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> Discover all available pages from the documentation index: https://mastra.ai/llms.txt

# Streaming

Mastra supports real-time, incremental responses from agents and workflows, allowing users to see output as it’s generated instead of waiting for completion. This is useful for chat, long-form content, multi-step workflows, or any scenario where immediate feedback matters.

## Getting started

[`Agent.stream()`](https://mastra.ai/reference/streaming/agents/stream) is the standard streaming API for agents. It returns a `MastraModelOutput` that exposes `textStream` for progressive text and promises such as `text`, `steps`, and `usage` that resolve when the stream finishes.

## Streaming with agents

Pass a single string for a basic prompt. When providing multiple pieces of context, use an array of strings. An array of message objects with `role` and `content` gives you precise control over roles and conversational flow.

### Using `Agent.stream()`

A `textStream` breaks the response into chunks as it's generated, allowing output to stream progressively instead of arriving all at once. Iterate over the `textStream` using a `for await` loop to inspect each stream chunk.

```typescript
const testAgent = mastra.getAgent('testAgent')

const stream = await testAgent.stream([{ role: 'user', content: 'Help me organize my day' }])

for await (const chunk of stream.textStream) {
  process.stdout.write(chunk)
}
```

Visit [Agent.stream()](https://mastra.ai/reference/streaming/agents/stream) for more information.

> **Tip:** For agents that dispatch [background tasks](https://mastra.ai/docs/harness/background-tasks), use `stream()` with the [`untilIdle` option](https://mastra.ai/reference/streaming/agents/stream), such as `untilIdle: true`, to keep the stream open until those tasks complete and the agent has had a chance to respond to their results. This option requires agent memory.

### Output from `Agent.stream()`

The output streams the generated response from the agent.

```text
Of course!
To help you organize your day effectively, I need a bit more information.
Here are some questions to consider:
...
```

### Agent stream properties

An agent stream provides access to these response properties:

- **`stream.textStream`**: A readable stream that emits text chunks.
- **`stream.text`**: A promise that resolves to the full text response.
- **`stream.steps`**: A promise that resolves to the completed model steps.
- **`stream.finishReason`**: A promise that resolves to the reason the agent stopped streaming.
- **`stream.usage`**: A promise that resolves to token usage information.
- **`stream.objectStream`** and **`stream.object`**: Partial and final structured output when `structuredOutput` is passed.

See the [`MastraModelOutput` reference](https://mastra.ai/reference/streaming/agents/MastraModelOutput) for the complete set of properties.

### AI SDK integration

Use `toAISdkStream()` and `toAISdkMessages()` to convert Mastra streams and stored messages to AI SDK-compatible formats. The converters default to AI SDK v5 for backward compatibility. Pass the version that matches your installed AI SDK, such as `version: 'v7'` for AI SDK v7.

```typescript
import { toAISdkStream } from '@mastra/ai-sdk'

const testAgent = mastra.getAgent('testAgent')
const stream = await testAgent.stream([{ role: 'user', content: 'Help me organize my day' }])

const aiSDKStream = toAISdkStream(stream, {
  from: 'agent',
  version: 'v7',
})
```

Convert stored messages for `useChat()`'s `initialMessages` with `toAISdkMessages()`:

```typescript
import { toAISdkMessages } from '@mastra/ai-sdk/ui'

const initialMessages = toAISdkMessages([{ role: 'user', content: 'Hello' }], { version: 'v7' })
```

For route handlers, see [AI SDK UI](https://mastra.ai/integrations/agentic-ui/ai-sdk-ui), which covers `handleChatStream()`, `handleWorkflowStream()`, and `handleNetworkStream()`. Pass the version that matches your installed AI SDK to these handlers too. See the [`toAISdkStream()`](https://mastra.ai/reference/ai-sdk/to-ai-sdk-stream) and [`toAISdkMessages()`](https://mastra.ai/reference/ai-sdk/to-ai-sdk-messages) references for all options.

## Streaming with workflows

Streaming from a workflow returns a sequence of structured events describing the run lifecycle, rather than incremental text chunks. This event-based format makes it possible to track and respond to workflow progress in real time once a run is created using `.createRun()`.

### Using `Run.stream()`

The `stream()` method returns a `WorkflowRunOutput`. Its `fullStream` property is a `ReadableStream` of workflow events.

```typescript
const run = await testWorkflow.createRun()

const stream = run.stream({
  inputData: {
    value: 'initial data',
  },
})

for await (const chunk of stream.fullStream) {
  console.log(chunk)
}
```

Visit [Run.stream()](https://mastra.ai/reference/streaming/workflows/stream) for more information.

### Output from `Run.stream()`

The event structure includes `runId` and `from` at the top level, making it easier to identify and track workflow runs without digging into the payload.

```typescript
{
  type: 'workflow-start',
  runId: '1eeaf01a-d2bf-4e3f-8d1b-027795ccd3df',
  from: 'WORKFLOW',
  payload: {
    workflowId: 'testWorkflow'
  }
}
```

### Workflow stream properties

A workflow stream provides access to these response properties:

- **`stream.status`**: The status of the workflow run.
- **`stream.result`**: The result of the workflow run.
- **`stream.usage`**: The total token usage of the workflow run.

Streaming from agents or workflows provides real-time visibility into either the LLM’s output or the status of a workflow run. Pass this feedback directly to the user, or use it in an application to display workflow status as it changes.

Events emitted from agents or workflows represent different stages of generation and execution, such as when a run starts, when text is produced, or when a tool is invoked.

## Event types

Agent and workflow streams emit different event types during execution.

