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# Agent.stream()

The `.stream()` method enables real-time streaming of responses from an agent with enhanced capabilities and format flexibility. This method accepts messages and optional streaming options, providing a current streaming experience with support for both Mastra's native format and AI SDK v5+ compatibility.

## Usage example

```ts
const stream = await agent.stream('message for agent')
```

> **Note:** **Model Compatibility**: This method is designed for V2 models. V1 models should use the [`.streamLegacy()`](https://mastra.ai/reference/streaming/agents/streamLegacy) method. The framework automatically detects your model version and will throw an error if there's a mismatch.

## Parameters

**messages** (`string | string[] | CoreMessage[] | AiMessageType[] | UIMessageWithMetadata[]`): The messages to send to the agent. Can be a single string, array of strings, or structured message objects.

**options** (`AgentExecutionOptions<Output, Format>`): Optional configuration for the streaming process.

**options.hideSignals** (`boolean | AgentSignalType[]`): Use true to hide all recognized signals, false to show all, or an array to hide selected types from this caller's fullStream after experimental transforms. Does not filter model context, storage, aggregates, or other subscribers. See Signal visibility below.

**options.maxSteps** (`number`): Maximum number of steps to run during execution.

**options.scorers** (`MastraScorers | Record<string, { scorer: MastraScorer['name']; sampling?: ScoringSamplingConfig }>`): Evaluation scorers to run on the execution results.

**options.scorers.scorer** (`string`): Name of the scorer to use.

**options.scorers.sampling** (`ScoringSamplingConfig`): Sampling configuration for the scorer.

**options.scorers.sampling.type** (`'none' | 'ratio'`): Type of sampling strategy. Use 'none' to disable sampling or 'ratio' for percentage-based sampling.

**options.scorers.sampling.rate** (`number`): Sampling rate (0-1). Required when type is 'ratio'.

**options.onIterationComplete** (`(context: IterationCompleteContext) => { continue?: boolean; feedback?: string } | void | Promise<{ continue?: boolean; feedback?: string } | void>`): Callback function called after each iteration completes. Use this to monitor progress, provide feedback to guide the agent, or stop execution early. The callback receives context about the iteration including the current text, tool calls, and finish reason.

**options.onIterationComplete.context.iteration** (`number`): Current iteration number (1-based).

**options.onIterationComplete.context.maxIterations** (`number | undefined`): Maximum iterations allowed (if set).

**options.onIterationComplete.context.text** (`string`): The text response from this iteration.

**options.onIterationComplete.context.isFinal** (`boolean`): Whether this is the final iteration.

**options.onIterationComplete.context.finishReason** (`string`): Reason why this iteration finished (e.g., 'stop', 'length', 'tool-calls').

**options.onIterationComplete.context.toolCalls** (`ToolCall[]`): Tool calls made in this iteration.

**options.onIterationComplete.context.messages** (`MastraDBMessage[]`): All messages accumulated so far.

**options.onIterationComplete.return.continue** (`boolean`): Set to false to stop execution early.

**options.onIterationComplete.return.feedback** (`string`): Feedback message to guide the agent's next iteration.

**options.isTaskComplete** (`IsTaskCompleteConfig`): Task completion scoring configuration that validates whether the task is complete. Uses Mastra's evaluation scorers to automatically check if the agent's response satisfies the completion criteria.

**options.isTaskComplete.scorers** (`MastraScorer[]`): Array of scorers that evaluate task completion. Each scorer returns 0 (failed) or 1 (passed).

**options.isTaskComplete.strategy** (`'all' | 'any'`): Strategy for combining scorer results. 'all' requires all scorers to pass, 'any' requires at least one.

**options.isTaskComplete.onComplete** (`(result: IsTaskCompleteRunResult) => void | Promise<void>`): Callback called when the task completion check finishes. Receives the result with individual scorer scores.

**options.isTaskComplete.parallel** (`boolean`): Whether to run scorers in parallel.

**options.isTaskComplete.timeout** (`number`): Maximum time in milliseconds to wait for all scorers to complete.

**options.isTaskComplete.suppressFeedback** (`boolean`): When true, marks the completion check feedback so consumers can hide it from displayed output. Feedback is only added to the conversation when the check fails, to guide the next iteration.

