mirror of
https://github.com/harivansh-afk/clanker-agent.git
synced 2026-04-22 00:00:31 +00:00
move pi-mono into companion-cloud as apps/companion-os
- Copy all pi-mono source into apps/companion-os/ - Update Dockerfile to COPY pre-built binary instead of downloading from GitHub Releases - Update deploy-staging.yml to build pi from source (bun compile) before Docker build - Add apps/companion-os/** to path triggers - No more cross-repo dispatch needed Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
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0250f72976
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630
packages/ai/test/image-tool-result.test.ts
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630
packages/ai/test/image-tool-result.test.ts
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|
@ -0,0 +1,630 @@
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|||
import { readFileSync } from "node:fs";
|
||||
import { join } from "node:path";
|
||||
import { Type } from "@sinclair/typebox";
|
||||
import { describe, expect, it } from "vitest";
|
||||
import type {
|
||||
Api,
|
||||
Context,
|
||||
Model,
|
||||
Tool,
|
||||
ToolResultMessage,
|
||||
} from "../src/index.js";
|
||||
import { complete, getModel } from "../src/index.js";
|
||||
import type { StreamOptions } from "../src/types.js";
|
||||
|
||||
type StreamOptionsWithExtras = StreamOptions & Record<string, unknown>;
|
||||
|
||||
import {
|
||||
hasAzureOpenAICredentials,
|
||||
resolveAzureDeploymentName,
|
||||
} from "./azure-utils.js";
|
||||
import { hasBedrockCredentials } from "./bedrock-utils.js";
|
||||
import { resolveApiKey } from "./oauth.js";
|
||||
|
||||
// Resolve OAuth tokens at module level (async, runs before tests)
|
||||
const oauthTokens = await Promise.all([
|
||||
resolveApiKey("anthropic"),
|
||||
resolveApiKey("github-copilot"),
|
||||
resolveApiKey("google-gemini-cli"),
|
||||
resolveApiKey("google-antigravity"),
|
||||
resolveApiKey("openai-codex"),
|
||||
]);
|
||||
const [
|
||||
anthropicOAuthToken,
|
||||
githubCopilotToken,
|
||||
geminiCliToken,
|
||||
antigravityToken,
|
||||
openaiCodexToken,
|
||||
] = oauthTokens;
|
||||
|
||||
/**
|
||||
* Test that tool results containing only images work correctly across all providers.
|
||||
* This verifies that:
|
||||
* 1. Tool results can contain image content blocks
|
||||
* 2. Providers correctly pass images from tool results to the LLM
|
||||
* 3. The LLM can see and describe images returned by tools
|
||||
*/
|
||||
async function handleToolWithImageResult<TApi extends Api>(
|
||||
model: Model<TApi>,
|
||||
options?: StreamOptionsWithExtras,
|
||||
) {
|
||||
// Check if the model supports images
|
||||
if (!model.input.includes("image")) {
|
||||
console.log(
|
||||
`Skipping tool image result test - model ${model.id} doesn't support images`,
|
||||
);
|
||||
return;
|
||||
}
|
||||
|
||||
// Read the test image
|
||||
const imagePath = join(__dirname, "data", "red-circle.png");
|
||||
const imageBuffer = readFileSync(imagePath);
|
||||
const base64Image = imageBuffer.toString("base64");
|
||||
|
||||
// Define a tool that returns only an image (no text)
|
||||
const getImageSchema = Type.Object({});
|
||||
const getImageTool: Tool<typeof getImageSchema> = {
|
||||
name: "get_circle",
|
||||
description: "Returns a circle image for visualization",
|
||||
parameters: getImageSchema,
|
||||
};
|
||||
|
||||
const context: Context = {
|
||||
systemPrompt: "You are a helpful assistant that uses tools when asked.",
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content:
|
||||
"Call the get_circle tool to get an image, and describe what you see, shapes, colors, etc.",
|
||||
timestamp: Date.now(),
|
||||
},
|
||||
],
|
||||
tools: [getImageTool],
|
||||
};
|
||||
|
||||
// First request - LLM should call the tool
|
||||
const firstResponse = await complete(model, context, options);
|
||||
expect(firstResponse.stopReason).toBe("toolUse");
|
||||
|
||||
// Find the tool call
|
||||
const toolCall = firstResponse.content.find((b) => b.type === "toolCall");
|
||||
expect(toolCall).toBeTruthy();
|
||||
if (!toolCall || toolCall.type !== "toolCall") {
|
||||
throw new Error("Expected tool call");
|
||||
}
|
||||
expect(toolCall.name).toBe("get_circle");
|
||||
|
