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

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

note

Model Compatibility: This method is designed for V2 models. V1 models should use the .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.

AgentExecutionOptions<Output, Format>

maxSteps?:

number

Maximum number of steps to run during execution.

scorers?:

MastraScorers | Record<string, { scorer: MastraScorer['name']; sampling?: ScoringSamplingConfig }>

Evaluation scorers to run on the execution results.

scorer:

string

Name of the scorer to use.

sampling?:

ScoringSamplingConfig

Sampling configuration for the scorer.

type:

'none' | 'ratio'

Type of sampling strategy. Use 'none' to disable sampling or 'ratio' for percentage-based sampling.

rate?:

number

Sampling rate (0-1). Required when type is 'ratio'.

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.

context.iteration:

number

Current iteration number (1-based).

context.maxIterations:

number | undefined

Maximum iterations allowed (if set).

context.text:

string

The text response from this iteration.

context.isFinal:

boolean

Whether this is the final iteration.

context.finishReason:

string

Reason why this iteration finished (e.g., 'stop', 'length', 'tool-calls').

context.toolCalls:

ToolCall[]

Tool calls made in this iteration.

context.messages:

MastraDBMessage[]

All messages accumulated so far.

return.continue?:

boolean

Set to false to stop execution early.

return.feedback?:

string

Feedback message to guide the agent's next iteration.

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.

scorers:

MastraScorer[]

Array of scorers that evaluate task completion. Each scorer returns 0 (failed) or 1 (passed).

strategy?:

'all' | 'any'

Strategy for combining scorer results. 'all' requires all scorers to pass, 'any' requires at least one.

onComplete?:

(result: IsTaskCompleteRunResult) => void | Promise<void>

Callback called when the task completion check finishes. Receives the result with individual scorer scores.

parallel?:

boolean

Whether to run scorers in parallel.

timeout?:

number

Maximum time in milliseconds to wait for all scorers to complete.

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.

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.

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.

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.

messageFilter?:

(context: MessageFilterContext) => MastraDBMessage[] | Promise<MastraDBMessage[]>

Callback function called before delegating to a subagent. Use this to filter the messages that are passed to the subagent.

tracingContext?:

TracingContext

Tracing context for span hierarchy and metadata.

returnScorerData?:

boolean

Whether to return detailed scoring data in the response.

onChunk?:

(chunk: ChunkType) => Promise<void> | void

Callback function called for each chunk during streaming.

onError?:

({ error }: { error: Error | string }) => Promise<void> | void

Callback function called when an error occurs during streaming.

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.

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.

activeTools?:

Array<keyof ToolSet> | undefined

Array of active tool names that can be used during execution.

prepareStep?:

PrepareStepFunction<any>

Callback function called before each step of multi-step execution.

context?:

ModelMessage[]

Additional context messages to provide to the agent.

structuredOutput?:

StructuredOutputOptions<S extends ZodTypeAny = ZodTypeAny>

Options to fine tune your structured output generation.

schema:

StandardJSONSchemaV1

Standard JSON Schema defining the expected output structure.

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

errorStrategy?:

'strict' | 'warn' | 'fallback'

Strategy for handling schema validation errors. 'strict' throws errors, 'warn' logs warnings, 'fallback' uses fallback values.

fallbackValue?:

<S extends ZodTypeAny>

Fallback value to use when schema validation fails and errorStrategy is 'fallback'.

instructions?:

string

Additional instructions for the structured output model.

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.

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' } }).

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.

includeRawChunks?:

boolean

Whether to include raw chunks in the stream output (not available on all model providers).

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.

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.

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.

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.

memory?:

object

Configuration for memory. This is the preferred way to manage 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.

resource:

string

Identifier for the user or resource associated with the thread.

options?:

MemoryConfig

Configuration for memory behavior including lastMessages, readOnly, semanticRecall, workingMemory, and filterIncompleteToolCalls.

