### Install @mastra/ai-sdk with npm

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Install the necessary packages using npm to begin integrating Mastra with AI SDK UI.

```bash
npm install @mastra/ai-sdk@latest @ai-sdk/react ai
```

--------------------------------

### Install @mastra/ai-sdk with Bun

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Install the necessary packages using Bun to begin integrating Mastra with AI SDK UI.

```bash
bun add @mastra/ai-sdk@latest @ai-sdk/react ai
```

--------------------------------

### Install @mastra/ai-sdk with pnpm

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Install the necessary packages using pnpm to begin integrating Mastra with AI SDK UI.

```bash
pnpm add @mastra/ai-sdk@latest @ai-sdk/react ai
```

--------------------------------

### handleChatStream() API route setup

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Sets up a chat route at the /chat endpoint using an agent with the ID weatherAgent. The version is explicitly set to v7 to match the installed AI SDK.

```typescript
import { handleChatStream } from '@mastra/ai-sdk'
import { createUIMessageStreamResponse } from 'ai'
import { mastra } from '@/src/mastra'

export async function POST(req: Request) {
  const params = await req.json()
  const stream = await handleChatStream({
    mastra,
    agentId: 'weatherAgent',
    version: 'v7',
    params,
  })
  return createUIMessageStreamResponse({ stream })
}
```

--------------------------------

### Install @mastra/ai-sdk with Yarn

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Install the necessary packages using Yarn to begin integrating Mastra with AI SDK UI.

```bash
yarn add @mastra/ai-sdk@latest @ai-sdk/react ai
```

--------------------------------

### handleNetworkStream() API route setup

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Sets up a network route at the /network endpoint using an agent with the ID routingAgent.

```typescript
import { handleNetworkStream } from '@mastra/ai-sdk'
import { createUIMessageStreamResponse } from 'ai'
import { mastra } from '@/src/mastra'

export async function POST(req: Request) {
  const params = await req.json()
  const stream = await handleNetworkStream({
    mastra,
    agentId: 'routingAgent',
    params,
  })
  return createUIMessageStreamResponse({ stream })
}
```

--------------------------------

### handleWorkflowStream() API route setup

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Sets up a workflow route at the /workflow endpoint using a workflow with the ID weatherWorkflow.

```typescript
import { handleWorkflowStream } from '@mastra/ai-sdk'
import { createUIMessageStreamResponse } from 'ai'
import { mastra } from '@/src/mastra'

export async function POST(req: Request) {
  const params = await req.json()
  const stream = await handleWorkflowStream({
    mastra,
    workflowId: 'weatherWorkflow',
    params,
  })
  return createUIMessageStreamResponse({ stream })
}
```

--------------------------------

### Implement Workflow Suspend/Resume with User Approval

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

This component uses useChat to manage workflow state, including starting a workflow and resuming it via approval or rejection. It accesses the suspend payload through step.suspendPayload and requires configured storage for persistence.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
import { useMemo, useState } from 'react'
import type { WorkflowDataPart } from '@mastra/ai-sdk'

type WorkflowData = WorkflowDataPart['data']

export function ApprovalWorkflow() {
  const [requestId, setRequestId] = useState('')
  const [summary, setSummary] = useState('')

  const { messages, sendMessage, setMessages, status } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/workflow/approvalWorkflow',
      prepareSendMessagesRequest: ({ messages }) => {
        const lastMessage = messages[messages.length - 1]
        const text = lastMessage.parts.find(p => p.type === 'text')?.text
        const metadata = lastMessage.metadata as Record<string, string>

        // Resuming: send runId, step, and resumeData
        if (text === 'Approve' || text === 'Reject') {
          return {
            body: {
              runId: metadata.runId,
              step: 'request-approval',
              resumeData: { approved: text === 'Approve' },
            },
          }
        }
        // Starting: send inputData
        return {
          body: { inputData: { requestId: metadata.requestId, summary: metadata.summary } },
        }
      },
    }),
  })

