# Tool: Resume & CV Parser to JSON (`scrapers_lat/resume-parser-tool`) Actor
Turn raw resume or CV text into clean structured JSON with name, contact, skills, work history and education using premium AI. Paid Apify plans only.
- **URL**: https://apify.com/scrapers\_lat/resume-parser-tool.md
- **Developed by:** [Scrapers Lat](https://apify.com/scrapers_lat) (community)
- **Categories:** AI, Business, Developer tools
- **Stats:** 2 total users, 1 monthly users, 66.7% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $64.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
[](https://console.apify.com/actors/resume-parser-tool)
## Tool: Resume & CV Parser to JSON
> Turn raw resume and CV text into clean, structured JSON. Get name, contact details, skills, work history and education ready for your ATS, database or spreadsheet.
Contact fields name, email, phone, location
Full history skills, experience, education
JSON / CSV / Excel output formats
### What it does
Give it one or more resumes as plain text or as a link. For each resume it extracts a clean, structured record you can load straight into a hiring system:
- **fullName**, **email**, **phone** and **location**
- **headline** and a short **summary**
- **skills** as an array
- **experience** as a list of roles with title, company, start date, end date and description
- **education** as a list of degree, school and year
- **links** found in the document
Export to JSON, CSV or Excel, or pull it through the Apify API and drop it into your ATS.
### Paid plans only
This tool runs on the latest, most reliable premium AI models. Because each run has a real model cost, it is available to paid Apify accounts only. A run started from a free account stops immediately with an upgrade message. Upgrade your Apify subscription to a paid plan and run it again.
### Who is it for
| Use case | Who benefits |
|---|---|
| Recruiting | Recruiters loading candidates into an ATS |
| HR ops | Teams standardizing resumes into one schema |
| Job boards | Products that need parsed profiles from uploads |
| Staffing | Agencies building searchable candidate databases |
### Input
- **resumes**: a list of items, each either `{ "text": "..." }` with raw resume text or `{ "url": "..." }` linking to a resume page.
- **maxItems**: optional cap on how many resumes to parse this run.
### Notes
- Fields that are not present in a resume come back as null or an empty list. The parser does not invent facts.
- Long documents are trimmed to a safe length before parsing, and the record marks when that happened.
### FAQ
**What formats can I send?**
Plain resume text works best. You can also pass a URL to a resume page and the tool will pull and clean the text first.
**Does it guess missing details?**
No. If an email or a date is not in the text, the field is null.
**Can I parse many resumes at once?**
Yes. Pass an array and each resume returns its own structured record.
### Example use cases
Ready-to-run example tasks, each preconfigured for a common scenario. Open one and press run, or use it as a template:
- [Parse a Software Engineer Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-software-engineer-resume): Extract name, roles, skills and education from a software engineer resume into structured data for your hiring pipeline.
- [Parse a Marketing Manager Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-marketing-manager-resume): Turn a marketing manager resume into clean fields for roles, skills and results so recruiters screen candidates faster.
- [Parse a Nurse Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-nurse-healthcare-resume): Pull licenses, roles and specialties from a nursing resume into structured data so healthcare recruiters match roles fast.
- [Parse a Sales Representative Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-sales-rep-resume): Extract quota, roles and skills from a sales resume into structured fields so hiring teams rank reps by track record.
- [Parse Two Resumes in One Batch](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-two-resumes-in-batch): Parse two resumes at once into structured candidate profiles so recruiters compare applicants side by side quickly.
- [Parse a Recent Graduate Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-recent-graduate-resume): Structure a new graduate resume with education, internships and skills so campus recruiters process applications at scale.
- [Parse a Project Manager Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-project-manager-resume): Extract certifications, roles and skills from a project manager resume into fields your applicant tracking system can use.
- [Parse an Accountant Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-accountant-finance-resume): Turn an accounting resume into structured data with roles, credentials and skills so finance recruiters shortlist faster.
- [Parse a Teacher Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-teacher-education-resume): Extract subjects, roles and certifications from a teacher resume into structured fields for school hiring workflows.
- [Parse a DevOps Engineer Resume](https://apify.com/scrapers_lat/resume-parser-tool/examples/resume-parse-devops-engineer-resume): Pull cloud skills, roles and tools from a DevOps resume into structured data so tech recruiters match infrastructure roles.
### Export, API and AI agents (x402 + MCP)
Export the scraped data to **JSON, CSV or Excel**, pull it as a **dataset** through the Apify **API**, or wire it into your app with **no code**. This web scraper and data extractor also works for bulk data extraction and scheduled runs.
For AI agents: this Actor is available on **x402**, Apify's agentic payment standard built with Coinbase. An AI agent can discover, pay for and run it on its own with a funded wallet and a single HTTP request: no account, no subscription, no API key and no human in the loop. It also runs as an **MCP** tool inside Claude, Cursor and other AI clients out of the box. Learn more about [x402 agentic payments on Apify](https://docs.apify.com/platform/integrations/x402).
Built by [scrapers.lat](https://scrapers.lat).
# Actor input Schema
## `resumes` (type: `array`):
A list of resumes or CVs. Each item is either an object with a text field holding raw resume text, or an object with a url field pointing to a resume page. Provide one item per candidate.
## `maxItems` (type: `integer`):
Cap how many resumes to parse this run. Optional.
