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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>
167 lines
4.4 KiB
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167 lines
4.4 KiB
JSON
{
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"name": "trigger.dev",
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"version": "4.5.0",
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"description": "A Command-Line Interface for Trigger.dev projects",
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"type": "module",
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".": "./src/index.ts"
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"scripts": {
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