Merge branch 'main' into docs/self-host-env-vars

Author: isshaddad

.changeset/agent-skills.md

@@ -0,0 +1,16 @@
+---
+"@trigger.dev/sdk": patch
+"@trigger.dev/core": patch
+"@trigger.dev/build": patch
+"trigger.dev": patch
+---
+
+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).
+
+```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.

.changeset/ai-prompts.md

@@ -0,0 +1,52 @@
+---
+"@trigger.dev/sdk": minor
+---
+
+**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()`.
+
+```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`).

.changeset/ai-tool-helpers.md

@@ -0,0 +1,15 @@
+---
+"@trigger.dev/sdk": patch
+---
+
+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.
+
+```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.

.changeset/bundle-skills-single-pass.md

@@ -0,0 +1,5 @@
+---
+"trigger.dev": patch
+---
+
+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.

.changeset/cap-idempotency-key-length.md

@@ -0,0 +1,5 @@
+---
+"@trigger.dev/core": patch
+---
+
+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.

.changeset/chat-agent-on-boot-hook.md

@@ -0,0 +1,21 @@
+---
+"@trigger.dev/sdk": minor
+---
+
+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.
+
+```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.

.changeset/chat-agent.md

@@ -0,0 +1,44 @@
+---
+"@trigger.dev/sdk": minor
+"@trigger.dev/core": patch
+---
+
+**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.
+
+```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.

.changeset/chat-history-read-primitives.md

@@ -0,0 +1,21 @@
+---
+"@trigger.dev/sdk": minor
+---
+
+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.
+
+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 });
+  }
+};
+```

.changeset/chat-session-attributes.md

@@ -0,0 +1,6 @@
+---
+"@trigger.dev/sdk": patch
+"@trigger.dev/core": patch
+---
+
+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.

.changeset/chat-slim-wire-merge.md

@@ -0,0 +1,31 @@
+---
+"@trigger.dev/sdk": patch
+---
+
+Fix `chat.agent` HITL continuations on reasoning-heavy turns. Two changes that work together:
+
+- The per-turn merge now overlays the wire copy's tool-part state advancement onto the agent's existing chain — `state` + the matching resolution field (`output` / `errorText` / `approval`) come from the wire, everything else (text, reasoning, tool `input`, provider metadata) stays whatever the snapshot or `hydrateMessages` returned. Previously a full-message replace overwrote those fields with whatever the client shipped, so a slimmed wire copy landed a tool call with no `arguments` on the next LLM call. Covers `output-available` / `output-error` (HITL `addToolOutput`) and `approval-responded` / `output-denied` (approval flow).
+- `TriggerChatTransport.sendMessages` and `AgentChat.sendRaw` now slim assistant messages that carry advanced tool parts. The wire payload is just `{ id, role, parts: [<state + resolution field>] }` for `submit-message` continuations; everything else passes through. Reasoning blobs and full tool inputs no longer ride the wire on every `addToolOutput` / `addToolApproveResponse`, so continuation payloads stay well under the `.in/append` cap on long agent loops.
+
+Note: `onValidateMessages` receives the slim wire on HITL turns. If you call `validateUIMessages` from `ai` against the full `messages` array it will reject the slim assistant; filter to user messages (or skip on HITL turns) — see the updated docstring on `onValidateMessages` for the recommended pattern.
+
+For `hydrateMessages` hooks that persist the chain, this release also adds a small helper to the `@trigger.dev/sdk/ai` surface:
+
+```ts
+import { chat, upsertIncomingMessage } from "@trigger.dev/sdk/ai";
+
+chat.agent({
+  hydrateMessages: async ({ chatId, trigger, incomingMessages }) => {
+    const record = await db.chat.findUnique({ where: { id: chatId } });
+    const stored = record?.messages ?? [];
+    if (upsertIncomingMessage(stored, { trigger, incomingMessages })) {
+      await db.chat.update({ where: { id: chatId }, data: { messages: stored } });
+    }
+    return stored;
+  },
+});
+```
+
+It pushes fresh user messages by id, no-ops on HITL continuations (the incoming shares an id with the existing assistant — the runtime overlays the new tool-state advance), and skips on non-`submit-message` triggers. Returns `true` if it mutated `stored` so the caller knows whether to persist.
+
+Net effect: `chat.addToolOutput(...)` / `chat.addToolApproveResponse(...)` on multi-step reasoning agents (OpenAI Responses with `store: false`, Anthropic extended thinking, etc.) no longer blows the cap and no longer corrupts the LLM input.