||||
// Add the tool call to context
|
||||
context.messages.push(firstResponse);
|
||||
|
||||
// Create tool result with ONLY an image (no text)
|
||||
const toolResult: ToolResultMessage = {
|
||||
role: "toolResult",
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.name,
|
||||
content: [
|
||||
{
|
||||
type: "image",
|
||||
data: base64Image,
|
||||
mimeType: "image/png",
|
||||
},
|
||||
],
|
||||
isError: false,
|
||||
timestamp: Date.now(),
|
||||
};
|
||||
|
||||
context.messages.push(toolResult);
|
||||
|
||||
// Second request - LLM should describe the image from the tool result
|
||||
const secondResponse = await complete(model, context, options);
|
||||
expect(secondResponse.stopReason).toBe("stop");
|
||||
expect(secondResponse.errorMessage).toBeFalsy();
|
||||
|
||||
// Verify the LLM can see and describe the image
|
||||
const textContent = secondResponse.content.find((b) => b.type === "text");
|
||||
expect(textContent).toBeTruthy();
|
||||
if (textContent && textContent.type === "text") {
|
||||
const lowerContent = textContent.text.toLowerCase();
|
||||
// Should mention red and circle since that's what the image shows
|
||||
expect(lowerContent).toContain("red");
|
||||
expect(lowerContent).toContain("circle");
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Test that tool results containing both text and images work correctly across all providers.
|
||||
* This verifies that:
|
||||
* 1. Tool results can contain mixed content blocks (text + images)
|
||||
* 2. Providers correctly pass both text and images from tool results to the LLM
|
||||
* 3. The LLM can see both the text and images in tool results
|
||||
*/
|
||||
async function handleToolWithTextAndImageResult<TApi extends Api>(
|
||||
model: Model<TApi>,
|
||||
options?: StreamOptionsWithExtras,
|
||||
) {
|
||||
// Check if the model supports images
|
||||
if (!model.input.includes("image")) {
|
||||
console.log(
|
||||
`Skipping tool text+image result test - model ${model.id} doesn't support images`,
|
||||
);
|
||||
return;
|
||||
}
|
||||
|
||||
// Read the test image
|
||||
const imagePath = join(__dirname, "data", "red-circle.png");
|
||||
const imageBuffer = readFileSync(imagePath);
|
||||
const base64Image = imageBuffer.toString("base64");
|
||||
|
||||
// Define a tool that returns both text and an image
|
||||
const getImageSchema = Type.Object({});
|
||||
const getImageTool: Tool<typeof getImageSchema> = {
|
||||
name: "get_circle_with_description",
|
||||
description: "Returns a circle image with a text description",
|
||||
parameters: getImageSchema,
|
||||
};
|
||||
|
||||
const context: Context = {
|
||||
systemPrompt: "You are a helpful assistant that uses tools when asked.",
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content:
|
||||
"Use the get_circle_with_description tool and tell me what you learned. Also say what color the shape is.",
|
||||
timestamp: Date.now(),
|
||||
},
|
||||
],
|
||||
tools: [getImageTool],
|
||||
};
|
||||
|
||||
// First request - LLM should call the tool
|
||||
const firstResponse = await complete(model, context, options);
|
||||
expect(firstResponse.stopReason).toBe("toolUse");
|
||||
|
||||
// Find the tool call
|
||||
const toolCall = firstResponse.content.find((b) => b.type === "toolCall");
|
||||
expect(toolCall).toBeTruthy();
|
||||
if (!toolCall || toolCall.type !== "toolCall") {
|
||||
throw new Error("Expected tool call");
|
||||
}
|
||||
expect(toolCall.name).toBe("get_circle_with_description");
|
||||
|
||||
// Add the tool call to context
|
||||
context.messages.push(firstResponse);
|
||||
|
||||
// Create tool result with BOTH text and image
|
||||
const toolResult: ToolResultMessage = {
|
||||
role: "toolResult",
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.name,
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: "This is a geometric shape with specific properties: it has a diameter of 100 pixels.",
|
||||
},
|
||||
{
|
||||
type: "image",
|
||||
data: base64Image,
|
||||
mimeType: "image/png",
|
||||
},
|
||||
],
|
||||
isError: false,
|
||||
timestamp: Date.now(),
|
||||
};
|
||||
|
||||
context.messages.push(toolResult);
|
||||
|
||||
// Second request - LLM should describe both the text and image from the tool result
|
||||
const secondResponse = await complete(model, context, options);
|
||||
expect(secondResponse.stopReason).toBe("stop");
|
||||
expect(secondResponse.errorMessage).toBeFalsy();
|
||||
|
||||
// Verify the LLM can see both text and image
|
||||
const textContent = secondResponse.content.find((b) => b.type === "text");
|
||||
expect(textContent).toBeTruthy();
|
||||
if (textContent && textContent.type === "text") {
|
||||
const lowerContent = textContent.text.toLowerCase();
|
||||
// Should mention details from the text (diameter/pixels)
|
||||
expect(lowerContent.match(/diameter|100|pixel/)).toBeTruthy();
|
||||
// Should also mention the visual properties (red and circle)
|
||||
expect(lowerContent).toContain("red");
|
||||
expect(lowerContent).toContain("circle");
|
||||
}
|
||||
}
|
||||
|
||||
describe("Tool Results with Images", () => {
|
||||
describe.skipIf(!process.env.GEMINI_API_KEY)(
|
||||
"Google Provider (gemini-2.5-flash)",
|
||||
() => {
|
||||
const llm = getModel("google", "gemini-2.5-flash");
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!process.env.OPENAI_API_KEY)(
|
||||
"OpenAI Completions Provider (gpt-4o-mini)",
|
||||
() => {
|
||||
const { compat: _compat, ...baseModel } = getModel(
|
||||
"openai",
|
||||
"gpt-4o-mini",
|
||||
);
|
||||
void _compat;
|
||||
const llm: Model<"openai-completions"> = {
|
||||
...baseModel,
|
||||
api: "openai-completions",
|
||||
};
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!process.env.OPENAI_API_KEY)(
|
||||
"OpenAI Responses Provider (gpt-5-mini)",
|
||||
() => {
|
||||
const llm = getModel("openai", "gpt-5-mini");
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!hasAzureOpenAICredentials())(
|
||||
"Azure OpenAI Responses Provider (gpt-4o-mini)",
|
||||
() => {
|
||||
const llm = getModel("azure-openai-responses", "gpt-4o-mini");
|
||||
const azureDeploymentName = resolveAzureDeploymentName(llm.id);
|
||||
const azureOptions = azureDeploymentName ? { azureDeploymentName } : {};
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm, azureOptions);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm, azureOptions);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!process.env.ANTHROPIC_API_KEY)(
|
||||
"Anthropic Provider (claude-haiku-4-5)",
|
||||
() => {
|
||||
const model = getModel("anthropic", "claude-haiku-4-5");
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(model);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(model);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!process.env.OPENROUTER_API_KEY)(
|
||||
"OpenRouter Provider (glm-4.5v)",
|
||||
() => {
|
||||
const llm = getModel("openrouter", "z-ai/glm-4.5v");
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!process.env.MISTRAL_API_KEY)(
|
||||
"Mistral Provider (pixtral-12b)",
|
||||
() => {
|
||||
const llm = getModel("mistral", "pixtral-12b");
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 5, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 5, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!process.env.KIMI_API_KEY)(
|
||||
"Kimi For Coding Provider (k2p5)",
|
||||
() => {
|
||||
const llm = getModel("kimi-coding", "k2p5");
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!process.env.AI_GATEWAY_API_KEY)(
|
||||
"Vercel AI Gateway Provider (google/gemini-2.5-flash)",
|
||||
() => {
|
||||
const llm = getModel("vercel-ai-gateway", "google/gemini-2.5-flash");
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
describe.skipIf(!hasBedrockCredentials())(
|
||||
"Amazon Bedrock Provider (claude-sonnet-4-5)",
|
||||
() => {
|
||||
const llm = getModel(
|
||||
"amazon-bedrock",
|
||||
"global.anthropic.claude-sonnet-4-5-20250929-v1:0",
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(llm);
|
||||
},
|
||||
);
|
||||
|
||||
it(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(llm);
|
||||
},
|
||||
);
|
||||
},
|
||||
);
|
||||
|
||||
// =========================================================================
|
||||
// OAuth-based providers (credentials from ~/.pi/agent/oauth.json)
|
||||
// =========================================================================
|
||||
|
||||
describe("Anthropic OAuth Provider (claude-sonnet-4-5)", () => {
|
||||
const model = getModel("anthropic", "claude-sonnet-4-5");
|
||||
|
||||
it.skipIf(!anthropicOAuthToken)(
|
||||