### Agent events

Common agent events include:

- **`start`**: The agent run begins.
- **`text-start`**, **`text-delta`**, and **`text-end`**: The start and end events mark text generation boundaries. Delta events carry incremental text.
- **`reasoning-start`**, **`reasoning-delta`**, and **`reasoning-end`**: The start and end events mark reasoning generation boundaries. Delta events carry incremental reasoning.
- **`tool-call`** and **`tool-result`**: A tool is called and returns a result.
- **`step-start`** and **`step-finish`**: A model step begins and ends.
- **`finish`**: The agent run completes.

This list isn't exhaustive. See the [`ChunkType` reference](https://mastra.ai/reference/streaming/ChunkType) for all agent chunk types and payloads.

### Workflow events

Workflow events include:

- **`workflow-start`**: The workflow run begins.
- **`workflow-step-start`**: A workflow step begins.
- **`workflow-step-output`**: A step emits custom output.
- **`workflow-step-progress`**: A step reports progress.
- **`workflow-step-result`**: A step completes with a result.
- **`workflow-finish`**: The workflow run completes.
- **`workflow-paused`**, **`workflow-step-suspended`**, and **`workflow-canceled`**: The workflow run is interrupted.

See the [`Run.stream()` reference](https://mastra.ai/reference/streaming/workflows/stream) for workflow event details.

## Inspecting agent streams

Iterate over `stream.fullStream` with a `for await` loop to inspect all emitted event chunks.

```typescript
const testAgent = mastra.getAgent('testAgent')

const stream = await testAgent.stream([{ role: 'user', content: 'Help me organize my day' }])

for await (const chunk of stream.fullStream) {
  console.log(chunk)
}
```

Visit [Agent.stream()](https://mastra.ai/reference/streaming/agents/stream) for more information.

### Example agent output

Below is an example of events that may be emitted. Each event always includes a `type` and can include additional fields like `from` and `payload`.

```typescript
{
  type: 'start',
  from: 'AGENT',
  // ..
}
{
  type: 'step-start',
  from: 'AGENT',
  payload: {
    messageId: 'msg-cdUrkirvXw8A6oE4t5lzDuxi',
    // ...
  }
}
{
  type: 'tool-call',
  from: 'AGENT',
  payload: {
    toolCallId: 'call_jbhi3s1qvR6Aqt9axCfTBMsA',
    toolName: 'testTool'
    // ..
  }
}
```

## Writer API

The `writer` API is shared by tools and workflow steps. See the Tools and Workflows docs for feature-specific examples.

## Agent using tool

Agent streaming can be combined with tool calls, allowing tool outputs to be written directly into the agent’s streaming response. This surfaces tool activity as part of the interaction.

```typescript
import { Agent } from '@mastra/core/agent'
import { testTool } from '../tools/test-tool'

export const testAgent = new Agent({
  id: 'test-agent',
  name: 'Test Agent',
  instructions: 'You are a weather agent.',
  model: 'openai/gpt-5.6-sol',
  tools: { testTool },
})
```

### Using `context.writer`

The `context.writer` object is available in a tool's `execute()` function and can emit custom events, data, or values into the active stream. Tools use these events to provide intermediate results or status updates during execution.

> **Warning:** You must `await` the call to `writer.write()` or else you will lock the stream and get a `WritableStream is locked` error.

```typescript
import { createTool } from '@mastra/core/tools'

export const testTool = createTool({
  execute: async (inputData, context) => {
    const { value } = inputData

    await context?.writer?.write({
      type: 'custom-event',
      status: 'pending',
    })

    const response = await fetch()

    await context?.writer?.write({
      type: 'custom-event',
      status: 'success',
    })

    return {
      value: '',
    }
  },
})
```

You can also use `writer.custom()` to emit top-level stream chunks. This is useful when integrating with UI frameworks.

```typescript
import { createTool } from '@mastra/core/tools'

export const testTool = createTool({
  execute: async (inputData, context) => {
    const { value } = inputData

    await context?.writer?.custom({
      type: 'data-tool-progress',
      status: 'pending',
    })

    const response = await fetch()

    await context?.writer?.custom({
      type: 'data-tool-progress',
      status: 'success',
    })

    return {
      value: '',
    }
  },
})
```

### Transient data chunks

By default, `data-*` chunks emitted with `writer.custom()` are persisted to storage as part of the message history. For chunks that are only needed during live streaming, such as progress updates or verbose log output, set `transient: true` to skip storage persistence. Transient chunks are still streamed to the client in real time but aren't saved to the database.

```typescript
await context?.writer?.custom({
  type: 'data-build-log',
  data: { line: 'Compiling module 3 of 12...' },
  transient: true,
})
```

Use transient chunks when the data is large or high-frequency and only relevant during the live session. After a page refresh, transient chunks are no longer available. Only the tool's return value and any non-transient chunks are loaded from storage.

## Using the `writer` argument

The `writer` argument is passed to a workflow step's `execute` function and can emit custom events, data, or values into the active stream. Workflow steps use these events to provide intermediate results or status updates during execution.

> **Warning:** You must `await` the call to `writer.write(...)` or else you will lock the stream and get a `WritableStream is locked` error.

```typescript
import { createStep } from '@mastra/core/workflows'
import { z } from 'zod'

export const testStep = createStep({
  id: 'test-step',
  inputSchema: z.object({ url: z.url() }),
  outputSchema: z.object({ status: z.number() }),
  execute: async ({ inputData, writer }) => {
    const { url } = inputData

    await writer.write({
      type: 'custom-event',
      status: 'pending',
    })

    const response = await fetch(url)

    await writer.write({
      type: 'custom-event',
      status: 'success',
    })

    return {
      status: response.status,
    }
  },
})
```