import Steps from "@site/src/components/Steps"; import StepItem from "@site/src/components/StepItem";

Building an AI recruiter

In this guide, you'll learn how Mastra helps you build workflows with LLMs.

You'll create a workflow that gathers information from a candidate's resume, then branches to either a technical or behavioral question based on the candidate's profile. Along the way, you'll see how to structure workflow steps, handle branching, and integrate LLM calls.

Prerequisites

Node.js v22.13.0 or later installed
An API key from a supported Model Provider
An existing Mastra project (Follow the installation guide to set up a new project)

Building the workflow

Set up the Workflow, define steps to extract and classify candidate data, and then ask suitable follow-up questions.

<Steps> <StepItem> Create a new file `src/mastra/workflows/candidate-workflow.ts` and define your workflow:

```ts title="src/mastra/workflows/candidate-workflow.ts"
import { createWorkflow, createStep } from '@mastra/core/workflows'
import { z } from 'zod'

export const candidateWorkflow = createWorkflow({
  id: 'candidate-workflow',
  inputSchema: z.object({
    resumeText: z.string(),
  }),
  outputSchema: z.object({
    askAboutSpecialty: z.object({
      question: z.string(),
    }),
    askAboutRole: z.object({
      question: z.string(),
    }),
  }),
}).commit()
```

</StepItem> <StepItem> You want to extract candidate details from the resume text and classify the person as "technical" or "non-technical". This step calls an LLM to parse the resume and returns structured JSON, including the name, technical status, specialty, and the original resume text. Defined through the `inputSchema` you get access to the `resumeText` inside `execute()`. Use it to prompt an LLM and return the organized fields.

To the existing `src/mastra/workflows/candidate-workflow.ts` file add the following:

```ts title="src/mastra/workflows/candidate-workflow.ts"
import { Agent } from '@mastra/core/agent'

const recruiter = new Agent({
  id: 'recruiter-agent',
  name: 'Recruiter Agent',
  instructions: `You are a recruiter.`,
  model: 'openai/gpt-5.4',
})

const gatherCandidateInfo = createStep({
  id: 'gatherCandidateInfo',
  inputSchema: z.object({
    resumeText: z.string(),
  }),
  outputSchema: z.object({
    candidateName: z.string(),
    isTechnical: z.boolean(),
    specialty: z.string(),
    resumeText: z.string(),
  }),
  execute: async ({ inputData }) => {
    const resumeText = inputData?.resumeText

    const prompt = `Extract details from the resume text:
"${resumeText}"`

    const res = await recruiter.generate(prompt, {
      structuredOutput: {
        schema: z.object({
          candidateName: z.string(),
          isTechnical: z.boolean(),
          specialty: z.string(),
          resumeText: z.string(),
        }),
      },
    })

    return res.object
  },
})
```

Since you're using a Recruiter agent inside `execute()` you need to define it above the step and add the necessary imports.

</StepItem> <StepItem> This step prompts a candidate who is identified as "technical" for more information about how they got into their specialty. It uses the entire resume text so the LLM can craft a relevant follow-up question.

To the existing `src/mastra/workflows/candidate-workflow.ts` file add the following:

```ts title="src/mastra/workflows/candidate-workflow.ts"
const askAboutSpecialty = createStep({
  id: 'askAboutSpecialty',
  inputSchema: z.object({
    candidateName: z.string(),
    isTechnical: z.boolean(),
    specialty: z.string(),
    resumeText: z.string(),
  }),
  outputSchema: z.object({
    question: z.string(),
  }),
  execute: async ({ inputData: candidateInfo }) => {
    const prompt = `You are a recruiter. Given the resume below, craft a short question
for ${candidateInfo?.candidateName} about how they got into "${candidateInfo?.specialty}".
Resume: ${candidateInfo?.resumeText}`
    const res = await recruiter.generate(prompt)

    return { question: res?.text?.trim() || '' }
  },
})
```

</StepItem> <StepItem> If the candidate is "non-technical", you want a different follow-up question. This step asks what interests them most about the role, again referencing their complete resume text. The `execute()` function solicits a role-focused query from the LLM.

