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…#3684) ## Summary Type `chat.createStartSessionAction` against the chat agent so `clientData` is typed end-to-end on the first turn. Closes the gap where `useTriggerChatTransport`'s `startSession` callback already hands you a typed `clientData` (via the transport generic) but the server-side action couldn't accept it without untyped routing through the `metadata` field. ## Design `ChatStartSessionParams` gains a typed `clientData` field via the new generic: ```ts export type ChatStartSessionParams<TChat extends AnyTask = AnyTask> = { chatId: string; clientData?: InferChatClientData<TChat>; triggerConfig?: Partial<SessionTriggerConfig>; metadata?: Record<string, unknown>; }; function createChatStartSessionAction<TChat extends AnyTask = AnyTask>( taskId: string, options?: CreateChatStartSessionActionOptions ): (params: ChatStartSessionParams<TChat>) => Promise<ChatStartSessionResult> ``` When provided, `clientData` is folded into the first run's `triggerConfig.basePayload.metadata`, so `onPreload` / `onChatStart` see the same shape per-turn `metadata` carries via the transport. The opaque session-level `metadata` field stays exactly as before — it lands on the Session row, not the run payload. ## Usage ```ts // actions.ts import { chat } from "@trigger.dev/sdk/ai"; import type { myChat } from "@/trigger/chat"; export const startChatSession = chat.createStartSessionAction<typeof myChat>("my-chat"); ``` ```tsx // Chat.tsx const transport = useTriggerChatTransport<typeof myChat>({ task: "my-chat", accessToken: ({ chatId }) => mintChatAccessToken(chatId), startSession: ({ chatId, clientData }) => startChatSession({ chatId, clientData }), }); ``` ## Test plan - [x] `pnpm run build --filter @trigger.dev/sdk` passes - [ ] Verify a `chat.agent` with `clientDataSchema` reads the typed clientData from `onPreload` payload metadata on the first turn
…3685) ## Summary Pre-existing typecheck errors in `references/ai-chat` against the current SDK shape. Unblocks `pnpm exec tsc --noEmit` in the reference project. ## What changed Three categories of fixes inside `references/ai-chat`. No SDK changes. ### 1. `payload.messages` → `payload.message` The wire payload is now delta-only — one new message per trigger, optional. Old code in two raw-task files reads `payload.messages` (plural array) which no longer exists. ```ts // before const messages = await conversation.addIncoming(currentPayload.messages, ...); // after const messages = await conversation.addIncoming( currentPayload.message ? [currentPayload.message] : [], ... ); ``` Same fix to the `chat.messages.on` handler, reading `msg.message` (singular) instead of `msg.messages[length - 1]`. ### 2. `clientData` non-null assertion in `cf-trust-test` `ChatTurnContext.clientData` is typed as `?: TClientData` on `onTurnStart` / `run` event objects even when the agent declares a `clientDataSchema`. The runtime validates against the schema before the hook fires, so it's structurally non-null — but TypeScript can't know that. Non-null assert for now. Follow-up worth filing: narrow `ChatTurnContext.clientData` to non-optional when the agent has a `clientDataSchema`. Same friction the docs friction-test subagent flagged. ### 3. `stress-emit.parseConfig` retyped against `ModelMessage[]` The `run` callback hands `messages: ModelMessage[]`, not `UIMessage[]`. Update `parseConfig` to accept `ModelMessage[]` and pull text from `content` (string or array-of-parts). ## Test plan - [x] `pnpm exec tsc --noEmit` in `references/ai-chat` passes (was 8 errors, now 0)
## Summary Two CI workflows were blocking the v4.5.0-rc.0 release PR (#3563) and would block every future changeset release PR. ### 1. `changesets-pr.yml` — self-report `All PR Checks` The changesets bot pushes commits authored by `GITHUB_TOKEN`. By GitHub design, `GITHUB_TOKEN`-authored pushes can't trigger downstream workflows (loop-prevention). That means `pr_checks.yml` never fires on release-PR commits, leaving the required `All PR Checks` status permanently `Expected — Waiting for status to be reported`. The PR can't merge. The fix: after `changesets/action` creates the PR, post a `success` check with the exact `All PR Checks` context onto the PR's head SHA. GitHub's required-check evaluation is satisfied by any check with the right context name — the source doesn't have to be `pr_checks.yml`. **Why this is safe:** the release PR only mechanically bumps `package.json`, `pnpm-lock.yaml`, and `CHANGELOG.md` from changesets that were already on `main` (and already ran full CI when they merged). If a human ever pushes a commit to `changeset-release/main`, `pr_checks.yml` fires on that push (real user, not `GITHUB_TOKEN`) and posts its own `All PR Checks` status — last write wins for the same context on the same SHA, so the human-push result overrides the auto-success. ### 2. `vouch-check-pr.yml` — exempt `github-actions[bot]` The `require-draft` job auto-closes any non-draft PR whose author is not a `MEMBER`/`OWNER`/`COLLABORATOR`, with an explicit allowlist for `devin-ai-integration[bot]` and `dependabot[bot]`. The changesets bot publishes as `github-actions[bot]` with `author_association: CONTRIBUTOR`, so every release PR was getting auto-closed on open with a "please re-open as draft" comment. Add `github-actions[bot]` to the exemption list. ## Test plan - [ ] After merge, the next changeset bot push to `changeset-release/main` should post `All PR Checks: success` on the release PR's head SHA, and the PR should not get auto-closed by `Vouch - Check PR`. - [ ] Confirm `pr_checks.yml` still fires + gates normal (human-authored) PRs to `main`.
