Trigger Chat Agent Advanced

chat.agent: advanced and operational

chat.agent is built on Sessions: a durable, task-bound, bi-directional I/O channel pair keyed

on a stable externalId (e.g. chatId) that outlives any single run. This skill covers the layers

beneath and around the everyday agent: the raw sessions API, server-side AgentChat, durable

sub-agents, actions / background injection, fast starts, compaction and recovery, and the wire

protocol for custom transports.

Two chat namespaces are easy to confuse: the agent definition imports chat from

@trigger.dev/sdk/ai; Head Start / Node-listener server entries import chat from

@trigger.dev/sdk/chat-server.

Setup

Happy path: drive an agent from server-side code (task, webhook, or script) with AgentChat.

import { AgentChat } from "@trigger.dev/sdk/chat";
import type { myAgent } from "./trigger/my-agent";

const chat = new AgentChat<typeof myAgent>({ agent: "my-chat", clientData: { userId: "user_123" } });
const stream = await chat.sendMessage("Review PR #42");
const text = await stream.text();
await chat.close();

sendMessage() triggers a run on the first call, then reuses it via input streams. ChatStream

exposes text(), result() ({ text, toolCalls, toolResults }), messages() (UIMessage

snapshots), and the raw .stream. Other methods: steer(text), stop(), sendRaw(uiMessages),

sendAction(action), preload(), reconnect().

Core patterns

1. Raw Sessions for non-chat, bi-directional I/O

Reach for sessions directly when the chat abstraction does not fit: agent inboxes, approval flows,

server-to-server pipelines. sessions.start is idempotent on (env, externalId); externalId

cannot start with session_.

import { sessions } from "@trigger.dev/sdk";

const { id, publicAccessToken } = await sessions.start({
  type: "chat.agent",
  externalId: chatId,
  taskIdentifier: "my-chat",
  triggerConfig: { tags: [`chat:${chatId}`], basePayload: { chatId, trigger: "preload" } },
});

const session = sessions.open(chatId); // no network call; methods are lazy
await session.out.append({ kind: "message", text: "hello" });
const next = await session.in.once<MyEvent>({ timeoutMs: 30_000 });

sessions.open(id).in also has send, on(handler), peek, wait (suspends the run, only inside

task.run()), and waitWithIdleTimeout. .out has append, pipe, writer, read,

writeControl, and trimTo. List with sessions.list({ type, tag, status, ... }) (for await),

mutate with sessions.update, end with sessions.close (terminal, idempotent).

2. Durable sub-agent as a streaming tool

AgentChat inside an AI SDK tool() delegates to a durable sub-agent; its response streams as

preliminary tool results. Give the tool a toModelOutput so the model sees a compact summary.

import { tool } from "ai";
import { AgentChat } from "@trigger.dev/sdk/chat";
import { z } from "zod";

const researchTool = tool({
  description: "Delegate research to a specialist agent.",
  inputSchema: z.object({ topic: z.string() }),
  execute: async function* ({ topic }, { abortSignal }) {
    const chat = new AgentChat({ agent: "research-agent" });
    const stream = await chat.sendMessage(topic, { abortSignal });
    yield* stream.messages(); // UIMessage snapshots become preliminary tool results
    await chat.close();
  },
  toModelOutput: ({ output: message }) => {
    const lastText = message?.parts?.findLast((p: { type: string }) => p.type === "text") as
      | { text?: string }
      | undefined;
    return { type: "text", value: lastText?.text ?? "Done." };
  },
});

For a subtask exposed via execute: ai.toolExecute(task), stream progress to the agent's run with

chat.stream.writer({ target: "root" }). target accepts "self" | "parent" | "root" | <runId>.

Inside the subtask, read context with ai.toolCallId() and ai.chatContextOrThrow<typeof myChat>()

({ chatId, turn, continuation, clientData }).

import { chat, ai } from "@trigger.dev/sdk/ai";

const { waitUntilComplete } = chat.stream.writer({
  target: "root",
  execute: ({ write }) =>
    write({ type: "data-research-status", id: partId, data: { query, status: "in-progress" } }),
});
await waitUntilComplete();

3. Background injection: defer + inject

chat.defer(promise) runs work in parallel with streaming (all deferred promises are awaited, with a

5s timeout, before onTurnComplete). chat.inject(messages) queues ModelMessage[] that drain at

the next turn start or prepareStep boundary.

export const myChat = chat.agent({
  id: "my-chat",
  onTurnComplete: async ({ messages }) => {
    chat.defer(
      (async () => {
        const analysis = await analyzeConversation(messages);
        chat.inject([{ role: "system", content: `[Analysis]\n\n${analysis}` }]);
      })()
    );
  },
  run: async ({ messages, signal }) =>
    streamText({ ...chat.toStreamTextOptions({ registry }), messages, abortSignal: signal, stopWhen: stepCountIs(15) }),
});

4. Compaction (threshold-based)

compaction.shouldCompact decides when, summarize produces the summary that replaces the model

messages. UI messages are preserved by default (customize via compactUIMessages). The prepareStep

that performs inner-loop compaction is auto-injected by chat.toStreamTextOptions(); a prepareStep

you pass after the spread wins.