**options.delegation** (`DelegationConfig`): Configuration for subagent delegation. Use this to control and monitor when the agent delegates tasks to other agents, including the ability to modify, reject delegations, and provide feedback to guide the supervisor.

**options.delegation.onDelegationStart** (`(context: DelegationStartContext) => DelegationStartResult | void | Promise<DelegationStartResult | void>`): Called before delegating to a subagent. Use this to modify the delegation parameters, reject the delegation entirely, or mutate context.requestContext to add entries to the subagent run's request context.

**options.delegation.onDelegationComplete** (`(context: DelegationCompleteContext) => { feedback?: string } | void | Promise<{ feedback?: string } | void>`): Called after a subagent delegation completes. The context includes a bail() method to stop further execution, and you can return { feedback } to guide the supervisor's next action. Feedback is saved to supervisor memory as an assistant message.

**options.delegation.messageFilter** (`(context: MessageFilterContext) => ModelMessage[] | Promise<ModelMessage[]>`): Callback function called before delegating to a subagent. Use this to filter the messages that are passed to the subagent.

**options.tracingContext** (`TracingContext`): Tracing context for span hierarchy and metadata.

**options.returnScorerData** (`boolean`): Whether to return detailed scoring data in the response.

**options.onChunk** (`(chunk: ChunkType) => Promise<void> | void`): Callback function called for each chunk during streaming.

**options.onError** (`({ error }: { error: Error | string }) => Promise<void> | void`): Callback function called when an error occurs during streaming.

**options.onAbort** (`(event: { steps: any[]; text?: string }) => Promise<void> | void`): Callback function called when the stream is aborted. steps contains the steps that completed before the abort, and text contains the assistant text streamed so far for the step that was in flight.

**options.abortSignal** (`AbortSignal`): Signal object that allows you to abort the agent's execution, including in-flight subagent runs. Canceled runs continue through output processors. When writable memory is configured, built-in memory processors persist submitted messages, completed tool results, and assistant output available when terminal processing runs. Output that doesn't reach terminal processing isn't persisted. Custom output processors can transform or interrupt this behavior before memory processors run.

**options.activeTools** (`Array<keyof ToolSet> | undefined`): Array of active tool names that can be used during execution.

**options.prepareStep** (`PrepareStepFunction<any>`): Callback function called before each step of multi-step execution.

**options.context** (`ModelMessage[]`): Additional context messages to provide to the agent.

**options.structuredOutput** (`StructuredOutputOptions<S extends ZodTypeAny = ZodTypeAny>`): Options to fine tune your structured output generation.

**options.structuredOutput.schema** (`StandardJSONSchemaV1`): Standard JSON Schema defining the expected output structure.

**options.structuredOutput.model** (`MastraLanguageModel`): Language model to use for structured output generation. If provided, enables the agent to respond in multi step with tool calls, text, and structured output

**options.structuredOutput.errorStrategy** (`'strict' | 'warn' | 'fallback'`): Strategy for handling schema validation errors. 'strict' throws errors, 'warn' logs warnings, 'fallback' uses fallback values.

**options.structuredOutput.fallbackValue** (`<S extends ZodTypeAny>`): Fallback value to use when schema validation fails and errorStrategy is 'fallback'.

**options.structuredOutput.instructions** (`string`): Additional instructions for the structured output model. With jsonPromptInjection and no structuring model, this text is injected into the prompt in place of the serialized schema.

**options.structuredOutput.jsonPromptInjection** (`boolean | 'system' | 'inline' | 'auto'`): Controls how the JSON schema reaches the model. Set to 'auto' to use native structured output when supported and inline prompt injection otherwise.

**options.structuredOutput.providerOptions** (`ProviderOptions`): Provider-specific options passed to the internal structuring agent. Use this to control model behavior like reasoning effort for thinking models (e.g., { openai: { reasoningEffort: 'low' } }).

**options.outputProcessors** (`Processor[]`): Overrides the output processors set on the agent. Output processors that can modify or validate messages from the agent before they are returned to the user. Must implement either (or both) of the processOutputResult and processOutputStream functions.

