feat(mollifier): trigger burst smoothing — Phase 1 (monitoring)#3614
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WalkthroughThis PR implements the first two phases of a trigger burst-smoothing system ("mollifier"): it adds a Redis-backed MollifierBuffer and MollifierDrainer, Zod schemas and payload (de)serialization, a real trip evaluator and mollifier gate wired into RunEngineTriggerTaskService, OpenTelemetry metrics, worker startup drainer wiring, environment configuration and feature flag, package re-exports, and comprehensive tests (unit, integration, and fuzz) validating buffer, drainer, gate, and evaluator behavior while keeping fail-open semantics and deferring full activation to later phases. Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes 🚥 Pre-merge checks | ✅ 3 | ❌ 2❌ Failed checks (2 warnings)
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Code reviewFound 2 issues:
trigger.dev/apps/webapp/app/v3/mollifier/mollifierGate.server.ts Lines 83 to 88 in 452ebda
trigger.dev/apps/webapp/app/runEngine/services/triggerTask.server.ts Lines 338 to 346 in 452ebda 🤖 Generated with Claude Code - If this code review was useful, please react with 👍. Otherwise, react with 👎. |
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Nitpicks (test-mock removal) also addressed in 98c1520:
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Code review (follow-up)Four additional issues from the earlier scan, posting now since the gate-naming fix landed:
trigger.dev/apps/webapp/test/engine/triggerTask.test.ts Lines 1343 to 1347 in 98c1520
trigger.dev/apps/webapp/app/services/worker.server.ts Lines 129 to 152 in 98c1520
trigger.dev/apps/webapp/app/v3/mollifier/mollifierGate.server.ts Lines 11 to 16 in 98c1520 trigger.dev/packages/redis-worker/src/mollifier/buffer.ts Lines 294 to 312 in 98c1520 🤖 Generated with Claude Code - If this code review was useful, please react with 👍. Otherwise, react with 👎. |
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Redis-backed burst-smoothing layer behind MOLLIFIER_ENABLED=0 (default).
With the kill switch off, the gate short-circuits on its first env check
and production behaviour is identical to main.
@trigger.dev/redis-worker:
- MollifierBuffer: atomic Lua-backed FIFO with accept / pop / ack /
requeue / fail + TTL. Per-env queues with HSET entry storage,
atomic RPOP + status transition, FIFO retry ordering.
- MollifierDrainer: generic round-robin worker with concurrency cap,
retry semantics, and a stop deadline to avoid livelock on a hung
handler. Phase 3 will wire the handler to engine.trigger().
- Full testcontainers-backed test suite (21 tests).
apps/webapp:
- evaluateGate cascade-check (kill switch -> org feature flag ->
shadow mode -> trip evaluator -> mollify / shadow_log / pass_through).
Dependencies injected for testability; the trip evaluator stub
returns { divert: false } in phase 1.
- Inserted into RunEngineTriggerTaskService.call() before
traceEventConcern.traceRun. The mollify branch throws (unreachable
in phase 1).
- Lazy MollifierBuffer + MollifierDrainer singletons; no Redis
connection unless MOLLIFIER_ENABLED=1.
- 12 MOLLIFIER_* env vars (all safe defaults) and a mollifierEnabled
feature flag in the global catalog.
- Drainer booted from worker.server.ts on first import.
- Read-fallback stub for phase 3.
- Gate cascade tests + .env loader so env.server validates in vitest
workers.
Phase 2 will land the real trip evaluator; phase 3 will activate the
buffer-write + drain path.