compaction: {
  shouldCompact: ({ totalTokens }) => (totalTokens ?? 0) > 80_000,
  summarize: async ({ messages }) =>
    (await generateText({
      model: anthropic("claude-haiku-4-5"),
      messages: [...messages, { role: "user", content: "Summarize concisely." }],
    })).text,
},

5. Actions: mutate state without a turn

actionSchema validates; onAction mutates via chat.history (slice, replace, rollbackTo,

remove, getPendingToolCalls, extractNewToolResults). Actions fire hydrateMessages and

onAction only, never run() or the turn hooks. Return a StreamTextResult, string, or UIMessage

to also emit a model response.

export const myChat = chat.agent({
  id: "my-chat",
  actionSchema: z.discriminatedUnion("type", [
    z.object({ type: z.literal("undo") }),
    z.object({ type: z.literal("rollback"), targetMessageId: z.string() }),
  ]),
  onAction: async ({ action }) => {
    if (action.type === "undo") chat.history.slice(0, -2);
    if (action.type === "rollback") chat.history.rollbackTo(action.targetMessageId);
  },
  run: async ({ messages, signal }) => streamText({ model: anthropic("claude-sonnet-4-5"), messages, abortSignal: signal }),
});

Send from the browser with transport.sendAction(chatId, { type: "undo" }), or server-side with

agentChat.sendAction({ type: "rollback", targetMessageId: "msg-3" }).

6. Fast starts: Head Start

chat.headStart (from @trigger.dev/sdk/chat-server, NOT /ai) returns a Web Fetch handler that

serves turn 1 from your own warm process, then hands off to the agent on turn 2+. Tools passed here

must be schema-only (a module importing ai + zod only); heavy executes stay in the task.

import { chat } from "@trigger.dev/sdk/chat-server";
import { streamText, stepCountIs } from "ai";
import { anthropic } from "@ai-sdk/anthropic";
import { headStartTools } from "@/lib/chat-tools/schemas";

export const chatHandler = chat.headStart({
  agentId: "my-chat",
  run: async ({ chat: helper }) =>
    streamText({
      ...helper.toStreamTextOptions({ tools: headStartTools }),
      model: anthropic("claude-sonnet-4-6"),
      system: "You are helpful.",
      stopWhen: stepCountIs(15),
    }),
});
// Next.js: export const POST = chatHandler;  Transport: headStart: "/api/chat"

Node-only frameworks wrap a Web Fetch handler with chat.toNodeListener(handler). Use the **same

model** on both sides to avoid a tone shift between turn 1 and turn 2+.

7. chat.local: init in onBoot, not onChatStart

chat.local<T>({ id }) is module-level, shallow-proxy, run-scoped state. Initialize it in onBoot

(fires on every fresh worker, including continuation runs), never onChatStart.

const userContext = chat.local<{ name: string; plan: "free" | "pro" }>({ id: "userContext" });

export const myChat = chat.agent({
  id: "my-chat",
  onBoot: async ({ clientData }) => userContext.init({ name: "Alice", plan: "pro" }),
  run: async ({ messages, signal }) => streamText({ /* ... */ }),
});

8. Pending messages (mid-stream user input)

A message sent while a turn is streaming should NOT cancel the stream. Configure

pendingMessages (shouldInject, prepare, onReceived, onInjected) on the agent so the SDK's

auto-injected prepareStep folds them in at the next boundary. On the frontend, usePendingMessages

returns pending, steer(text), queue(text), and promoteToSteering(id); send via

transport.sendPendingMessage(chatId, uiMessage, metadata?).

9. Recovery and version upgrades

onRecoveryBoot fires only when a partial assistant message exists on the tail (interrupted

deploy, crash, OOM retry). It does NOT fire on chat.requestUpgrade(), which is a graceful exit with

no partial. chat.requestUpgrade() (called in onTurnStart / onValidateMessages to skip run(),

or in run() / chat.defer() to exit after the turn) rotates the Session's currentRunId to a run

on the latest deployment without a client reconnect. Pair it with a contract version on clientData.

const SUPPORTED_VERSIONS = new Set(["v2", "v3"]);
onTurnStart: async ({ clientData }) => {
  if (clientData?.protocolVersion && !SUPPORTED_VERSIONS.has(clientData.protocolVersion)) {
    chat.requestUpgrade();
  }
},

For OOM resilience, set oomMachine (and machine) on the agent so retries land on a larger preset.

