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
v20.0or 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.
Define the Workflow
Create a new file src/mastra/workflows/candidate-workflow.ts and define your workflow:
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();Step: Gather Candidate Info
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:
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";
const recruiter = new Agent({
name: "Recruiter Agent",
instructions: `You are a recruiter.`,
model: openai("gpt-4o-mini"),
});
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, {
output: 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.
Step: Technical Question
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:
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() || "" };
},
});Step: Behavioral Question
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:
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() || "" };
},
});Add Steps to the Workflow
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:
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();Register the Workflow with Mastra
In your src/mastra/index.ts file, register the workflow:
import { Mastra } from "@mastra/core";
import { candidateWorkflow } from "./workflows/candidate-workflow";
export const mastra = new Mastra({
workflows: { candidateWorkflow },
});Testing the Workflow
You can test your workflow inside Mastra’s playground by starting the development server:
mastra devIn 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:
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 .createRunAsync() and .start(). Create a new file src/test-workflow.ts and add the following:
import { mastra } from "./mastra";
const run = await mastra.getWorkflow("candidateWorkflow").createRunAsync();
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:
npx tsx src/test-workflow.tsYou’ve just built a workflow to parse a resume and decide which question to ask based on the candidate’s technical abilities. Congrats and happy hacking!