Agent.generate()

The .generate() method enables non-streaming response generation from an agent with enhanced capabilities. It accepts messages and optional generation options.

Usage examples
Direct link to Usage examples

Basic usage
Direct link to Basic usage

Call the agent with a message to generate a response:

const result = await agent.generate('message for agent')

With model settings
Direct link to With model settings

Example of limiting output tokens and setting temperature:

const limitedResult = await agent.generate('Write a short poem about coding', {
  modelSettings: {
    maxOutputTokens: 50,
    temperature: 0.7,
  },
})

With memory
Direct link to With memory

Give your agent access to conversation history and persistence by configuring memory options. This allows the agent to remember previous interactions and maintain context across messages.

const memoryResult = await agent.generate('Remember my favorite color is blue', {
  memory: {
    thread: 'user-123-thread',
    resource: 'user-123',
  },
})

Accessing response headers
Direct link to Accessing response headers

Some model providers return useful information in response headers, such as remaining token counts or rate limit status. You can access these headers from the result object after generation completes.

const result = await agent.generate('Hello!')
const remainingRequests = result.response?.headers?.['anthropic-ratelimit-requests-remaining']
const remainingTokens = result.response?.headers?.['x-ratelimit-remaining-tokens']
console.log(`Remaining requests: ${remainingRequests}, Remaining tokens: ${remainingTokens}`)

Analyzing images
Direct link to Analyzing images

Agents can analyze and describe images by processing both the visual content and any text within them. To enable image analysis, pass an object with type: 'image' and the image URL in the content array. You can combine image content with text prompts to guide the agent's analysis.

const response = await agent.generate([
  {
    role: 'user',
    content: [
      {
        type: 'image',
        image: 'https://placebear.com/cache/395-205.jpg',
        mimeType: 'image/jpeg',
      },
      {
        type: 'text',
        text: 'Describe the image in detail, and extract all the text in the image.',
      },
    ],
  },
])

console.log(response.text)

Using `maxSteps`
Direct link to using-maxsteps

The maxSteps parameter controls the maximum number of sequential LLM calls an agent can make. Each step includes generating a response, executing any tool calls, and processing the result. Limiting steps helps prevent infinite loops, reduce latency, and control token usage for agents that use tools. The default is 5, but can be increased:

const response = await agent.generate('Help me organize my day', {
  maxSteps: 10,
})

console.log(response.text)

Using `onStepFinish`
Direct link to using-onstepfinish

You can monitor the progress of multi-step operations using the onStepFinish callback. This is useful for debugging or providing progress updates to users.

onStepFinish is only available when streaming or generating text without structured output.

const response = await agent.generate('Help me organize my day', {
  onStepFinish: ({ text, toolCalls, toolResults, finishReason, usage }) => {
    console.log({ text, toolCalls, toolResults, finishReason, usage })
  },
})

Parameters
Direct link to 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 generation process.

AgentExecutionOptions<Output, Format>

maxSteps?:

number

Maximum number of steps to run during execution.

stopWhen?:

LoopOptions['stopWhen']

Conditions for stopping execution (e.g., step count, token limit).

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.

IterationCompleteContext

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.

IsTaskCompleteConfig

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.

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.

DelegationConfig

onDelegationStart?:

(context: DelegationStartContext) => DelegationStartResult | void | Promise<DelegationStartResult | void>

Called before delegating to a subagent. Use this to modify the delegation parameters or reject the delegation entirely.

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.

scorers?:

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

Evaluation scorers to run on the execution results.

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

scorer:

string

Name of the scorer to use.

sampling?:

ScoringSamplingConfig

Sampling configuration for the scorer.

ScoringSamplingConfig

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'.

returnScorerData?:

boolean

Whether to return detailed scoring data in the response.

onChunk?:

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

Callback function called for each chunk during generation.

onError?:

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

Callback function called when an error occurs during generation.

onAbort?:

(event: any) => Promise<void> | void

Callback function called when the generation is aborted.

activeTools?:

Array<keyof ToolSet> | undefined

Array of tool names that should be active during execution. If undefined, all available tools are active.

abortSignal?:

AbortSignal

Signal object that allows you to abort the agent's execution. When the signal is aborted, all ongoing operations will be terminated.

prepareStep?:

PrepareStepFunction

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

requireToolApproval?:

boolean

When true, all tool calls require explicit approval before execution. The generate() method will return with `finishReason: 'suspended'` and include a `suspendPayload` with tool call details (`toolCallId`, `toolName`, `args`). Use `approveToolCallGenerate()` or `declineToolCallGenerate()` to proceed. See [Agent Approval](/docs/agents/agent-approval#tool-approval-with-generate) for details.

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.

context?:

ModelMessage[]

Additional context messages to provide to the agent.

structuredOutput?:

StructuredOutputOptions<S extends ZodTypeAny = ZodTypeAny>

Options to fine tune your structured output generation.

StructuredOutputOptions<S extends ZodTypeAny = ZodTypeAny>

schema:

z.ZodSchema<S>

Zod 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?:

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

instructions?:

string

Additional instructions for the structured output model.

jsonPromptInjection?:

boolean

Injects system prompt into the main agent instructing it to return structured output, useful for when a model does not natively support structured outputs.

logger?:

IMastraLogger

Optional logger instance for structured logging during output generation.

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?:

OutputProcessorOrWorkflow[]

Output processors to use for this execution (overrides agent's default).

maxProcessorRetries?:

number

Maximum number of times processors can trigger a retry for this generation. Overrides agent's default maxProcessorRetries.

inputProcessors?:

InputProcessorOrWorkflow[]

Input processors to use for this execution (overrides agent's default).

instructions?:

Custom instructions that override the agent's default instructions for this execution. Can be a single string, message object, or array of either.

system?:

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

Memory configuration for conversation persistence and retrieval.

object

thread:

string | { id: string; metadata?: Record<string, any>, title?: string }

Thread identifier for conversation continuity. Can be a string ID or an object with ID and optional metadata/title.

resource:

string

Resource identifier for organizing conversations by user, session, or context.

options?:

MemoryConfig

Additional memory configuration options including lastMessages, readOnly, semanticRecall, and workingMemory.

onFinish?:

LoopConfig['onFinish']

Callback fired when generation completes.

onStepFinish?:

LoopConfig['onStepFinish']

Callback fired after each generation step.

telemetry?:

TelemetrySettings

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

TelemetrySettings

isEnabled?:

boolean

Whether telemetry collection is enabled.

recordInputs?:

boolean

Whether to record input data in telemetry.

recordOutputs?:

boolean

Whether to record output data in telemetry.

functionId?:

string

Identifier for the function being executed.

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.

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 tools are selected during generation.

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

'auto':

string

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

'none':

string

Disable tool usage entirely.

'required':

string

Force the model to use at least one tool.

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

object

Force the model to use a specific tool.

toolsets?:

ToolsetsInput

Additional tool sets that can be used for this execution.

clientTools?:

ToolsInput

Client-side tools available during execution.

savePerStep?:

boolean

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

providerOptions?:

Record<string, Record<string, JSONValue>>

Provider-specific options passed to the language model.

Record<string, Record<string, JSONValue>>

openai?:

Record<string, JSONValue>

OpenAI-specific options like reasoningEffort, responseFormat, etc.

anthropic?:

Record<string, JSONValue>

Anthropic-specific options like maxTokens, etc.

google?:

Record<string, JSONValue>

Google-specific options.

[providerName]?:

Record<string, JSONValue>

Any provider-specific options.

runId?:

string

Unique identifier for this execution run.

requestContext?:

RequestContext

Request Context containing dynamic configuration and state.

tracingContext?:

TracingContext

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

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.

tracingOptions?:

TracingOptions

Options for Tracing configuration.

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.

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.

includeRawChunks?:

boolean

Whether to include raw chunks in the stream output. Not available on all model providers.

Response structure
Direct link to Response structure

Agent.generate() returns the final data collected during execution. steps is an array of step objects. The tool arrays in the result, including top-level toolCalls and toolResults and the nested step.toolCalls and step.toolResults arrays, use Mastra's chunk format.

That means tool data is wrapped in payload:

const response = await agent.generate('Check the weather in Lagos')

for (const toolCall of response.toolCalls) {
  console.log(toolCall.type) // 'tool-call'
  console.log(toolCall.runId)
  console.log(toolCall.from)
  console.log(toolCall.payload.toolName)
  console.log(toolCall.payload.args)
}

for (const step of response.steps) {
  for (const toolResult of step.toolResults) {
    console.log(toolResult.type) // 'tool-result'
    console.log(toolResult.payload.toolName)
    console.log(toolResult.payload.result)
  }
}

For the streaming version of the same chunk shape, see the ChunkType reference.

Returns
Direct link to Returns

result:

Awaited<ReturnType<MastraModelOutput<Output>['getFullOutput']>>

Returns the full output of the generation process including text, object (if structured output), tool calls, tool results, usage statistics, and step information.

text:

string

The generated text response from the agent.

object?:

Output | undefined

The structured output object if structuredOutput was provided, validated against the schema.

toolCalls:

ToolCallChunk[]

Array of tool call chunks made during generation.

ToolCallChunk

type:

'tool-call'

Chunk type identifier.

runId:

string

Execution run identifier.

from:

ChunkFrom

Source of the chunk, such as AGENT or WORKFLOW.

payload:

ToolCallPayload

Tool call data.

ToolCallPayload

toolCallId:

string

Unique identifier for the tool call.

toolName:

string

Name of the tool that was called.

args?:

Record<string, unknown>

Arguments passed to the tool.

providerExecuted?:

boolean

Whether the model provider executed the tool directly.

toolResults:

ToolResultChunk[]

Array of tool result chunks from tool executions.

ToolResultChunk

type:

'tool-result'

Chunk type identifier.

runId:

string

Execution run identifier.

from:

ChunkFrom

Source of the chunk, such as AGENT or WORKFLOW.

payload:

ToolResultPayload

Tool result data.

