# Agent Approval

Agents sometimes require the same [human-in-the-loop](https://mastra.ai/docs/workflows/human-in-the-loop) oversight used in workflows when calling tools that handle sensitive operations, like deleting resources or performing running long processes. With agent approval you can suspend a tool call and provide feedback to the user, or approve or decline a tool call based on targeted application conditions.

## Tool call approval

Tool call approval can be enabled at the agent level and apply to every tool the agent uses, or at the tool level providing more granular control over individual tool calls.

### Storage

Agent approval uses a snapshot to capture the state of the request. Ensure you've enabled a storage provider in your main Mastra instance. If storage isn't enabled you'll see an error relating to snapshot not found.

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

export const mastra = new Mastra({
  storage: new LibSQLStore({
    id: 'mastra-storage',
    url: ':memory:',
  }),
})
```

## Agent-level approval

When calling an agent using `.stream()` set `requireToolApproval` to `true` which will prevent the agent from calling any of the tools defined in its configuration.

```typescript
const stream = await agent.stream("What's the weather in London?", {
  requireToolApproval: true,
})
```

### Approving tool calls

To approve a tool call, access `approveToolCall` from the `agent`, passing in the `runId` of the stream. This will let the agent know its now OK to call its tools.

```typescript
const handleApproval = async () => {
  const approvedStream = await agent.approveToolCall({ runId: stream.runId })

  for await (const chunk of approvedStream.textStream) {
    process.stdout.write(chunk)
  }
  process.stdout.write('\n')
}
```

### Declining tool calls

To decline a tool call, access the `declineToolCall` from the `agent`. You will see the streamed response from the agent, but it won't call its tools.

```typescript
const handleDecline = async () => {
  const declinedStream = await agent.declineToolCall({ runId: stream.runId })

  for await (const chunk of declinedStream.textStream) {
    process.stdout.write(chunk)
  }
  process.stdout.write('\n')
}
```

## Tool approval with generate()

Tool approval also works with the `generate()` method for non-streaming use cases. When using `generate()` with `requireToolApproval: true`, the method returns immediately when a tool requires approval instead of executing it.

### How it works

When a tool requires approval during a `generate()` call, the response includes:

- `finishReason: 'suspended'` - indicates the agent is waiting for approval
- `suspendPayload` - contains tool call details (`toolCallId`, `toolName`, `args`)
- `runId` - needed to approve or decline the tool call

### Approving tool calls

To approve a tool call with `generate()`, use the `approveToolCallGenerate` method:

```typescript
const output = await agent.generate('Find user John', {
  requireToolApproval: true,
})

if (output.finishReason === 'suspended') {
  console.log('Tool requires approval:', output.suspendPayload.toolName)
  console.log('Arguments:', output.suspendPayload.args)

  // Approve the tool call and get the final result
  const result = await agent.approveToolCallGenerate({
    runId: output.runId,
    toolCallId: output.suspendPayload.toolCallId,
  })

  console.log('Final result:', result.text)
}
```

### Declining tool calls

To decline a tool call, use the `declineToolCallGenerate` method:

```typescript
if (output.finishReason === 'suspended') {
  const result = await agent.declineToolCallGenerate({
    runId: output.runId,
    toolCallId: output.suspendPayload.toolCallId,
  })

  // Agent will respond acknowledging the declined tool
  console.log(result.text)
}
```

### Stream vs Generate comparison

| Aspect             | `stream()`                   | `generate()`                                     |
| ------------------ | ---------------------------- | ------------------------------------------------ |
| Response type      | Streaming chunks             | Complete response                                |
| Approval detection | `tool-call-approval` chunk   | `finishReason: 'suspended'`                      |
| Approve method     | `approveToolCall({ runId })` | `approveToolCallGenerate({ runId, toolCallId })` |
| Decline method     | `declineToolCall({ runId })` | `declineToolCallGenerate({ runId, toolCallId })` |
| Result             | Stream to iterate            | Full output object                               |

## Tool-level approval

There are two types of tool call approval. The first uses `requireApproval`, which is a property on the tool definition, while `requireToolApproval` is a parameter passed to `agent.stream()`. The second uses `suspend` and lets the agent provide context or confirmation prompts so the user can decide whether the tool call should continue.

### Tool approval using `requireToolApproval`

In this approach, `requireApproval` is configured on the tool definition (shown below) rather than on the agent.

