docs/ai-chat/custom-agents.mdx
A custom agent is a task you register with chat.customAgent() and drive yourself — either with the managed turn iterator from chat.createSession(), or with a fully hand-rolled loop over the raw chat primitives. You give up chat.agent()'s lifecycle hooks and automatic continuation recovery; you gain inline control over every turn, and (at the lowest level) full control over the stream conversion.
See the comparison table before dropping down. The frontend is unchanged either way: all levels speak the same wire protocol, so useTriggerChatTransport points at a custom agent exactly like a chat.agent().
chat.customAgent() is a thin wrapper around task() that does two things: it registers the task as an agent (so it appears in the agent dashboard, the playground, and the MCP server's list_agents), and it binds the run to its backing Session so the chat.* primitives resolve to the right .in/.out channels. There is no managed lifecycle — no turn loop, no hooks, no preload handling.
A plain task() works with the same primitives but stays invisible to the agent surfaces, so prefer customAgent unless you specifically don't want the task listed as an agent.
Inside the wrapper, pick one of two loop styles:
chat.createSession() yields turns; the SDK handles stop signals, accumulation, idle suspend/resume, and turn-complete signaling. You write the turn body.chat.messages, MessageAccumulator, pipeAndCapture, and writeTurnComplete. The right choice when you need complete control over .toUIMessageStream() (e.g. onFinish, originalMessages) beyond what chat.setUIMessageStreamOptions() provides, or you're implementing a custom protocol.chat.createSession() gives you an async iterator of ChatTurn objects. Each turn arrives with the accumulated history, a combined stop+cancel signal, and helpers to finish the turn:
import { chat, type ChatTaskWirePayload } from "@trigger.dev/sdk/ai";
import { streamText, stepCountIs } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
export const myChat = chat.customAgent({
id: "my-chat",
run: async (payload: ChatTaskWirePayload, { signal }) => {
// One-time initialization — plain code, no hooks. Upsert, not create:
// continuation runs boot with the row already in place.
const clientData = payload.metadata as { userId: string };
await db.chat.upsert({
where: { id: payload.chatId },
create: { id: payload.chatId, userId: clientData.userId },
update: {},
});
const session = chat.createSession(payload, {
signal,
idleTimeoutInSeconds: 60,
timeout: "1h",
});
for await (const turn of session) {
// Persist the incoming user message BEFORE streaming — this is your
// onTurnStart equivalent. Without it, a page reload mid-stream
// restores the assistant text (replayed from the session) but loses
// the user message that prompted it.
await db.chat.update({
where: { id: turn.chatId },
data: { messages: turn.uiMessages },
});
const result = streamText({
model: anthropic("claude-sonnet-4-5"),
messages: turn.messages,
abortSignal: turn.signal,
stopWhen: stepCountIs(15),
});
// Pipe, capture, accumulate, and signal turn-complete — all in one call
await turn.complete(result);
// Persist the full exchange after the turn — your onTurnComplete equivalent
await db.chat.update({
where: { id: turn.chatId },
data: { messages: turn.uiMessages },
});
}
},
});
| Option | Type | Default | Description |
|---|---|---|---|
signal | AbortSignal | required | Run-level cancel signal (from task context) |
idleTimeoutInSeconds | number | 30 | Seconds to stay idle between turns before suspending |
timeout | string | "1h" | Duration string for suspend timeout |
maxTurns | number | 100 | Max turns before ending |
compaction | ChatAgentCompactionOptions | undefined | Automatic context compaction — same options as on chat.agent() |
pendingMessages | PendingMessagesOptions | undefined | Mid-execution message injection — same options as on chat.agent() |
Between turns the run idles on waitWithIdleTimeout: after idleTimeoutInSeconds with no message it suspends (compute is freed), and the next message restores it on the same run — the same warm/suspended pipeline chat.agent() uses.