…dual-write monitoring + drainer ack loop) Phase 1 of the trigger-burst smoothing initiative. Adds the A-side trip evaluator (atomic Lua sliding-window per env) and wires it into the trigger hot path. When the per-org mollifierEnabled feature flag is on AND the evaluator says divert, the canonical replay payload is buffered to Redis (via buffer.accept) AND the trigger continues through engine.trigger — i.e. dual-write. The drainer pops + acks (no-op handler) to prove the dequeue mechanism works end-to-end. Operators audit by joining mollifier.buffered (write) and mollifier.drained (consume) logs by runId. Buffer primitives hardened: - accept is idempotent on duplicate runId (Lua EXISTS guard) - pop skips orphan queue references (entry HASH TTL'd while runId queued) - fail no-ops on missing entry (no partial FAILED hash leak) - mollifier:envs set pruned on draining pop, restored on requeue - 16-row truth-table test enumerates the gate cascade - BufferedTriggerPayload defines the canonical replay shape Phase 2 will use to invoke engine.trigger - payload hash for audit-equivalence computed off the hot path (in the drainer) to avoid CPU during a spike Regression tests in apps/webapp/test/engine/triggerTask.test.ts pin the mollifier integration: - validation throws BEFORE the gate runs (no orphan buffer write on rejected triggers) - mollify dual-write happy path (Postgres + Redis both reflect the run) - pass_through path does NOT call buffer.accept - engine.trigger throwing AFTER buffer.accept leaves an orphan (documented behaviour — drainer auto-cleans; audit-trail surfaces it) - idempotency-key match short-circuits BEFORE the gate is consulted - debounce match produces an orphan (documented behaviour — Phase 2 must lift handleDebounce upfront before buffer.accept) Behaviour with MOLLIFIER_ENABLED=0 (default) is byte-identical to main. With MOLLIFIER_ENABLED=1 and the flag off, only mollifier.would_mollify logs fire (no buffer state). With the flag on, dual-write activates. Includes two opt-in *.fuzz.test.ts suites (gated on FUZZ=1) that randomise operation sequences against evaluateTrip and the drainer to find timing edges. They are clearly marked TEMPORARY in their headers.
- changeset: drop "deferred" wording — phase-1 actively dual-writes + runs the drainer ack loop. - worker.server.ts: wrap mollifier drainer init in try/catch + register SIGTERM/SIGINT handlers so the polling loop stops cleanly on shutdown. - bufferedTriggerPayload: only serialise idempotencyKeyExpiresAt when an idempotencyKey is present (avoid impossible orphan-expiry payloads). - mollifierTelemetry: narrow recordDecision reason to DecisionReason union to keep OTEL attribute cardinality bounded. - mollifierGate: rename resolveOrgFlag → resolveFlag. The underlying FeatureFlag table is global by key, so the "org" prefix was misleading; per-org gating is out of scope for phase-1. - tests: drop vi.fn mocks. mollifierGate now uses plain closure spies; mollifierTripEvaluator runs against a real MollifierBuffer backed by a redisTest container (closed client exercises the fail-open path). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…stacking Worker.init() is called per request from entry.server.tsx, so the process.once SIGTERM/SIGINT pair added in 98c1520 would stack a fresh listener every request under dev hot-reload (process.once only removes after firing). Gate registration on a process-global flag, matching the existing __worker__ pattern. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…tion.featureFlags The mollifier gate's resolveOrgFlag was a global feature-flag lookup named as if org-scoped. Phase-1 plan and design doc both intended per-org gating; the implementation regressed because the global flag() helper has no orgId parameter. Adopt the existing per-org feature-flag pattern (used by canAccessAi, canAccessPrivateConnections, compute beta gating): pass `Organization.featureFlags` through as `flag()` overrides. Per-org opt-in now works admin-toggleable via the existing Organization.featureFlags JSON column — no schema migration needed. - mollifierGate: revert resolveFlag/flagEnabled back to resolveOrgFlag/orgFlagEnabled (the name now matches reality). GateInputs gains `orgFeatureFlags`; the default resolver passes them as overrides to `flag()`. - triggerTask.server.ts: thread `environment.organization.featureFlags` into the gate call. - tests: three new postgresTest cases exercise the real DB-backed resolveOrgFlag end-to-end, proving (a) per-org opt-in isolation, (b) unrelated beta flags don't bleed across, (c) per-org overrides take precedence over the global FeatureFlag row. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…nect The unit cascade tests in mollifierGate.test.ts import the gate module, which transitively pulls in ~/db.server. That module constructs the prisma singleton at import time and eagerly calls $connect(), which fails against localhost:5432 in the unit-test shard and surfaces as an unhandled rejection that fails the whole vitest run. Mocking the module keeps the cascade tests pure and leaves the postgresTest cases on the testcontainer-fixture prisma untouched.
- Gate drainer init on WORKER_ENABLED so only worker replicas run the polling loop. - Update the enqueueSystem TTL comment now that delayed/pending-version are first enqueues. - Correct the mollifier gate docstring to describe the fixed-window counter and tripped-key rearm. - Swap findUnique for findFirst in the trigger task test to match the webapp Prisma rule.
…eFlags The gate's `GateInputs` now requires `orgFeatureFlags`, but the surface type used by the trigger service was still the pre-org-scope shape, so the default evaluator wasn't assignable and the call site couldn't pass the flag overrides.
