跳至主要內容

toAISdkStream()

將 Mastra 串流(Agent、網絡或 Workflow)轉換成 AI SDK 兼容串流。當你需要手動轉換 Mastra 串流,以配合 AI SDK 的 createUIMessageStream()createUIMessageStreamResponse() 使用時,請使用此函數。

在 Mastra 所提供的 chatRoute()workflowRoute() 等路由輔助函數以外建立自訂串流端點時,此函數尤其有用。

toAISdkStream() 保留現有的 AI SDK v5/預設行為。如果你的應用程式以 AI SDK v6 型別為準,請在選項物件中傳入 version: 'v6'

UI 串流中的結構化輸出
UI 串流中的結構化輸出 的直接連結

當來源 Agent 串流包含最終結構化輸出物件時,toAISdkStream() 會將其作為自訂 AI SDK UI 資料部分輸出:

{
"type": "data-structured-output",
"data": {
"object": {}
}
}

object 欄位包含完整的結構化輸出值。這會將 Mastra 的最終結構化輸出區塊對應至 AI SDK UI 串流。系統不會輸出部分結構化輸出區塊。

使用範例
使用範例 的直接連結

Next.js App Router 範例:

app/api/chat/route.ts
import { mastra } from '../../mastra'
import { createUIMessageStream, createUIMessageStreamResponse } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'

export async function POST(req: Request) {
const { messages } = await req.json()
const myAgent = mastra.getAgent('weatherAgent')
const stream = await myAgent.stream(messages)

const uiMessageStream = createUIMessageStream({
originalMessages: messages,
execute: async ({ writer }) => {
for await (const part of toAISdkStream(stream, { from: 'agent' })) {
await writer.write(part)
}
},
})

return createUIMessageStreamResponse({
stream: uiMessageStream,
})
}
提示

createUIMessageStream() 中將 messages 傳給 originalMessages,以免 UI 出現重複的助手訊息。詳情請參閱疑難排解:重複的助手訊息

參數
參數 的直接連結

第一個參數是要轉換的 Mastra 串流,可以是以下其中一種:

  • MastraModelOutput - 來自 agent.stream() 的 Agent 串流
  • MastraAgentNetworkStream - 來自 agent.network() 的網絡串流
  • MastraWorkflowStreamWorkflowRunOutput - Workflow 串流

第二個參數是選項物件:

version?:

'v5' | 'v6'
= 'v5'
選擇要輸出的 AI SDK 串流合約。省略此項或傳入 'v5',即可使用現有的預設行為。當你的應用程式以 AI SDK v6 回應輔助函數的型別為準時,請傳入 'v6'

from:

'agent' | 'network' | 'workflow'
= 'agent'
正在轉換的 Mastra 串流類型。

lastMessageId?:

string
(僅限 Agent)對話中最後一則訊息的 ID。

sendStart?:

boolean
= true
(僅限 Agent)是否在串流中傳送開始事件。

sendFinish?:

boolean
= true
(僅限 Agent)是否在串流中傳送完成事件。

sendReasoning?:

boolean
= false
(僅限 Agent)是否在串流中包含 reasoning-delta 區塊。設為 true,可串流支援延伸思考模型的推理內容。

sendSources?:

boolean
= false
(僅限 Agent)是否在輸出中包含來源引文。

includeTextStreamParts?:

boolean
= true
(僅限 Workflow)是否在輸出中包含文字串流部分。

messageMetadata?:

(options: { part: UIMessageStreamPart }) => Record<string, unknown> | undefined
(僅限 Agent)接收目前串流部分,並傳回要附加至開始及完成區塊的中繼資料之函數。

onError?:

(error: unknown) => string
(僅限 Agent)用於處理串流轉換期間錯誤的函數。此函數會接收錯誤,並應傳回其字串表示。

範例
範例 的直接連結

轉換 Workflow 串流
轉換 Workflow 串流 的直接連結

app/api/workflow/route.ts
import { mastra } from '../../mastra'
import { createUIMessageStream, createUIMessageStreamResponse } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'

export async function POST(req: Request) {
const { input } = await req.json()
const workflow = mastra.getWorkflow('myWorkflow')
const run = workflow.createRun()
const stream = await run.stream({ inputData: input })

const uiMessageStream = createUIMessageStream({
execute: async ({ writer }) => {
for await (const part of toAISdkStream(stream, { from: 'workflow' })) {
await writer.write(part)
}
},
})

