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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>
Build and deploy fully‑managed AI agents and workflows
Website | Docs | Issues | Example projects | Feature requests | Public roadmap | Self-hosting
About Trigger.dev
Trigger.dev is the open-source platform for building AI workflows in TypeScript. Long-running tasks with retries, queues, observability, and elastic scaling.
The platform designed for building AI agents
Build AI agents using all the frameworks, services and LLMs you're used to, deploy them to Trigger.dev and get durable, long-running tasks with retries, queues, observability, and elastic scaling out of the box.
-
Long-running without timeouts: Execute your tasks with absolutely no timeouts, unlike AWS Lambda, Vercel, and other serverless platforms.
-
Durability, retries & queues: Build rock solid agents and AI applications using our durable tasks, retries, queues and idempotency.
-
True runtime freedom: Customize your deployed tasks with system packages – run browsers, Python scripts, FFmpeg and more.
-
Human-in-the-loop: Programmatically pause your tasks until a human can approve, reject or give feedback.
-
Realtime apps & streaming: Move your background jobs to the foreground by subscribing to runs or streaming AI responses to your app.
-
Observability & monitoring: Each run has full tracing and logs. Configure error alerts to catch bugs fast.
Key features:
- JavaScript and TypeScript SDK - Build background tasks using familiar programming models
- Long-running tasks - Handle resource-heavy tasks without timeouts
- Durable cron schedules - Create and attach recurring schedules of up to a year
- Trigger.dev Realtime - Trigger, subscribe to, and get real-time updates for runs, with LLM streaming support
- Build extensions - Hook directly into the build system and customize the build process. Run Python scripts, FFmpeg, browsers, and more.
- React hooks - Interact with the Trigger.dev API on your frontend using our React hooks package
- Batch triggering - Use batchTrigger() to initiate multiple runs of a task with custom payloads and options
- Structured inputs / outputs - Define precise data schemas for your tasks with runtime payload validation
- Waits - Add waits to your tasks to pause execution for a specified duration
- Preview branches - Create isolated environments for testing and development. Integrates with Vercel and git workflows
- Waitpoints - Add human-in-the-loop judgment at critical decision points without disrupting workflow
- Concurrency & queues - Set concurrency rules to manage how multiple tasks execute
- Multiple environments - Support for DEV, PREVIEW, STAGING, and PROD environments
- No infrastructure to manage - Auto-scaling infrastructure that eliminates timeouts and server management
- Automatic retries - If your task encounters an uncaught error, we automatically attempt to run it again
- Checkpointing - Tasks are inherently durable, thanks to our checkpointing feature
- Versioning - Atomic versioning allows you to deploy new versions without affecting running tasks
- Machines - Configure the number of vCPUs and GBs of RAM you want the task to use
- Observability & monitoring - Monitor every aspect of your tasks' performance with comprehensive logging and visualization tools
- Logging & tracing - Comprehensive logging and tracing for all your tasks
- Tags - Attach up to ten tags to each run, allowing you to filter via the dashboard, realtime, and the SDK
- Run metadata - Attach metadata to runs which updates as the run progresses and is available to use in your frontend for live updates
- Bulk actions - Perform actions on multiple runs simultaneously, including replaying and cancelling
- Real-time alerts - Choose your preferred notification method for run failures and deployments
Write tasks in your codebase
Create tasks where they belong: in your codebase. Version control, localhost, test and review like you're already used to.
import { task } from "@trigger.dev/sdk";
//1. You need to export each task
export const helloWorld = task({
//2. Use a unique id for each task
id: "hello-world",
//3. The run function is the main function of the task
run: async (payload: { message: string }) => {
//4. You can write code that runs for a long time here, there are no timeouts
console.log(payload.message);
},
});
Deployment
Use our SDK to write tasks in your codebase. There's no infrastructure to manage, your tasks automatically scale and connect to our cloud. Or you can always self-host.
Environments
We support Development, Staging, Preview, and Production environments, allowing you to test your tasks before deploying them to production.
Full visibility of every job run
View every task in every run so you can tell exactly what happened. We provide a full trace view of every task run so you can see what happened at every step.
Getting started
The quickest way to get started is to create an account and project in our web app, and follow the instructions in the onboarding. Build and deploy your first task in minutes.
Useful links:
- Quick start - get up and running in minutes
- How it works - understand how Trigger.dev works under the hood
- Guides and examples - walk-through guides and code examples for popular frameworks and use cases
Self-hosting
If you prefer to self-host Trigger.dev, you can follow our self-hosting guides:
- Docker self-hosting guide - use Docker Compose to spin up a Trigger.dev instance
- Kubernetes self-hosting guide - use our official Helm chart to deploy Trigger.dev to your Kubernetes cluster
Support and community
We have a large active community in our official Discord server for support, including a dedicated channel for self-hosting.
Development
To setup and develop locally or contribute to the open source project, follow our development guide.