chore: import upstream snapshot with attribution

2026-07-13 13:32:57 +08:00
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+---
+title: "Streaming data from tasks"
+sidebarTitle: "Streams"
+description: "Pipe continuous data from your Trigger.dev tasks to frontend or backend clients in real time. Stream AI completions, file chunks, progress updates, and more."
+---
+
+**Streams let you pipe data from a running task to your frontend or backend as it's produced.** Think AI completions token by token, progress updates, or file chunks. You can also **send data into** running tasks with [Input Streams](#input-streams) for bidirectional flows (cancel buttons, approvals).
+
+For subscribing to **run state changes** (status, metadata, tags) instead, see [Realtime](/realtime/overview).
+
+<Note>
+  Streams require SDK version **4.1.0 or later** (`@trigger.dev/sdk` and `@trigger.dev/react-hooks`).
+  This doc describes the current streams behavior (v2 is the default). For pre-4.1.0 streams, see
+  [Pre-4.1.0 streams (legacy)](#pre-410-streams-legacy) below.
+</Note>
+
+## Overview
+
+Streams provide:
+
+- **Unlimited stream length** (previously capped at 2000 chunks)
+- **Unlimited active streams per run** (previously 5)
+- **Improved reliability** with automatic resumption on connection loss
+- **28-day stream retention** (previously 1 day)
+- **Multiple client streams** can pipe to a single stream
+- **Enhanced dashboard visibility** for viewing stream data in real-time
+
+Streams v2 is the **default** when using SDK 4.1.0 or later. If you trigger tasks outside the SDK, set the `x-trigger-realtime-streams-version=v2` header. To opt out, use `auth.configure({ future: { v2RealtimeStreams: false } })` or `TRIGGER_V2_REALTIME_STREAMS=0`.
+
+## Limits Comparison
+
+| Limit                            | Legacy (pre-4.1.0) | Current   |
+| -------------------------------- | ------------------ | --------- |
+| Maximum stream length            | 2000               | Unlimited |
+| Number of active streams per run | 5                  | Unlimited |
+| Maximum streams per run          | 10                 | Unlimited |
+| Maximum stream TTL               | 1 day              | 28 days   |
+| Maximum stream size              | 10MB               | 300 MiB   |
+
+## Quick Start
+
+The recommended workflow for **output** streams (data from task to client):
+
+1. **Define your streams** in a shared location using `streams.define()`
+2. **Use the defined stream** in your tasks with `.pipe()`, `.append()`, or `.writer()`
+3. **Read from the stream** using `.read()` or the `useRealtimeStream` hook in React
+
+This approach gives you full type safety, better code organization, and easier maintenance as your application grows. For **input** streams (sending data into a running task), see [Input Streams](#input-streams) below.
+
+## Defining Typed Streams (Recommended)
+
+The recommended way to work with streams is to define them once with `streams.define()`. This allows you to specify the chunk type and stream ID in one place, and then reuse that definition throughout your codebase with full type safety.
+
+### Creating a Defined Stream
+
+Define your streams in a shared location (like `app/streams.ts` or `trigger/streams.ts`):
+
+```ts
+import { streams, InferStreamType } from "@trigger.dev/sdk";
+
+// Define a stream with a specific type
+export const aiStream = streams.define<string>({
+  id: "ai-output",
+});
+
+// Export the type for use in frontend components
+export type AIStreamPart = InferStreamType<typeof aiStream>;
+```
+
+You can define streams for any JSON-serializable type:
+
+```ts
+import { streams, InferStreamType } from "@trigger.dev/sdk";
+import { UIMessageChunk } from "ai";
+
+// Stream for AI UI message chunks
+export const aiStream = streams.define<UIMessageChunk>({
+  id: "ai",
+});
+
+// Stream for progress updates
+export const progressStream = streams.define<{ step: string; percent: number }>({
+  id: "progress",
+});
+
+// Stream for simple text
+export const logStream = streams.define<string>({
+  id: "logs",
+});
+
+// Export types
+export type AIStreamPart = InferStreamType<typeof aiStream>;
+export type ProgressStreamPart = InferStreamType<typeof progressStream>;
+export type LogStreamPart = InferStreamType<typeof logStream>;
+```
+
+### Using Defined Streams in Tasks
+
+Once defined, you can use all stream methods on your defined stream:
+
+```ts
+import { task } from "@trigger.dev/sdk";
+import { aiStream } from "./streams";
+
+export const streamTask = task({
+  id: "stream-task",
+  run: async (payload: { prompt: string }) => {
+    // Get a stream from an AI service, database, etc.
