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Trigger.dev/Trigger.dev/v4.5.0-rc.0

trigger.dev v4.5.0-rc.0

v4.5.0-rc.0

May 21, 2026Trigger.devView original ↗

Upgrade

npx trigger.dev@4.5.0-rc.0 update
pnpm dlx trigger.dev@4.5.0-rc.0 update
yarn dlx trigger.dev@4.5.0-rc.0 update 
bunx trigger.dev@4.5.0-rc.0 update

Self-hosted Docker image: ghcr.io/triggerdotdev/trigger.dev:v4.5.0-rc.0

What's changed

Improvements

  • AI Prompts — define prompt templates as code alongside your tasks, version them on deploy, and override the text or model from the dashboard without redeploying. Prompts integrate with the Vercel AI SDK via toAISDKTelemetry() (links every generation span back to the prompt) and with chat.agent via chat.prompt.set() + chat.toStreamTextOptions(). (#3629)
  • Code-defined, deploy-versioned templates — define with prompts.define({ id, model, config, variables, content }). Every deploy creates a new version visible in the dashboard. Mustache-style placeholders ({{var}}, {{#cond}}...{{/cond}}) with Zod / ArkType / Valibot-typed variables.
  • Dashboard overrides — change a prompt's text or model from the dashboard without redeploying. Overrides take priority over the deployed "current" version and are environment-scoped (dev / staging / production independent).
  • Resolve API — prompt.resolve(vars, { version?, label? }) returns the compiled text, resolved model, version, and labels. Standalone prompts.resolve<typeof handle>(slug, vars) for cross-file resolution with full type inference on slug and variable shape.
  • AI SDK integration — spread resolved.toAISDKTelemetry({ ...extra }) into any generateText / streamText call and every generation span links to the prompt in the dashboard alongside its input variables, model, tokens, and cost.
  • chat.agent integration — chat.prompt.set(resolved) stores the resolved prompt run-scoped; chat.toStreamTextOptions({ registry }) pulls system, model (resolved via the AI SDK provider registry), temperature / maxTokens / etc., and telemetry into a single spread for streamText.
  • Management SDK — prompts.list(), prompts.versions(slug), prompts.promote(slug, version), prompts.createOverride(slug, body), prompts.updateOverride(slug, body), prompts.removeOverride(slug), prompts.reactivateOverride(slug, version).
  • Dashboard — prompts list with per-prompt usage sparklines; per-prompt detail with Template / Details / Versions / Generations / Metrics tabs. AI generation spans get a custom inspector showing the linked prompt's metadata, input variables, and template content alongside model, tokens, cost, and the message thread.
  • Adds onBoot to chat.agent — a lifecycle hook that fires once per worker process picking up the chat. Runs for the initial run, preloaded runs, AND reactive continuation runs (post-cancel, crash, endRun, requestUpgrade, OOM retry), before any other hook. Use it to initialize chat.local, open per-process resources, or re-hydrate state from your DB on continuation — anywhere the SAME run picking up after suspend/resume isn't enough. (#3543)
  • AI SDK useChat integration — a custom ChatTransport (useTriggerChatTransport) plugs straight into Vercel AI SDK's useChat hook. Text streaming, tool calls, reasoning, and data-* parts all work natively over Trigger.dev's realtime streams. No custom API routes needed.
  • First-turn fast path (chat.headStart) — opt-in handler that runs the first turn's streamText step in your warm server process while the agent run boots in parallel, cutting cold-start TTFC by roughly half (measured 2801ms → 1218ms on claude-sonnet-4-6). The agent owns step 2+ (tool execution, persistence, hooks) so heavy deps stay where they belong. Web Fetch handler works natively in Next.js, Hono, SvelteKit, Remix, Workers, etc.; bridge to Express/Fastify/Koa via chat.toNodeListener. New @trigger.dev/sdk/chat-server subpath.
  • Multi-turn durability via Sessions — every chat is backed by a durable Session that outlives any individual run. Conversations resume across page refreshes, idle timeout, crashes, and deploys; resume: true reconnects via lastEventId so clients only see new chunks. sessions.list enumerates chats for inbox-style UIs.
  • Auto-accumulated history, delta-only wire — the backend accumulates the full conversation across turns; clients only ship the new message each turn. Long chats never hit the 512 KiB body cap. Register hydrateMessages to be the source of truth yourself.