**options.includeRawChunks** (`boolean`): Whether to include raw chunks in the stream output (not available on all model providers).

**options.inputProcessors** (`Processor[]`): Overrides the input processors set on the agent. Input processors that can modify or validate messages before they are processed by the agent. Must implement the processInput function.

**options.instructions** (`string`): Custom instructions that override the agent's default instructions for this specific generation. Useful for dynamically modifying agent behavior without creating a new agent instance.

**options.system** (`string | string[] | CoreSystemMessage | SystemModelMessage | CoreSystemMessage[] | SystemModelMessage[]`): Custom system message(s) to include in the prompt. Can be a single string, message object, or array of either. System messages provide additional context or behavior instructions that supplement the agent's main instructions.

**options.output** (`Zod schema | JsonSchema7`): \*\*Deprecated.\*\* Use structuredOutput without a model to achieve the same thing. Defines the expected structure of the output. Can be a JSON Schema object or a Zod schema.

**options.memory** (`object`): Configuration for memory. This is the preferred way to manage memory.

**options.memory.thread** (`string | { id: string; metadata?: Record<string, any>, title?: string }`): The conversation thread, as a string ID or an object with an id and optional metadata.

**options.memory.resource** (`string`): Identifier for the user or resource associated with the thread.

**options.memory.options** (`MemoryConfig`): Configuration for memory behavior including lastMessages, readOnly, semanticRecall, workingMemory, and filterIncompleteToolCalls.

**options.memory.onTitleGenerated** (`(title: string) => void | Promise<void>`): Callback fired asynchronously when a thread title is generated and persisted to storage. Title generation runs in the background and may complete after the stream ends, unless generateTitle.emitEvent is enabled — then the title is also emitted as a data-thread-title chunk on the stream before finish. Only fires when generateTitle is enabled in memory options and the thread has no existing title.

**options.onFinish** (`StreamTextOnFinishCallback<any> | StreamObjectOnFinishCallback<OUTPUT>`): Callback function called when streaming completes. Receives the final result.

**options.onStepFinish** (`StreamTextOnStepFinishCallback<any> | never`): Callback function called after each execution step. Receives step details as a JSON string. Unavailable for structured output

**options.telemetry** (`TelemetrySettings`): Settings for OTLP telemetry collection during streaming (not Tracing).

**options.telemetry.isEnabled** (`boolean`): Enable or disable telemetry. Disabled by default while experimental.

**options.telemetry.recordInputs** (`boolean`): Enable or disable input recording. Enabled by default. You might want to disable input recording to avoid recording sensitive information.

**options.telemetry.recordOutputs** (`boolean`): Enable or disable output recording. Enabled by default. You might want to disable output recording to avoid recording sensitive information.

**options.telemetry.functionId** (`string`): Identifier for this function. Used to group telemetry data by function.

**options.modelSettings** (`CallSettings`): Model-specific settings like temperature, maxOutputTokens, topP, etc. These settings control how the language model generates responses.

**options.modelSettings.temperature** (`number`): Controls randomness in generation (0-2). Higher values make output more random.

**options.modelSettings.maxOutputTokens** (`number`): Maximum number of tokens to generate in the response. Note: Use maxOutputTokens (not maxTokens) as per AI SDK v5 convention.

**options.modelSettings.maxRetries** (`number`): Maximum number of retry attempts for failed requests.

**options.modelSettings.topP** (`number`): Nucleus sampling parameter (0-1). Controls diversity of generated text.

**options.modelSettings.topK** (`number`): Top-k sampling parameter. Limits vocabulary to k most likely tokens.

**options.modelSettings.presencePenalty** (`number`): Penalty for token presence (-2 to 2). Reduces repetition.

**options.modelSettings.frequencyPenalty** (`number`): Penalty for token frequency (-2 to 2). Reduces repetition of frequent tokens.