+    const stream = await getAIStream(payload.prompt);
+
+    // Pipe the stream using your defined stream
+    const { stream: readableStream, waitUntilComplete } = aiStream.pipe(stream);
+
+    // Option A: Iterate over the stream locally
+    for await (const chunk of readableStream) {
+      console.log("Received chunk:", chunk);
+    }
+
+    // Option B: Wait for the stream to complete
+    await waitUntilComplete();
+
+    return { message: "Stream completed" };
+  },
+});
+```
+
+#### Reading from a Stream
+
+Use the defined stream's `read()` method to consume data from anywhere (frontend, backend, or another task):
+
+```ts
+import { aiStream } from "./streams";
+
+const stream = await aiStream.read(runId);
+
+for await (const chunk of stream) {
+  console.log(chunk); // chunk is typed as the stream's chunk type
+}
+```
+
+With options:
+
+```ts
+const stream = await aiStream.read(runId, {
+  timeoutInSeconds: 60, // Stop if no data for 60 seconds
+  startIndex: 10, // Start from the 10th chunk
+});
+```
+
+#### Appending to a Stream
+
+Use the defined stream's `append()` method to add a single chunk:
+
+```ts
+import { task } from "@trigger.dev/sdk";
+import { aiStream, progressStream, logStream } from "./streams";
+
+export const appendTask = task({
+  id: "append-task",
+  run: async (payload) => {
+    // Append to different streams with full type safety
+    await logStream.append("Processing started");
+    await progressStream.append({ step: "Initialization", percent: 0 });
+
+    // Do some work...
+
+    await progressStream.append({ step: "Processing", percent: 50 });
+    await logStream.append("Step 1 complete");
+
+    // Do more work...
+
+    await progressStream.append({ step: "Complete", percent: 100 });
+    await logStream.append("All steps complete");
+  },
+});
+```
+
+#### Writing Multiple Chunks
+
+Use the defined stream's `writer()` method for more complex stream writing:
+
+```ts
+import { task } from "@trigger.dev/sdk";
+import { logStream } from "./streams";
+
+export const writerTask = task({
+  id: "writer-task",
+  run: async (payload) => {
+    const { waitUntilComplete } = logStream.writer({
+      execute: ({ write, merge }) => {
+        // Write individual chunks
+        write("Chunk 1");
+        write("Chunk 2");
+
+        // Merge another stream
+        const additionalStream = ReadableStream.from(["Chunk 3", "Chunk 4", "Chunk 5"]);
+        merge(additionalStream);
+      },
+    });
+
+    await waitUntilComplete();
+  },
+});
+```
+
+### Using Defined Streams in React
+
+Defined streams work seamlessly with the `useRealtimeStream` hook:
+
+```tsx
+"use client";
+
+import { useRealtimeStream } from "@trigger.dev/react-hooks";
+import { aiStream } from "@/app/streams";
+
+export function StreamViewer({ accessToken, runId }: { accessToken: string; runId: string }) {
+  // Pass the defined stream directly - full type safety!
+  const { parts, error } = useRealtimeStream(aiStream, runId, {
+    accessToken,
+    timeoutInSeconds: 600,
+  });
+
+  if (error) return <div>Error: {error.message}</div>;
+  if (!parts) return <div>Loading...</div>;
+
+  return (
+    <div>
+      {parts.map((part, i) => (
+        <span key={i}>{part}</span>
+      ))}
+    </div>
+  );
+}
+```
+
+## Direct Stream Methods (Without Defining)
+
+<Warning>
+  We strongly recommend using `streams.define()` instead of direct methods. Defined streams provide
+  better organization, full type safety, and make it easier to maintain your codebase as it grows.
+</Warning>
+
+If you have a specific reason to avoid defined streams, you can use stream methods directly by specifying the stream key each time.