  • Lifecycle hooks — onPreload, onChatStart, onValidateMessages, hydrateMessages, onTurnStart, onBeforeTurnComplete, onTurnComplete, onChatSuspend, onChatResume — for persistence, validation, and post-turn work.
  • Stop generation — client-driven transport.stopGeneration(chatId) aborts mid-stream; the run stays alive for the next message, partial response is captured, and aborted parts (stuck partial-call tools, in-progress reasoning) are auto-cleaned.
  • Tool approvals (HITL) — tools with needsApproval: true pause until the user approves or denies via addToolApprovalResponse. The runtime reconciles the updated assistant message by ID and continues streamText.
  • Steering and background injection — pendingMessages injects user messages between tool-call steps so users can steer the agent mid-execution; chat.inject() + chat.defer() adds context from background work (self-review, RAG, safety checks) between turns.
  • Actions — non-turn frontend commands (undo, rollback, regenerate, edit) sent via transport.sendAction. Fire hydrateMessages + onAction only — no turn hooks, no run(). onAction can return a StreamTextResult for a model response, or void for side-effect-only.
  • Typed state primitives — chat.local<T> for per-run state accessible from hooks, run(), tools, and subtasks (auto-serialized through ai.toolExecute); chat.store for typed shared data between agent and client; chat.history for reading and mutating the message chain; clientDataSchema for typed clientData in every hook.
  • chat.toStreamTextOptions() — one spread into streamText wires up versioned system Prompts, model resolution, telemetry metadata, compaction, steering, and background injection.
  • Multi-tab coordination — multiTab: true + useMultiTabChat prevents duplicate sends and syncs state across browser tabs via BroadcastChannel. Non-active tabs go read-only with live updates.
  • Network resilience — built-in indefinite retry with bounded backoff, reconnect on online / tab refocus / bfcache restore, Last-Event-ID mid-stream resume. No app code needed.
  • Sessions — a durable, run-aware stream channel keyed on a stable externalId. A Session is the unit of state that owns a multi-run conversation: messages flow through .in, responses through .out, both survive run boundaries. Sessions back the new chat.agent runtime, and you can build on them directly for any pattern that needs durable bi-directional streaming across runs. (#3542)
  • Add ai.toolExecute(task) so you can wire a Trigger subtask in as the execute handler of an AI SDK tool() while defining description and inputSchema yourself — useful when you want full control over the tool surface and just need Trigger's subtask machinery for the body. (#3546)
  • Type chat.createStartSessionAction against your chat agent so clientData is typed end-to-end on the first turn: (#3684)
  • Add region to the runs list / retrieve API: filter runs by region (runs.list({ region: "..." }) / filter[region]=<masterQueue>) and read each run's executing region from the new region field on the response. (#3612)
  • Add TRIGGER_BUILD_SKIP_REWRITE_TIMESTAMP=1 escape hatch for local self-hosted builds whose buildx driver doesn't support rewrite-timestamp alongside push (e.g. orbstack's default docker driver). (#3618)
  • Reject overlong idempotencyKey values at the API boundary so they no longer trip an internal size limit on the underlying unique index and surface as a generic 500. Inputs are capped at 2048 characters — well above what idempotencyKeys.create() produces (a 64-character hash) and above any realistic raw key. Applies to tasks.trigger, tasks.batchTrigger, batch.create (Phase 1 streaming batches), wait.createToken, wait.forDuration, and the input/session stream waitpoint endpoints. Over-limit requests now return a structured 400 instead. (#3560)
  • AI SDK useChat integration — a custom ChatTransport (useTriggerChatTransport) plugs straight into Vercel AI SDK's useChat hook. Text streaming, tool calls, reasoning, and data-* parts all work natively over Trigger.dev's realtime streams. No custom API routes needed.