```typescript
export const testTool = createTool({
  id: 'test-tool',
  description: 'Fetches weather for a location',
  inputSchema: z.object({
    location: z.string(),
  }),
  outputSchema: z.object({
    weather: z.string(),
  }),
  resumeSchema: z.object({
    approved: z.boolean(),
  }),
  execute: async inputData => {
    const response = await fetch(`https://wttr.in/${inputData.location}?format=3`)
    const weather = await response.text()

    return { weather }
  },
  requireApproval: true,
})
```

When `requireApproval` is true for a tool, the stream will include chunks of type `tool-call-approval` to indicate that the call is paused. To continue the call, invoke `resumeStream` with the required `resumeSchema` and the `runId`.

```typescript
const stream = await agent.stream("What's the weather in London?")

for await (const chunk of stream.fullStream) {
  if (chunk.type === 'tool-call-approval') {
    console.log('Approval required.')
  }
}

const handleResume = async () => {
  const resumedStream = await agent.resumeStream({ approved: true }, { runId: stream.runId })

  for await (const chunk of resumedStream.textStream) {
    process.stdout.write(chunk)
  }
  process.stdout.write('\n')
}
```

### Tool approval using `suspend`

With this approach, neither the agent nor the tool uses `requireApproval`. Instead, the tool implementation calls `suspend` to pause execution and return context or confirmation prompts to the user.

```typescript
export const testToolB = createTool({
  id: 'test-tool-b',
  description: 'Fetches weather for a location',
  inputSchema: z.object({
    location: z.string(),
  }),
  outputSchema: z.object({
    weather: z.string(),
  }),
  resumeSchema: z.object({
    approved: z.boolean(),
  }),
  suspendSchema: z.object({
    reason: z.string(),
  }),
  execute: async (inputData, context) => {
    const { resumeData: { approved } = {}, suspend } = context?.agent ?? {}

    if (!approved) {
      return suspend?.({ reason: 'Approval required.' })
    }

    const response = await fetch(`https://wttr.in/${inputData.location}?format=3`)
    const weather = await response.text()

    return { weather }
  },
})
```

With this approach the stream will include a `tool-call-suspended` chunk, and the `suspendPayload` will contain the `reason` defined by the tool's `suspendSchema`. To continue the call, invoke `resumeStream` with the required `resumeSchema` and the `runId`.

```typescript
const stream = await agent.stream("What's the weather in London?")

for await (const chunk of stream.fullStream) {
  if (chunk.type === 'tool-call-suspended') {
    console.log(chunk.payload.suspendPayload)
  }
}

const handleResume = async () => {
  const resumedStream = await agent.resumeStream({ approved: true }, { runId: stream.runId })

  for await (const chunk of resumedStream.textStream) {
    process.stdout.write(chunk)
  }
  process.stdout.write('\n')
}
```

## Automatic tool resumption

When using tools that call `suspend()`, you can enable automatic resumption so the agent resumes suspended tools based on the user's next message. This creates a conversational flow where users provide the required information naturally, without your application needing to call `resumeStream()` explicitly.

### Enabling auto-resume

Set `autoResumeSuspendedTools` to `true` in the agent's default options or when calling `stream()`:

```typescript
import { Agent } from '@mastra/core/agent'
import { Memory } from '@mastra/memory'

// Option 1: In agent configuration
const agent = new Agent({
  id: 'my-agent',
  name: 'My Agent',
  instructions: 'You are a helpful assistant',
  model: 'openai/gpt-4o-mini',
  tools: { weatherTool },
  memory: new Memory(),
  defaultOptions: {
    autoResumeSuspendedTools: true,
  },
})

