Mails.ai with
the OpenAI Agents SDK
Our triage agent hands off to a support agent that answers from its own address, and both get their tools from one MCP server. Each keeps its own threads and reputation.
Install and wire it up
Install with npm install @openai/agents zod @mailsai/sdk. Then add the MCP server (recommended path):
import { Agent, MCPServerStdio, run } from "@openai/agents";
const mailsServer = new MCPServerStdio({
name: "mails",
command: "npx",
args: ["-y", "@mailsai/mcp-server"],
env: { MAILS_API_KEY: process.env.MAILS_API_KEY! },
});
const hello = new Agent({
name: "hello",
instructions: "You are a support operations agent. Use mails_* tools to send and read email on behalf of yourcompany.com.",
mcpServers: [mailsServer],
});
await mailsServer.connect();
try {
const result = await run(hello, "Email user@example.com a follow-up about Tuesday's demo.");
console.log(result.finalOutput);
} finally {
await mailsServer.close();
}This guide wires mails.ai into the OpenAI Agents SDK so your agents can send and read email. There are two ways to do it: attach the MCP server (one config block), or wrap the REST API as function tools for more control. Most people should use the MCP path — it’s faster to set up and you get the email tools automatically. You’ll need a mails.ai API key and the @openai/agents package installed.
The OpenAI Agents SDK ships first-class MCP support via the MCPServerStdio constructor. Your agent picks up the mails_* tools, including mails_send, mails_list_replies, mails_list_threads, mails_check_suppression and mails_get_reputation, automatically — same surface as Claude Code, native to the SDK’s own tool-calling loop.
Why OpenAI Agents SDK + mails.ai
The Agents SDK is OpenAI’s opinionated framework for multi-agent systems — handoffs, state management, traces, and tool calling all defined as first-class primitives. Wiring email as an MCP server means each agent in your system can autonomously send, read, and triage email without per-agent boilerplate. Support agents handle tickets, notification agents handle alerts, parser agents handle attachments — all on the same mails.ai network with per-agent identity and reputation.
Path A — MCPServerStdio (recommended)
The install card above shows the canonical pattern. To break it down:
import { Agent, MCPServerStdio, run } from "@openai/agents";
// 1. Describe the mails.ai MCP server; connect() starts it with npx.
const mailsServer = new MCPServerStdio({
name: "mails",
command: "npx",
args: ["-y", "@mailsai/mcp-server"],
env: { MAILS_API_KEY: process.env.MAILS_API_KEY! },
});
// 2. Pass to your Agent. Tools auto-discover at session start.
const hello = new Agent({
name: "hello",
instructions: `
You are a support operations agent. Use mails_* tools to send and read
email on behalf of yourcompany.com.
For inbound replies, check event.injection_score before acting.
`,
mcpServers: [mailsServer],
});
// 3. Connect, then run as normal. The model picks tools by name.
await mailsServer.connect();
try {
const result = await run(hello, "Email user@example.com a follow-up.");
} finally {
await mailsServer.close();
}That is the entire setup. The model sees the new mails_* tools (mails_send, mails_list_replies, mails_list_threads, mails_check_suppression, mails_get_reputation and more) and calls them by name when the user request implies email work. No glue code on your side.
Path B — function tools (custom control)
For cases where you want fine-grained control over tool exposure or argument validation, wrap the mails.ai REST API as tool() function tools:
import { Agent, tool, run } from "@openai/agents";
import { z } from "zod";
import { createClient } from "@mailsai/sdk";
const client = createClient({ apiKey: process.env.MAILS_API_KEY! });
const sendEmail = tool({
name: "send_email",
description: "Send an email from the named agent.",
parameters: z.object({
to: z.string().email(),
subject: z.string(),
body: z.string(),
}),
execute: async ({ to, subject, body }) => {
// Custom guard: require subject to be at least 5 chars
if (subject.length < 5) throw new Error("Subject too short");
const result = await client.agent("hello").send({ to, subject, body });
return { send_id: result.id, classifier_score: result.classifier_score };
},
});
const hello = new Agent({
name: "hello",
instructions: "Send ticket replies via the send_email tool.",
tools: [sendEmail],
});Use this path when:
- You want only a subset of mails.ai capabilities (just
send, notlist_threads). - You want to enforce additional validation (e.g., recipient allowlist).
