feat(sdk): chat.agent — runtime + browser transport (2/4)#3543
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🪛 LanguageTool.changeset/chat-agent-delta-wire-snapshots.md[grammar] ~8-~8: Ensure spelling is correct (QB_NEW_EN_ORTHOGRAPHY_ERROR_IDS_1) .changeset/chat-head-start.md[style] ~5-~5: Consider a different verb to strengthen your wording. (DROP_DECLINE) .changeset/chat-history-read-primitives.md[grammar] ~7-~7: Ensure spelling is correct (QB_NEW_EN_ORTHOGRAPHY_ERROR_IDS_1) 🔇 Additional comments (4)
WalkthroughAdds Trigger v3 chat transport (browser) and server head-start handler, a server/client chat client, durable sessions API with streaming I/O, skills API/runtime with catalog support, multi-tab coordination, and deployment helpers. Webapp consolidates Streamdown rendering and expands AI UI features. Build system adds a secure-exec extension. Core adds conversation-id instrumentation and skill catalog methods. Extensive tests and changesets document behavior, wire formats, snapshots/replay, head-start, and new hooks. Estimated code review effort🎯 5 (Critical) | ⏱️ ~120 minutes ✨ Finishing Touches📝 Generate docstrings
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…3542) ## Summary A `/sessions` dashboard for inspecting durable Sessions, an `AGENT` / `SCHEDULED` task-kind filter for the runs list, and the server-side hardening (rate-limit exemption for packets, retry-with-backoff on stream appends, typed too-large-chunk error) that the `chat.agent` runtime in #3543 needs. Builds on the Sessions primitive shipped in #3417. ## Design The Sessions list + detail routes mirror the run inspector pattern. `TaskTriggerSource` gains `AGENT` and `SCHEDULED` values, persisted on `BackgroundWorker.taskKind` and `TaskRun.taskKind` (plus a matching Clickhouse column), so the runs list can filter by kind. New `@trigger.dev/core` modules — `sessionStreams`, `inputStreams`, a `sessionStreamInstance` for realtime streams, and the `realtime-streams-api` / `session-streams-api` surfaces — expose the typed shapes that chat.agent will use to drive `session.out`. `ChatChunkTooLargeError` lets the runtime drop oversized chunks with a typed surface instead of failing the run. `s2Append` retries transient failures with exponential backoff. `/api/v[12]/packets/*` is exempt from customer rate limits so chat snapshot reads and writes don't get throttled under load. ## Stack Part of a 4-PR stack. Merge bottom-up. 1. **This PR** (#3542) → `main` 2. #3543 → #3542 — `chat.agent` runtime + browser transport 3. #3545 → #3543 — agent-view dashboard 4. #3546 → #3545 — ai-chat reference + MCP tooling Replaces #3173 (closed). <!-- GitButler Footer Boundary Top --> --- This is **part 5 of 5 in a stack** made with GitButler: - <kbd> 5 </kbd> #3612 - <kbd> 4 </kbd> #3546 - <kbd> 3 </kbd> #3545 - <kbd> 2 </kbd> #3543 - <kbd> 1 </kbd> #3542 👈 <!-- GitButler Footer Boundary Bottom -->
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Adds the chat.agent({...}) task definition (server runtime) and the
browser-side TriggerChatTransport + AgentChat that drives it from a
React or Next.js app. The runtime sits on top of the Sessions primitive
and handles the durable conversational task lifecycle.
Server runtime:
- chat.agent({...}) — session-aware task definition
- Lifecycle hooks: onChatStart, onTurnStart, onTurnComplete, onAction,
onValidateMessages, hydrateMessages
- chat.history read primitives for HITL flows
- chat.local, chat.headStart, chat.handover, oomMachine
- Delta-only wire + S3 snapshot reconstruction at run boot
- Actions are no longer turns
Browser transport:
- TriggerChatTransport (ai-sdk Transport): delta-only wire sends,
SSE reconnection with lastEventId resume, stop/abort cleanup,
dynamic accessToken refresh
- AgentChat: direct programmatic API
- useTriggerChatTransport (React hook)
- chat-tab-coordinator: cross-tab leader election
Includes the chat-agent, chat-agent-delta-wire-snapshots,
chat-history-read-primitives, chat-head-start, chat-actions-no-turn,
chat-session-attributes, agent-skills, and mock-chat-agent-test-harness
changesets.
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## Summary A chat-aware run inspector and a `/playground` UI for testing `chat.agent` tasks interactively. Builds on #3543's runtime. ## Design The run inspector grows a new tab that renders the conversation chain for any `chat.agent`-kind run. It subscribes to the run's session streams, threads chat parts through a per-message renderer, and uses a shared markdown + Shiki component for code highlighting (also used by the test-payload panel). The playground is a standalone `/playground` route that lets you drive a deployed chat agent from the dashboard — pick a task, send messages, watch tool calls render, and see span detail on every turn. The matching `/agents` list view shows all deployed agents in the project.
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## 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>
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Summary
Adds
chat.agent({...}), a durable conversational task runtime, plus the browser-sideTriggerChatTransport+AgentChatthat drive it from a React or Next.js app. Conversations survive page refreshes, network blips, idle suspend, and process restarts, with built-in tools, HITL approvals, multi-turn state, and stop-mid-stream cancellation. Builds on #3542.Design
Each
/in/appendrequest carries at most one new message. The agent reconstructs prior history at run boot from an object-store snapshot plus asession.outreplay tail, so conversation context lives server-side instead of bloating the wire. Awaited snapshot writes after everyonTurnCompletekeep the chain durable across idle suspend. RegisteringhydrateMessagesshort-circuits both paths for customers who own their own conversation store.Lifecycle hooks —
onChatStart,onTurnStart,onTurnComplete,onAction,onValidateMessages,hydrateMessages— cover validation, persistence, and post-turn work.chat.historyexposes read primitives (getPendingToolCalls,getResolvedToolCalls,extractNewToolResults,findMessage,all) for HITL flows.chat.localgives per-run typed state with Proxy access and dirty tracking.chat.headStartbridges first-turn TTFC via a customer HTTP handler.oomMachineopts a chat into one-shot OOM-retry on a larger machine.TriggerChatTransportis aTransportimplementation for Vercel's ai-sdkuseChat: delta-only wire sends, SSE reconnection withlastEventIdresume, stop/abort cleanup, dynamicaccessTokenrefresh,X-Peek-Settledfast-close.AgentChatis the direct programmatic equivalent. A cross-tab coordinator does leader election so multiple open tabs share a single SSE.Test plan
useChatagainst a local webappstop(), verify clean abortStack
Part of a 4-PR stack. Merge bottom-up.
main— Sessions dashboard + chat-ready hardeningOriginally split across #3543 and #3544. Folded into a single PR because the SDK package.json subpath exports for
./chatand./chat/reactmade the runtime and the browser transport install-coupled.This is part 4 of 5 in a stack made with GitButler: