect(out.buffer.length).toBeGreaterThan(0);
|
|
});
|
|
|
|
it("rejects non-image data URLs", () => {
|
|
expect(() => __testing.decodeDataUrl("data:text/plain;base64,SGVsbG8=")).toThrow(
|
|
/Unsupported data URL type/i,
|
|
);
|
|
});
|
|
});
|
|
|
|
describe("image tool MiniMax VLM routing", () => {
|
|
const pngB64 =
|
|
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/woAAn8B9FD5fHAAAAAASUVORK5CYII=";
|
|
const priorFetch = global.fetch;
|
|
|
|
beforeEach(() => {
|
|
vi.stubEnv("MINIMAX_API_KEY", "");
|
|
vi.stubEnv("COPILOT_GITHUB_TOKEN", "");
|
|
vi.stubEnv("GH_TOKEN", "");
|
|
vi.stubEnv("GITHUB_TOKEN", "");
|
|
});
|
|
|
|
afterEach(() => {
|
|
vi.unstubAllEnvs();
|
|
global.fetch = priorFetch;
|
|
});
|
|
|
|
async function createMinimaxVlmFixture(baseResp: { status_code: number; status_msg: string }) {
|
|
const fetch = stubMinimaxFetch(baseResp, baseResp.status_code === 0 ? "ok" : "");
|
|
|
|
const agentDir = await fs.mkdtemp(path.join(os.tmpdir(), "openclaw-minimax-vlm-"));
|
|
vi.stubEnv("MINIMAX_API_KEY", "minimax-test");
|
|
const cfg: OpenClawConfig = {
|
|
agents: { defaults: { model: { primary: "minimax/MiniMax-M2.5" } } },
|
|
};
|
|
const tool = createRequiredImageTool({ config: cfg, agentDir });
|
|
return { fetch, tool };
|
|
}
|
|
|
|
it("accepts image for single-image requests and calls /v1/coding_plan/vlm", async () => {
|
|
const { fetch, tool } = await createMinimaxVlmFixture({ status_code: 0, status_msg: "" });
|
|
|
|
const res = await tool.execute("t1", {
|
|
prompt: "Describe the image.",
|
|
image: `data:image/png;base64,${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,${pngB64}`, `data:image/gif;base64,${ONE_PIXEL_GIF_B64}`],
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
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/gif;base64,${ONE_PIXEL_GIF_B64}`,
|
|
`data:image/gif;base64,${ONE_PIXEL_GIF_B64}`,
|
|
],
|
|
});
|
|
|
|
expect(fetch).toHaveBeenCalledTimes(1);
|
|
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(1);
|
|
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");
|
|
});
|
|
});
|