+
+### Direct Piping
+
+```ts
+import { streams, task } from "@trigger.dev/sdk";
+
+export const directStreamTask = task({
+  id: "direct-stream",
+  run: async (payload: { prompt: string }) => {
+    const stream = await getAIStream(payload.prompt);
+
+    // Specify the stream key directly
+    const { stream: readableStream, waitUntilComplete } = streams.pipe("ai-output", stream);
+
+    await waitUntilComplete();
+  },
+});
+```
+
+### Direct Reading
+
+```ts
+import { streams } from "@trigger.dev/sdk";
+
+// Specify the stream key when reading
+const stream = await streams.read(runId, "ai-output");
+
+for await (const chunk of stream) {
+  console.log(chunk);
+}
+```
+
+### Direct Appending
+
+```ts
+import { streams, task } from "@trigger.dev/sdk";
+
+export const directAppendTask = task({
+  id: "direct-append",
+  run: async (payload) => {
+    // Specify the stream key each time
+    await streams.append("logs", "Processing started");
+    await streams.append("progress", "50%");
+    await streams.append("logs", "Complete");
+  },
+});
+```
+
+### Direct Writing
+
+```ts
+import { streams, task } from "@trigger.dev/sdk";
+
+export const directWriterTask = task({
+  id: "direct-writer",
+  run: async (payload) => {
+    const { waitUntilComplete } = streams.writer("output", {
+      execute: ({ write, merge }) => {
+        write("Chunk 1");
+        write("Chunk 2");
+      },
+    });
+
+    await waitUntilComplete();
+  },
+});
+```
+
+## Default Stream
+
+Every run has a "default" stream, allowing you to skip the stream key entirely. This is useful for simple cases where you only need one stream per run.
+
+Using direct methods:
+
+```ts
+import { streams, task } from "@trigger.dev/sdk";
+
+export const defaultStreamTask = task({
+  id: "default-stream",
+  run: async (payload) => {
+    const stream = getDataStream();
+
+    // No stream key needed - uses "default"
+    const { waitUntilComplete } = streams.pipe(stream);
+
+    await waitUntilComplete();
+  },
+});
+
+// Reading from the default stream
+const readStream = await streams.read(runId);
+```
+
+## Targeting Different Runs
+
+You can pipe streams to parent, root, or any other run using the `target` option. This works with both defined streams and direct methods.
+
+### With Defined Streams
+
+```ts
+import { task } from "@trigger.dev/sdk";
+import { logStream } from "./streams";
+
+export const childTask = task({
+  id: "child-task",
+  run: async (payload, { ctx }) => {
+    const stream = getDataStream();
+
+    // Pipe to parent run
+    logStream.pipe(stream, { target: "parent" });
+
+    // Pipe to root run
+    logStream.pipe(stream, { target: "root" });
+
+    // Pipe to self (default behavior)
+    logStream.pipe(stream, { target: "self" });
+
+    // Pipe to a specific run ID
+    logStream.pipe(stream, { target: payload.otherRunId });
+  },
+});
+```
+
+### With Direct Methods
+
+```ts
+import { streams, task } from "@trigger.dev/sdk";
+
+export const childTask = task({
+  id: "child-task",
+  run: async (payload, { ctx }) => {
+    const stream = getDataStream();
+
+    // Pipe to parent run
+    streams.pipe("output", stream, { target: "parent" });
+
+    // Pipe to root run
+    streams.pipe("output", stream, { target: "root" });
+
+    // Pipe to a specific run ID
+    streams.pipe("output", stream, { target: payload.otherRunId });
+  },
+});
+```
+
+## Streaming from Outside a Task
+
+If you specify a `target` run ID, you can pipe streams from anywhere (like a Next.js API route):
+
+```ts
+import { streams } from "@trigger.dev/sdk";
+import { openai } from "@ai-sdk/openai";
+import { streamText } from "ai";
+
+export async function POST(req: Request) {
+  const { messages, runId } = await req.json();
+
+  const result = streamText({
+    model: openai("gpt-4o"),
+    messages,
+  });
+
+  // Pipe AI stream to a Trigger.dev run
+  const { stream } = streams.pipe("ai-stream", result.toUIMessageStream(), {
+    target: runId,
+  });
+
+  return new Response(stream as any, {
+    headers: { "Content-Type": "text/event-stream" },
+  });
+}
+```
+
+## React Hook
+
+Use the `useRealtimeStream` hook to subscribe to streams in your React components.
