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# Step class

The Step class defines individual units of work within a workflow, encapsulating execution logic, data validation, and input/output handling. It can take a tool, agent, or configured classifier as a parameter to automatically create a step from it.

## Usage example

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

const step1 = createStep({
  id: 'step-1',
  description: 'passes value from input to output',
  inputSchema: z.object({
    value: z.number(),
  }),
  outputSchema: z.object({
    value: z.number(),
  }),
  execute: async ({ inputData }) => {
    const { value } = inputData
    return {
      value,
    }
  },
})
```

## Define schemas

You can define the step's `inputSchema` and `outputSchema` with any library that supports [Standard JSON Schema](https://standardschema.dev/json-schema). This includes libraries like [Zod](https://zod.dev/), [Valibot](https://valibot.dev/), and [ArkType](https://arktype.io/).

**Zod**:

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

const step1 = createStep({
  id: 'step-1',
  inputSchema: z.object({
    message: z.string(),
  }),
  outputSchema: z.object({
    formatted: z.string(),
  }),
  execute: async ({ inputData }) => {
    const { message } = inputData

    return {
      formatted: message.toUpperCase(),
    }
  },
})
```

**Valibot**:

```typescript
import { createStep } from '@mastra/core/workflows'
import * as v from 'valibot'
import { toStandardJsonSchema } from '@valibot/to-json-schema'

const step1 = createStep({
  id: 'step-1',
  inputSchema: toStandardJsonSchema(
    v.object({
      message: v.string(),
    }),
  ),
  outputSchema: toStandardJsonSchema(
    v.object({
      formatted: v.string(),
    }),
  ),
  execute: async ({ inputData }) => {
    const { message } = inputData

    return {
      formatted: message.toUpperCase(),
    }
  },
})
```

**ArkType**:

```typescript
import { createStep } from '@mastra/core/workflows'
import { type } from 'arktype'

const step1 = createStep({
  id: 'step-1',
  inputSchema: type({
    message: 'string',
  }),
  outputSchema: type({
    formatted: 'string',
  }),
  execute: async ({ inputData }) => {
    const { message } = inputData

    return {
      formatted: message.toUpperCase(),
    }
  },
})
```

## Creating steps from agents

You can create a step directly from an agent. The step will use the agent's name as its ID.

### Basic agent step

```typescript
import { testAgent } from '../agents/test-agent'

const agentStep = createStep(testAgent)
// inputSchema: { prompt: string }
// outputSchema: { text: string }
```

### Agent step with structured output

Pass `structuredOutput` to have the agent return typed structured data:

```typescript
const articleSchema = z.object({
  title: z.string(),
  summary: z.string(),
  tags: z.array(z.string()),
})

const agentStep = createStep(testAgent, {
  structuredOutput: { schema: articleSchema },
})
// inputSchema: { prompt: string }
// outputSchema: { title: string, summary: string, tags: string[] }
```

### Agent step options

**structuredOutput** (`{ schema: StandardJSONSchemaV1 }`): When provided, the agent returns structured data matching this schema instead of plain text. The step's outputSchema is set to the provided schema.

**onFinish** (`(result: AgentResult) => void`): Callback invoked when the agent completes generation.

## Creating steps from classifiers

Pass a [`Classifier`](https://mastra.ai/reference/classifier/classifier) with constructor-configured questions to `createStep()`. By default, the classifier evaluates the complete `inputData`. Use `state` to select a JSON value from the step context.

```typescript
import { Classifier } from '@mastra/core/classifier'
import { typeSafeAi } from '@ai-sdk/typesafe-ai'
import { createStep } from '@mastra/core/workflows'

const router = new Classifier({
  id: 'ticket-router',
  model: typeSafeAi.decisionModel('jev-latest'),
  questions: {
    route: {
      type: 'choice',
      criteria: {
        billing: 'Billing, invoices, and payments',
        support: 'Account access and product help',
        other: 'Anything else',
      },
    },
    urgent: { type: 'boolean' },
  },
})

const classifyTicket = createStep(router, {
  maxRetries: 2,
  retries: 1,
})
```

The typed step output is JSON-safe:

- `answers.route.choice` is the selected choice literal.
- `answers.urgent.probability` is the probability that the boolean answer is `true`, not a thresholded boolean.
- Choice and score answers may include distributions.
- `usage` contains normalized token usage.

`maxRetries` controls classifier model-call retries. `retries` controls workflow step retries. Classifier warnings, response metadata, and raw provider data aren't included in workflow output.

A classifier without constructor-configured questions can't be used as a workflow step. The step evaluates its complete input. Add a preceding `.map()` call when the workflow needs to select or reshape that input.