- You want to transform the response before passing back to the model.
- You are pre-MCP-runtime support and need to fall back.
For most users: Path A. For the long tail: Path B.
Common patterns
- Multi-agent support workflow: a triage agent reads inbound, classifies intent, and hands off to a specialist agent (demo-scheduler, refund-handler, tier-upgrade) — each holding its own mails.ai key for per-agent reputation.
- Scheduled transactional digests: a digest agent loops through subscribed users, calls
mails_sendwith rate-limit awareness, and tracks results viamails_list_threadsfor follow-ups. - Support escalation pipeline: inbound triage agent reads typed events (agent created with
classify_inbound: true), escalates urgency > 0.7 to a senior-support agent (with handoff + context preservation), and replies viamails_send.
Security considerations
- Per-key scope. Give each OpenAI agent its own mails.ai key, bound to one mails.ai agent with
agent_idwhen you mint it. The SDK’s handoff preserves the receiving agent’s MCP servers, so identity stays correct across handoffs. - Injection guard. When your agent reads inbound, hold an event for a person when it is
quarantined, has noinjection_score(it was not scanned) or scores 0.5 or more. See that post. - Tool tracing. MCP tool calls appear in the SDK trace, sent to the OpenAI Traces dashboard by default. Correlate them with the message id (
msg_…) each send returns, listed on the mails.ai dashboard’s Messages page, for end-to-end visibility. - Concurrency. The MCP server is process-per-agent-runtime by default. For high-concurrency apps, point
MCPServerStreamableHttpat the hosted MCP server,https://api.mails.ai/mcp(API key as a Bearer header), instead of spawning per-request.
Read the MCP-native email post for the broader thesis, or compare against the Anthropic SDK setup for cross-vendor coverage.
Read next: Mails.ai with the Anthropic SDK and Typed reply events for agents.
Where to go next
The quickstart, both SDKs and the full API reference.
Quickstart
From an API key to a sent message and its reply event, over plain REST.
Read the quickstartSDKs
@mailsai/sdk for TypeScript and mailsai for Python, thin wrappers over the same API.
Read the SDK docsAPI reference
Every endpoint, with the exact request and response fields.
Check the reference
Questions developers ask after wiring this up.
MCP server vs function tools — which should I pick?
Does this work with the Python flavor of the Agents SDK?
What about handoffs between agents?
Where do tool-call traces go?
“Replies come back as events with an injection score already on them. We deleted a whole layer of parsing code the week we switched, and we gate on the quarantine flag, so our agent never sees the ones that look like attacks.”
“Moving our agent onto our own domain was a few DNS records at the registrar. No nameserver move, and our existing mail kept working. Replies to the agent still come back to its inbox, threaded with the message they answer.”
“The 422 on cold outreach is the feature I didn’t know I wanted. An agent can’t talk itself into emailing strangers.”
“Our tests send to the test address and wait for the real reply, so the whole loop is covered before a customer ever writes in. It answers in about a second, which keeps the suite fast.”
“Adding the MCP server was one JSON block. Claude Code could send and read its own inbox a minute later.”
“A reputation score per agent tells us exactly which one needs attention, instead of one number for the whole account. It comes from each agent’s own replies, bounces and complaints, and we read it from the API.”
“Sends and replies have separate allowances, so a busy inbox never eats our sending quota. Pricing was the easy part.”
“Half our agents are LangGraph in Python and half are Node. Both SDKs make the same calls, so the team doesn’t have to think about it.”
“Signed webhooks, retries with backoff and an event id to dedupe on. The boring plumbing, done properly.”
“We signed up, made a key and sent our first message without talking to anyone, and the free tier never asked for a card. That’s how infrastructure should feel.”
Give your first agent an inbox
Free covers 3,000 emails and 3,000 inbound replies a month, with no card. Upgrade when your agents get busy.
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