  // Find suspended workflow
  const suspended = useMemo(() => {
    for (const m of messages) {
      for (const p of m.parts) {
        if (p.type === 'data-workflow' && (p.data as WorkflowData).status === 'suspended') {
          return { data: p.data as WorkflowData, runId: p.id }
        }
      }
    }
    return null
  }, [messages])

  const handleApprove = () => {
    setMessages([])
    sendMessage({ text: 'Approve', metadata: { runId: suspended?.runId } })
  }

  const handleReject = () => {
    setMessages([])
    sendMessage({ text: 'Reject', metadata: { runId: suspended?.runId } })
  }

  return (
    <div>
      {!suspended ? (
        <form
          onSubmit={e => {
            e.preventDefault()
            setMessages([])
            sendMessage({ text: 'Start', metadata: { requestId, summary } })
          }}
        >
          <input
            value={requestId}
            onChange={e => setRequestId(e.target.value)}
            placeholder="Request ID"
          />
          <input value={summary} onChange={e => setSummary(e.target.value)} placeholder="Summary" />
          <button type="submit" disabled={status !== 'ready'}>
            Submit
          </button>
        </form>
      ) : (
        <div>
          <p>
            {
              (suspended.data.steps['request-approval']?.suspendPayload as { message: string })
                ?.message
            }
          </p>
          <button onClick={handleApprove}>Approve</button>
          <button onClick={handleReject}>Reject</button>
        </div>
      )}
    </div>
  )
}
```

--------------------------------

### Emit custom events in a Mastra tool

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use writer.custom() within the execute function to send progress updates. Custom event types must start with 'data-'.

```typescript
import { createTool } from '@mastra/core/tools'
import { z } from 'zod'

export const taskTool = createTool({
  id: 'process-task',
  description: 'Process a task with progress updates',
  inputSchema: z.object({
    task: z.string().describe('The task to process'),
  }),
  outputSchema: z.object({
    result: z.string(),
    status: z.string(),
  }),
  execute: async (inputData, context) => {
    const { task } = inputData

    // Emit "in progress" custom event
    await context?.writer?.custom({
      type: 'data-tool-progress',
      data: {
        status: 'in-progress',
        message: 'Gathering information...',
      },
    })

    // Simulate work
    await new Promise(resolve => setTimeout(resolve, 3000))

    // Emit "done" custom event
    await context?.writer?.custom({
      type: 'data-tool-progress',
      data: {
        status: 'done',
        message: `Successfully processed "${task}"`,
      },
    })

    return {
      result: `Task "${task}" has been completed successfully!`,
      status: 'completed',
    }
  },
})
```

--------------------------------

### Configure an agent to use the nested tool

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Initializes an agent configured with the nested agent tool.

```typescript
import { Agent } from '@mastra/core/agent'
import { nestedAgentTool } from '../tools/nested-agent-tool'

export const forecastAgent = new Agent({
  id: 'forecast-agent',
  instructions: 'Use the nested-agent-stream tool when asked about weather.',
  model: 'openai/gpt-5.6-sol',
  tools: { nestedAgentTool },
})
```

--------------------------------

### Create a workflow step with streaming

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Define a workflow step that uses the writer to pipe agent responses for real-time streaming. The step returns the final analysis and location for subsequent steps.

```typescript
import { createStep, createWorkflow } from '@mastra/core/workflows'
import { z } from 'zod'
import { weatherAgent } from '../agents/weather-agent'

const analyzeWeather = createStep({
  id: 'analyze-weather',
  inputSchema: z.object({ location: z.string() }),
  outputSchema: z.object({ analysis: z.string(), location: z.string() }),
  execute: async ({ inputData, writer }) => {
    const response = await weatherAgent.stream(
      `Analyze the weather in ${inputData.location} and provide insights.`,
    )

    // Pipe agent stream to step writer for real-time text streaming
    await response.fullStream.pipeTo(writer)

    return {
      analysis: await response.text,
      location: inputData.location,
    }
  },
})

const calculateScore = createStep({
  id: 'calculate-score',
  inputSchema: z.object({ analysis: z.string(), location: z.string() }),
  outputSchema: z.object({ score: z.number(), summary: z.string() }),
  execute: async ({ inputData }) => {
    const score = inputData.analysis.includes('sunny') ? 85 : 50
    return { score, summary: `Comfort score for ${inputData.location}: ${score}/100` }
  },
})

export const weatherWorkflow = createWorkflow({
  id: 'weather-workflow',
  inputSchema: z.object({ location: z.string() }),
  outputSchema: z.object({ score: z.number(), summary: z.string() }),
})
  .then(analyzeWeather)
  .then(calculateScore)

weatherWorkflow.commit()
```

--------------------------------

### Mastra Server Backend for useCompletion

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Backend implementation using registerApiRoute to handle completion requests and stream responses via handleChatStream.