// Option 2: Per-request
const stream = await agent.stream("What's the weather?", {
  autoResumeSuspendedTools: true,
})
```

### How it works

When `autoResumeSuspendedTools` is enabled:

1. A tool suspends execution by calling `suspend()` with a payload (e.g., requesting more information)

2. The suspension is persisted to memory along with the conversation

3. When the user sends their next message on the same thread, the agent:

   - Detects the suspended tool from message history
   - Extracts `resumeData` from the user's message based on the tool's `resumeSchema`
   - Automatically resumes the tool with the extracted data

### Example

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

export const weatherTool = createTool({
  id: 'weather-info',
  description: 'Fetches weather information for a city',
  suspendSchema: z.object({
    message: z.string(),
  }),
  resumeSchema: z.object({
    city: z.string(),
  }),
  execute: async (_inputData, context) => {
    // Check if this is a resume with data
    if (!context?.agent?.resumeData) {
      // First call - suspend and ask for the city
      return context?.agent?.suspend({
        message: 'What city do you want to know the weather for?',
      })
    }

    // Resume call - city was extracted from user's message
    const { city } = context.agent.resumeData
    const response = await fetch(`https://wttr.in/${city}?format=3`)
    const weather = await response.text()

    return { city, weather }
  },
})

const agent = new Agent({
  id: 'my-agent',
  name: 'My Agent',
  instructions: 'You are a helpful assistant',
  model: 'openai/gpt-4o-mini',
  tools: { weatherTool },
  memory: new Memory(),
  defaultOptions: {
    autoResumeSuspendedTools: true,
  },
})

const stream = await agent.stream("What's the weather like?")

for await (const chunk of stream.fullStream) {
  if (chunk.type === 'tool-call-suspended') {
    console.log(chunk.payload.suspendPayload)
  }
}

const handleResume = async () => {
  const resumedStream = await agent.stream('San Francisco')

  for await (const chunk of resumedStream.textStream) {
    process.stdout.write(chunk)
  }
  process.stdout.write('\n')
}
```

**Conversation flow:**

```text
User: "What's the weather like?"
Agent: "What city do you want to know the weather for?"

User: "San Francisco"
Agent: "The weather in San Francisco is: San Francisco: ☀️ +72°F"
```

The second message automatically resumes the suspended tool - the agent extracts `{ city: "San Francisco" }` from the user's message and passes it as `resumeData`.

### Requirements

For automatic tool resumption to work:

- **Memory configured**: The agent needs memory to track suspended tools across messages
- **Same thread**: The follow-up message must use the same memory thread and resource identifiers
- **`resumeSchema` defined**: The tool must define a `resumeSchema` so the agent knows what data structure to extract from the user's message

### Manual vs automatic resumption

| Approach                               | Use case                                                                 |
| -------------------------------------- | ------------------------------------------------------------------------ |
| Manual (`resumeStream()`)              | Programmatic control, webhooks, button clicks, external triggers         |
| Automatic (`autoResumeSuspendedTools`) | Conversational flows where users provide resume data in natural language |

Both approaches work with the same tool definitions. Automatic resumption triggers only when suspended tools exist in the message history and the user sends a new message on the same thread.

## Tool approval: Supervisor pattern

The [supervisor pattern](https://mastra.ai/docs/agents/networks) lets a supervisor agent coordinate multiple subagents using `.stream()` or `.generate()`. The supervisor delegates tasks to subagents, which may use tools that require approval. When this happens, tool approvals properly propagate through the delegation chain -- the approval request surfaces at the supervisor level where you can handle it, regardless of which subagent triggered it.

### How it works

1. The supervisor agent delegates a task to a subagent.
2. The subagent calls a tool that has `requireApproval: true` or uses `suspend()`.
3. The approval request bubbles up through the delegation chain to the supervisor.
4. You handle the approval or decline at the supervisor level.
5. The decision propagates back down to the subagent, which continues or terminates accordingly.

### Example

The following example creates a subagent with a database lookup tool that requires approval. The supervisor delegates to this subagent, and when the tool triggers an approval request, it surfaces in the supervisor's stream as a `tool-call-approval` chunk. You then approve the tool call using `approveToolCall` with the stream's `runId`.

```typescript
import { Agent } from '@mastra/core/agent'
import { createTool } from '@mastra/core/tools'
import { Memory } from '@mastra/memory'
import { z } from 'zod'

// subagent with approval-required tool
const findUserTool = createTool({
  id: 'find-user',
  description: 'Finds user by ID in the database',
  inputSchema: z.object({
    userId: z.string(),
  }),
  outputSchema: z.object({
    user: z.object({
      id: z.string(),
      name: z.string(),
      email: z.string(),
    }),
  }),
  requireApproval: true, // Requires approval before execution
  execute: async input => {
    const user = await database.findUser(input.userId)
    return { user }
  },
})

const dataAgent = new Agent({
  id: 'data-agent',
  name: 'Data Agent',
  description: 'Handles database queries and user data retrieval',
  model: 'openai/gpt-4o-mini',
  tools: { findUserTool },
})

const supervisorAgent = new Agent({
  id: 'supervisor',
  name: 'Supervisor Agent',
  instructions: `You coordinate data retrieval tasks.
    Delegate to data-agent for user lookups.`,
  model: 'openai/gpt-5.1',
  agents: { dataAgent },
  memory: new Memory(),
})

// When supervisor delegates to dataAgent and tool requires approval
const stream = await supervisorAgent.stream('Find user with ID 12345')

for await (const chunk of stream.fullStream) {
  if (chunk.type === 'tool-call-approval') {
    console.log('Tool requires approval:', chunk.payload.toolName)
    console.log('Arguments:', chunk.payload.args)

    // Approve the tool call
    const resumeStream = await supervisorAgent.approveToolCall({
      runId: stream.runId,
      toolCallId: chunk.payload.toolCallId,
    })

    // Process resumed stream
    for await (const resumeChunk of resumeStream.textStream) {
      process.stdout.write(resumeChunk)
    }
  }
}
```