**options.modelSettings.timeout** (`object`): Time-based execution budget for the run. It must be an object whose configured values are positive, finite numbers of milliseconds. Accepts totalMs, the maximum duration of the entire agent run across every loop iteration, tool call and retry, and stepMs, the maximum duration of a single model call including the time spent consuming its stream. Exceeding either budget fails with a MastraTimeoutError. A totalMs timeout ends the run and does not try fallback models, because it is a hard deadline for the whole run. A stepMs timeout is not retried against the same model but does advance to the next entry in models when fallback models are configured. Also accepts firstChunkMs, which only applies to streaming calls and is the maximum time the model may take to emit its first content-bearing chunk (text, reasoning, tool call, file or source; stream-start and metadata chunks do not count). A firstChunkMs timeout fails with timeoutType: 'firstChunk', behaves like stepMs for fallback, and is reset for each provider retry attempt. Nested timeout keys are merged across call-time and per-model settings.

**options.modelSettings.stopSequences** (`string[]`): Stop sequences. If set, the model will stop generating text when one of the stop sequences is generated.

**options.toolChoice** (`'auto' | 'none' | 'required' | { type: 'tool'; toolName: string }`): Controls how the agent uses tools during streaming.

**options.toolChoice.'auto'** (`string`): Let the model decide whether to use tools (default).

**options.toolChoice.'none'** (`string`): Do not use any tools.

**options.toolChoice.'required'** (`string`): Require the model to use at least one tool.

**options.toolChoice.{ type: 'tool'; toolName: string }** (`object`): Require the model to use a specific tool by name.

**options.toolsets** (`ToolsetsInput`): Additional toolsets to make available to the agent during streaming.

**options.clientTools** (`ToolsInput`): Tools that are executed on the 'client' side of the request. These tools do not have execute functions in the definition.

**options.hooks** (`ToolHooks`): Per-execution hooks that run before and after tool calls. Overrides matching agent-level hooks for this execution. beforeToolCall can return { proceed: false, output } to skip the tool call.

**options.savePerStep** (`boolean`): Save messages incrementally after each stream step completes (default: false).

**options.requireToolApproval** (`boolean`): When true, all tool calls require explicit approval before execution. The stream will emit tool-call-approval chunks and pause until approveToolCall() or declineToolCall() is called.

**options.autoResumeSuspendedTools** (`boolean`): When true, automatically resumes suspended tools when the user sends a new message on the same thread. The agent extracts resumeData from the user's message based on the tool's resumeSchema. Requires memory to be configured.

**options.toolCallConcurrency** (`number`): Maximum number of tool calls to execute concurrently. Defaults to 1 when approval may be required, otherwise 10.

**options.eagerToolExecution** (`boolean`): Defaults to true. A tool call whose arguments have finished streaming starts executing immediately, instead of waiting for the model to finish the whole step. Set it to false to restore the previous scheduling. Tool results and message history keep their model-call order either way. Only server-side tools are started early: approval-gated, suspend-schema-declaring, provider-executed, client-side, background-dispatched (by argument or by backgroundTasks config), missing and inactive tools are left to the post-stream pass, as is any run using autoResumeSuspendedTools or an output processor that runs after the stream completes. That last case is wider than it sounds: a processor workflow you build yourself counts whatever it contains, because its contents are not inspected, and so does structuredOutput when given an explicit model. A processor implementing processToolResult is excluded too when a provider-executed tool is configured: that hook runs inside the stream when a provider result arrives there, and its abort bails the turn before the post-stream pass, so starting a tool early would change whether it runs, not just when. Without a provider tool, the hook only sees results after the pass and early dispatch stays on. Also left to the post-stream pass is any run using toolCallConcurrency.strategy: 'called' (the full set of called tools is not known until the step finishes). Eager executions honour the same toolCallConcurrency limit (they are counted separately from the post-stream pass, so the limit constrains each rather than being pooled across both) and aborting the run waits for work already running, then keeps its result, as the post-stream pass does. A tool that already ran is never run again, and its finished result is never thrown away. If the last model fails mid-stream with no retry, its calls still run through the post-stream pass, which adopts eager work instead of restarting it. If the attempt is retried or failed over, tools still running are allowed to finish, and every started call's result or error is written into the conversation before the replacement attempt begins, even if the retry never runs. A tool that calls suspend() at runtime without declaring a suspend schema cannot be detected in advance; the early attempt hands its suspension back to that call's own post-stream iteration, which raises the real suspension with the tool's own payload, so the tool body runs exactly once — the same as it would without early execution. Because that pre-suspend work already happened, an attempt holding such a call is never retried or failed over: the run suspends on that call instead, so resuming it is the only way the tool runs again. Not supported by durable agents, which reject an explicit true.