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. Only fires when generateTitle is enabled in memory options and the thread has no existing title.

onFinish?:

StreamTextOnFinishCallback<any> | StreamObjectOnFinishCallback<OUTPUT>

Callback function called when streaming completes. Receives the final result.

onStepFinish?:

StreamTextOnStepFinishCallback<any> | never

Callback function called after each execution step. Receives step details as a JSON string. Unavailable for structured output

telemetry?:

TelemetrySettings

Settings for OTLP telemetry collection during streaming (not Tracing).

isEnabled?:

boolean

Enable or disable telemetry. Disabled by default while experimental.

recordInputs?:

boolean

Enable or disable input recording. Enabled by default. You might want to disable input recording to avoid recording sensitive information.

recordOutputs?:

boolean

Enable or disable output recording. Enabled by default. You might want to disable output recording to avoid recording sensitive information.

functionId?:

string

Identifier for this function. Used to group telemetry data by function.

modelSettings?:

CallSettings

Model-specific settings like temperature, maxOutputTokens, topP, etc. These settings control how the language model generates responses.

temperature?:

number

Controls randomness in generation (0-2). Higher values make output more random.

maxOutputTokens?:

number

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

maxRetries?:

number

Maximum number of retry attempts for failed requests.

topP?:

number

Nucleus sampling parameter (0-1). Controls diversity of generated text.

topK?:

number

Top-k sampling parameter. Limits vocabulary to k most likely tokens.

presencePenalty?:

number

Penalty for token presence (-2 to 2). Reduces repetition.

frequencyPenalty?:

number

Penalty for token frequency (-2 to 2). Reduces repetition of frequent tokens.

timeout?:

object

Time-based execution budget for the run. 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.

stopSequences?:

string[]

Stop sequences. If set, the model will stop generating text when one of the stop sequences is generated.

toolChoice?:

'auto' | 'none' | 'required' | { type: 'tool'; toolName: string }

Controls how the agent uses tools during streaming.

'auto':

string

Let the model decide whether to use tools (default).

'none':

string

Do not use any tools.

'required':

string

Require the model to use at least one tool.

{ type: 'tool'; toolName: string }:

object

Require the model to use a specific tool by name.

toolsets?:

ToolsetsInput

Additional toolsets to make available to the agent during streaming.

clientTools?:

ToolsInput

Tools that are executed on the 'client' side of the request. These tools do not have execute functions in the definition.

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.

savePerStep?:

boolean

Save messages incrementally after each stream step completes (default: false).

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.

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.

toolCallConcurrency?:

number

Maximum number of tool calls to execute concurrently. Defaults to 1 when approval may be required, otherwise 10.

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 } }.

openai?:

Record<string, JSONValue>

OpenAI-specific options. Example: { reasoningEffort: 'high' }

anthropic?:

Record<string, JSONValue>

Anthropic-specific options. Example: { maxTokens: 1000 }

google?:

Record<string, JSONValue>

Google-specific options. Example: { safetySettings: [...] }

[providerName]?:

Record<string, JSONValue>

Other provider-specific options. The key is the provider name and the value is a record of provider-specific options.

runId?:

string

Unique ID for this generation run. Useful for tracking and debugging purposes.

requestContext?:

RequestContext

Request Context for dependency injection and contextual information.

tracingContext?:

TracingContext

Tracing context for creating child spans and adding metadata. Automatically injected when using Mastra's tracing system.

currentSpan?:

Span

Current span for creating child spans and adding metadata. Use this to create custom child spans or update span attributes during execution.

tracingOptions?:

TracingOptions

Options for Tracing configuration.

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.

requestContextKeys?:

string[]

Additional RequestContext keys to extract as metadata for this trace. Supports dot notation for nested values (e.g., 'user.id').

traceId?:

string

Trace ID to use for this execution (1-32 hexadecimal characters). If provided, this trace will be part of the specified trace.

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.

tags?:

string[]

Tags to apply to this trace. String labels for categorizing and filtering traces.

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.

VersionOverrides

agents?:

Record<string, VersionSelector>

A map of agent IDs to their version selectors.

VersionSelector

versionId?:

string

Target a specific version by ID.

status?:

'draft' | 'published'

Target the latest version with this publication status.

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.

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.

Extended usage example

Mastra Format (Default)

index.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.

index.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
    },
  },
})

Exceeding either budget fails with a MastraTimeoutError, which carries a timeoutType of 'total' or 'step'. Each budget behaves differently when the agent is configured with fallback models:

• 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.

AI SDK v5+ Format

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

index.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.

index.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

index.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.

index.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.

index.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.

index.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
• Streaming responses
• Agent Approval