+
+### With Defined Streams (Recommended)
+
+```tsx
+"use client";
+
+import { useRealtimeStream } from "@trigger.dev/react-hooks";
+import { aiStream } from "@/app/streams";
+
+export function StreamViewer({ accessToken, runId }: { accessToken: string; runId: string }) {
+  // Pass the defined stream directly for full type safety
+  const { parts, error } = useRealtimeStream(aiStream, runId, {
+    accessToken,
+    timeoutInSeconds: 600,
+    onData: (chunk) => {
+      console.log("New chunk:", chunk); // chunk is typed!
+    },
+  });
+
+  if (error) return <div>Error: {error.message}</div>;
+  if (!parts) return <div>Loading...</div>;
+
+  return (
+    <div>
+      {parts.map((part, i) => (
+        <span key={i}>{part}</span>
+      ))}
+    </div>
+  );
+}
+```
+
+### With Direct Stream Keys
+
+If you prefer not to use defined streams, you can specify the stream key directly:
+
+```tsx
+"use client";
+
+import { useRealtimeStream } from "@trigger.dev/react-hooks";
+
+export function StreamViewer({ accessToken, runId }: { accessToken: string; runId: string }) {
+  const { parts, error } = useRealtimeStream<string>(runId, "ai-output", {
+    accessToken,
+    timeoutInSeconds: 600,
+  });
+
+  if (error) return <div>Error: {error.message}</div>;
+  if (!parts) return <div>Loading...</div>;
+
+  return (
+    <div>
+      {parts.map((part, i) => (
+        <span key={i}>{part}</span>
+      ))}
+    </div>
+  );
+}
+```
+
+### Using Default Stream
+
+```tsx
+// Omit stream key to use the default stream
+const { parts, error } = useRealtimeStream<string>(runId, {
+  accessToken,
+});
+```
+
+### Hook Options
+
+```tsx
+const { parts, error } = useRealtimeStream(streamDef, runId, {
+  accessToken: "pk_...", // Required: Public access token
+  baseURL: "https://api.trigger.dev", // Optional: Custom API URL
+  timeoutInSeconds: 60, // Optional: Timeout (default: 60)
+  startIndex: 0, // Optional: Start from specific chunk
+  throttleInMs: 16, // Optional: Throttle updates (default: 16ms)
+  onData: (chunk) => {}, // Optional: Callback for each chunk
+});
+```
+
+## Input Streams
+
+Input Streams let you send data **into** a running task from your backend or frontend. While output streams (above) send data out of tasks, input streams complete the loop — enabling bidirectional communication.
+
+<Note>
+  Input Streams require SDK version **4.4.2 or later** and use the same streams infrastructure (v2 is the default). If you're on an older SDK, calling `.on()` or `.once()` will throw with instructions to enable v2 streams. See [Pre-4.1.0 streams (legacy)](#pre-410-streams-legacy) for the older metadata-based API.
+</Note>
+
+### Input Streams overview
+
+Input Streams solve three common problems:
+
+- **Cancelling AI streams mid-generation.** When you use AI SDK's `streamText` inside a task, the LLM keeps generating until it's done — even if the user clicked "Stop." With input streams, your frontend sends a cancel signal and the task aborts the LLM call immediately.
+- **Human-in-the-loop workflows.** A task generates a draft, then pauses and waits for the user to approve or edit it before continuing.
+- **Interactive agents.** An AI agent running as a task needs follow-up information from the user mid-execution — clarifying a question, choosing between options, or providing additional context.
+
+### Quick Start (Input Streams)
+
+1. **Define** input streams in a shared file with `streams.input<T>({ id: "..." })`.
+2. **Receive** in your task with `.wait()`, `.once()`, `.on()`, or `.peek()`.
+3. **Send** from your backend with `.send(runId, data)` or from the frontend with the `useInputStreamSend` hook (see [Realtime React hooks](/realtime/react-hooks/streams#useinputstreamsend)).