"should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithImageResult(model, { apiKey: anthropicOAuthToken });
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!anthropicOAuthToken)(
|
||||
"should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
await handleToolWithTextAndImageResult(model, {
|
||||
apiKey: anthropicOAuthToken,
|
||||
});
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
describe("GitHub Copilot Provider", () => {
|
||||
it.skipIf(!githubCopilotToken)(
|
||||
"gpt-4o - should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("github-copilot", "gpt-4o");
|
||||
await handleToolWithImageResult(llm, { apiKey: githubCopilotToken });
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!githubCopilotToken)(
|
||||
"gpt-4o - should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("github-copilot", "gpt-4o");
|
||||
await handleToolWithTextAndImageResult(llm, {
|
||||
apiKey: githubCopilotToken,
|
||||
});
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!githubCopilotToken)(
|
||||
"claude-sonnet-4 - should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("github-copilot", "claude-sonnet-4");
|
||||
await handleToolWithImageResult(llm, { apiKey: githubCopilotToken });
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!githubCopilotToken)(
|
||||
"claude-sonnet-4 - should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("github-copilot", "claude-sonnet-4");
|
||||
await handleToolWithTextAndImageResult(llm, {
|
||||
apiKey: githubCopilotToken,
|
||||
});
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
describe("Google Gemini CLI Provider", () => {
|
||||
it.skipIf(!geminiCliToken)(
|
||||
"gemini-2.5-flash - should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("google-gemini-cli", "gemini-2.5-flash");
|
||||
await handleToolWithImageResult(llm, { apiKey: geminiCliToken });
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!geminiCliToken)(
|
||||
"gemini-2.5-flash - should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("google-gemini-cli", "gemini-2.5-flash");
|
||||
await handleToolWithTextAndImageResult(llm, { apiKey: geminiCliToken });
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
describe("Google Antigravity Provider", () => {
|
||||
it.skipIf(!antigravityToken)(
|
||||
"gemini-3-flash - should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("google-antigravity", "gemini-3-flash");
|
||||
await handleToolWithImageResult(llm, { apiKey: antigravityToken });
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!antigravityToken)(
|
||||
"gemini-3-flash - should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("google-antigravity", "gemini-3-flash");
|
||||
await handleToolWithTextAndImageResult(llm, {
|
||||
apiKey: antigravityToken,
|
||||
});
|
||||
},
|
||||
);
|
||||
|
||||
/** These two don't work, the model simply won't call the tool, works in pi
|
||||
it.skipIf(!antigravityToken)(
|
||||
"claude-sonnet-4-5 - should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("google-antigravity", "claude-sonnet-4-5");
|
||||
await handleToolWithImageResult(llm, { apiKey: antigravityToken });
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!antigravityToken)(
|
||||
"claude-sonnet-4-5 - should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("google-antigravity", "claude-sonnet-4-5");
|
||||
await handleToolWithTextAndImageResult(llm, { apiKey: antigravityToken });
|
||||
},
|
||||
);**/
|
||||
|
||||
// Note: gpt-oss-120b-medium does not support images, so not tested here
|
||||
});
|
||||
|
||||
describe("OpenAI Codex Provider", () => {
|
||||
it.skipIf(!openaiCodexToken)(
|
||||
"gpt-5.2-codex - should handle tool result with only image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("openai-codex", "gpt-5.2-codex");
|
||||
await handleToolWithImageResult(llm, { apiKey: openaiCodexToken });
|
||||
},
|
||||
);
|
||||
|
||||
it.skipIf(!openaiCodexToken)(
|
||||
"gpt-5.2-codex - should handle tool result with text and image",
|
||||
{ retry: 3, timeout: 30000 },
|
||||
async () => {
|
||||
const llm = getModel("openai-codex", "gpt-5.2-codex");
|
||||
await handleToolWithTextAndImageResult(llm, {
|
||||
apiKey: openaiCodexToken,
|
||||
});
|
||||
},
|
||||
);
|
||||
});
|
||||
});
|
||||
Loading…
Add table
Add a link
Reference in a new issue