## 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>
…3688) On a warm worker process, a task whose `task()` definition is loaded via `await import(...)` from inside another task's `run()` could end up permanently missing from the catalog: the `task()` call fired with no `_currentFileContext` set, `registerTaskMetadata` silently returned, and Node's ESM module cache then blocked the worker's setContext + re-import recovery from ever firing the call again. Subsequent runs of that task on the same warm process failed with `COULD_NOT_FIND_EXECUTOR` until the process hit `maxExecutionsPerProcess` and exited. All five of these had to coincide on the same worker for the bug to surface: 1. `processKeepAlive` enabled (so catalog state survives across runs). 2. A `run()` function (or lifecycle hook) does `await import(...)`. 3. The import's transitive static graph reaches a `task()` / `schemaTask()` call. 4. The task containing the dynamic import is the **first** task to run on a given warm worker process — so the dropped `task()` calls fire on this process for the first time, are silently dropped, and Node's module cache locks the wrong outcome in. 5. A subsequent run for one of the dropped task ids lands on the same warm worker before it recycles. The runtime workers now set a sentinel file context (`<no-context>`) around the `executor.execute(...)` call, so `task()` invocations firing during a run register normally. The catalog detects the sentinel and emits a one-time `console.warn` per task id so the pattern stays visible without spamming. The indexer never sets this context, so deploy-time behavior is unchanged. Repro is `references/hello-world/src/trigger/dynamicImportRepro*.ts`. Verified end-to-end against a deployed image with firestarter warm-starts on: pre-fix saw `COULD_NOT_FIND_EXECUTOR` on children that landed on the parent-poisoned worker; post-fix all 23/23 runs succeeded and the warning surfaces in the parent's run trace.
…ev (#3690) ## Summary `trigger.dev dev` was silently dropping registered `chat.agent` skills for any project whose task files read `process.env` at module top level — e.g. a third-party SDK client initialized at import. The agent would boot fine, but `skill.local()` failed at runtime with `ENOENT` because the skill folder was never copied into `.trigger/skills/`. ## Design The CLI ran two indexer passes in dev: the worker's own indexer (with the full env it eventually executes tasks in), and a separate skill-discovery indexer with only the CLI process's env. Top-level reads of vars like `TRIGGER_API_URL` imported cleanly in the worker pass and threw in the skill pass — the latter caught the error, warned, and skipped skill copying. Failure was silent enough that `skill.local()` only surfaced it at task runtime. The skill registry is already part of the worker manifest. This PR drops the duplicate pass and copies skill folders from that manifest after the worker initializes. One indexer instead of two; a bad `SKILL.md` now surfaces as a startup error instead of silently disappearing skills. Deploy is unaffected — its skill discovery uses the project's environment variables (fetched via the API, which fills in `TRIGGER_API_URL` etc.), so the dev failure mode doesn't reach there. ## Test plan - [x] New `references/agent-skills` reference project with `skills.define` + a task that calls `skill.local()` and runs a bundled script - [x] On `main`, adding a top-level `process.env.TRIGGER_API_URL!.includes(...)` read in any task file reproduces the symptom: warning at dev startup, no `.trigger/skills/` folder, `skill.local()` fails with ENOENT - [x] On this branch, same project boots clean and `skill.local()` works end-to-end - [x] Deploy still works end-to-end with the new reference project
## Summary 2 bug fixes. ## Bug fixes - Fix `chat.agent` skills silently missing in `trigger dev` for projects whose task files read `process.env` at module top level (e.g. a third-party SDK client initialized at import). Skill folders now bundle into `.trigger/skills/` reliably regardless of which env vars are set when the CLI launches. ([#3690](#3690)) - Fix `COULD_NOT_FIND_EXECUTOR` when a task's definition is loaded via `await import(...)