**options.providerOptions** (`Record<string, Record<string, JSONValue>>`): Additional provider-specific options that are passed through to the underlying LLM provider. The structure is { providerName: { optionKey: value } }. For example: { openai: { reasoningEffort: 'high' }, anthropic: { maxTokens: 1000 } }.

**options.providerOptions.openai** (`Record<string, JSONValue>`): OpenAI-specific options. Example: { reasoningEffort: 'high' }

**options.providerOptions.anthropic** (`Record<string, JSONValue>`): Anthropic-specific options. Example: { maxTokens: 1000 }

**options.providerOptions.google** (`Record<string, JSONValue>`): Google-specific options. Example: { safetySettings: \[...] }

**options.providerOptions.\[providerName]** (`Record<string, JSONValue>`): Other provider-specific options. The key is the provider name and the value is a record of provider-specific options.

**options.runId** (`string`): Unique ID for this generation run. Useful for tracking and debugging purposes.

**options.requestContext** (`RequestContext`): Request Context for dependency injection and contextual information.

**options.tracingContext** (`TracingContext`): Tracing context for creating child spans and adding metadata. Automatically injected when using Mastra's tracing system.

**options.tracingContext.currentSpan** (`Span`): Current span for creating child spans and adding metadata. Use this to create custom child spans or update span attributes during execution.

**options.tracingOptions** (`TracingOptions`): Options for Tracing configuration.

**options.tracingOptions.rootSpanName** (`string`): Display name for the root span of this trace, replacing the default workflow run: '\<id>' or agent run: '\<id>' name. Use it to tell runs apart in trace lists.

**options.tracingOptions.metadata** (`Record<string, any>`): Metadata to add to the root trace span. Useful for adding custom attributes like user IDs, session IDs, or feature flags.

**options.tracingOptions.requestContextKeys** (`string[]`): Additional RequestContext keys to extract as metadata for this trace. Supports dot notation for nested values (e.g., 'user.id').

**options.tracingOptions.traceId** (`string`): Trace ID to use for this execution (1-32 hexadecimal characters). If provided, this trace will be part of the specified trace.

**options.tracingOptions.parentSpanId** (`string`): Parent span ID to use for this execution (1-16 hexadecimal characters). If provided, the root span will be created as a child of this span.

**options.tracingOptions.nestUnderParent** (`boolean`): Set to true together with parentSpanId to attach this run as a child of that span inside the existing trace, for example an LLM judge under the run it scores. Exporters then keep the trace's own name, input, output, and tags. Leave unset when parentSpanId comes from your own tracing system. Ignored without a valid traceId and parentSpanId.

**options.tracingOptions.tags** (`string[]`): Tags to apply to this trace. String labels for categorizing and filtering traces.

**options.versions** (`VersionOverrides`): Per-invocation version overrides for sub-agent delegation. Merged on top of Mastra instance-level versions and propagated automatically through sub-agent calls via requestContext. Requires the editor package. See Editor versioning.

**options.versions.agents** (`Record<string, VersionSelector>`): A map of agent IDs to their version selectors.

**options.versions.agents.versionId** (`string`): Target a specific version by ID.

**options.versions.agents.status** (`'draft' | 'published'`): Target the latest version with this publication status.

**options.untilIdle** (`boolean | { maxIdleMs?: number }`): When set, keeps the stream open across background-task continuations. The agent will automatically re-invoke the LLM when background tasks complete, streaming continuation turns through the same fullStream. Pass true for default settings (5 min idle timeout), or an object with maxIdleMs to configure. Requires memory. Replaces the standalone streamUntilIdle() method.

**options.untilIdle.maxIdleMs** (`number`): Closes the outer stream after this many ms of idleness between turns. The timer only runs while the wrapper is between turns. Default: 5 minutes.

## Returns

**stream** (`MastraModelOutput<Output>`): Returns a MastraModelOutput instance that provides access to the streaming output.