ToolResultPayload

toolCallId:

string

Unique identifier for the tool call.

toolName:

string

Name of the tool that produced the result.

result:

unknown

Value returned by the tool.

isError?:

boolean

Whether the tool execution failed.

usage:

TokenUsage

Token usage statistics for the generation.

steps:

object[]

Array of execution steps, useful for debugging multi-step generations.

object

text:

string

Text generated in this step.

toolCalls:

ToolCallChunk[]

Tool calls emitted during this step.

toolResults:

ToolResultChunk[]

Tool results emitted during this step.

finishReason?:

string

Why this step finished.

usage:

LanguageModelUsage

Token usage for this step.

request:

{ body?: unknown }

Request metadata for this step.

response:

object

Response metadata for this step.

finishReason:

string

The reason generation finished. Values include 'stop' (normal completion), 'tool-calls' (ended with tool calls), 'suspended' (waiting for tool approval), or 'error' (error occurred).

response:

object

Response metadata from the model provider. Useful for accessing rate limit headers and request IDs.

object

id?:

string

Response ID from the model provider.

timestamp?:

Date

Timestamp when the response was generated.

modelId?:

string

Model identifier used for this response.

headers?:

Record<string, string>

HTTP response headers from the model provider. Contains rate limit information (e.g., `anthropic-ratelimit-requests-remaining`, `x-ratelimit-remaining-tokens`) and other provider-specific metadata.

messages?:

ResponseMessage[]

Response messages in model format.

uiMessages?:

UIMessage[]

Response messages in UI format, includes any metadata added by output processors.

request?:

object

The request that was sent to the model.

object

body?:

unknown

The request body sent to the model provider.

warnings?:

LanguageModelWarning[]

Any warnings from the model provider during generation.

providerMetadata?:

Record<string, unknown>

Provider-specific metadata returned with the response.

reasoning?:

ReasoningChunk[]

Reasoning details from models that support reasoning (e.g., OpenAI o1 series).

reasoningText?:

string

Combined reasoning text from reasoning models.

sources?:

SourceChunk[]

Sources referenced by the model during generation.

files?:

FileChunk[]

Files generated by the model.

suspendPayload?:

object

Present when `finishReason` is 'suspended'. Contains tool call details needed to approve or decline the pending tool call.

object

toolCallId:

string

Unique identifier for the pending tool call.

toolName:

string

Name of the tool that requires approval.

args:

Record<string, any>

Arguments that will be passed to the tool.

runId?:

string

Unique identifier for this execution run. Required when calling `approveToolCallGenerate()` or `declineToolCallGenerate()` to resume a suspended execution.

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.

messages:

MastraDBMessage[]

All messages from this execution including input, memory history, and response.

rememberedMessages:

MastraDBMessage[]

Only messages loaded from memory (conversation history).

error?:

Error

Error object if the generation failed.

tripwire?:

StepTripwireData

Tripwire data if content was blocked by a processor.

scoringData?:

object

Scoring data for evals when `returnScorerData` is enabled.

Usage examplesDirect link to Usage examples

Basic usageDirect link to Basic usage

With model settingsDirect link to With model settings

With memoryDirect link to With memory

Accessing response headersDirect link to Accessing response headers

Analyzing imagesDirect link to Analyzing images

Using maxStepsDirect link to using-maxsteps

Using onStepFinishDirect link to using-onstepfinish

ParametersDirect link to Parameters

messages:

options?:

maxSteps?:

stopWhen?:

onIterationComplete?:

context.iteration:

context.maxIterations:

context.text:

context.isFinal:

context.finishReason:

context.toolCalls:

context.messages:

return.continue?:

return.feedback?:

isTaskComplete?:

scorers:

strategy?:

onComplete?:

parallel?:

timeout?:

delegation?:

onDelegationStart?:

onDelegationComplete?:

messageFilter?:

scorers?:

scorer:

sampling?:

type:

rate?:

returnScorerData?:

onChunk?:

onError?:

onAbort?:

activeTools?:

abortSignal?:

prepareStep?:

requireToolApproval?:

autoResumeSuspendedTools?:

toolCallConcurrency?:

context?:

structuredOutput?:

schema:

model?:

errorStrategy?:

fallbackValue?:

instructions?:

jsonPromptInjection?:

logger?:

providerOptions?:

outputProcessors?:

maxProcessorRetries?:

inputProcessors?:

instructions?:

system?:

output?:

memory?:

thread:

resource:

options?:

onFinish?:

onStepFinish?:

telemetry?:

isEnabled?:

recordInputs?:

recordOutputs?:

functionId?:

modelSettings?:

temperature?:

maxOutputTokens?:

maxRetries?:

topP?:

Usage examples
Direct link to Usage examples

Basic usage
Direct link to Basic usage

With model settings
Direct link to With model settings

With memory
Direct link to With memory

Accessing response headers
Direct link to Accessing response headers

Analyzing images
Direct link to Analyzing images

Using `maxSteps`
Direct link to using-maxsteps

Using `onStepFinish`
Direct link to using-onstepfinish

Parameters
Direct link to Parameters

Response structure
Direct link to Response structure

Returns
Direct link to Returns