To the existing `src/mastra/workflows/candidate-workflow.ts` file add the following:

```ts title="src/mastra/workflows/candidate-workflow.ts"
const askAboutRole = createStep({
  id: 'askAboutRole',
  inputSchema: z.object({
    candidateName: z.string(),
    isTechnical: z.boolean(),
    specialty: z.string(),
    resumeText: z.string(),
  }),
  outputSchema: z.object({
    question: z.string(),
  }),
  execute: async ({ inputData: candidateInfo }) => {
    const prompt = `You are a recruiter. Given the resume below, craft a short question
for ${candidateInfo?.candidateName} asking what interests them most about this role.
Resume: ${candidateInfo?.resumeText}`
    const res = await recruiter.generate(prompt)
    return { question: res?.text?.trim() || '' }
  },
})
```

</StepItem> <StepItem> You now combine the steps to implement branching logic based on the candidate's technical status. The workflow first gathers candidate data, then either asks about their specialty or about their role, depending on `isTechnical`. This is done by chaining `gatherCandidateInfo` with `askAboutSpecialty` and `askAboutRole`.

To the existing `src/mastra/workflows/candidate-workflow.ts` file change the `candidateWorkflow` like so:

```ts title="src/mastra/workflows/candidate-workflow.ts" {10-14}
export const candidateWorkflow = createWorkflow({
  id: 'candidate-workflow',
  inputSchema: z.object({
    resumeText: z.string(),
  }),
  outputSchema: z.object({
    askAboutSpecialty: z.object({
      question: z.string(),
    }),
    askAboutRole: z.object({
      question: z.string(),
    }),
  }),
})
  .then(gatherCandidateInfo)
  .branch([
    [async ({ inputData: { isTechnical } }) => isTechnical, askAboutSpecialty],
    [async ({ inputData: { isTechnical } }) => !isTechnical, askAboutRole],
  ])
  .commit()
```

</StepItem> <StepItem> In your `src/mastra/index.ts` file, register the workflow:

```ts title="src/mastra/index.ts" {2, 5}
import { Mastra } from '@mastra/core'
import { candidateWorkflow } from './workflows/candidate-workflow'

export const mastra = new Mastra({
  workflows: { candidateWorkflow },
})
```

</StepItem> </Steps>

Testing the workflow

You can test your workflow inside Studio by starting the development server:

bash

mastra dev

In the sidebar, navigate to Workflows and select candidate-workflow. In the middle you'll see a graph view of your workflow and on the right sidebar the Run tab is selected by default. Inside this tab you can enter a resume text, for example:

text

Knowledgeable Software Engineer with more than 10 years of experience in software development. Proven expertise in the design and development of software databases and optimization of user interfaces.

After entering the resume text, press the Run button. You should now see two status boxes (GatherCandidateInfo and AskAboutSpecialty) which contain the output of the workflow steps.

You can also test the workflow programmatically by calling .createRun() and .start(). Create a new file src/test-workflow.ts and add the following:

import { mastra } from './mastra'

const run = await mastra.getWorkflow('candidateWorkflow').createRun()

const res = await run.start({
  inputData: {
    resumeText:
      'Knowledgeable Software Engineer with more than 10 years of experience in software development. Proven expertise in the design and development of software databases and optimization of user interfaces.',
  },
})

// Dump the complete workflow result (includes status, steps and result)
console.log(JSON.stringify(res, null, 2))

// Get the workflow output value
if (res.status === 'success') {
  const question = res.result.askAboutRole?.question ?? res.result.askAboutSpecialty?.question

  console.log(`Output value: ${question}`)
}

Now, run the workflow and get output in your terminal:

bash

npx tsx src/test-workflow.ts

You've built a workflow to parse a resume and decide which question to ask based on the candidate's technical abilities. Congrats and happy hacking!

Guide: Building an AI recruiter

Building an AI recruiter

Prerequisites

Building the workflow

Testing the workflow