How to Build an AI SDR (With the Vercel AI SDK)

export LEADOCEAN_API_KEY="paste-your-key-here"

// leadocean.ts
const BASE = "https://api.leadocean.io";
const KEY = process.env.LEADOCEAN_API_KEY!;
export const spent = { records: 0 };
const RECORD_CAP = 20; // hard ceiling for one run

export async function lo(method: "GET" | "POST", path: string, body?: unknown) {
  for (let attempt = 0; attempt < 2; attempt++) {
    const r = await fetch(BASE + path, {
      method,
      headers: { "x-api-key": KEY, "content-type": "application/json" },
      body: body ? JSON.stringify(body) : undefined,
    });
    if (r.status === 429 && attempt === 0) {
      const wait = Number(r.headers.get("retry-after") ?? "1");
      await new Promise((res) => setTimeout(res, wait * 1000));
      continue;
    }
    if (r.status === 402) return { error: "records_spent", hint: "Stop. The free records are spent." };
    if (r.status === 429) return { error: "rate_limited", hint: "Stop. Retry later." };
    if (r.status === 404) return { error: "not_found", hint: "Nobody matched. This cost nothing." };
    if (!r.ok) return { error: `http_${r.status}`, detail: await r.text() };
    return r.json();
  }
}

export function reserve(n: number) {
  if (spent.records + n > RECORD_CAP) return false;
  spent.records += n;
  return true;
}

// tools.ts
import { tool } from "ai";
import { z } from "zod";
import { lo, reserve } from "./leadocean";

export const tools = {
  countAudience: tool({
    description: "Free. Count people matching filters before searching.",
    inputSchema: z.object({
      title: z.array(z.string()),
      country: z.array(z.string()).describe("ISO 3166-1 alpha-2, e.g. US"),
    }),
    execute: async (f) =>
      lo("POST", "/v1/people/search?count=true&limit=1", f),
  }),

  searchPeople: tool({
    description: "Search people. Costs 1 record per person returned. Max 10.",
    inputSchema: z.object({
      title: z.array(z.string()),
      country: z.array(z.string()),
      limit: z.number().int().min(1).max(10).default(10),
    }),
    execute: async (f) => {
      if (!reserve(f.limit)) return { error: "run_cap", hint: "Record cap reached." };
      return lo("POST", "/v1/people/search", f);
    },
  }),

  enrichPerson: tool({
    description: "Enrich one person by person_id. Costs 1 record if found.",
    inputSchema: z.object({ person_id: z.string() }),
    execute: async ({ person_id }) => {
      if (!reserve(1)) return { error: "run_cap", hint: "Record cap reached." };
      return lo("POST", "/v1/people/enrich", { person_id });
    },
  }),
};

// agent.ts
import { generateText, isStepCount } from "ai";
import { tools } from "./tools";
import { spent } from "./leadocean";

const { text } = await generateText({
  model: "anthropic/claude-sonnet-5.5", // any model string your setup supports
  tools,
  stopWhen: isStepCount(10),
  system:
    "You are an SDR assistant. Never send anything. " +
    "If a tool returns records_spent or rate_limited, stop and say so.",
  prompt: `Our product: usage-based billing for SaaS.
1. Count VP-level or Head of Sales people in the US with countAudience.
2. Search 10 with searchPeople.
3. Pick the 3 best fits and enrich each with enrichPerson, using meta.source_ids.person_id.
4. Write one 60-word first-touch email per person. Output drafts only.`,
});

console.log(text);
console.log(`LeadOcean records spent: ${spent.records}`);

{
  "total": 18520,
  "totalIsExact": true,
  "totalAll": 56852,
  "distinctCompanies": 12932,
  "credits": 0
}

{
  "profile_data": { "profile_full_name": "Jane Doe" },
  "contact_data": {
    "has_email": true,
    "email_status": "verified",
    "contact_current_experiences": [{ "title": "VP Sales", "company": "Acme" }]
  },
  "resume_data": {},
  "meta": { "source_ids": { "person_id": "1234567890" } }
}