Build a small pipeline that turns an incoming support message into typed structured data.

Define the agent

Start with one agent and give it an output schema.

server.ts

import { betterAgent, defineAgent } from "@better-agent/core";
import { createOpenAI } from "@better-agent/providers/openai";
import { z } from "zod";

const openai = createOpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

const triageAgent = defineAgent({
  name: "triageAgent",
  model: openai.text("gpt-5-mini"),
  instruction: `
    Classify the support message and return a short summary.
    Choose the best category and priority from the schema.
  `,
  outputSchema: {
    schema: z.object({
      category: z.enum(["billing", "technical", "account", "general"]),
      priority: z.enum(["low", "medium", "high"]),
      summary: z.string(),
      needsEscalation: z.boolean(),
    }),
  },
});

const app = betterAgent({
  agents: [triageAgent],
  baseURL: "/api",
  secret: "dev-secret",
});

export default app;

Create the client

client.ts

import { createClient } from "@better-agent/client";
import type app from "./server";

export const client = createClient<typeof app>({
  baseURL: "/api",
  secret: "dev-secret",
});

Build the form

Extraction usually fits a simple request and result flow, so this example uses client.run(...) directly.

Page.tsx

import { useState } from "react";
import { client } from "./client";

export function Page() {
  const [input, setInput] = useState("");
  const [result, setResult] = useState<null | {
    category: "billing" | "technical" | "account" | "general";
    priority: "low" | "medium" | "high";
    summary: string;
    needsEscalation: boolean;
  }>(null);
  const [loading, setLoading] = useState(false);

  return (
    <div>
      <form
        onSubmit={async (event) => {
          event.preventDefault();
          if (!input.trim()) return;

          setLoading(true);
          try {
            const result = await client.run("triageAgent", {
              input,
            });
            setResult(result.structured);
          } finally {
            setLoading(false);
          }
        }}
      >
        <textarea value={input} onChange={(event) => setInput(event.target.value)} />
        <button type="submit">{loading ? "Running..." : "Extract"}</button>
      </form>

      {result ? (
        <pre>{JSON.stringify(result, null, 2)}</pre>
      ) : null}
    </div>
  );
}

This same pattern works for support tickets, forms, CRM notes, and other unstructured text.

Build a Structured Extraction Pipeline

Define the agent

Create the client

Build the form

On this page