Resume Parser API

v1.0

AI-powered API that extracts 20+ structured fields from any resume: contact details, work history, skills, education, and more.

Overview

Resume Parser API automates the tedious process of manually entering candidate information from resumes into your systems. Upload a PDF, DOCX, image, or archive, and receive clean structured JSON ready to import into your ATS, CRM, or recruitment workflow.

🤖 AI-Powered

Frontier models extract 20+ fields regardless of layout or template

📄 All Formats

PDF, DOCX, JPEG, PNG, ZIP, and 7z — one unified endpoint

📸 OCR Included

Process scanned PDFs and resume photos directly

💡 Perfect For

• Recruitment agencies and ATS platforms automating candidate data entry
• HR departments eliminating manual profile creation
• Job boards and HRIS platforms normalizing candidate profiles
• Staffing firms processing high volumes of applications

Quick Start

Base URL

https://api.cparse.com/resume/v1

Authentication

http

X-API-Key: YOUR_API_KEY

Get your API key from the dashboard. New accounts include free credit — no credit card required.

Quick Example

Upload a resume and extract all fields:

cURL

bash

curl --request POST \
  --url https://api.cparse.com/resume/v1/parse \
  --header 'X-API-Key: YOUR_API_KEY' \
  --form 'file=@resume.pdf'

Endpoints

GET/health

Health check endpoint. Returns service status.

Response

json

{ "status": "ok", "service": "resume-parser", "version": "0.1.0" }

POST/parse

Parse a resume file and return structured JSON with all extracted fields.

Request

Content-Type: multipart/form-data
file (required): PDF, DOCX, JPEG, PNG, or ZIP / 7z archive containing one of the above

Response

json

[
  {
    "success": true,
    "file_name": "resume.pdf",
    "data": {
      "name": "Jane Smith",
      "headline": "Senior Software Engineer",
      "email": "jane.smith@example.com",
      "phone": "+12025551234",
      "linkedin": "https://linkedin.com/in/janesmith",
      "links": ["https://github.com/janesmith"],
      "location": "San Francisco, CA",
      "summary": "Experienced full-stack engineer with 7 years...",
      "skills": ["python", "javascript", "react", "aws", "docker"],
      "languages": [
        { "language": "English", "proficiency": "Native" },
        { "language": "Spanish", "proficiency": "B2" }
      ],
      "certifications": ["AWS Certified Solutions Architect - Associate (2023)"],
      "experience": [
        {
          "title": "Senior Software Engineer",
          "company": "Tech Corp",
          "location": "San Francisco, CA",
          "employment_type": "Full-time",
          "work_style": "Hybrid",
          "start_date": "2021-03",
          "end_date": "Present",
          "description": "- Led development of microservices architecture.\n- Reduced deploy time by refactoring CI/CD pipelines.",
          "tech_stack": ["react", "node.js", "aws", "docker"]
        }
      ],
      "education": [
        {
          "degree": "BSc Computer Science",
          "institution": "University of Edinburgh",
          "date": "2019"
        }
      ],
      "projects": [
        {
          "name": "OpenRecruite",
          "description": "Open-source ATS built with Next.js",
          "technologies": ["next.js", "postgres", "prisma"]
        }
      ]
    },
    "ocr_used": false,
    "pages_processed": 2
  }
]

Response Fields

Field	Type	Description
`name`	string	Full name
`headline`	string	Professional headline or current title
`email`	string	Primary email address
`phone`	string	Phone number in E.164 format (+12025551234)
`linkedin`	string	Full LinkedIn profile URL
`links`	string[]	Other URLs: GitHub, portfolio, personal site, etc.
`location`	string	City, Country or full location string
`summary`	string	Professional summary or profile text
`skills`	string[]	Skills — lowercase, deduplicated (technical and soft)
`languages`	object[]	Spoken languages with optional proficiency level
`certifications`	string[]	Certifications, with year in parentheses if present
`courses`	object[]	Courses with name, optional provider and date
`experience`	object[]	Work history — see structure below
`education`	object[]	Education: degree, institution, date
`projects`	object[]	Projects: name, description, technologies
`publications`	object[]	Publications: title, publisher, date
`awards`	object[]	Awards: title, issuer, date
`volunteering`	object[]	Volunteering: role, organization, dates, description
`patents`	object[]	Patents: title, patent_number, date
`talks`	object[]	Talks: title, event, date
`professional_affiliations`	object[]	Memberships: organization, role, dates
`referees`	object[]	References: name, title, organization, phone, email

Experience Object

Field	Type	Description
`title`	string	Job title
`company`	string	Company name
`location`	string	Geographic location (City, Country)
`employment_type`	string	Full-time, Contract, Freelance, Internship, etc.
`work_style`	string	Remote, Hybrid, or On-site
`start_date`	string	YYYY-MM or YYYY
`end_date`	string	YYYY-MM, YYYY, or "Present"
`description`	string	Full role description — bullet points joined with \n
`tech_stack`	string[]	Technologies used specifically in this role

Error Handling

Status	Error	Solution
400	`No file provided`	Include a file in the multipart form body
400	`Invalid or corrupted archive`	Ensure archive contains exactly one supported file
401	`Authentication required`	Pass X-API-Key header with a valid key
413	`File size exceeds limit`	Reduce the file size and retry
413	`Document exceeds page limit`	Split the document and retry
413	`Image dimensions exceed limit`	Resize image to 2048px or smaller per side
415	`Unsupported file type`	Use PDF, DOCX, JPEG, PNG, ZIP, or 7z
415	`Image processing requires OCR support`	Use a text-based PDF or DOCX for best results
500	`AI processing failed`	Retry or contact support

Tips

📌 Best Practices

Images and scanned PDFs — JPEG/PNG resumes and scanned PDFs use OCR. Images must be 2048px or smaller on each side.
Image size limit — Resize images to 2048px or smaller before uploading.
Omit vs. null — Fields absent from the resume are omitted from the response entirely, not returned as null.
Tech stack vs. skills — skills lists all skills across the resume. tech_stack inside each experience entry lists technologies specific to that role.
Archives — ZIP and 7z archives must contain exactly one supported file. Multiple files are rejected.

🔒 Security & Privacy

✅ Files processed in memory, never written to disk or stored
✅ All requests over HTTPS
✅ API key authentication required on every request
✅ GDPR and CCPA compliant

Code Examples

Python

python

import requests

url = "https://api.cparse.com/resume/v1/parse"
headers = {"X-API-Key": "YOUR_API_KEY"}

with open("resume.pdf", "rb") as f:
    response = requests.post(url, files={"file": f}, headers=headers)

result = response.json()
resume = result[0]["data"]
print(resume.get("name"), resume.get("email"))
print("Skills:", resume.get("skills"))
print("Experience:", len(resume.get("experience", [])), "roles")

JavaScript (Node.js)

javascript

import FormData from 'form-data';
import fs from 'fs';
import axios from 'axios';

const form = new FormData();
form.append('file', fs.createReadStream('resume.pdf'));

const response = await axios.post(
  'https://api.cparse.com/resume/v1/parse',
  form,
  {
    headers: {
      ...form.getHeaders(),
      'X-API-Key': 'YOUR_API_KEY',
    },
  }
);

const [result] = response.data;
const { name, email, skills, experience } = result.data;
console.log(name, email);
console.log('Skills:', skills);
console.log('Experience:', experience?.length, 'roles');