```ts
import { Mastra } from '@mastra/core/mastra'
import { registerApiRoute } from '@mastra/core/server'
import { handleChatStream } from '@mastra/ai-sdk'
import { createUIMessageStreamResponse } from 'ai'

export const mastra = new Mastra({
  server: {
    apiRoutes: [
      registerApiRoute('/completion', {
        method: 'POST',
        handler: async c => {
          const { prompt } = await c.req.json()
          const mastra = c.get('mastra')
          const stream = await handleChatStream({
            mastra,
            agentId: 'weatherAgent',
            params: {
              messages: [
                {
                  id: '1',
                  role: 'user',
                  parts: [
                    {
                      type: 'text',
                      text: prompt,
                    },
                  ],
                },
              ],
            },
          })

          return createUIMessageStreamResponse({ stream })
        },
      }),
    ],
  },
})
```

--------------------------------

### Set up networkRoute for Mastra server

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Configures a network route at the /network endpoint using the weatherAgent.

```typescript
import { Mastra } from '@mastra/core'
import { networkRoute } from '@mastra/ai-sdk'

export const mastra = new Mastra({
  server: {
    apiRoutes: [
      networkRoute({
        path: '/network',
        agent: 'weatherAgent',
      }),
    ],
  },
})
```

--------------------------------

### Set up chatRoute for Mastra server

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Configures a chat route at the /chat endpoint using the weatherAgent.

```typescript
import { Mastra } from '@mastra/core'
import { chatRoute } from '@mastra/ai-sdk'

export const mastra = new Mastra({
  server: {
    apiRoutes: [
      chatRoute({
        path: '/chat',
        agent: 'weatherAgent',
      }),
    ],
  },
})
```

--------------------------------

### Register workflow with storage

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Configures the Mastra instance with the approval workflow and LibSQL storage, which is required for persisting state during suspension.

```typescript
import { Mastra } from '@mastra/core'
import { workflowRoute } from '@mastra/ai-sdk'
import { LibSQLStore } from '@mastra/libsql'

export const mastra = new Mastra({
  workflows: { approvalWorkflow },
  storage: new LibSQLStore({
    id: 'mastra-storage',
    url: 'file:../mastra.db',
  }),
  server: {
    apiRoutes: [
      workflowRoute({ path: '/workflow/approvalWorkflow', workflow: 'approvalWorkflow' }),
    ],
  },
})
```

--------------------------------

### Set up workflowRoute for Mastra server

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Configures a workflow route at the /workflow endpoint using the weatherWorkflow.

```typescript
import { Mastra } from '@mastra/core'
import { workflowRoute } from '@mastra/ai-sdk'

export const mastra = new Mastra({
  server: {
    apiRoutes: [
      workflowRoute({
        path: '/workflow',
        workflow: 'weatherWorkflow',
      }),
    ],
  },
})
```

--------------------------------

### Define a weather tool with outputSchema

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Define a tool using createTool with an outputSchema to ensure the frontend receives the expected data structure for rendering.

```typescript
import { createTool } from '@mastra/core/tools'
import { z } from 'zod'

export const weatherTool = createTool({
  id: 'get-weather',
  description: 'Get current weather for a location',
  inputSchema: z.object({
    location: z.string().describe('The location to get the weather for'),
  }),
  outputSchema: z.object({
    temperature: z.number(),
    feelsLike: z.number(),
    humidity: z.number(),
    windSpeed: z.number(),
    conditions: z.string(),
    location: z.string(),
  }),
  execute: async inputData => {
    const response = await fetch(
      `https://api.weatherapi.com/v1/current.json?key=${process.env.WEATHER_API_KEY}&q=${inputData.location}`,
    )
    const data = await response.json()
    return {
      temperature: data.current.temp_c,
      feelsLike: data.current.feelslike_c,
      humidity: data.current.humidity,
      windSpeed: data.current.wind_kph,
      conditions: data.current.condition.text,
      location: data.location.name,
    }
  },
})
```

--------------------------------

### Create approval workflow step and workflow

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Defines a workflow step with suspend and resume schemas to handle user approval, followed by the workflow definition.

