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# ClassifierProcessor

The `ClassifierProcessor` is a hybrid processor that runs a [`Classifier`](https://mastra.ai/reference/classifier/classifier) over message text and passes the typed answers to `onResult`. In `onResult`, call `abort(reason)` to stop the request with a tripwire, call `filter()` to drop the content and continue, or do nothing to let it through. The classifier provides evidence such as probabilities, choices, and scores. `onResult` owns application policy.

Abort reasons are always the string supplied by the caller. Provider-generated text is never included in the abort message.

## Usage example

```typescript
import { Classifier } from '@mastra/core/classifier'
import { ClassifierProcessor } from '@mastra/core/processors'

const safety = new Classifier({
  id: 'safety',
  model,
  questions: {
    unsafe: {
      type: 'boolean',
      criteria: { true: 'The message is unsafe', false: 'The message is safe' },
    },
  },
})

const processor = new ClassifierProcessor({
  classifier: safety,
  onResult: (answers, { abort }) => {
    if (answers.unsafe.probability > 0.8) abort('Message rejected by safety policy')
  },
  lastMessageOnly: true,
})
```

Several conditions can share one `onResult`, or use separate processors when they need different classifiers. Give each processor a unique `id`. Processor instances with the same `id` share per-run state, including accumulated stream chunks.

```typescript
inputProcessors: [
  new ClassifierProcessor({
    id: 'safety-check',
    classifier: safety,
    onResult: (a, { abort }) => {
      if (a.unsafe.probability > 0.8) abort('Rejected by safety policy')
    },
  }),
  new ClassifierProcessor({
    id: 'topic-check',
    classifier: topic,
    onResult: (a, { abort }) => {
      if (a.topic.choice === 'other') abort('Support questions only')
    },
  }),
]
```

## Constructor parameters

**options** (`ClassifierProcessorOptions`): Configuration for the processor

**options.classifier** (`Classifier | string`): A Classifier instance with configured questions, or the key or ID of a classifier registered on the Mastra instance. Registered classifiers are resolved lazily through mastra.getClassifierById() on first use.

**options.onResult** (`(answers, context) => void | Promise<void>`): Called after each classification. answers is typed from the classifier questions. context provides abort(reason) to stop the request with a tripwire, filter() to drop the message or chunk, phase, and the full ClassifierResult including usage and provider metadata.

**options.id** (`string`): Processor identifier. Use a unique ID for each ClassifierProcessor in the same phase because instances with the same ID share per-run processor state.

**options.errorStrategy** (`'warn' | 'strict'`): How to handle evaluation model failures. 'strict' is fail-closed: it aborts the request, including during streaming. 'warn' is fail-open: it logs the failure and lets the content through. Classifier configuration errors always throw.

**options.lastMessageOnly** (`boolean`): When true, only the last message is classified in the input and output phases.

**options.chunkWindow** (`number`): Non-negative integer number of trailing stream chunks to classify together, including the current text-delta chunk. A value of 0 classifies only the current chunk. Each text chunk triggers a classifier call, so larger windows increase cost.

**options.maxInputLength** (`number`): Non-negative integer maximum number of characters sent to the classifier. Longer text is truncated.

**options.providerOptions** (`SharedV4ProviderOptions`): Provider-specific options forwarded to the evaluation model.

## Behavior by phase

| Phase                              | no call        | `abort(reason)`      | `filter()`                        |
| ---------------------------------- | -------------- | -------------------- | --------------------------------- |
| Input (`inputProcessors`)          | message passes | abort before the LLM | message removed from context      |
| Output result (`outputProcessors`) | message passes | abort the response   | message removed from the response |
| Stream (`outputProcessors`)        | chunk emitted  | abort the stream     | chunk not emitted                 |

Messages with no text content are passed through without calling the classifier.

## Extended usage examples

### Topic scoping

```typescript
import { Agent } from '@mastra/core/agent'
import { Classifier } from '@mastra/core/classifier'
import { ClassifierProcessor } from '@mastra/core/processors'

const topic = new Classifier({
  id: 'topic',
  model,
  questions: {
    topic: {
      type: 'choice',
      criteria: {
        billing: 'Questions about invoices or payments',
        account: 'Questions about account settings',
        other: 'Anything else',
      },
    },
  },
})

export const agent = new Agent({
  id: 'support-agent',
  name: 'support-agent',
  instructions: 'You help customers with billing and account questions.',
  model: 'openai/gpt-5.6-sol',
  inputProcessors: [
    new ClassifierProcessor({
      classifier: topic,
      lastMessageOnly: true,
      onResult: (answers, { abort }) => {
        if (answers.topic.choice === 'other') {
          abort('This assistant only handles billing and account questions')
        }
      },
    }),
  ],
})
```

### Output quality gate

```typescript
import { Agent } from '@mastra/core/agent'
import { Classifier } from '@mastra/core/classifier'
import { ClassifierProcessor } from '@mastra/core/processors'

const quality = new Classifier({
  id: 'quality',
  model,
  questions: {
    quality: {
      type: 'score',
      instructions: 'Rate how well the response answers the user',
      criteria: ['Off-topic', 'Partial', 'Complete'],
    },
  },
})

export const agent = new Agent({
  id: 'quality-gated-agent',
  name: 'quality-gated-agent',
  instructions: 'You are a helpful assistant',
  model: 'openai/gpt-5.6-sol',
  outputProcessors: [
    new ClassifierProcessor({
      classifier: quality,
      onResult: (answers, { filter }) => {
        if (answers.quality.score < 1) filter()
      },
    }),
  ],
})
```

### Registered classifier

Register the classifier on the `Mastra` instance and reference it by key or ID. The processor resolves it on first use.

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

export const mastra = new Mastra({
  classifiers: {
    safety: new Classifier({ id: 'safety', model, questions: {/* ... */} }),
  },
})
```

```typescript
import { ClassifierProcessor } from '@mastra/core/processors'

type SafetyQuestions = {
  unsafe: { type: 'boolean' }
}

const processor = new ClassifierProcessor<SafetyQuestions>({
  classifier: 'safety',
  onResult: (answers, { abort }) => {
    if (answers.unsafe.probability > 0.8) abort('Message rejected by safety policy')
  },
})
```

When using a registered classifier by string, answer types aren't inferred from the registered instance. Pass its question map as the `ClassifierProcessor` type argument to type the `answers` parameter.

## Related

- [Classifier](https://mastra.ai/reference/classifier/classifier)
- [Guardrails](https://mastra.ai/docs/agents/guardrails)