  • First-turn fast path (chat.headStart) — opt-in handler that runs the first turn's streamText step in your warm server process while the agent run boots in parallel, cutting cold-start TTFC by roughly half (measured 2801ms → 1218ms on claude-sonnet-4-6). The agent owns step 2+ (tool execution, persistence, hooks) so heavy deps stay where they belong. Web Fetch handler works natively in Next.js, Hono, SvelteKit, Remix, Workers, etc.; bridge to Express/Fastify/Koa via chat.toNodeListener. New @trigger.dev/sdk/chat-server subpath.
  • Multi-turn durability via Sessions — every chat is backed by a durable Session that outlives any individual run. Conversations resume across page refreshes, idle timeout, crashes, and deploys; resume: true reconnects via lastEventId so clients only see new chunks. sessions.list enumerates chats for inbox-style UIs.
  • Auto-accumulated history, delta-only wire — the backend accumulates the full conversation across turns; clients only ship the new message each turn. Long chats never hit the 512 KiB body cap. Register hydrateMessages to be the source of truth yourself.
  • Lifecycle hooks — onPreload, onChatStart, onValidateMessages, hydrateMessages, onTurnStart, onBeforeTurnComplete, onTurnComplete, onChatSuspend, onChatResume — for persistence, validation, and post-turn work.
  • Stop generation — client-driven transport.stopGeneration(chatId) aborts mid-stream; the run stays alive for the next message, partial response is captured, and aborted parts (stuck partial-call tools, in-progress reasoning) are auto-cleaned.
  • Tool approvals (HITL) — tools with needsApproval: true pause until the user approves or denies via addToolApprovalResponse. The runtime reconciles the updated assistant message by ID and continues streamText.
  • Steering and background injection — pendingMessages injects user messages between tool-call steps so users can steer the agent mid-execution; chat.inject() + chat.defer() adds context from background work (self-review, RAG, safety checks) between turns.
  • Actions — non-turn frontend commands (undo, rollback, regenerate, edit) sent via transport.sendAction. Fire hydrateMessages + onAction only — no turn hooks, no run(). onAction can return a StreamTextResult for a model response, or void for side-effect-only.
  • Typed state primitives — chat.local<T> for per-run state accessible from hooks, run(), tools, and subtasks (auto-serialized through ai.toolExecute); chat.store for typed shared data between agent and client; chat.history for reading and mutating the message chain; clientDataSchema for typed clientData in every hook.
  • chat.toStreamTextOptions() — one spread into streamText wires up versioned system Prompts, model resolution, telemetry metadata, compaction, steering, and background injection.
  • Multi-tab coordination — multiTab: true + useMultiTabChat prevents duplicate sends and syncs state across browser tabs via BroadcastChannel. Non-active tabs go read-only with live updates.
  • Network resilience — built-in indefinite retry with bounded backoff, reconnect on online / tab refocus / bfcache restore, Last-Event-ID mid-stream resume. No app code needed.
  • Retry TASK_PROCESS_SIGSEGV task crashes under the user's retry policy instead of failing the run on the first segfault. SIGSEGV in Node tasks is frequently non-deterministic (native addon races, JIT/GC interaction, near-OOM in native code, host issues), so retrying on a fresh process often succeeds. The retry is gated by the task's existing retry config + maxAttempts — same path TASK_PROCESS_SIGTERM and uncaught exceptions already use — so tasks without a retry policy still fail fast. (#3552)
  • The public interfaces for a plugin system. Initially consolidated authentication and authorization interfaces. (#3499)
  • Add MollifierBuffer and MollifierDrainer primitives for trigger burst smoothing. (#3614)

Bug fixes

  • Fix LocalsKey<T> type incompatibility across dual-package builds. The phantom value-type brand no longer uses a module-level unique symbol, so a single TypeScript compilation that resolves the type from both the ESM and CJS outputs (which can happen under certain pnpm hoisting layouts) no longer sees two structurally-incompatible variants of the same type. (#3626)

All packages: v4.5.0-rc.0

@trigger.dev/build, @trigger.dev/core, @trigger.dev/plugins, @trigger.dev/python, @trigger.dev/react-hooks, @trigger.dev/redis-worker, @trigger.dev/rsc, @trigger.dev/schema-to-json, @trigger.dev/sdk, trigger.dev

Full changelog: https://github.com/triggerdotdev/trigger.dev/compare/v4.4.0.0...v4.5.0-rc.0

Fetched May 21, 2026