```typescript
import { createStep, createWorkflow } from '@mastra/core/workflows'
import { z } from 'zod'

const requestApproval = createStep({
  id: 'request-approval',
  inputSchema: z.object({ requestId: z.string(), summary: z.string() }),
  outputSchema: z.object({
    approved: z.boolean(),
    requestId: z.string(),
    approvedBy: z.string().optional(),
  }),
  resumeSchema: z.object({
    approved: z.boolean(),
    approverName: z.string().optional(),
  }),
  suspendSchema: z.object({
    message: z.string(),
    requestId: z.string(),
  }),
  execute: async ({ inputData, resumeData, suspend, bail }) => {
    // User rejected - bail out
    if (resumeData?.approved === false) {
      return bail({ message: 'Request rejected' })
    }
    // User approved - continue
    if (resumeData?.approved) {
      return {
        approved: true,
        requestId: inputData.requestId,
        approvedBy: resumeData.approverName || 'User',
      }
    }
    // First execution - suspend and wait
    return await suspend({
      message: `Please approve: ${inputData.summary}`,
      requestId: inputData.requestId,
    })
  },
})

export const approvalWorkflow = createWorkflow({
  id: 'approval-workflow',
  inputSchema: z.object({ requestId: z.string(), summary: z.string() }),
  outputSchema: z.object({
    approved: z.boolean(),
    requestId: z.string(),
    approvedBy: z.string().optional(),
  }),
}).then(requestApproval)

approvalWorkflow.commit()
```

--------------------------------

### Create a tool with nested agent streaming

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Defines a tool that retrieves an agent, streams its response, and pipes the full stream to the tool's writer.

```typescript
import { createTool } from '@mastra/core/tools'
import { z } from 'zod'

export const nestedAgentTool = createTool({
  id: 'nested-agent-stream',
  description: 'Analyze weather using a nested agent',
  inputSchema: z.object({
    city: z.string().describe('The city to analyze'),
  }),
  outputSchema: z.object({
    summary: z.string(),
  }),
  execute: async (inputData, context) => {
    const agent = context?.mastra?.getAgent('weatherAgent')
    if (!agent) {
      return { summary: 'Weather agent not available' }
    }

    const stream = await agent.stream(
      `Analyze the weather in ${inputData.city} and provide a summary.`,
    )

    // Pipe the agent's stream to emit data-tool-agent parts
    await stream.fullStream.pipeTo(context!.writer!)

    return { summary: (await stream.text) ?? 'No summary available' }
  },
})
```

--------------------------------

### Define a multi-step workflow

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Create a workflow with multiple steps that emit workflow state and step data upon execution.

```typescript
import { createStep, createWorkflow } from '@mastra/core/workflows'
import { z } from 'zod'

const fetchWeather = createStep({
  id: 'fetch-weather',
  inputSchema: z.object({
    location: z.string(),
  }),
  outputSchema: z.object({
    temperature: z.number(),
    conditions: z.string(),
  }),
  execute: async ({ inputData }) => {
    // Fetch weather data...
    return { temperature: 22, conditions: 'Sunny' }
  },
})

const planActivities = createStep({
  id: 'plan-activities',
  inputSchema: z.object({
    temperature: z.number(),
    conditions: z.string(),
  }),
  outputSchema: z.object({
    activities: z.string(),
  }),
  execute: async ({ inputData, mastra }) => {
    const agent = mastra?.getAgent('activityAgent')
    const response = await agent?.generate(
      `Suggest activities for ${inputData.conditions} weather at ${inputData.temperature}°C`,
    )
    return { activities: response?.text || '' }
  },
})

export const activitiesWorkflow = createWorkflow({
  id: 'activities-workflow',
  inputSchema: z.object({
    location: z.string(),
  }),
  outputSchema: z.object({
    activities: z.string(),
  }),
})
  .then(fetchWeather)
  .then(planActivities)

activitiesWorkflow.commit()
```

--------------------------------

### Frontend Implementation for useCompletion

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

A basic React component using the useCompletion hook to send prompts and display streamed responses.