+
+### Defining Input Streams
+
+Use `streams.input()` to define a typed input stream. The generic parameter controls the shape of data that can be sent:
+
+```ts
+import { streams } from "@trigger.dev/sdk";
+
+export const cancelSignal = streams.input<{ reason?: string }>({
+  id: "cancel",
+});
+
+export const approval = streams.input<{ approved: boolean; reviewer: string }>({
+  id: "approval",
+});
+
+export const userResponse = streams.input<{
+  action: "approve" | "reject" | "edit";
+  message?: string;
+  edits?: Record<string, string>;
+}>({
+  id: "user-response",
+});
+```
+
+Type safety is enforced through the generic — both `.send()` and the receiving methods (`.wait()`, `.once()`, `.on()`, `.peek()`) share the same type.
+
+### Receiving data inside a task
+
+| Method | Task suspended? | Compute cost while waiting | Best for |
+|--------|-----------------|----------------------------|-----------|
+| `.wait()` | **Yes** | **None** — process freed | Approval gates, human-in-the-loop, long waits |
+| `.once()` | No | Full — process stays alive | Short waits, concurrent work; returns result object with `.unwrap()` |
+| `.on(handler)` | No | Full — process stays alive | Continuous listening (cancel signals, live updates) |
+| `.peek()` | No | None | Non-blocking check for latest buffered value |
+
+#### `wait()` — Suspend until data arrives
+
+Suspends the task entirely, freeing compute resources. The task resumes when data arrives via `.send()`. Returns a [`ManualWaitpointPromise`](/wait-for-token) — the same type as `wait.forToken()`.
+
+```ts
+import { task } from "@trigger.dev/sdk";
+import { approval } from "./streams";
+
+export const publishPost = task({
+  id: "publish-post",
+  run: async (payload: { postId: string }) => {
+    const draft = await prepareDraft(payload.postId);
+    await notifyReviewer(draft);
+
+    const result = await approval.wait({ timeout: "7d" });
+
+    if (result.ok) {
+      if (result.output.approved) {
+        await publish(draft);
+        return { published: true, reviewer: result.output.reviewer };
+      }
+      return { published: false, reviewer: result.output.reviewer };
+    }
+    return { published: false, timedOut: true };
+  },
+});
+```
+
+Use `.unwrap()` to throw on timeout: `const data = await approval.wait({ timeout: "24h" }).unwrap();`
+
+**Options:** `timeout` (e.g. `"30s"`, `"5m"`, `"24h"`, `"7d"`), `idempotencyKey`, `idempotencyKeyTTL`, `tags`. Use `idempotencyKey` when your task has retries so the same waitpoint is resumed across retries.
+
+#### `once()` — Wait for the next value (non-suspending)
+
+Blocks until data arrives but keeps the task process alive. Returns a result object; use `.unwrap()` to get the data or throw on timeout.
+
+```ts
+const result = await approval.once({ timeoutMs: 300_000 });
+if (result.ok) {
+  console.log(result.output.approved);
+}
+// Or: const data = await approval.once({ timeoutMs: 300_000 }).unwrap();
+```
+
+`once()` also accepts a `signal` (e.g. `AbortController.signal`) for cancellation.
+
+#### `on()` — Listen for every value
+
+Registers a persistent handler that fires on every piece of data. Handlers are automatically cleaned up when the task run completes. Call `.off()` on the returned subscription to stop listening early.
+
+```ts
+const controller = new AbortController();
+cancelSignal.on((data) => {
+  console.log("Cancelled:", data.reason);
+  controller.abort();
+});
+const result = streamText({ ..., abortSignal: controller.signal });
+```
+
+#### `peek()` — Non-blocking check
+
+Returns the most recent buffered value without waiting, or `undefined` if nothing has been received yet.
+
+```ts
+const latest = cancelSignal.peek();
+if (latest) {
+  // A cancel was already sent before we checked
+}
+```
+
+### Sending data to a running task
+
+Use `.send(runId, data)` from your backend to push data into a running task. See the [backend input streams guide](/realtime/backend/input-streams) for API route patterns.
+
+```ts
+import { cancelSignal, approval } from "./trigger/streams";
+
+await cancelSignal.send(runId, { reason: "User clicked stop" });
+await approval.send(runId, { approved: true, reviewer: "alice@example.com" });
+```
+
+### Complete example: Cancellable AI streaming
+
+Stream an AI response while allowing the user to cancel mid-generation.