### Declining tool calls in supervisor pattern

You can also decline tool calls at the supervisor level by calling `declineToolCall`. The supervisor will respond acknowledging the declined tool without executing it:

```typescript
for await (const chunk of stream.fullStream) {
  if (chunk.type === 'tool-call-approval') {
    console.log('Declining tool call:', chunk.payload.toolName)

    // Decline the tool call
    const declineStream = await supervisorAgent.declineToolCall({
      runId: stream.runId,
      toolCallId: chunk.payload.toolCallId,
    })

    // The supervisor will respond acknowledging the declined tool
    for await (const declineChunk of declineStream.textStream) {
      process.stdout.write(declineChunk)
    }
  }
}
```

### Using suspend() in supervisor pattern

Tools can also use [`suspend()`](#tool-approval-using-suspend) to pause execution and return context to the user. This approach works through the supervisor delegation chain the same way `requireApproval` does -- the suspension surfaces at the supervisor level:

```typescript
const conditionalTool = createTool({
  id: 'conditional-operation',
  description: 'Performs an operation that may require confirmation',
  inputSchema: z.object({
    operation: z.string(),
  }),
  suspendSchema: z.object({
    message: z.string(),
  }),
  resumeSchema: z.object({
    confirmed: z.boolean(),
  }),
  execute: async (input, context) => {
    const { resumeData } = context?.agent ?? {}

    if (!resumeData?.confirmed) {
      return context?.agent?.suspend({
        message: `Confirm: ${input.operation}?`,
      })
    }

    // Proceed with operation
    return await performOperation(input.operation)
  },
})

// When using this tool through a subagent in supervisor pattern
for await (const chunk of stream.fullStream) {
  if (chunk.type === 'tool-call-suspended') {
    console.log('Tool suspended:', chunk.payload.suspendPayload.message)

    // Resume with confirmation
    const resumeStream = await supervisorAgent.resumeStream(
      { confirmed: true },
      { runId: stream.runId },
    )

    for await (const resumeChunk of resumeStream.textStream) {
      process.stdout.write(resumeChunk)
    }
  }
}
```

### Tool approval with generate()

Tool approval propagation also works with `generate()` in supervisor pattern:

```typescript
const output = await supervisorAgent.generate('Find user with ID 12345', {
  maxSteps: 10,
})

if (output.finishReason === 'suspended') {
  console.log('Tool requires approval:', output.suspendPayload.toolName)

  // Approve
  const result = await supervisorAgent.approveToolCallGenerate({
    runId: output.runId,
    toolCallId: output.suspendPayload.toolCallId,
  })

  console.log('Final result:', result.text)
}
```

### Multi-level delegation

Tool approvals propagate through multiple levels of delegation. For example, if a supervisor delegates to subagent A, which in turn delegates to subagent B that has a tool with `requireApproval: true`, the approval request still surfaces at the top-level supervisor. You handle the approval or decline there, and the result flows back down through the entire delegation chain to the tool that requested it.

## Related

- [Using Tools](https://mastra.ai/docs/agents/using-tools)
- [Agent Overview](https://mastra.ai/docs/agents/overview)
- [Tools Overview](https://mastra.ai/docs/mcp/overview)
- [Agent Memory](https://mastra.ai/docs/agents/agent-memory)
- [Request Context](https://mastra.ai/docs/server/request-context)