```typescript
import { useCompletion } from '@ai-sdk/react'

export default function Page() {
  const { completion, input, handleInputChange, handleSubmit } = useCompletion({
    api: '/api/completion',
  })

  return (
    <form onSubmit={handleSubmit}>
      <input name="prompt" value={input} onChange={handleInputChange} id="input" />
      <button type="submit">Submit</button>
      <div>{completion}</div>
    </form>
  )
}
```

--------------------------------

### Render network data on the frontend

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use the NetworkDataPart type to safely parse and render agent execution steps from data-network parts received via the AI SDK chat transport.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
import type { NetworkDataPart } from '@mastra/ai-sdk'

type NetworkData = NetworkDataPart['data']

function AgentStep({ step }: { step: NetworkData['steps'][number] }) {
  return (
    <div className="agent-step">
      <div className="step-header">
        <span className="agent-name">{step.name}</span>
        <span className={`status status-${step.status}`}>{step.status}</span>
      </div>
      {step.input && (
        <div className="step-input">
          <strong>Input:</strong>
          <pre>{JSON.stringify(step.input, null, 2)}</pre>
        </div>
      )}
      {step.output && (
        <div className="step-output">
          <strong>Output:</strong>
          <pre>
            {typeof step.output === 'string' ? step.output : JSON.stringify(step.output, null, 2)}
          </pre>
        </div>
      )}
    </div>
  )
}

export function NetworkChat() {
  const { messages, sendMessage, status } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/network',
    }),
  })

  return (
    <div>
      {messages.map(message => (
        <div key={message.id}>
          {message.parts.map((part, index) => {
            if (part.type === 'data-network') {
              const networkData = part.data as NetworkData

              return (
                <div key={index} className="network-execution">
                  <div className="network-header">
                    <h3>Agent Network: {networkData.name}</h3>
                    <span className={`status status-${networkData.status}`}>
                      {networkData.status}
                    </span>
                  </div>
                  <div className="network-steps">
                    {networkData.steps.map((step, stepIndex) => (
                      <AgentStep key={stepIndex} step={step} />
                    ))}
                  </div>
                </div>
              )
            }
            return null
          })}
        </div>
      ))}
    </div>
  )
}
```

--------------------------------

### Next.js Backend for useCompletion

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Backend implementation for a Next.js route handler to process completion requests and stream responses.

```ts
import { handleChatStream } from '@mastra/ai-sdk'
import { createUIMessageStreamResponse } from 'ai'
import { mastra } from '@/src/mastra'

// Allow streaming responses up to 30 seconds
export const maxDuration = 30

export async function POST(req: Request) {
  const { prompt }: { prompt: string } = await req.json()

  const stream = await handleChatStream({
    mastra,
    agentId: 'weatherAgent',
    params: {
      messages: [
        {
          id: '1',
          role: 'user',
          parts: [
            {
              type: 'text',
              text: prompt,
            },
          ],
        },
      ],
    },
  })
  return createUIMessageStreamResponse({ stream })
}
```

--------------------------------

### Render custom tool output component in Chat

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use the useChat hook to process message parts and render a WeatherCard component based on the tool-weatherTool state.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
import { WeatherCard } from './weather-card'
import { Loader } from './loader'

export function Chat() {
  const { messages, sendMessage } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/chat/weatherAgent',
    }),
  })

  return (
    <div>
      {messages.map(message => (
        <div key={message.id}>
          {message.parts.map((part, index) => {
            // Handle user text messages
            if (part.type === 'text' && message.role === 'user') {
              return <p key={index}>{part.text}</p>
            }

            // Handle weather tool output
            if (part.type === 'tool-weatherTool') {
              switch (part.state) {
                case 'input-available':
                  return <Loader key={index} />
                case 'output-available':
                  return <WeatherCard key={index} {...part.output} />
                case 'output-error':
                  return <div key={index}>Error: {part.errorText}</div>
                default:
                  return null
              }
            }

            return null
          })}
        </div>
      ))}
    </div>
  )
}
```

--------------------------------

### Register and expose workflow via workflowRoute

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Register the workflow in the Mastra instance and expose it using workflowRoute to enable streaming events to the frontend.

