Agent.generateLegacy()（遗留）

🌐 Agent.generateLegacy() (Legacy)

warning

已弃用：此方法已被弃用，仅适用于 V1 模型。对于 V2 模型，请改用新的 .generate() 方法。

.generateLegacy() 方法是代理生成 API 的旧版本，用于与 V1 模型代理交互以生成文本或结构化响应。该方法接受消息和可选的生成选项。

🌐 The .generateLegacy() method is the legacy version of the agent generation API, used to interact with V1 model agents to produce text or structured responses. This method accepts messages and optional generation options.

使用示例

🌐 Usage example

await agent.generateLegacy("message for agent");

参数

🌐 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 with multimodal content (text, images, etc.).

options?:

AgentGenerateOptions

Optional configuration for the generation process.

选项参数

🌐 Options parameters

abortSignal?:

AbortSignal

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

context?:

CoreMessage[]

Additional context messages to provide to the agent.

structuredOutput?:

StructuredOutputOptions<S extends ZodTypeAny = ZodTypeAny>

Enables structured output generation with better developer experience. Automatically creates and uses a StructuredOutputProcessor internally.

schema:

z.ZodSchema<S>

Zod schema to validate the output against.

model:

MastraLanguageModel

Model to use for the internal structuring agent.

errorStrategy?:

'strict' | 'warn' | 'fallback'

Strategy when parsing or validation fails. Defaults to 'strict'.

fallbackValue?:

<S extends ZodTypeAny>

Fallback value when errorStrategy is 'fallback'.

instructions?:

string

Custom instructions for the structuring agent.

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.

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.

experimental_output?:

Zod schema | JsonSchema7

Note, the preferred route is to use the `structuredOutput` property. Enables structured output generation alongside text generation and tool calls. The model will generate responses that conform to the provided schema.

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.

output?:

Zod schema | JsonSchema7

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, like message history and semantic recall. See `MemoryConfig` below.

maxSteps?:

number

= 5

Maximum number of execution steps allowed.

maxRetries?:

number

= 2

Maximum number of retries. Set to 0 to disable retries.

onStepFinish?:

GenerateTextOnStepFinishCallback<any> | never

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

runId?:

string

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

telemetry?:

TelemetrySettings

Settings for telemetry collection during generation.

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.

temperature?:

number

Controls randomness in the model's output. Higher values (e.g., 0.8) make the output more random, lower values (e.g., 0.2) make it more focused and deterministic.

toolChoice?:

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

= 'auto'

Controls how the agent uses tools during generation.

'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 generation.

clientTools?:

ToolsInput

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

savePerStep?:

boolean

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

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 } }`. Since Mastra extends AI SDK, see the [AI SDK documentation](https://sdk.vercel.ai/docs/providers/ai-sdk-providers) for complete provider options.

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.

requestContext?:

RequestContext

Request Context for dependency injection and contextual information.

maxTokens?:

number

Maximum number of tokens to generate.

topP?:

number

Nucleus sampling. This is a number between 0 and 1. It is recommended to set either `temperature` or `topP`, but not both.

topK?:

number

Only sample from the top K options for each subsequent token. Used to remove 'long tail' low probability responses.

presencePenalty?:

number

Presence penalty setting. It affects the likelihood of the model to repeat information that is already in the prompt. A number between -1 (increase repetition) and 1 (maximum penalty, decrease repetition).

frequencyPenalty?:

number

Frequency penalty setting. It affects the likelihood of the model to repeatedly use the same words or phrases. A number between -1 (increase repetition) and 1 (maximum penalty, decrease repetition).

stopSequences?:

string[]

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

seed?:

number

The seed (integer) to use for random sampling. If set and supported by the model, calls will generate deterministic results.

headers?:

Record<string, string | undefined>

Additional HTTP headers to be sent with the request. Only applicable for HTTP-based providers.

返回

🌐 Returns

text?:

string

The generated text response. Present when output is 'text' (no schema provided).

object?:

object

The generated structured response. Present when a schema is provided via `output`, `structuredOutput`, or `experimental_output`.

toolCalls?:

Array<ToolCall>

The tool calls made during the generation process. Present in both text and object modes.

toolName:

string

The name of the tool invoked.

args:

any

The arguments passed to the tool.

迁移到新API

🌐 Migration to New API

info

新的 .generate() 方法提供了增强的功能，包括兼容 AI SDK v5+、更好的结构化输出处理以及改进的流式支持。有关详细的迁移说明，请参阅 迁移指南。

🌐 The new .generate() method offers enhanced capabilities including AI SDK v5+ compatibility, better structured output handling, and improved streaming support. See the migration guide for detailed migration instructions.

快速迁移示例

🌐 Quick Migration Example

以前（传统）

🌐 Before (Legacy)

const result = await agent.generateLegacy("message", {
  temperature: 0.7,
  maxSteps: 3,
});

在（新 API）之后

🌐 After (New API)

const result = await agent.generate("message", {
  modelSettings: {
    temperature: 0.7,
  },
  maxSteps: 3,
});

扩展使用示例

🌐 Extended usage example

import { z } from "zod";
import {
  ModerationProcessor,
  TokenLimiterProcessor,
} from "@mastra/core/processors";

await agent.generateLegacy(
  [
    { role: "user", content: "message for agent" },
    {
      role: "user",
      content: [
        {
          type: "text",
          text: "message for agent",
        },
        {
          type: "image",
          imageUrl: "https://example.com/image.jpg",
          mimeType: "image/jpeg",
        },
      ],
    },
  ],
  {
    temperature: 0.7,
    maxSteps: 3,
    memory: {
      thread: "user-123",
      resource: "test-app",
    },
    toolChoice: "auto",
    providerOptions: {
      openai: {
        reasoningEffort: "high",
      },
    },
    // Structured output with better DX
    structuredOutput: {
      schema: z.object({
        sentiment: z.enum(["positive", "negative", "neutral"]),
        confidence: z.number(),
      }),
      model: "openai/gpt-5.1",
      errorStrategy: "warn",
    },
    // Output processors for response validation
    outputProcessors: [
      new ModerationProcessor({ model: "openai/gpt-4.1-nano" }),
      new TokenLimiterProcessor({ maxTokens: 1000 }),
    ],
  },
);

相关

🌐 Related

• 迁移指南
• 新的 .generate() 方法
• 生成响应
• 流式响应