return createUIMessageStreamResponse({
stream: uiMessageStream,
})
}

轉換網絡串流
轉換網絡串流 的直接連結

app/api/network/route.ts
import { mastra } from '../../mastra'
import { createUIMessageStream, createUIMessageStreamResponse } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'

export async function POST(req: Request) {
const { messages } = await req.json()
const routingAgent = mastra.getAgent('routingAgent')
const stream = await routingAgent.network(messages)

const uiMessageStream = createUIMessageStream({
execute: async ({ writer }) => {
for await (const part of toAISdkStream(stream, { from: 'network' })) {
await writer.write(part)
}
},
})

return createUIMessageStreamResponse({
stream: uiMessageStream,
})
}

轉換已啟用推理的 Agent 串流
轉換已啟用推理的 Agent 串流 的直接連結

app/api/reasoning/route.ts
import { mastra } from '../../mastra'
import { createUIMessageStream, createUIMessageStreamResponse } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'

export async function POST(req: Request) {
const { messages } = await req.json()
const reasoningAgent = mastra.getAgent('reasoningAgent')
const stream = await reasoningAgent.stream(messages, {
providerOptions: {
openai: { reasoningEffort: 'high' },
},
})

const uiMessageStream = createUIMessageStream({
originalMessages: messages,
execute: async ({ writer }) => {
for await (const part of toAISdkStream(stream, {
from: 'agent',
sendReasoning: true,
})) {
await writer.write(part)
}
},
})

return createUIMessageStreamResponse({
stream: uiMessageStream,
})
}

為 AI SDK v6 轉換 Agent 串流
為 AI SDK v6 轉換 Agent 串流 的直接連結

app/api/chat-v6/route.ts
import { mastra } from '../../mastra'
import { createUIMessageStream, createUIMessageStreamResponse } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'

export async function POST(req: Request) {
const { messages } = await req.json()
const myAgent = mastra.getAgent('weatherAgent')
const stream = await myAgent.stream(messages)

const uiMessageStream = createUIMessageStream({
originalMessages: messages,
execute: async ({ writer }) => {
for await (const part of toAISdkStream(stream, {
from: 'agent',
version: 'v6',
})) {
await writer.write(part)
}
},
})

return createUIMessageStreamResponse({
stream: uiMessageStream,
})
}

使用 messageMetadata
using-messagemetadata 的直接連結

app/api/chat-with-metadata/route.ts
import { mastra } from '../../mastra'
import { createUIMessageStream, createUIMessageStreamResponse } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'

export async function POST(req: Request) {
const { messages } = await req.json()
const myAgent = mastra.getAgent('weatherAgent')
const stream = await myAgent.stream(messages)

const uiMessageStream = createUIMessageStream({
originalMessages: messages,
execute: async ({ writer }) => {
for await (const part of toAISdkStream(stream, {
from: 'agent',
messageMetadata: ({ part }) => ({
timestamp: Date.now(),
partType: part.type,
}),
})) {
await writer.write(part)
}
},
})

return createUIMessageStreamResponse({
stream: uiMessageStream,
})
}

客戶端串流轉換
客戶端串流轉換 的直接連結

如果你在客戶端使用 Mastra client SDK(@mastra/client-js),並希望將串流轉換成 AI SDK 格式:

client-stream-to-ai-sdk.ts
import { MastraClient } from '@mastra/client-js'
import { createUIMessageStream } from 'ai'
import { toAISdkStream } from '@mastra/ai-sdk'
import type { ChunkType, MastraModelOutput } from '@mastra/core/stream'

const client = new MastraClient({
baseUrl: 'http://localhost:4111',
})

const agent = client.getAgent('weatherAgent')
const response = await agent.stream('What is the weather in Tokyo?')

// Convert the client SDK stream to a ReadableStream<ChunkType>
const chunkStream = new ReadableStream<ChunkType>({
async start(controller) {
await response.processDataStream({
onChunk: async chunk => {
controller.enqueue(chunk)
},
})
controller.close()
},
})

// Transform to AI SDK format
const uiMessageStream = createUIMessageStream({
execute: async ({ writer }) => {
for await (const part of toAISdkStream(chunkStream as unknown as MastraModelOutput, {
from: 'agent',
})) {
await writer.write(part)
}
},
})

for await (const part of uiMessageStream) {
console.log(part)
}