```typescript
import { Mastra } from '@mastra/core'
import { workflowRoute } from '@mastra/ai-sdk'

export const mastra = new Mastra({
  workflows: { activitiesWorkflow },
  server: {
    apiRoutes: [
      workflowRoute({
        path: '/workflow/activitiesWorkflow',
        workflow: 'activitiesWorkflow',
      }),
    ],
  },
})
```

--------------------------------

### Consume workflow stream on the frontend

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use the useChat hook to connect to the workflow route and render data-workflow and data-workflow-step parts.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
import type { WorkflowDataPart, WorkflowStepDataPart } from '@mastra/ai-sdk'

type WorkflowData = WorkflowDataPart['data']
type WorkflowStepData = WorkflowStepDataPart['data']
type StepStatus = 'running' | 'success' | 'failed' | 'suspended' | 'waiting'

function StepIndicator({
  name,
  status,
  output,
}: {
  name: string
  status: StepStatus
  output: unknown
}) {
  return (
    <div className="step">
      <div className="step-header">
        <span>{name}</span>
        <span className={`status status-${status}`}>{status}</span>
      </div>
      {status === 'success' && output && <pre>{JSON.stringify(output, null, 2)}</pre>}
    </div>
  )
}

export function WorkflowChat() {
  const { messages, sendMessage, status } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/workflow/activitiesWorkflow',
      prepareSendMessagesRequest: ({ messages }) => ({
        body: {
          inputData: {
            location: messages[messages.length - 1]?.parts[0]?.text,
          },
        },
      }),
    }),
  })

  return (
    <div>
      {messages.map(message => (
        <div key={message.id}>
          {message.parts.map((part, index) => {
            if (part.type === 'data-workflow') {
              const workflowData = part.data as WorkflowData
              const steps = Object.values(workflowData.steps)

              return (
                <div key={index} className="workflow-progress">
                  <h3>Workflow: {workflowData.name}</h3>
                  <p>Status: {workflowData.status}</p>
                  {steps.map(step => (
                    <StepIndicator
                      key={step.name}
                      name={step.name}
                      status={step.status}
                      output={step.output}
                    />
                  ))}
                </div>
              )
            }
            if (part.type === 'data-workflow-step') {
              const stepData = part.data as WorkflowStepData
              return (
                <StepIndicator
                  key={index}
                  name={stepData.step.name}
                  status={stepData.step.status}
                  output={stepData.step.output}
                />
              )
            }
            return null
          })}
        </div>
      ))}
    </div>
  )
}
```

--------------------------------

### Handle nested agent streams in the frontend

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

A React component using useChat to process data-tool-agent and data-tool-agent-step parts from the backend stream.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
import { useState } from 'react'
import type { AgentDataPart, AgentStepDataPart } from '@mastra/ai-sdk'

export function NestedAgentChat() {
  const [input, setInput] = useState('')
  const { messages, sendMessage, status } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/chat/forecastAgent',
    }),
  })

  return (
    <div>
      <form
        onSubmit={e => {
          e.preventDefault()
          sendMessage({ text: input })
          setInput('')
        }}
      >
        <input value={input} onChange={e => setInput(e.target.value)} placeholder="Enter a city" />
        <button type="submit" disabled={status !== 'ready'}>
          Get Forecast
        </button>
      </form>

      {messages.map(message => (
        <div key={message.id}>
          {message.parts.map((part, index) => {
            if (part.type === 'text') {
              return <p key={index}>{part.text}</p>
            }
            if (part.type === 'data-tool-agent') {
              const { id, data } = part as AgentDataPart
              return (
                <div key={index} className="nested-agent">
                  <strong>Nested Agent: {id}</strong>
                  {data.text && <p>{data.text}</p>}
                </div>
              )
            }
            if (part.type === 'data-tool-agent-step') {
              const { data } = part as AgentStepDataPart
              return (
                <div key={index} className="nested-agent-step">
                  <strong>Completed nested step {data.stepIndex + 1}</strong>
                  {data.step.text && <p>{data.step.text}</p>}
                </div>
              )
            }
            return null
          })}
        </div>
      ))}
    </div>
  )
}
```

--------------------------------

### Register networkRoute in Mastra backend

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Expose the routing agent via networkRoute() within the Mastra server configuration to stream network execution events.

```typescript
import { Mastra } from '@mastra/core'
import { networkRoute } from '@mastra/ai-sdk'

export const mastra = new Mastra({
  agents: { routingAgent, researchAgent, weatherAgent },
  server: {
    apiRoutes: [
      networkRoute({
        path: '/network',
        agent: 'routingAgent',
      }),
    ],
  },
})
```

--------------------------------

### Register workflow with workflowRoute

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Expose the workflow via an API route. Text streaming is enabled by default.