10. Offline testing with mockChatAgent

@trigger.dev/sdk/ai/test runs the real turn loop in-memory. Import it before the agent module

so the resource catalog is installed. Drive with sendMessage, sendRegenerate, sendAction,

sendStop, sendHeadStart, sendHandover; seed state with seedSnapshot / seedSessionOutTail /

seedSessionOutPartial / seedSessionInTail; assert against turn.chunks and harness.allChunks.

import { mockChatAgent } from "@trigger.dev/sdk/ai/test"; // BEFORE the agent module
import { myChatAgent } from "./my-chat.js";

const harness = mockChatAgent(myChatAgent, { chatId: "test-1", clientData: { model } });
try {
  const turn = await harness.sendMessage({ id: "u1", role: "user", parts: [{ type: "text", text: "hi" }] });
  // assert against turn.chunks
} finally {
  await harness.close();
}

Options include mode ("preload" | "submit-message" | "handover-prepare" | "continuation"),

preload, continuation, previousRunId, snapshot, taskContext, and setupLocals. Set

taskContext.ctx.attempt.number > 1 to simulate an OOM-retry attempt. runInMockTaskContext drives a

non-chat task offline.

11. Custom transport: the wire protocol

Endpoints: POST /api/v1/sessions (create), GET /realtime/v1/sessions/{id}/out (SSE),

POST /realtime/v1/sessions/{id}/in/append, POST /api/v1/sessions/{id}/close. ChatInputChunk is

{ kind: "message"; payload: ChatTaskWirePayload } | { kind: "stop"; message? }. The

ChatTaskWirePayload carries chatId, trigger (`submit-message | regenerate-message | preload |

close | action | handover-prepare), message?, metadata?, action?, continuation?`,

previousRunId?, and more. Control records are header-form: trigger-control: turn-complete (with

optional public-access-token, session-in-event-id) and trigger-control: upgrade-required. The

TS helpers SSEStreamSubscription and controlSubtype(headers) (documented in

docs/ai-chat/client-protocol.mdx) handle batch decoding and control-record filtering for you.

Common mistakes

• CRITICAL: sending a follow-up by re-POSTing POST /api/v1/sessions.

  // Wrong - a cached re-POST silently drops basePayload.message; basePayload is trigger config, not a channel
  await fetch("/api/v1/sessions", { method: "POST", body: JSON.stringify({ ...createBody }) });
  // Correct - append to the session's input channel
  await fetch(`/realtime/v1/sessions/${id}/in/append`, { method: "POST", body: JSON.stringify({ kind: "message", payload }) });

• Using the wrong token for .in / .out. Use publicAccessToken from the create response

body (session-scoped). The x-trigger-jwt response header is run-scoped and cannot subscribe.

• Initializing chat.local in onChatStart. It is skipped on continuation runs, so run()

crashes with chat.local can only be modified after initialization. Init in onBoot.

• chat.defer for the message-history write. A mid-stream refresh would read []. await that

write inline before the model streams; reserve chat.defer for analytics, audit, cache warming.

• Giving the HITL tool an execute. streamText calls it immediately. Leave it execute-less;

the frontend supplies the answer via addToolOutput + sendAutomaticallyWhen.

• Declaring sub-agent / heavy tools only on streamText. Also declare them on

chat.agent({ tools }) (or pass to convertToModelMessages(uiMessages, { tools }) in a custom

agent) so toModelOutput re-applies on every turn.

• Importing heavy-execute tools into the Head Start route module. This is a build-time import

chain problem; runtime strip helpers do not fix it. Keep schemas in an ai + zod-only module.

• Returning a megabyte tool output on the stream. One tool-output-available record over ~1 MiB

throws ChatChunkTooLargeError. Persist to your store, write the row first, then emit only an id.

• Setting X-Peek-Settled: 1 on the active-send path. It races the new turn's first chunk and

closes the stream early. Use it only on reconnect-on-reload paths.

> Note on docs vocabulary: agent-side examples in some docs still use the legacy

> trigger:turn-complete chunk type. That is the agent-emit vocabulary. A custom reader must

> filter on the trigger-control header, not on chunk.type.

>

> MCP-driven agent chats (list_agents, start_agent_chat, send_agent_message,

> close_agent_chat) are MCP server tools used from Claude Code / Cursor, not importable SDK

> functions. See /mcp-tools#agent-chat-tools.

References

• trigger-authoring-chat-agent skill - the everyday chat.agent({...}) definition, lifecycle hooks, and

the useTriggerChatTransport happy path. Start there before reaching for this skill.

• trigger-realtime-and-frontend skill - Realtime hooks and frontend streaming beyond the chat transport.
• trigger-authoring-tasks skill - base task() semantics, ctx, and standard lifecycle hooks.

Reference docs ship beside this skill in the same package, read them locally (no network), pinned to your installed version. The sources: frontmatter above lists every doc this skill draws from, all under @trigger.dev/sdk/docs/ai-chat/ (including patterns/). For HITL, sessions, and sub-agents start with sessions.mdx, server-chat.mdx, client-protocol.mdx, patterns/human-in-the-loop.mdx, patterns/sub-agents.mdx.

For trigger.config.ts and build extensions a chat-agent task may need (Prisma, Playwright, Python, etc.), read the bundled config docs under @trigger.dev/sdk/docs/config/ (config/extensions/ for the per-extension setup).

Version

This skill is bundled inside @trigger.dev/sdk and read directly from node_modules, so it always matches your installed SDK version (see the adjacent package.json). The full documentation for these APIs ships alongside it under @trigger.dev/sdk/docs/.