Each turn yielded by the iterator provides:
| Field | Type | Description |
|---|---|---|
number | number | Turn number (0-indexed) |
chatId | string | Chat session ID |
trigger | string | What triggered this turn |
clientData | unknown | Client data from the transport |
messages | ModelMessage[] | Full accumulated model messages — pass to streamText |
uiMessages | UIMessage[] | Full accumulated UI messages — use for persistence |
signal | AbortSignal | Combined stop+cancel signal (fresh each turn) |
stopped | boolean | Whether the user stopped generation this turn |
continuation | boolean | Whether this is a continuation run |
previousTurnUsage | LanguageModelUsage | undefined | Token usage from the previous turn (undefined on turn 0) |
totalUsage | LanguageModelUsage | Cumulative token usage across all completed turns |
handover | { isFinal: boolean } | null | The chat.headStart handover for this turn (turn 0 only); null otherwise |
| Method | Description |
|---|---|
turn.complete(source?) | Pipe stream, capture response, accumulate, and signal turn-complete. Call with no source on a final head-start handover (turn.handover.isFinal), where the warm step-1 partial is already the response |
turn.done() | Signal turn-complete only (when you have piped manually) |
turn.addResponse(response) | Add a response to the accumulator manually |
turn.setMessages(uiMessages) | Replace the accumulated messages — continuation seeding and on-demand compaction |
turn.prepareStep() | prepareStep callback wiring compaction + injection — pass to streamText when not spreading chat.toStreamTextOptions() |
chat.agent() rebuilds conversation history automatically when a chat continues on a fresh run (after a cancel, crash, version upgrade, or TTL expiry) — via its snapshot/replay boot or your hydrateMessages hook. Custom agents do none of that: a continuation run starts with an empty accumulator, and history restoration is your job.
With createSession, check turn.continuation on the first turn and seed from your store with turn.setMessages():
for await (const turn of session) {
if (turn.continuation && turn.number === 0) {
const row = await db.chat.findUnique({ where: { id: turn.chatId } });
const stored = (row?.messages ?? []) as UIMessage[];
if (stored.length > 0) {
// Keep any incoming message that isn't already persisted
const incoming = turn.uiMessages.filter((m) => !stored.some((s) => s.id === m.id));
await turn.setMessages([...stored, ...incoming]);
}
}
// ... streamText + turn.complete as usual
}
Without this, a resumed chat silently loses its history: the model sees only the message that triggered the continuation. In a hand-rolled loop, seed by passing the stored history into the turn-0 addIncoming call — shown in the example below.
turn.complete(result) is the one-call path — it handles piping, capturing the response, accumulating messages, cleaning up aborted parts on a stop, and writing the turn-complete chunk.
For more control, you can do each step manually:
for await (const turn of session) {
const result = streamText({
model: anthropic("claude-sonnet-4-5"),
messages: turn.messages,
abortSignal: turn.signal,
stopWhen: stepCountIs(15),
});
// Manual: pipe and capture separately
const response = await chat.pipeAndCapture(result, { signal: turn.signal });
if (response) {
// Custom processing before accumulating
await turn.addResponse(response);
}
// Custom persistence, analytics, etc.
await db.chat.update({ ... });
// Must call done() when not using complete()
await turn.done();
}
The frontend stops a turn with transport.stopGeneration(chatId), which writes a stop signal to the session's input stream. It aborts the current turn's generation but keeps the run alive, so the next message continues on the same session.
turn.signal is a combined stop-and-cancel AbortSignal, fresh each turn. Pass it to streamText so the stop reaches the model, then let turn.complete() finish the turn:
for await (const turn of session) {
const result = streamText({
model: anthropic("claude-sonnet-4-5"),
messages: turn.messages,
abortSignal: turn.signal, // fires on a user stop OR a run cancel
stopWhen: stepCountIs(15),
});
await turn.complete(result);
if (turn.stopped) {
// user stopped this turn — the partial response is already accumulated
}
}
On a stop, turn.complete() cleans up the aborted parts of the partial response, accumulates it as its own assistant message, and writes turn-complete. The run does not end — the loop continues to the next turn.
Read turn.stopped to tell a user stop from a full run cancel:
transport.stopGeneration): turn.signal aborts, turn.stopped is true, the partial response is accumulated, and the run stays alive for the next message.maxDuration exceeded): turn.signal aborts, turn.stopped is false, and turn.complete() returns without accumulating because the run is ending.A hand-rolled loop wires this itself with chat.createStopSignal() and chat.cleanupAbortedParts(). Two things createSession handles for you are easy to get wrong there — see the hand-rolled loop checklist.
For full control, skip createSession and compose the primitives directly:
| Primitive | Description |
|---|---|
chat.messages | Input stream for incoming messages — use .waitWithIdleTimeout() to wait for the next turn |
chat.createStopSignal() | Create a managed stop signal wired to the stop input stream |
chat.pipeAndCapture(result) | Pipe a StreamTextResult to the chat stream and capture the response |
chat.writeTurnComplete() | Signal the frontend that the current turn is complete |
chat.MessageAccumulator | Accumulates conversation messages across turns |
chat.pipe(stream) | Pipe a stream to the frontend (no response capture) |
chat.cleanupAbortedParts(msg) | Clean up incomplete parts from a stopped response |
A complete loop:
import { chat, type ChatTaskWirePayload } from "@trigger.dev/sdk/ai";
import { streamText, stepCountIs } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
export const myChat = chat.customAgent({
id: "my-chat-raw",
run: async (payload: ChatTaskWirePayload, { signal: runSignal }) => {
let currentPayload = payload;
// Handle preload — wait for the first real message
if (currentPayload.trigger === "preload") {
const result = await chat.messages.waitWithIdleTimeout({
idleTimeoutInSeconds: 60,
timeout: "1h",
spanName: "waiting for first message",
});
if (!result.ok) return;
currentPayload = result.output;
}
const stop = chat.createStopSignal();
const conversation = new chat.MessageAccumulator();
// Continuation runs (cancel, crash, upgrade) start with an empty
// accumulator — fetch stored history so turn 0 can seed it.