` from inside another task's `run()`. The runtime workers now register such tasks with a sentinel file context, and the catalog logs a one-time warning per task id. ([#3688](#3688)) <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/build@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` ## trigger.dev@4.5.0-rc.1 ### Patch Changes - Fix `chat.agent` skills silently missing in `trigger dev` for projects whose task files read `process.env` at module top level (e.g. a third-party SDK client initialized at import). Skill folders now bundle into `.trigger/skills/` reliably regardless of which env vars are set when the CLI launches. ([#3690](#3690)) - Fix `COULD_NOT_FIND_EXECUTOR` when a task's definition is loaded via `await import(...)` from inside another task's `run()`. The runtime workers now register such tasks with a sentinel file context, and the catalog logs a one-time warning per task id. ([#3688](#3688)) - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` - `@trigger.dev/build@4.5.0-rc.1` - `@trigger.dev/schema-to-json@4.5.0-rc.1` ## @trigger.dev/core@4.5.0-rc.1 ### Patch Changes - Fix `COULD_NOT_FIND_EXECUTOR` when a task's definition is loaded via `await import(...)` from inside another task's `run()`. The runtime workers now register such tasks with a sentinel file context, and the catalog logs a one-time warning per task id. ([#3688](#3688)) ## @trigger.dev/plugins@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` ## @trigger.dev/python@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` - `@trigger.dev/build@4.5.0-rc.1` - `@trigger.dev/sdk@4.5.0-rc.1` ## @trigger.dev/react-hooks@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` ## @trigger.dev/redis-worker@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` ## @trigger.dev/rsc@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` ## @trigger.dev/schema-to-json@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` ## @trigger.dev/sdk@4.5.0-rc.1 ### Patch Changes - Updated dependencies: - `@trigger.dev/core@4.5.0-rc.1` </details> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
## Summary Lands the full AI Agents documentation surface alongside the v4.5 release candidate of `@trigger.dev/sdk`. Covers `chat.agent` end to end — defining agents, lifecycle hooks, the frontend transport, sub-agents, recovery from cancel/crash/OOM, AI Prompts integration — and the Sessions primitive that backs it. ## Coverage - **Conceptual**: Overview, Quick Start, How it works. - **Building agents**: Backend (`chat.agent` / `chat.createSession` / raw primitives), Lifecycle hooks, Frontend transport, Server-side `AgentChat`, Sessions reference, `chat.local` state primitive, TypeScript types. - **Features**: AI Prompts integration, Fast starts (Preload + Head Start), Compaction, Pending Messages (steering), Background Injection (`chat.inject` + `chat.defer`), Actions (undo / regenerate / edit), Error handling. - **Patterns (13)**: Sub-agents, Branching conversations, Code sandbox, Database persistence, Persistence and replay, HITL, Tool result auditing, Large payloads, Agent skills, OOM resilience, Recovery boot, Trusted edge signals, Version upgrades. - **Reference**: API Reference, Client Protocol (wire format), Testing harness (`mockChatAgent`), MCP server tools, Upgrade guide, Changelog. ## Structure changes - Top-level nav: AI → **Agents**, with sub-groups for *Building agents / Features / Patterns / Reference*. - New RC banner snippet on every page links to the supported AI SDK versions table on the API Reference. - All examples use Anthropic with `stopWhen: stepCountIs(15)`. --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Summary Three post-merge fixes for the AI Agents docs (#3226), all caught by review after merge. ## Fixes - **`onTurnComplete` examples now use `db.$transaction`** — both the Database persistence "Complete example" and the Lifecycle hooks reference example were doing two separate `await` calls (`db.chat.update` then `db.chatSession.upsert`). That's the exact non-atomic pattern the warning earlier on the persistence page calls out as ❌: a refresh between the two writes reads a stale `lastEventId` and duplicates the assistant message on resume. Both examples now use the recommended atomic form. - **Background injection self-review prose aligned with the code** — the prose said "gpt-4o-mini" but the example above it had been swapped to `claude-haiku-4-5`. The Anthropic-sweep script only touched code blocks; this prose line wasn't picked up. ## Test plan - [x] Both updated examples use `db.$transaction([...])` - [x] Prose matches the model used in the code block - [ ] Mintlify deployment passes
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