…est startup The per-org isolation suite uses `postgresTest`, which spins up a fresh Postgres testcontainer per case. On CI the 5s vitest default regularly times out on container start before the test body runs. Match the 30s `vi.setConfig` used by other postgresTest suites in this app.
…rrors resolveOrgFlag now checks the per-org Organization.featureFlags override in-memory before falling back to the global flag() helper, so the common per-org enablement path resolves without a Prisma round-trip on every trigger call. evaluateGate also wraps the flag resolution in try/catch and fails open to false on error, mirroring the trip evaluator.
…exit Pass a configurable timeout to drainer.stop() so SIGTERM/SIGINT can't hang forever if an in-flight handler is wedged. Matches the precedent set by BATCH_TRIGGER_WORKER_SHUTDOWN_TIMEOUT_MS (default 30s).
processOneFromEnv now catches buffer.pop() failures so one env's hiccup doesn't reject Promise.all and bubble up to the loop's outer catch. The polling loop itself wraps each runOnce in try/catch and backs off with capped exponential delay (up to 5s) instead of exiting permanently on the first listEnvs/pop error. Stop semantics are unchanged: only the stopping flag breaks the loop. Adds two regression tests using a stub buffer (no Redis container) so fault injection is deterministic.
The phase-1 scaffolding referenced MollifierBuffer, getMollifierBuffer, and deserialiseMollifierSnapshot without importing them — CI typecheck fails with TS2304. The runtime path is gated behind MOLLIFIER_ENABLED=0 so this never produced a runtime symptom, but the types must resolve.
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…oop wins the race The Promise.race between this.loopPromise and this.delay(timeoutMs) discarded the timeout's underlying setTimeout handle whenever the loop branch won. The discarded timer was still ref'd by libuv and pinned the Node event loop alive for the remainder of `timeoutMs` — exactly the shutdown slack the timeout was supposed to bound. Inline the timer in stop() with a captured handle and clearTimeout() it in a finally block, so every exit path (loop-won, timeout-won, throw) releases the ref. The in-loop delay() calls are unchanged — they're awaited normally and their timers fire-and-clear themselves.
`process.once("SIGTERM", stopDrainer)` was the odd one out — every
other webapp service (runsReplicationInstance, llmPricingRegistry,
dynamicFlushScheduler, marqs, eventLoopMonitor) registers through
`signalsEmitter` from `~/services/signals.server`, an EventEmitter
backed by a single `process.on()` that fans out to all listeners.
Switching gets us:
- codebase consistency;
- `.on` (not `.once`) so a second SIGTERM, if the orchestrator emits
one before SIGKILL, still reaches us;
- if SIGTERM lands in the narrow gap between the listener attaching
and drainer.start() below, the first invocation no-ops (stop()
returns early because isRunning is false) but the listener stays
attached for any subsequent signal, instead of being consumed and
leaving the now-running drainer with no graceful-stop path.
This addition was applied while phase-2 was already in review and is out of scope for the mollifier PR. The underlying clarification is worth landing — just not on this branch.
evaluateGate ran on every trigger regardless of TRIGGER_MOLLIFIER_ENABLED.
With the flag off (the default everywhere it hasn't been opted in), the
gate still produced a `pass_through` decision after allocating a
GateInputs object, spreading defaultGateDependencies inside evaluateGate,
and incrementing the `mollifier.decisions{outcome=pass_through}` OTel
counter. Cheap individually, but triggerTask is the hottest code path in
the system — multiply by trigger rate and the unnecessary work compounds.
Guard the gate call with a direct env.TRIGGER_MOLLIFIER_ENABLED check at
the call site. When the flag is off, mollifierOutcome is null and the
downstream `mollifierOutcome?.action === "mollify"` branch skips the
buffer dual-write entirely — zero allocation, zero counter increment on
the disabled path. When the flag is on, behaviour is unchanged.
Lost-signal note: with mollifier off, we no longer count "pass_through"
decisions in the OTel counter (the gate never runs). That's a non-issue
— "pass_through count when feature is off" is just total trigger rate,
which is already observable via the trigger handler's own spans/counters
upstream. The gate counter remains the source of truth for the
mollify/shadow/pass_through ratio when the feature is on, which is the
load-bearing signal.
…DI hook
The previous commit added a perf short-circuit at the call site that
read `env.TRIGGER_MOLLIFIER_ENABLED` directly. That broke three
mollifier integration tests in CI: the tests inject a custom
`evaluateGate` via the existing DI seam expecting the buffer-write
branch to be reached, but CI has no `.env` (the `apps/webapp/.env`
symlink target is absent), the Zod default `"0"` wins, the call site
short-circuits to `null` before the injected gate runs, and
`buffer.accepted` stays empty.
