Route a question to a different AI model

Create an AI agent workflow that routes a question to a different AI model depending on its complexity

Overview

Routing is a workflow pattern that classifies an input and directs it to a specialized followup task. This pattern allows for separation of concerns and building more specialized prompts, which is particularly effective when there are distinct categories that are better handled separately. Without routing, optimizing for one kind of input can hurt performance on other inputs.

Example task

In this example, we’ll create a workflow that routes a question to a different AI model depending on its complexity. This approach is particularly effective when tasks require different models or approaches for different inputs.

This task:

• Uses generateText from Vercel’s AI SDK to interact with OpenAI models
• Uses experimental_telemetry in the source verification and historical analysis tasks to provide LLM logs
• Routes questions using a lightweight model (o1-mini) to classify complexity
• Directs simple questions to gpt-4o and complex ones to gpt-o3-mini
• Returns both the answer and metadata about the routing decision

import { openai } from "@ai-sdk/openai";
import { task } from "@trigger.dev/sdk/v3";
import { generateText } from "ai";
import { z } from "zod";

// Schema for router response
const routingSchema = z.object({
  model: z.enum(["gpt-4o", "gpt-o3-mini"]),
  reason: z.string(),
});

// Router prompt template
const ROUTER_PROMPT = `You are a routing assistant that determines the complexity of questions.
Analyze the following question and route it to the appropriate model:

- Use "gpt-4o" for simple, common, or straightforward questions
- Use "gpt-o3-mini" for complex, unusual, or questions requiring deep reasoning

Respond with a JSON object in this exact format:
{"model": "gpt-4o" or "gpt-o3-mini", "reason": "your reasoning here"}

Question: `;

export const routeAndAnswerQuestion = task({
  id: "route-and-answer-question",
  run: async (payload: { question: string }) => {
    // Step 1: Route the question
    const routingResponse = await generateText({
      model: openai("o1-mini"),
      messages: [
        {
          role: "system",
          content:
            "You must respond with a valid JSON object containing only 'model' and 'reason' fields. No markdown, no backticks, no explanation.",
        },
        {
          role: "user",
          content: ROUTER_PROMPT + payload.question,
        },
      ],
      temperature: 0.1,
      experimental_telemetry: {
        isEnabled: true,
        functionId: "route-and-answer-question",
      },
    });

    // Add error handling and cleanup
    let jsonText = routingResponse.text.trim();
    if (jsonText.startsWith("```")) {
      jsonText = jsonText.replace(/```json\n|\n```/g, "");
    }

    const routingResult = routingSchema.parse(JSON.parse(jsonText));

    // Step 2: Get the answer using the selected model
    const answerResult = await generateText({
      model: openai(routingResult.model),
      messages: [{ role: "user", content: payload.question }],
    });

    return {
      answer: answerResult.text,
      selectedModel: routingResult.model,
      routingReason: routingResult.reason,
    };
  },
});

Run a test

Triggering our task with a simple question shows it routing to the gpt-4o model and returning the answer with reasoning:

{
  "question": "How many planets are there in the solar system?"
}