+
+**Define the streams:**
+
+```ts
+import { streams } from "@trigger.dev/sdk";
+
+export const aiOutput = streams.define<string>({ id: "ai" });
+export const cancelStream = streams.input<{ reason?: string }>({ id: "cancel" });
+```
+
+**Task:** Register `cancelStream.on()` to abort an `AbortController`, then pipe `streamText(...).textStream` to `aiOutput`. **Backend:** POST to an API route that calls `cancelStream.send(runId, { reason: "User clicked stop" })`. **Frontend:** Use `useRealtimeStream(aiOutput, runId, { accessToken })` and a button that calls your cancel API (or use the `useInputStreamSend` hook; see [Realtime React hooks](/realtime/react-hooks/streams#useinputstreamsend)).
+
+**Important notes (input streams):** You cannot send to a completed, failed, or canceled run. Max payload per `.send()` is 1MB. Data sent before a listener is registered is buffered and delivered when a listener attaches; `.wait()` handles the buffering race automatically. Use `.wait()` for long waits to free compute; use `.once()` for short waits or concurrent work. Define input streams in a shared location and combine with output streams for full bidirectional communication.
+
+## Complete Example: AI Streaming
+
+### Define the stream
+
+```ts
+// app/streams.ts
+import { streams, InferStreamType } from "@trigger.dev/sdk";
+import { UIMessageChunk } from "ai";
+
+export const aiStream = streams.define<UIMessageChunk>({
+  id: "ai",
+});
+
+export type AIStreamPart = InferStreamType<typeof aiStream>;
+```
+
+### Create the task
+
+```ts
+// trigger/ai-task.ts
+import { task } from "@trigger.dev/sdk";
+import { openai } from "@ai-sdk/openai";
+import { streamText } from "ai";
+import { aiStream } from "@/app/streams";
+
+export const generateAI = task({
+  id: "generate-ai",
+  run: async (payload: { prompt: string }) => {
+    const result = streamText({
+      model: openai("gpt-4o"),
+      prompt: payload.prompt,
+    });
+
+    const { waitUntilComplete } = aiStream.pipe(result.toUIMessageStream());
+
+    await waitUntilComplete();
+
+    return { success: true };
+  },
+});
+```
+
+### Frontend component
+
+```tsx
+// components/ai-stream.tsx
+"use client";
+
+import { useRealtimeStream } from "@trigger.dev/react-hooks";
+import { aiStream } from "@/app/streams";
+
+export function AIStream({ accessToken, runId }: { accessToken: string; runId: string }) {
+  const { parts, error } = useRealtimeStream(aiStream, runId, {
+    accessToken,
+    timeoutInSeconds: 300,
+  });
+
+  if (error) return <div>Error: {error.message}</div>;
+  if (!parts) return <div>Loading...</div>;
+
+  return (
+    <div className="prose">
+      {parts.map((part, i) => (
+        <span key={i}>{part}</span>
+      ))}
+    </div>
+  );
+}
+```
+
+## Migration from v1
+
+If you're using the old `metadata.stream()` API, here's how to migrate to the recommended v2 approach:
+
+### Step 1: Define Your Streams
+
+Create a shared streams definition file:
+
+```ts
+// app/streams.ts or trigger/streams.ts
+import { streams, InferStreamType } from "@trigger.dev/sdk";
+
+export const myStream = streams.define<string>({
+  id: "my-stream",
+});
+
+export type MyStreamPart = InferStreamType<typeof myStream>;
+```
+
+### Step 2: Update Your Tasks
+
+Replace `metadata.stream()` with the defined stream's `pipe()` method:
+
+```ts
+// Before (v1)
+import { metadata, task } from "@trigger.dev/sdk";
+
+export const myTask = task({
+  id: "my-task",
+  run: async (payload) => {
+    const stream = getDataStream();
+    await metadata.stream("my-stream", stream);
+  },
+});
+```
+
+```ts
+// After (v2 - Recommended)
+import { task } from "@trigger.dev/sdk";
+import { myStream } from "./streams";
+
+export const myTask = task({
+  id: "my-task",
+  run: async (payload) => {
+    const stream = getDataStream();
+
+    // Don't await - returns immediately
+    const { waitUntilComplete } = myStream.pipe(stream);
+
+    // Optionally wait for completion
+    await waitUntilComplete();
+  },
+});
+```
+
+### Step 3: Update Your Frontend
+
+Use the defined stream with `useRealtimeStream`:
+
+```tsx
+// Before