Make the global-enabled check itself injectable:
- New constructor opt `isMollifierGloballyEnabled?: () => boolean`,
defaulting to `() => env.TRIGGER_MOLLIFIER_ENABLED === "1"`. Each
DI hook now represents one decision (gate, buffer, global-enabled),
so a test that wants the buffer-write branch reached can inject
`isMollifierGloballyEnabled: () => true` alongside its custom gate.
- Call site now reads `this.isMollifierGloballyEnabled()` instead of
`env.TRIGGER_MOLLIFIER_ENABLED` directly. In production, with no DI
override, the default closure resolves `env` exactly once per call
just as before — same perf win when the flag is off.
- All six mollifier DI injection sites in triggerTask.test.ts now also
pass `isMollifierGloballyEnabled: () => true` so the tests' DI
surface matches the new contract regardless of CI env state.
The bootstrap in mollifierDrainerWorker.server.ts wrapped getMollifierDrainer()
in a try/catch that logged-and-continued on any error, which absorbed the two
designed-to-crash throws in initializeMollifierDrainer():
- "MollifierDrainer initialised without a buffer" (missing buffer client)
- "TRIGGER_MOLLIFIER_DRAIN_SHUTDOWN_TIMEOUT_MS must be at least ... below
GRACEFUL_SHUTDOWN_TIMEOUT" (shutdown-timeout reconciliation)
Both are deploy-time mistakes: silently disabling the drainer means the
gate keeps writing to the buffer, the drainer never reads, and entries
TTL out in 10min. Bounded in phase 1 (monitoring-only) but customer-
visible data loss in phase 2/3 where the drainer replays into engine.trigger.
Better to fail loud now than retrofit the contract later.
Introduce MollifierConfigurationError for the two deterministic throws.
The bootstrap's catch now rethrows that class (process crashes at module
top-level → orchestrator health check fails → deploy rolls back) while
still logging-and-continuing on transient errors (Redis blip during init
shouldn't take the whole webapp down). instanceof + name fallback covers
the Remix dev hot-reload realm edge case.
Adds the smallest DI surface to `initMollifierDrainerWorker` (`isEnabled`
and `getDrainer`, both optional, default to live env/singleton) so the
catch-block policy can be tested without manipulating module-level env:
- rethrows MollifierConfigurationError — deterministic misconfig
escapes, which is what makes the production-path crash on boot
(the call site in entry.server.tsx runs sync at module top level,
before `process.on("uncaughtException", ...)` is registered, so an
escape becomes a Node default-handler exit-1).
- rethrows when `name === "MollifierConfigurationError"` even when
`instanceof` fails — covers the Remix dev hot-reload realm edge
case where the catch holds a stale class reference.
- swallows non-configuration errors — a transient Redis blip during
buffer init shouldn't take the whole webapp down.
- no-op when disabled — the factory isn't invoked when the enabled
predicate returns false.
Also updates the existing mollifier server-changes note to: rename env
vars to TRIGGER_MOLLIFIER_* prefix, document the TRIGGER_MOLLIFIER_DRAINER_ENABLED
split for multi-replica drainer placement, and call out the new fail-loud
behaviour on drainer misconfiguration.
…keep batch alive If buffer.requeue() or buffer.fail() throws during error recovery inside processEntry, the rejection used to escape processOneFromEnv and reject runOnce's Promise.all — discarding handler results from sibling envs in the same tick. Wrap processEntry in try/catch so the failed env is just counted as "failed" for the tick, matching the invariant stated in the processOneFromEnv comment. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…out test Node's setTimeout can fire a millisecond or two early under CI load, causing the existing `>= 500ms` lower bound to flake (saw 499ms in CI). Loosen to `>= 450ms` — the behaviour being pinned is "stop honors the deadline instead of waiting for the hung handler indefinitely", not millisecond-precise timing. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Shadow-mode and live-divert logs both fire on the trigger hot path; rely on the mollifier.decisions OTel counter for production visibility.