## Constructor parameters

**id** (`string`): Unique identifier for the step

**description** (`string`): Optional description of what the step does

**inputSchema** (`StandardJSONSchemaV1`): Standard JSON Schema defining the input structure

**outputSchema** (`StandardJSONSchemaV1`): Standard JSON Schema defining the output structure

**resumeSchema** (`StandardJSONSchemaV1`): Optional Standard JSON Schema for resuming the step

**suspendSchema** (`StandardJSONSchemaV1`): Optional Standard JSON Schema for suspending the step

**stateSchema** (`StandardJSONSchemaV1`): Optional Standard JSON Schema for the step state. Automatically injected when using Mastra's state system. The stateSchema must be a subset of the workflow's stateSchema. If not specified, type is 'any'.

**requestContextSchema** (`StandardJSONSchemaV1`): Standard JSON Schema for validating request context values. When provided, the context is validated before the step's execute() runs, failing the step if validation fails.

**execute** (`(params: ExecuteParams) => Promise<any>`): Async function containing step logic

**execute.inputData** (`z.infer<TStepInput>`): The input data matching the inputSchema

**execute.resumeData** (`z.infer<TResumeSchema>`): The resume data matching the resumeSchema, when resuming the step from a suspended state. Only exists if the step is being resumed.

**execute.suspendData** (`z.infer<TSuspendSchema>`): The suspend data that was originally passed to suspend() when the step was suspended. Only exists if the step is being resumed and was previously suspended with data.

**execute.mastra** (`Mastra`): Access to Mastra services (agents, tools, etc.)

**execute.getStepResult** (`(step: Step | string) => any`): Function to access results from other steps

**execute.getInitData** (`() => any`): Function to access the initial input data of the workflow in any step

**execute.suspend** (`(suspendPayload: any, suspendOptions?: { resumeLabel?: string }) => Promise<void>`): Function to pause workflow execution

**execute.state** (`z.infer<TState>`): The current workflow state. Contains shared values that persist across all steps and suspend/resume cycles. The structure is defined by the step's stateSchema.

**execute.setState** (`(state: z.infer<TState>) => void`): Function to set the state of the workflow. Inject via reducer-like pattern, such as 'setState({ ...state, ...newState })'

**execute.runId** (`string`): Current run id

**execute.requestContext** (`RequestContext`): Request Context for dependency injection and contextual information.

**execute.retryCount** (`number`): The retry count for this specific step, it automatically increases each time the step is retried

**scorers** (`MastraScorers | (({ requestContext }) => MastraScorers | Promise<MastraScorers>)`): Scorers that run automatically after the step completes successfully. Each scorer evaluates the step's own input and output, and the results are stored and attached to the step's trace. Provide a map of { \[name]: { scorer, sampling? } }, or a function that returns one. Scoring runs asynchronously and doesn't block the workflow. See Scoring step output.

**retries** (`number`): Number of times to retry the step's execute function if it throws.

**metadata** (`Record<string, any>`): Optional key-value pairs for storing additional step information. Values must be serializable (no functions, circular references, etc.).

## Scoring step output

Attach `scorers` to a step to evaluate that step's output automatically, at the point it runs, instead of only scoring the workflow's final answer. This is useful for multi-step and RAG workflows, where you want to see which step degraded quality, for example whether a retrieval step returned relevant chunks before later steps reason over them.

Each scorer receives the step's own `input` and `output`. Scoring runs asynchronously after the step succeeds, and the result is stored against the step's trace. Use `sampling` to control how often a scorer runs.

The following example attaches a scorer to a retrieval step so every execution is scored:

```typescript
import { createStep } from '@mastra/core/workflows'
import { z } from 'zod'
import { retrievalRelevanceScorer } from '../scorers/retrieval-relevance'

const retrievalStep = createStep({
  id: 'retrieval',
  inputSchema: z.object({ query: z.string() }),
  outputSchema: z.object({ query: z.string(), chunks: z.array(z.string()) }),
  scorers: {
    retrievalRelevance: {
      scorer: retrievalRelevanceScorer(),
      sampling: { type: 'ratio', rate: 1 },
    },
  },
  execute: async ({ inputData }) => {
    const chunks = await retrieve(inputData.query)
    return { query: inputData.query, chunks }
  },
})
```

Attach a scorer to each step you want to measure to build per-step scores across a multi-step workflow. Because scoring is scoped to a single step, you don't need a dedicated cross-step metric to see where quality changes.

Agent and tool steps added with [`Workflow.agent()`](https://mastra.ai/reference/workflows/workflow-methods/agent) and [`Workflow.tool()`](https://mastra.ai/reference/workflows/workflow-methods/tool) accept the same `scorers` option in their step options.

> **Note:** Visit the [Scorers overview](https://mastra.ai/docs/evals/overview) to learn how live evaluations run and where results are stored, and [Custom scorers](https://mastra.ai/docs/evals/custom-scorers) to build your own.

## Related

- [Workflow state](https://mastra.ai/docs/workflows/workflow-state)
- [Control flow](https://mastra.ai/docs/workflows/control-flow)
- [Using agents and tools](https://mastra.ai/docs/workflows/agents-and-tools)