```typescript
import { Mastra } from '@mastra/core'
import { workflowRoute } from '@mastra/ai-sdk'

export const mastra = new Mastra({
  agents: { weatherAgent },
  workflows: { weatherWorkflow },
  server: {
    apiRoutes: [workflowRoute({ path: '/workflow/weather', workflow: 'weatherWorkflow' })],
  },
})
```

--------------------------------

### Render streaming output in React

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use the useChat hook to consume streaming text and workflow progress data. The component handles both assistant text parts and data-workflow parts.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
import { useState } from 'react'
import type { WorkflowDataPart } from '@mastra/ai-sdk'

type WorkflowData = WorkflowDataPart['data']

export function WeatherWorkflow() {
  const [location, setLocation] = useState('')
  const { messages, sendMessage, status } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/workflow/weather',
      prepareSendMessagesRequest: ({ messages }) => ({
        body: {
          inputData: {
            location: messages[messages.length - 1].parts.find(p => p.type === 'text')?.text,
          },
        },
      }),
    }),
  })

  return (
    <div>
      <form
        onSubmit={e => {
          e.preventDefault()
          sendMessage({ text: location })
          setLocation('')
        }}
      >
        <input
          value={location}
          onChange={e => setLocation(e.target.value)}
          placeholder="Enter city"
        />
        <button type="submit" disabled={status !== 'ready'}>
          Analyze
        </button>
      </form>

      {messages.map(message => (
        <div key={message.id}>
          {message.parts.map((part, index) => {
            // Streaming agent text
            if (part.type === 'text' && message.role === 'assistant') {
              return (
                <div key={index}>
                  {status === 'streaming' && (
                    <p>
                      <em>Agent analyzing...</em>
                    </p>
                  )}
                  <p>{part.text}</p>
                </div>
              )
            }
            // Workflow step progress
            if (part.type === 'data-workflow') {
              const workflow = part.data as WorkflowData
              return (
                <div key={index}>
                  {Object.entries(workflow.steps).map(([stepId, step]) => (
                    <div key={stepId}>
                      <strong>{stepId}</strong>: {step.status}
                    </div>
                  ))}
                </div>
              )
            }
            return null
          })}
        </div>
      ))}
    </div>
  )
}
```

--------------------------------

### Configure useChat with Mastra Memory

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use the DefaultChatTransport to send only the latest message to the server while providing thread and resource identifiers for memory management.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'

const { messages, sendMessage } = useChat({
  transport: new DefaultChatTransport({
    api: 'http://localhost:4111/chat/weatherAgent',
    prepareSendMessagesRequest({ messages }) {
      return {
        body: {
          messages: [messages[messages.length - 1]],
          memory: {
            thread: 'user-thread-123',
            resource: 'user-123',
          },
        },
      }
    },
  }),
})
```

--------------------------------

### useChat() hook implementation

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Consumes a chat API endpoint using the useChat hook with DefaultChatTransport. Ensure the api URL matches your configured route.

```ts
import { useChat } from '@ai-sdk/react'
import { useState } from 'react'
import { DefaultChatTransport } from 'ai'

export default function Chat() {
  const [inputValue, setInputValue] = useState('')
  const { messages, sendMessage } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/chat',
    }),
  })

  const handleFormSubmit = (e: React.FormEvent) => {
    e.preventDefault()
    sendMessage({ text: inputValue })
  }

  return (
    <div>
      <pre>{JSON.stringify(messages, null, 2)}</pre>
      <form onSubmit={handleFormSubmit}>
        <input
          value={inputValue}
          onChange={e => setInputValue(e.target.value)}
          placeholder="Name of the city"
        />
      </form>
    </div>
  )
}
```

--------------------------------

### Implement RequestContext middleware in Mastra Server

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Add a server-level middleware to your Mastra configuration to extract data from the request body and set it into the RequestContext.