+const { parts, error } = useRealtimeStream<string>(runId, "my-stream", {
+  accessToken,
+});
+```
+
+```tsx
+// After
+import { myStream } from "@/app/streams";
+
+const { parts, error } = useRealtimeStream(myStream, runId, {
+  accessToken,
+});
+```
+
+### Alternative: Direct Methods (Not Recommended)
+
+If you prefer not to use defined streams, you can use direct methods:
+
+```ts
+import { streams, task } from "@trigger.dev/sdk";
+
+export const myTask = task({
+  id: "my-task",
+  run: async (payload) => {
+    const stream = getDataStream();
+    const { waitUntilComplete } = streams.pipe("my-stream", stream);
+    await waitUntilComplete();
+  },
+});
+```
+
+## Reliability Features
+
+Streams v2 includes automatic reliability improvements:
+
+- **Automatic resumption**: If a connection is lost, both appending and reading will automatically resume from the last successful chunk
+- **No data loss**: Network issues won't cause stream data to be lost
+- **Idempotent operations**: Duplicate chunks are automatically handled
+
+These improvements happen automatically - no code changes needed.
+
+## Dashboard Integration
+
+Streams are now visible in the Trigger.dev dashboard, allowing you to:
+
+- View stream data in real-time as it's generated
+- Inspect historical stream data for completed runs
+- Debug streaming issues with full visibility into chunk delivery
+
+<video src="https://content.trigger.dev/streams-v2-dashboard.mp4" controls muted autoPlay loop />
+
+## Best Practices
+
+1. **Always use `streams.define()`**: Define your streams in a shared location for better organization, type safety, and code reusability. This is the recommended approach for all streams.
+2. **Export stream types**: Use `InferStreamType` to export types for your frontend components
+3. **Handle errors gracefully**: Always check for errors when reading streams in your UI
+4. **Set appropriate timeouts**: Adjust `timeoutInSeconds` based on your use case (AI completions may need longer timeouts)
+5. **Target parent runs**: When orchestrating with child tasks, pipe to parent runs for easier consumption
+6. **Throttle frontend updates**: Use `throttleInMs` in `useRealtimeStream` to prevent excessive re-renders
+7. **Use descriptive stream IDs**: Choose clear, descriptive IDs like `"ai-output"` or `"progress"` instead of generic names
+
+## Pre-4.1.0 streams (legacy)
+
+Prior to SDK 4.1.0, streams used the older metadata-based API. If you're on an earlier version, see [metadata.stream()](/runs/metadata#stream) for legacy usage. With 4.4.2+, [Input Streams](#input-streams) are available and documented in this page.
+
+## Troubleshooting
+
+### Stream not appearing in dashboard
+
+- Verify your task is actually writing to the stream
+- Check that the stream key matches between writing and reading
+
+### Stream timeout errors
+
+- Increase `timeoutInSeconds` in your `read()` or `useRealtimeStream()` calls
+- Ensure your stream source is actively producing data
+- Check network connectivity between your application and Trigger.dev
+
+### Missing chunks
+
+- With the current streams implementation, chunks should not be lost due to automatic resumption
+- Verify you're reading from the correct stream key
+- Check the `startIndex` option if you're not seeing expected chunks
+
+### Input streams not working
+
+- Input streams require SDK **4.4.2 or later** and the default streams (v2) infrastructure. Ensure you're on a recent SDK and not using the legacy metadata.stream() API.
+- If `.on()` or `.once()` throw, follow the error message to enable v2 streams (they are default in 4.1.0+).
+
+### "Stream is being deleted" during long waits
+
+If a stream is created but stays empty for ~1 hour (for example, during a long `wait.forToken()` or `wait.for()`), the streams backend may garbage-collect it. When the run resumes and tries to use the stream, you'll see `S2Error: Stream is being deleted` and the task retries from the beginning.
+
+Two ways to avoid this:
+
+- Close the stream before the wait and open a new one when the run resumes.
+- Write a heartbeat record to the stream every 20–30 minutes during the wait so it's never empty long enough to be deleted.