let continuationSeed: UIMessage[] = [];
if (currentPayload.continuation) {
const row = await db.chat.findUnique({ where: { id: currentPayload.chatId } });
continuationSeed = (row?.messages ?? []) as UIMessage[];
}
for (let turn = 0; turn < 100; turn++) {
stop.reset();
// The wire payload carries at most one new message per turn. Turn 0
// REPLACES the accumulator, so seed stored history through
// addIncoming together with the incoming message — a setMessages
// call before the loop would be wiped here.
const incoming = currentPayload.message ? [currentPayload.message] : [];
const turnInput =
turn === 0 && continuationSeed.length > 0
? [...continuationSeed.filter((s) => !incoming.some((m) => m.id === s.id)), ...incoming]
: incoming;
const messages = await conversation.addIncoming(turnInput, currentPayload.trigger, turn);
// Persist the incoming user message before streaming so a
// mid-stream reload doesn't lose it.
await db.chat.update({
where: { id: currentPayload.chatId },
data: { messages: conversation.uiMessages },
});
const combinedSignal = AbortSignal.any([runSignal, stop.signal]);
const result = streamText({
model: anthropic("claude-sonnet-4-5"),
messages,
abortSignal: combinedSignal,
stopWhen: stepCountIs(15),
});
let response;
try {
response = await chat.pipeAndCapture(result, { signal: combinedSignal });
} catch (error) {
if (error instanceof Error && error.name === "AbortError") {
if (runSignal.aborted) break;
// Stop — fall through to accumulate partial
} else {
throw error;
}
}
if (response) {
const cleaned =
stop.signal.aborted && !runSignal.aborted ? chat.cleanupAbortedParts(response) : response;
await conversation.addResponse(cleaned);
}
if (runSignal.aborted) break;
// Persist, analytics, etc.
await db.chat.update({
where: { id: currentPayload.chatId },
data: { messages: conversation.uiMessages },
});
await chat.writeTurnComplete();
// Wait for the next message
const next = await chat.messages.waitWithIdleTimeout({
idleTimeoutInSeconds: 60,
timeout: "1h",
spanName: "waiting for next message",
});
if (!next.ok) break;
currentPayload = next.output;
}
stop.cleanup();
},
});
addIncoming(messages, trigger, turn) has two modes:
trigger === "regenerate-message": replaces the accumulator with exactly what you pass. This is why continuation seeding goes through addIncoming (above), and why a regenerate needs you to slice your own history — the wire omits the message on regenerate, so pass the stored history minus the last assistant message.const conversation = new chat.MessageAccumulator();
// Returns full accumulated ModelMessage[] for streamText
const messages = await conversation.addIncoming(
payload.message ? [payload.message] : [],
payload.trigger,
turn
);
// After piping, add the response
const response = await chat.pipeAndCapture(result);
if (response) await conversation.addResponse(response);
// Access accumulated messages for persistence
conversation.uiMessages; // UIMessage[]
conversation.modelMessages; // ModelMessage[]
The constructor also accepts compaction and pendingMessages options (same shapes as on chat.agent()); pass prepareStep: conversation.prepareStep() to streamText to activate them. See pending messages for the manual steering wiring.
Things the managed levels do for you that a raw loop has to get right:
Don't bare-await result.totalUsage. On a stop-abort the AI SDK's totalUsage promise never settles, which wedges the loop forever. Race it with a timeout:
const turnUsage = await Promise.race([
result.totalUsage,
new Promise((resolve) => setTimeout(() => resolve(undefined), 2000)),
]);
Persist the user message before streaming (shown in the example above). The session replay restores the assistant's streamed text after a page reload, but nothing restores a user message you haven't written down.
Seed history on continuation runs through the turn-0 addIncoming (shown above). payload.continuation is true when this run picked up an existing chat; the accumulator starts empty — and because turn 0 replaces the accumulator, a setMessages call before the loop gets wiped.
Clean up aborted parts on a stop with chat.cleanupAbortedParts() before accumulating, or the partial response carries half-open tool calls into the next turn's prompt.
Read payload.message (singular). The wire payload carries at most one new message per turn; there is no messages array on the payload.