chore: release v4.5.0

[github-actions]

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.

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.

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.

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.

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

[devin-ai-integration]

✅ Devin Review: No Issues Found

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