Document why the evaluator writes Redis in both shadow-only and flag-on modes: the trip threshold is computed from a counter, and a counter that doesn't increment isn't a counter. Also note env↔org is 1:1 so the per-env key is effectively per-org — no cross-org bleed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary 44 improvements, 1 bug fix. ## 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](#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](#3543)) - **AI SDK `useChat` integration** — a custom [`ChatTransport`](https://sdk.vercel.ai/docs/ai-sdk-ui/transport) (`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](https://trigger.dev/docs/ai/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](#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](#3546)) - Type `chat.createStartSessionAction` against your chat agent so `clientData` is typed end-to-end on the first turn: ([#3684](#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](#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](#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](#3560)) - **AI SDK `useChat` integration** — a custom [`ChatTransport`](https://sdk.vercel.ai/docs/ai-sdk-ui/transport) (`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](https://trigger.dev/docs/ai/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](#3552)) - The public interfaces for a plugin system. Initially consolidated authentication and authorization interfaces. ([#3499](#3499)) - Add MollifierBuffer and MollifierDrainer primitives for trigger burst smoothing. ([#3614](#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](#3626)) <details> <summary>Raw changeset output</summary>⚠️ ⚠️ ⚠️ ⚠️ ⚠️ ⚠️ `main` is currently in **pre mode** so this branch has prereleases rather than normal releases. If you want to exit prereleases, run `changeset pre exit` on `main`.⚠️ ⚠️ ⚠️ ⚠️ ⚠️ ⚠️ # Releases ## @trigger.dev/sdk@4.5.0-rc.0 ### Minor Changes - **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](#3629)) ```ts import { prompts } from "@trigger.dev/sdk"; import { generateText } from "ai"; import { openai } from "@ai-sdk/openai"; import { z } from "zod"; export const supportPrompt = prompts.define({ id: "customer-support", model: "gpt-4o", config: { temperature: 0.7 }, variables: z.object({ customerName: z.string(), plan: z.string(), issue: z.string(), }), content: `You are a support agent for Acme. Customer: {{customerName}} ({{plan}} plan) Issue: {{issue}}`, }); const resolved = await supportPrompt.resolve({ customerName: "Alice", plan: "Pro", issue: "Can't access billing", }); const result = await generateText({ model: openai(resolved.model ?? "gpt-4o"), system: resolved.text, prompt: "Can't access billing", ...resolved.toAISDKTelemetry(), }); ``` **What you get:** - **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. See [/docs/ai/prompts](https://trigger.dev/docs/ai/prompts) for the full reference — template syntax, version resolution order, override workflow, and type utilities (`PromptHandle`, `PromptIdentifier`, `PromptVariables`). - 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](#3543)) ```ts const userContext = chat.local<{ name: string; plan: string }>({ id: "userContext" }); export const myChat = chat.agent({ id: "my-chat", onBoot: async ({ clientData, continuation }) => { const user = await db.user.findUnique({ where: { id: clientData.userId } }); userContext.init({ name: user.name, plan: user.plan }); }, run: async ({ messages, signal }) => streamText({ model: openai("gpt-4o"), messages, abortSignal: signal }), }); ``` Use `onBoot` (not `onChatStart`) for state setup that must run every time a worker picks up the chat — `onChatStart` fires once per chat and won't run on continuation, leaving `chat.local` uninitialized when `run()` tries to use it. - **AI Agents** — run AI SDK chat completions as durable Trigger.dev agents instead of fragile API routes. Define an agent in one function, point `useChat` at it from React, and the conversation survives page refreshes, network blips, and process restarts. ([#3543](#3543)) ```ts import { chat } from "@trigger.dev/sdk/ai"; import { streamText } from "ai"; import { openai } from "@ai-sdk/openai"; export const myChat = chat.agent({ id: "my-chat", run: async ({ messages, signal }) => streamText({ model: openai("gpt-4o"), messages, abortSignal: signal }), }); ``` ```tsx import { useChat } from "@ai-sdk/react"; import { useTriggerChatTransport } from "@trigger.dev/sdk/chat/react"; const transport = useTriggerChatTransport({ task: "my-chat", accessToken, startSession }); const { messages, sendMessage } = useChat({ transport }); ``` **What you get:** - **AI SDK `useChat` integration** — a custom [`ChatTransport`](https://sdk.vercel.ai/docs/ai-sdk-ui/transport) (`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](https://trigger.dev/docs/ai/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. See [/docs/ai-chat](https://trigger.dev/docs/ai-chat/overview) for the full surface — quick start, three backend approaches (`chat.agent`, `chat.createSession`, raw task), persistence and code-sandbox patterns, type-level guides, and API reference. - Add read primitives to `chat.history` for HITL flows: `getPendingToolCalls()`, `getResolvedToolCalls()`, `extractNewToolResults(message)`, `getChain()`, and `findMessage(messageId)`. These lift the accumulator-walking logic that customers building human-in-the-loop tools were re-implementing into the SDK. ([#3543](#3543)) Use `getPendingToolCalls()` to gate fresh user turns while a tool call is awaiting an answer. Use `extractNewToolResults(message)` to dedup tool results when persisting to your own store — the helper returns only the parts whose `toolCallId` is not already resolved on the chain. ```ts const pending = chat.history.getPendingToolCalls(); if (pending.length > 0) { // an addToolOutput is expected before a new user message } onTurnComplete: async ({ responseMessage }) => { const newResults = chat.history.extractNewToolResults(responseMessage); for (const r of newResults) { await db.toolResults.upsert({ id: r.toolCallId, output: r.output, errorText: r.errorText }); } }; ``` - **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](#3542)) ```ts import { sessions, tasks } from "@trigger.dev/sdk"; // Trigger a task and subscribe to its session output in one call const { runId, stream } = await tasks.triggerAndSubscribe("my-task", payload, { externalId: "user-456", }); for await (const chunk of stream) { // ... } // Enumerate existing sessions (powers inbox-style UIs without a separate index) for await (const s of sessions.list({ type: "chat.agent", tag: "user:user-456" })) { console.log(s.id, s.externalId, s.createdAt, s.closedAt); } ``` See [/docs/ai-chat/overview](https://trigger.dev/docs/ai-chat/overview) for the full surface — Sessions powers the durable, resumable chat runtime described there. ### Patch Changes - Add Agent Skills for `chat.agent`. Drop a folder with a `SKILL.md` and any helper scripts/references next to your task code, register it with `skills.define({ id, path })`, and the CLI bundles it into the deploy image automatically — no `trigger.config.ts` changes. The agent gets a one-line summary in its system prompt and discovers full instructions on demand via `loadSkill`, with `bash` and `readFile` tools scoped per-skill (path-traversal guards, output caps, abort-signal propagation). ([#3543](#3543)) ```ts const pdfSkill = skills.define({ id: "pdf-extract", path: "./skills/pdf-extract" }); chat.skills.set([await pdfSkill.local()]); ``` Built on the [AI SDK cookbook pattern](https://ai-sdk.dev/cookbook/guides/agent-skills) — portable across providers. SDK + CLI only for now; dashboard-editable `SKILL.md` text is on the roadmap. - 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](#3546)) ```ts const myTool = tool({ description: "...", inputSchema: z.object({ ... }), execute: ai.toolExecute(mySubtask), }); ``` `ai.tool(task)` (`toolFromTask`) keeps doing the all-in-one wrap and now aligns its return type with AI SDK's `ToolSet`. Minimum `ai` peer raised to `^6.0.116` to avoid cross-version `ToolSet` mismatches in monorepos. - Stamp `gen_ai.conversation.id` (the chat id) on every span and metric emitted from inside a `chat.task` or `chat.agent` run. Lets you filter dashboard spans, runs, and metrics by the chat conversation that produced them — independent of the run boundary, so multi-run chats correlate cleanly. No code changes required on the user side. ([#3543](#3543)) - Type `chat.createStartSessionAction` against your chat agent so `clientData` is typed end-to-end on the first turn: ([#3684](#3684)) ```ts import { chat } from "@trigger.dev/sdk/ai"; import type { myChat } from "@/trigger/chat"; export const startChatSession = chat.createStartSessionAction<typeof myChat>("my-chat"); // In the browser, threaded from the transport's typed startSession callback: const transport = useTriggerChatTransport<typeof myChat>({ task: "my-chat", startSession: ({ chatId, clientData }) => startChatSession({ chatId, clientData }), // ... }); ``` `ChatStartSessionParams` gains a typed `clientData` field — folded into the first run's `payload.metadata` so `onPreload` / `onChatStart` see the same shape per-turn `metadata` carries via the transport. The opaque session-level `metadata` field is unchanged. - Unit-test `chat.agent` definitions offline with `mockChatAgent` from `@trigger.dev/sdk/ai/test`. Drives a real agent's turn loop in-process — no network, no task runtime — so you can send messages, actions, and stop signals via driver methods, inspect captured output chunks, and verify hooks fire. Pairs with `MockLanguageModelV3` from `ai/test` for model mocking. `setupLocals` lets you pre-seed `locals` (DB clients, service stubs) before `run()` starts. ([#3543](#3543)) The broader `runInMockTaskContext` harness it's built on lives at `@trigger.dev/core/v3/test` — useful for unit-testing any task code, not just chat. - 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](#3612)) - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` ## @trigger.dev/build@4.5.0-rc.0 ### Patch Changes - Add Agent Skills for `chat.agent`. Drop a folder with a `SKILL.md` and any helper scripts/references next to your task code, register it with `skills.define({ id, path })`, and the CLI bundles it into the deploy image automatically — no `trigger.config.ts` changes. The agent gets a one-line summary in its system prompt and discovers full instructions on demand via `loadSkill`, with `bash` and `readFile` tools scoped per-skill (path-traversal guards, output caps, abort-signal propagation). ([#3543](#3543)) ```ts const pdfSkill = skills.define({ id: "pdf-extract", path: "./skills/pdf-extract" }); chat.skills.set([await pdfSkill.local()]); ``` Built on the [AI SDK cookbook pattern](https://ai-sdk.dev/cookbook/guides/agent-skills) — portable across providers. SDK + CLI only for now; dashboard-editable `SKILL.md` text is on the roadmap. - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` ## trigger.dev@4.5.0-rc.0 ### Patch Changes - Add Agent Skills for `chat.agent`. Drop a folder with a `SKILL.md` and any helper scripts/references next to your task code, register it with `skills.define({ id, path })`, and the CLI bundles it into the deploy image automatically — no `trigger.config.ts` changes. The agent gets a one-line summary in its system prompt and discovers full instructions on demand via `loadSkill`, with `bash` and `readFile` tools scoped per-skill (path-traversal guards, output caps, abort-signal propagation). ([#3543](#3543)) ```ts const pdfSkill = skills.define({ id: "pdf-extract", path: "./skills/pdf-extract" }); chat.skills.set([await pdfSkill.local()]); ``` Built on the [AI SDK cookbook pattern](https://ai-sdk.dev/cookbook/guides/agent-skills) — portable across providers. SDK + CLI only for now; dashboard-editable `SKILL.md` text is on the roadmap. - 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](#3618)) - The CLI MCP server's agent-chat tools (`start_agent_chat`, `send_agent_message`, `close_agent_chat`) now run on the new Sessions primitive, so AI assistants driving a `chat.agent` get the same idempotent-by-`chatId`, durable-across-runs behavior the browser transport gets. Required PAT scopes go from `write:inputStreams` to `read:sessions` + `write:sessions`. ([#3546](#3546)) - MCP `list_runs` tool: add a `region` filter input and surface each run's executing region in the formatted summary. ([#3612](#3612)) - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` - `@trigger.dev/build@4.5.0-rc.0` - `@trigger.dev/schema-to-json@4.5.0-rc.0` ## @trigger.dev/core@4.5.0-rc.0 ### Patch Changes - Add Agent Skills for `chat.agent`. Drop a folder with a `SKILL.md` and any helper scripts/references next to your task code, register it with `skills.define({ id, path })`, and the CLI bundles it into the deploy image automatically — no `trigger.config.ts` changes. The agent gets a one-line summary in its system prompt and discovers full instructions on demand via `loadSkill`, with `bash` and `readFile` tools scoped per-skill (path-traversal guards, output caps, abort-signal propagation). ([#3543](#3543)) ```ts const pdfSkill = skills.define({ id: "pdf-extract", path: "./skills/pdf-extract" }); chat.skills.set([await pdfSkill.local()]); ``` Built on the [AI SDK cookbook pattern](https://ai-sdk.dev/cookbook/guides/agent-skills) — portable across providers. SDK + CLI only for now; dashboard-editable `SKILL.md` text is on the roadmap. - 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](#3560)) - **AI Agents** — run AI SDK chat completions as durable Trigger.dev agents instead of fragile API routes. Define an agent in one function, point `useChat` at it from React, and the conversation survives page refreshes, network blips, and process restarts. ([#3543](#3543)) ```ts import { chat } from "@trigger.dev/sdk/ai"; import { streamText } from "ai"; import { openai } from "@ai-sdk/openai"; export const myChat = chat.agent({ id: "my-chat", run: async ({ messages, signal }) => streamText({ model: openai("gpt-4o"), messages, abortSignal: signal }), }); ``` ```tsx import { useChat } from "@ai-sdk/react"; import { useTriggerChatTransport } from "@trigger.dev/sdk/chat/react"; const transport = useTriggerChatTransport({ task: "my-chat", accessToken, startSession }); const { messages, sendMessage } = useChat({ transport }); ``` **What you get:** - **AI SDK `useChat` integration** — a custom [`ChatTransport`](https://sdk.vercel.ai/docs/ai-sdk-ui/transport) (`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](https://trigger.dev/docs/ai/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. See [/docs/ai-chat](https://trigger.dev/docs/ai-chat/overview) for the full surface — quick start, three backend approaches (`chat.agent`, `chat.createSession`, raw task), persistence and code-sandbox patterns, type-level guides, and API reference. - Stamp `gen_ai.conversation.id` (the chat id) on every span and metric emitted from inside a `chat.task` or `chat.agent` run. Lets you filter dashboard spans, runs, and metrics by the chat conversation that produced them — independent of the run boundary, so multi-run chats correlate cleanly. No code changes required on the user side. ([#3543](#3543)) - 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](#3626)) - Unit-test `chat.agent` definitions offline with `mockChatAgent` from `@trigger.dev/sdk/ai/test`. Drives a real agent's turn loop in-process — no network, no task runtime — so you can send messages, actions, and stop signals via driver methods, inspect captured output chunks, and verify hooks fire. Pairs with `MockLanguageModelV3` from `ai/test` for model mocking. `setupLocals` lets you pre-seed `locals` (DB clients, service stubs) before `run()` starts. ([#3543](#3543)) The broader `runInMockTaskContext` harness it's built on lives at `@trigger.dev/core/v3/test` — useful for unit-testing any task code, not just chat. - 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](#3552)) - 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](#3612)) - **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](#3542)) ```ts import { sessions, tasks } from "@trigger.dev/sdk"; // Trigger a task and subscribe to its session output in one call const { runId, stream } = await tasks.triggerAndSubscribe("my-task", payload, { externalId: "user-456", }); for await (const chunk of stream) { // ... } // Enumerate existing sessions (powers inbox-style UIs without a separate index) for await (const s of sessions.list({ type: "chat.agent", tag: "user:user-456" })) { console.log(s.id, s.externalId, s.createdAt, s.closedAt); } ``` See [/docs/ai-chat/overview](https://trigger.dev/docs/ai-chat/overview) for the full surface — Sessions powers the durable, resumable chat runtime described there. ## @trigger.dev/plugins@4.5.0-rc.0 ### Patch Changes - The public interfaces for a plugin system. Initially consolidated authentication and authorization interfaces. ([#3499](#3499)) - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` ## @trigger.dev/python@4.5.0-rc.0 ### Patch Changes - Updated dependencies: - `@trigger.dev/sdk@4.5.0-rc.0` - `@trigger.dev/core@4.5.0-rc.0` - `@trigger.dev/build@4.5.0-rc.0` ## @trigger.dev/react-hooks@4.5.0-rc.0 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` ## @trigger.dev/redis-worker@4.5.0-rc.0 ### Patch Changes - Add MollifierBuffer and MollifierDrainer primitives for trigger burst smoothing. ([#3614](#3614)) MollifierBuffer (`accept`, `pop`, `ack`, `requeue`, `fail`, `evaluateTrip`) is a per-env FIFO over Redis with atomic Lua transitions for status tracking. `evaluateTrip` is a sliding-window trip evaluator the webapp gate uses to detect per-env trigger bursts. MollifierDrainer pops entries through a polling loop with a user-supplied handler. The loop survives transient Redis errors via capped exponential backoff (up to 5s), and per-env pop failures don't poison the rest of the batch — one env's blip is logged and counted as failed for that tick. Rotation is two-level: orgs at the top, envs within each org. The buffer maintains `mollifier:orgs` and `mollifier:org-envs:${orgId}` atomically with per-env queues, so the drainer walks orgs → envs directly without an in-memory cache. The `maxOrgsPerTick` option (default 500) caps how many orgs are scheduled per tick; for each picked org, one env is popped (rotating round-robin within the org). An org with N envs gets the same per-tick scheduling slot as an org with 1 env, so tenant-level drainage throughput is determined by org count rather than env count. - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` ## @trigger.dev/rsc@4.5.0-rc.0 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` ## @trigger.dev/schema-to-json@4.5.0-rc.0 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.0` </details> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Summary
trigger()API calls during traffic spikes, with a per-env trip evaluator and a drainer ack-loop.engine.trigger. No customer-facing behaviour change.mollifier.would_mollify,mollifier.buffered,mollifier.drained, plus themollifier.decisionscounter.Test plan
pnpm run test --filter @trigger.dev/redis-workerpnpm run test --filter webapp -- mollifiermollifier.buffered+mollifier.drainedlog pairs with matchingrunId