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# Trigger.dev v4.5.0
4.5.0 is the GA of the AI Agents platform. Everything built during the
prerelease line (durable agents, Sessions, AI Prompts) is now stable on
the `latest` tag, alongside a set of SDK and runtime improvements.
## AI Agents (`chat.agent`)
Run Vercel AI SDK chat completions as durable Trigger.dev tasks instead
of fragile API routes. A conversation runs as one long-lived task keyed
on `chatId`, so it survives page refreshes, network blips, redeploys,
and crashes, and every turn is a span in the dashboard.
```ts
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 }) => {
return streamText({
...chat.toStreamTextOptions(), // system prompt, compaction, steering, telemetry
model: anthropic("claude-sonnet-4-5"),
messages,
abortSignal: signal,
stopWhen: stepCountIs(15),
});
},
});
```
## Sessions
The durable primitive underneath `chat.agent`, usable on its own: a
run-aware, bidirectional stream channel keyed on a stable `externalId`
whose `.in` / `.out` streams survive run boundaries (suspend, crash,
idle-timeout, redeploy). One Session spans many runs, which makes it a
good fit for agent inboxes and approval flows.
```ts
import { sessions } from "@trigger.dev/sdk";
// Create the session and trigger its first run (idempotent on externalId)
await sessions.start({
type: "inbox",
externalId: userId,
taskIdentifier: "inbox-agent",
});
const session = sessions.open(userId);
await session.in.send({ text: "hello" });
const stream = await session.out.read({ signal: AbortSignal.timeout(30_000) });
for await (const chunk of stream) console.log(chunk); // durable across run swaps
```
## AI Prompts
Define prompt templates as code, versioned on every deploy, and override
the text or model from the dashboard without redeploying
(environment-scoped). Each generation links back to its prompt version
for usage, cost, and latency.
```ts
import { prompts } from "@trigger.dev/sdk";
import { z } from "zod";
export const supportPrompt = prompts.define({
id: "customer-support",
model: "gpt-4o",
variables: z.object({ customerName: z.string(), issue: z.string() }),
content: `You are a support agent for Acme.
Customer: {{customerName}}
Issue: {{issue}}`,
});
// Honors any active dashboard override, else the current deployed version
const resolved = await supportPrompt.resolve({ customerName: "Alice", issue: "Can't log in" });
// resolved.text, resolved.model, resolved.version
```
## `useChat` integration
`useTriggerChatTransport` is a Vercel AI SDK `ChatTransport` that runs
`useChat` over Trigger.dev realtime with no API routes. Text, tool
calls, reasoning, and `data-*` parts stream natively, and it works with
AI SDK v5, v6, and now v7.
## First-turn fast path (`chat.headStart`)
Runs the first turn in your warm server process while the agent boots in
parallel, cutting cold-start time-to-first-chunk roughly in half
(measured ~2.8s to ~1.2s). Available via the new
`@trigger.dev/sdk/chat-server` subpath.
## Human-in-the-loop, stop, and steering
The agent control surface: tool approvals (`needsApproval` +
`addToolApprovalResponse`), client-driven stop-generation, mid-execution
steering (`pendingMessages`), and between-turn context injection
(`chat.inject` / `chat.defer`), all durable across the conversation.
## Agent Skills
`skills.define({ id, path })` bundles a `SKILL.md` folder into your
deploy image. The agent gets a one-line summary up front and loads the
full instructions plus scoped `bash` / `readFile` tools on demand
(progressive disclosure), so a capability is something the model reaches
for rather than a pre-declared typed tool.
## `trigger skills` for coding assistants
`trigger skills` installs version-pinned Trigger.dev skills plus a
bundled docs snapshot into Claude Code, Cursor, GitHub Copilot, and
Codex, so your assistant's Trigger.dev knowledge stays current with your
installed SDK version. `trigger init` now offers to set up the MCP
server and skills too.
## Model library
A new Models page in the dashboard: a catalog of models grouped by
provider with context window, capabilities, and input / output pricing
per 1M tokens, plus a "Your models" tab showing per-model usage, cost,
and cache-hit sparklines from your actual traffic.
## Dev branches
Run multiple local `trigger dev` sessions in parallel (separate git
worktrees or coding agents) without runs colliding, each isolated with
its own dashboard, via `trigger dev --branch <name>`.
## `TriggerClient`
An instantiable client so one process can trigger and read across
projects, environments, and preview branches, each with its own auth and
baseURL, with no shared global state.
```ts
import { TriggerClient } from "@trigger.dev/sdk";
const prod = new TriggerClient({ accessToken: process.env.TRIGGER_PROD_KEY });
const preview = new TriggerClient({
accessToken: process.env.TRIGGER_PREVIEW_KEY,
previewBranch: "signup-flow",
});
await prod.tasks.trigger("send-email", { to: "user@example.com" });
await preview.runs.list({ status: ["COMPLETED"] });
```
## SDK and runtime
- AI SDK 7 support (v5 and v6 still supported), with OpenTelemetry
telemetry auto-wired
- Large trigger-payload offload: trigger payloads at or above 128KB
upload to object storage automatically, using the same auth and baseURL
as the trigger call
- Region support on the runs API: filter runs by region and read each
run's executing region (also on MCP `list_runs`)
- Duplicate task-id detection: `dev` and `deploy` fail with a clear
error instead of silently overwriting
- `envvars.upload` gains an `isSecret` flag to import redacted secret
variables
- Retry hardening: `TASK_MIDDLEWARE_ERROR` now retries under the task's
retry policy
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Official TypeScript SDK for Trigger.dev
The Trigger.dev SDK is a TypeScript/JavaScript library that allows you to define and trigger tasks in your projects.
About Trigger.dev
Trigger.dev is an open source platform for building and deploying fully-managed AI agents and workflows. Write workflows in normal async TypeScript for everything from simple tasks to long-running AI agents, heavy media processing, complex real-time systems and more. Complete with full observability, managed queues, and elastic infrastructure which handles the horizontal scaling.
Core features
- Task creation and execution
- CLI for development and deployment
- Build system with extensions
- Management API for runs, schedules, and environment variables
Key Components:
- Tasks: Background jobs written in TypeScript/JavaScript
- CLI: Commands for login, init, dev, deploy
- Build Extensions: Customize builds (Prisma, Python, FFmpeg, etc.)
- Management API: Programmatic control over runs and resources
Getting started
There are two ways to get started:
- Create an account in our web app, create a new project and follow the instructions in the onboarding. Build and deploy your first task in minutes.
- Manual setup in your existing project.
SDK documentation
For more information on our SDK, refer to our docs.
Support
If you have any questions, please reach out to us on Discord and we'll be happy to help.