## Actor input object example
```json
{
"resumes": [
{
"text": "JANE MARTINEZ\nSan Francisco, CA | jane.martinez@example.com | (415) 555-0198 | linkedin.com/in/janemartinez\n\nSenior Backend Engineer\n\nSUMMARY\nBackend engineer with 8 years building high-throughput payment and data platforms. Comfortable owning services end to end.\n\nSKILLS\nGo, Python, PostgreSQL, Kafka, AWS, Kubernetes, gRPC, Terraform\n\nEXPERIENCE\nStaff Engineer, Ramp (Jan 2021 - Present)\nLed the ledger service handling 4M transactions per day. Cut p99 latency by 40 percent. Mentored 5 engineers.\n\nSenior Software Engineer, Stripe (Jun 2017 - Dec 2020)\nBuilt fraud scoring pipelines in Go and Kafka. Owned the disputes API.\n\nEDUCATION\nB.S. Computer Science, University of California Berkeley, 2015\n\nLINKS\nhttps://github.com/janemartinez"
}
]
}
````
# Actor output Schema
## `results` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"resumes": [
{
"text": "JANE MARTINEZ\nSan Francisco, CA | jane.martinez@example.com | (415) 555-0198 | linkedin.com/in/janemartinez\n\nSenior Backend Engineer\n\nSUMMARY\nBackend engineer with 8 years building high-throughput payment and data platforms. Comfortable owning services end to end.\n\nSKILLS\nGo, Python, PostgreSQL, Kafka, AWS, Kubernetes, gRPC, Terraform\n\nEXPERIENCE\nStaff Engineer, Ramp (Jan 2021 - Present)\nLed the ledger service handling 4M transactions per day. Cut p99 latency by 40 percent. Mentored 5 engineers.\n\nSenior Software Engineer, Stripe (Jun 2017 - Dec 2020)\nBuilt fraud scoring pipelines in Go and Kafka. Owned the disputes API.\n\nEDUCATION\nB.S. Computer Science, University of California Berkeley, 2015\n\nLINKS\nhttps://github.com/janemartinez"
}
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapers_lat/resume-parser-tool").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "resumes": [{ "text": """JANE MARTINEZ
San Francisco, CA | jane.martinez@example.com | (415) 555-0198 | linkedin.com/in/janemartinez
Senior Backend Engineer
SUMMARY
Backend engineer with 8 years building high-throughput payment and data platforms. Comfortable owning services end to end.
SKILLS
Go, Python, PostgreSQL, Kafka, AWS, Kubernetes, gRPC, Terraform
EXPERIENCE
Staff Engineer, Ramp (Jan 2021 - Present)
Led the ledger service handling 4M transactions per day. Cut p99 latency by 40 percent. Mentored 5 engineers.
Senior Software Engineer, Stripe (Jun 2017 - Dec 2020)
Built fraud scoring pipelines in Go and Kafka. Owned the disputes API.
EDUCATION
B.S. Computer Science, University of California Berkeley, 2015
LINKS
https://github.com/janemartinez""" }] }
# Run the Actor and wait for it to finish
run = client.actor("scrapers_lat/resume-parser-tool").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"resumes": [
{
"text": "JANE MARTINEZ\\nSan Francisco, CA | jane.martinez@example.com | (415) 555-0198 | linkedin.com/in/janemartinez\\n\\nSenior Backend Engineer\\n\\nSUMMARY\\nBackend engineer with 8 years building high-throughput payment and data platforms. Comfortable owning services end to end.\\n\\nSKILLS\\nGo, Python, PostgreSQL, Kafka, AWS, Kubernetes, gRPC, Terraform\\n\\nEXPERIENCE\\nStaff Engineer, Ramp (Jan 2021 - Present)\\nLed the ledger service handling 4M transactions per day. Cut p99 latency by 40 percent. Mentored 5 engineers.\\n\\nSenior Software Engineer, Stripe (Jun 2017 - Dec 2020)\\nBuilt fraud scoring pipelines in Go and Kafka. Owned the disputes API.\\n\\nEDUCATION\\nB.S. Computer Science, University of California Berkeley, 2015\\n\\nLINKS\\nhttps://github.com/janemartinez"
}
]
}' |
apify call scrapers_lat/resume-parser-tool --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapers_lat/resume-parser-tool",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Tool: Resume & CV Parser to JSON",
"description": "Turn raw resume or CV text into clean structured JSON with name, contact, skills, work history and education using premium AI. Paid Apify plans only.",
"version": "0.1",
"x-build-id": "y9F74lfy5HAvBsClV"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapers_lat~resume-parser-tool/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapers_lat-resume-parser-tool",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapers_lat~resume-parser-tool/runs": {
"post": {
"operationId": "runs-sync-scrapers_lat-resume-parser-tool",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapers_lat~resume-parser-tool/run-sync": {
"post": {
"operationId": "run-sync-scrapers_lat-resume-parser-tool",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"resumes"
],
"properties": {
"resumes": {
"title": "Resumes to parse",
"type": "array",
"description": "A list of resumes or CVs. Each item is either an object with a text field holding raw resume text, or an object with a url field pointing to a resume page. Provide one item per candidate."
},
"maxItems": {
"title": "Max resumes",
"minimum": 1,
"maximum": 100000,
"type": "integer",
"description": "Cap how many resumes to parse this run. Optional."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```