**traceId** (`string`): The trace ID associated with this execution when Tracing is enabled. Use this to correlate logs and debug execution flow.

**spanId** (`string`): The root span ID associated with this execution when Tracing is enabled. Use this for span-level lookup and correlation.

## Signal visibility

Signals, including reactive reminders, appear in `fullStream` by default. Set `hideSignals` to omit selected signal chunks from your stream:

```ts
const stream = await agent.stream('Review the latest changes', {
  hideSignals: ['reactive', 'system-reminder'],
})

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

Set `hideSignals: true` to hide all recognized signal types, or `hideSignals: false` to show all signals. An array selects individual types.

The array accepts `user`, `state`, `reactive`, `notification`, and the legacy aliases `user-message` and `system-reminder`. An omitted option, `false`, or `[]` excludes nothing. Streaming normalizes `system-reminder` to `reactive` and `user-message` to `user`, then matches the encoded signal type in `data-signal` or `data-user-message` chunks. Unknown or malformed signal chunks pass through, as do text, errors, and completion events.

Exclusions apply after `experimentalTransform`, so transforms still receive the unfiltered input. Adapters consuming `fullStream` inherit the filter, but aggregate `content`, `getFullOutput()`, response messages, callbacks, model context, and saved messages remain unchanged.

Each [thread subscription](https://mastra.ai/reference/agents/agent) has its own exclusion policy, independent of the initiating stream.

The same policy applies to `resumeStream()`, `untilIdle` continuations, and the deprecated `streamUntilIdle()` and `resumeStreamUntilIdle()` methods, including durable agents. Shared execution options also accept `hideSignals` on `generate()` and `resumeGenerate()`, but it doesn't filter their returned results. This option isn't available on HTTP or client-js request options or legacy `streamLegacy()` APIs.

Unlike streams, [memory recall](https://mastra.ai/reference/memory/recall) hides reminders by default for compatibility. Recall exclusions match stored types exactly rather than normalizing aliases. Use `['reactive', 'system-reminder']` to exclude both reminder representations across surfaces.

Exclusions control returned data, not authorization or delivery. They aren't a security boundary. Signal options such as `ifActive`, `ifIdle`, `persist`/`discard`, and `transient` retain their delivery and persistence meanings.

## Extended usage example

### Mastra Format (Default)

```ts
import { stepCountIs } from 'ai-v5'

const stream = await agent.stream('Tell me a story', {
  stopWhen: stepCountIs(3), // Stop after 3 steps
  modelSettings: {
    temperature: 0.7,
  },
})

// Access text stream
for await (const chunk of stream.textStream) {
  console.log(chunk)
}

// or access full stream
for await (const chunk of stream.fullStream) {
  console.log(chunk)
}

// Get full text after streaming
const fullText = await stream.text
```

### Limiting execution time

Use `modelSettings.timeout` to bound how long a run may take. `totalMs` limits the entire run, including every loop iteration, tool call and retry. `stepMs` limits a single model call, covering both establishing the stream and consuming it. `firstChunkMs` only applies to streaming calls and limits how long the model may take to emit its first content-bearing chunk.

```ts
const stream = await agent.stream('Tell me a story', {
  modelSettings: {
    timeout: {
      totalMs: 30000, // fail the run if it takes longer than 30s
      stepMs: 10000, // fail an individual model call after 10s
      firstChunkMs: 3000, // fail a model call that produces no content within 3s
    },
  },
})
```

Exceeding a budget fails with a `MastraTimeoutError`, which carries a `timeoutType` of `'total'`, `'step'` or `'firstChunk'`. Each budget behaves differently when the agent is configured with fallback [`models`](https://mastra.ai/reference/agents/agent):

- A `totalMs` timeout ends the run immediately and doesn't try the next model, because it's a hard deadline for the run as a whole.
- A `stepMs` timeout isn't retried against the same model, but does advance to the next model, which makes it a way to fail over from a slow provider.
- A `firstChunkMs` timeout behaves like `stepMs`: it isn't retried against the same model, but does advance to the next model. Stream-start and metadata chunks don't satisfy the budget; only the first text, reasoning, tool call, file or source chunk does. Once content begins, only `stepMs` and `totalMs` remain active. Each provider retry attempt gets a fresh `firstChunkMs` budget.

Nested `timeout` settings are merged per key across call-time and per-model `modelSettings`, so a per-model `stepMs` override keeps a call-time `firstChunkMs`.

### AI SDK v5+ Format

To use the stream with AI SDK v5 (and later), you can convert it using our utility function `toAISdkStream`.

