co-mono/packages/ai/test/examples/openai-responses.ts
Mario Zechner cb4c32faaa refactor(ai): Add completion signal to onText/onThinking callbacks
- Update LLMOptions interface to include completion boolean parameter
- Modify all providers to signal when text/thinking blocks are complete
- Update examples to handle the completion parameter
- Move documentation files to docs/ directory
2025-08-24 20:33:26 +02:00

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TypeScript

import chalk from "chalk";
import { OpenAIResponsesLLMOptions, OpenAIResponsesLLM } from "../../src/providers/openai-responses.js";
import type { Context, Tool } from "../../src/types.js";
// Define a simple calculator tool
const tools: Tool[] = [
{
name: "calculate",
description: "Perform a mathematical calculation",
parameters: {
type: "object" as const,
properties: {
expression: {
type: "string",
description: "The mathematical expression to evaluate"
}
},
required: ["expression"]
}
}
];
const ai = new OpenAIResponsesLLM("gpt-5");
const context: Context = {
messages: [
{
role: "user",
content: "Think about birds briefly. Then give me a list of 10 birds. Finally, calculate 42 * 17 + 123 and 453 + 434 in parallel using the calculator tool.",
}
],
tools,
}
const options: OpenAIResponsesLLMOptions = {
onText: (t, complete) => process.stdout.write(t + (complete ? "\n" : "")),
onThinking: (t, complete) => process.stdout.write(chalk.dim(t + (complete ? "\n" : ""))),
reasoningEffort: "low",
reasoningSummary: "auto"
};
let msg = await ai.complete(context, options)
context.messages.push(msg);
console.log();
console.log(chalk.yellow(JSON.stringify(msg, null, 2)));
for (const toolCall of msg.toolCalls || []) {
if (toolCall.name === "calculate") {
const expression = toolCall.arguments.expression;
const result = eval(expression);
context.messages.push({
role: "toolResult",
content: `The result of ${expression} is ${result}.`,
toolCallId: toolCall.id,
isError: false
});
}
}
msg = await ai.complete(context, options);
console.log();
console.log(chalk.yellow(JSON.stringify(msg, null, 2)));