```typescript
import { Mastra } from '@mastra/core'

export const mastra = new Mastra({
  server: {
    middleware: [
      async (c, next) => {
        const requestContext = c.get('requestContext')

        if (c.req.method === 'POST') {
          const clonedReq = c.req.raw.clone()
          const body = await clonedReq.json()

          if (body?.data) {
            for (const [key, value] of Object.entries(body.data)) {
              requestContext.set(key, value)
            }
          }
        }
        await next()
      },
    ],
  },
})
```

--------------------------------

### Handle custom events in a React frontend

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Filter message parts for the custom event type and render UI components based on the event data.

```typescript
import { useChat } from '@ai-sdk/react'
import { DefaultChatTransport } from 'ai'
import { useMemo } from 'react'

type ProgressData = {
  status: 'in-progress' | 'done'
  message: string
}

function ProgressIndicator({ progress }: { progress: ProgressData }) {
  return (
    <div className="progress-indicator">
      {progress.status === 'in-progress' ? (
        <span className="spinner" />
      ) : (
        <span className="check-icon" />
      )}
      <span className={`status-${progress.status}`}>{progress.message}</span>
    </div>
  )
}

export function TaskChat() {
  const { messages, sendMessage } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/chat/taskAgent',
    }),
  })

  // Extract the latest progress event from messages
  const latestProgress = useMemo(() => {
    const allProgressParts: ProgressData[] = []
    messages.forEach(message => {
      message.parts.forEach(part => {
        if (part.type === 'data-tool-progress') {
          allProgressParts.push(part.data as ProgressData)
        }
      })
    })
    return allProgressParts[allProgressParts.length - 1]
  }, [messages])

  return (
    <div>
      {latestProgress && <ProgressIndicator progress={latestProgress} />}
      {messages.map(message => (
        <div key={message.id}>
          {message.parts.map((part, index) => {
            if (part.type === 'text') {
              return <p key={index}>{part.text}</p>
            }
            return null
          })}
        </div>
      ))}
    </div>
  )
}
```

--------------------------------

### Pass additional data with useChat

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use the sendMessage function from useChat to pass custom data from the frontend to the server, where it can be accessed as RequestContext.

```typescript
import { useChat } from '@ai-sdk/react'
import { useState } from 'react'
import { DefaultChatTransport } from 'ai'

export function ChatAdditional() {
  const [inputValue, setInputValue] = useState('')
  const { messages, sendMessage } = useChat({
    transport: new DefaultChatTransport({
      api: 'http://localhost:4111/chat-extra',
    }),
  })

  const handleFormSubmit = (e: React.FormEvent) => {
    e.preventDefault()
    sendMessage(
      { text: inputValue },
      {
        body: {
          data: {
            userId: 'user123',
            preferences: {
              language: 'en',
              temperature: 'celsius',
            },
          },
        },
      },
    )
  }

  return (
    <div>
      <pre>{JSON.stringify(messages, null, 2)}</pre>
      <form onSubmit={handleFormSubmit}>
        <input
          value={inputValue}
          onChange={e => setInputValue(e.target.value)}
          placeholder="Name of the city"
        />
      </form>
    </div>
  )
}
```

--------------------------------

### Transform streams with toAISdkStream

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Use toAISdkStream to convert Mastra streams to AI SDK-compatible format. Specify version: 'v6' if your application is typed against AI SDK v6.

```typescript
import { toAISdkStream } from '@mastra/ai-sdk'

const v5Stream = toAISdkStream(mastraStream, { from: 'agent' })
const v6Stream = toAISdkStream(mastraStream, { from: 'agent', version: 'v6' })
```

--------------------------------

### Handle RequestContext in Next.js API route

Source: https://mastra.ai/integrations/agentic-ui/ai-sdk-ui

Manually instantiate RequestContext in a Next.js POST route to process incoming data and pass it to handleChatStream.

```typescript
import { handleChatStream } from '@mastra/ai-sdk'
import { RequestContext } from '@mastra/core/request-context'
import { createUIMessageStreamResponse } from 'ai'
import { mastra } from '@/src/mastra'

export async function POST(req: Request) {
  const { messages, data } = await req.json()

  const requestContext = new RequestContext()

  if (data) {
    for (const [key, value] of Object.entries(data)) {
      requestContext.set(key, value)
    }
  }

  const stream = await handleChatStream({
    mastra,
    agentId: 'weatherAgent',
    params: {
      messages,
      requestContext,
    },
  })
  return createUIMessageStreamResponse({ stream })
}
```

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