```ts
import { stepCountIs, createUIMessageStreamResponse } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'

const stream = await agent.stream('Tell me a story', {
  stopWhen: stepCountIs(3), // Stop after 3 steps
  modelSettings: {
    temperature: 0.7,
  },
})

// In an API route for frontend integration
return createUIMessageStreamResponse({
  stream: toAISdkStream(stream, { from: 'agent' }),
})
```

### Using Callbacks

All callback functions are now available as top-level properties for a cleaner API experience.

```ts
const stream = await agent.stream('Tell me a story', {
  onFinish: result => {
    console.log('Streaming finished:', result)
  },
  onStepFinish: step => {
    console.log('Step completed:', step)
  },
  onChunk: chunk => {
    console.log('Received chunk:', chunk)
  },
  onError: ({ error }) => {
    console.error('Streaming error:', error)
  },
  onAbort: ({ steps, text }) => {
    console.log('Stream aborted after', steps.length, 'steps')
    console.log('Partial text:', text)
  },
})

// Process the stream
for await (const chunk of stream.textStream) {
  console.log(chunk)
}
```

### Advanced example with Options

```ts
import { z } from 'zod'
import { stepCountIs } from 'ai'

await agent.stream('message for agent', {
  stopWhen: stepCountIs(3), // Stop after 3 steps
  modelSettings: {
    temperature: 0.7,
  },
  memory: {
    thread: 'user-123',
    resource: 'test-app',
  },
  toolChoice: 'auto',
  // Structured output with better DX
  structuredOutput: {
    schema: z.object({
      sentiment: z.enum(['positive', 'negative', 'neutral']),
      confidence: z.number(),
    }),
    model: 'openai/gpt-5.6-sol',
    errorStrategy: 'warn',
  },
  // Output processors for streaming response validation
  outputProcessors: [
    new ModerationProcessor({ model: 'openrouter/openai/gpt-oss-safeguard-20b' }),
    new BatchPartsProcessor({ maxBatchSize: 3, maxWaitTime: 100 }),
  ],
})
```

## Responses WebSocket transport

Opt into Responses WebSocket streaming with provider options. This only applies to streaming calls and is supported for direct OpenAI models and Azure OpenAI Responses deployments. If WebSocket streaming is unavailable, Mastra falls back to HTTP streaming. By default, Mastra closes the WebSocket when the stream finishes.

```ts
const stream = await agent.stream('Hello', {
  providerOptions: {
    openai: {
      transport: 'websocket', // 'websocket' | 'fetch' | 'auto'
      websocket: {
        url: 'wss://api.openai.com/v1/responses',
        closeOnFinish: true, // default
      },
    },
  },
})
```

For Azure OpenAI, configure the gateway with `useResponsesAPI: true`, then use `providerOptions.azure.transport`.

```ts
const stream = await agent.stream('Hello', {
  providerOptions: {
    azure: {
      transport: 'websocket',
      store: false,
      websocket: { closeOnFinish: true },
    },
  },
})
```

To keep the connection open after the stream finishes, set `closeOnFinish: false` and close it manually.

```ts
const stream = await agent.stream('Hello', {
  providerOptions: {
    openai: {
      transport: 'websocket',
      websocket: { closeOnFinish: false },
    },
  },
})

// Later, when you're done with the connection:
stream.transport?.close()
```

Responses WebSocket connections run one response at a time. Mastra rejects overlapping continuation requests that include `previous_response_id` on the same WebSocket transport. Wait for the active stream to finish before sending the next turn in the response chain.

## Related

- [Generating responses](https://mastra.ai/docs/agents/overview)
- [Streaming responses](https://mastra.ai/docs/agents/overview)
- [Agent Approval](https://mastra.ai/docs/agents/human-in-the-loop)