> ## Documentation Index
> Fetch the complete documentation index at: https://trigger.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Agents

> Durable multi-turn AI chats — one Trigger.dev task per conversation, surviving refreshes, deploys, and crashes.

<Warning>
  The AI Agents and Prompts surface ships as part of the **v4.5 release candidate**. Install with `@trigger.dev/sdk@rc` (or pin `4.5.0-rc.0` or later) to use these features — they aren't yet on the latest stable, and APIs may still change before the 4.5.0 GA. See [supported AI SDK versions](/ai-chat/reference#compatibility) and the [AI chat changelog](/ai-chat/changelog) for details.
</Warning>

An AI chat isn't a request — it's a session. `chat.agent` runs every conversation as a single long-lived Trigger.dev task: you write the loop, it wakes up when a message arrives, freezes when none do, and the same in-memory state and on-disk workspace survive across page refreshes, deploys, idle gaps, and crashes. The substrate handles the parts most teams stitch together by hand — turn lifecycle, mid-stream resume, recovery from cancel/crash/OOM, HITL approvals, deploy upgrades — so your code is the loop you'd write anyway: messages in, `streamText` out.

## A minimal example

A `chat.agent` task takes `messages`, calls `streamText`, and returns the result. The frontend wires the [Vercel AI SDK's `useChat`](https://ai-sdk.dev/docs/reference/ai-sdk-ui/use-chat) to a `TriggerChatTransport`. No API routes.

```ts trigger/chat.ts theme={"theme":"css-variables"}
import { chat } from "@trigger.dev/sdk/ai";
import { streamText, stepCountIs } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

export const myChat = chat.agent({
  id: "my-chat",
  run: async ({ messages, signal }) =>
    streamText({
      model: anthropic("claude-sonnet-4-5"),
      messages,
      abortSignal: signal,
      stopWhen: stepCountIs(15),
    }),
});
```

```tsx app/components/Chat.tsx theme={"theme":"css-variables"}
import { useChat } from "@ai-sdk/react";
import { useTriggerChatTransport } from "@trigger.dev/sdk/chat/react";

export function Chat() {
  const transport = useTriggerChatTransport<typeof myChat>({
    task: "my-chat",
    accessToken: ({ chatId }) => mintChatAccessToken(chatId),
    startSession: ({ chatId, clientData }) =>
      startChatSession({ chatId, clientData }),
  });
  const { messages, sendMessage } = useChat({ transport });
  // ... render UI
}
```

See [Quick Start](/ai-chat/quick-start) for the matching server actions and a runnable project.

## Why use AI Agents on Trigger.dev

* **Resume across refreshes, deploys, and crashes.** A chat in progress when you redeploy keeps streaming on the new version. Mid-stream refreshes pick up where they left off.
* **Native AI SDK support.** Text, tool calls, reasoning, and custom `data-*` parts all flow through `useChat` over a custom `ChatTransport`. No custom protocol to maintain.
* **Multi-turn for free.** Each turn is a step inside the same durable task; conversation history accumulates server-side, so clients only ship the new message.
* **Fast cold starts.** Opt-in [Head Start](/ai-chat/fast-starts#head-start) runs the first `streamText` step in your warm Next.js / Hono / SvelteKit server while the agent boots in parallel — cuts time-to-first-chunk roughly in half.
* **Production primitives ship in the box.** Stop generation, steering, edits, branching, sub-agents, HITL tool approvals, version upgrades, recovery from cancel/crash/OOM — all first-class.
* **Observable.** Every turn is a span in the Trigger.dev dashboard. Sessions are queryable via `sessions.list` for inbox-style UIs.

## How it fits together

Three primitives, related but distinct:

* **Chat agents** — the SDK surface you define with [`chat.agent()`](/ai-chat/backend#chat-agent). Owns the turn loop, lifecycle hooks, and the response stream.
* **Sessions** — the durable, bi-directional channel keyed on `chatId` that holds the conversation across run boundaries. A chat agent runs *on top of* a [Session](/ai-chat/sessions).
* **Sub-agents** — Delegate work from one agent to another via [`AgentChat`](/ai-chat/patterns/sub-agents). The sub-agent runs as its own durable agent on its own session; its response streams back through the parent as preliminary tool results, so the frontend sees the sub-agent working inside the parent's tool card.

## Next steps

<CardGroup cols={2}>
  <Card title="Quick Start" icon="rocket" href="/ai-chat/quick-start">
    Get a working chat in three steps — agent, token, frontend.
  </Card>

  <Card title="How it works" icon="diagram-project" href="/ai-chat/how-it-works">
    Sessions, the turn loop, durable streams, and what survives a refresh.
  </Card>

  <Card title="Backend" icon="server" href="/ai-chat/backend">
    `chat.agent` options, lifecycle hooks, and the raw-task primitives.
  </Card>

  <Card title="Tools" icon="wrench" href="/ai-chat/tools">
    Declare tools so `toModelOutput` survives across turns, typed in `run()`.
  </Card>

  <Card title="Patterns" icon="puzzle-piece" href="/ai-chat/patterns/sub-agents">
    HITL approvals, branching, sub-agents, OOM/crash recovery.
  </Card>
</CardGroup>