# HireLayer

> HireLayer provides five recruiting REST APIs behind one API key: Extract (resume file → structured candidate JSON), Job Extract (job description → weighted criteria), Match (one candidate vs. one job, criterion by criterion), Rank (up to 10 candidates for one job) and Skills (free-text skills → a taxonomy of 11,061 skills). Every call is synchronous (resume parsing can also answer asynchronously for automation platforms); each successful call costs 1 credit.

- Base URL: https://hirelayer.co/api — authenticate with the `X-API-Key` header.
- Every documentation page below is Markdown; the HTML version is the same URL without `.md`. Website pages, from "About HireLayer" on, are HTML.
- Documentation version: 2026-10-01.

## Get started

- [HireLayer API documentation](https://hirelayer.co/api-docs.md): Five recruiting APIs behind one API key: parse resumes, extract job criteria, match and rank candidates, and normalize skills.
- [Quickstart](https://hirelayer.co/api-docs/quickstart.md): Create an API key, check connectivity and parse a resume with cURL, Python or TypeScript in about five minutes.
- [Authentication](https://hirelayer.co/api-docs/authentication.md): Authenticate every request with an X-API-Key header. Create, rotate and revoke keys from the dashboard.
- [Errors and retries](https://hirelayer.co/api-docs/errors.md): Error format, every status code and message, and when to retry a HireLayer API call.
- [Limits and credits](https://hirelayer.co/api-docs/limits.md): How credits are consumed, request size and batch limits, timeouts and concurrency guidance for the HireLayer APIs.

## APIs

- [HireLayer CV Extract](https://hirelayer.co/api-docs/extract.md): Parse a resume file into structured candidate JSON with one multipart request: contact details, experience, education, languages and typed skills.
- [HireLayer Job Extract](https://hirelayer.co/api-docs/job-extract.md): Extract explicit, weighted and mandatory criteria from a job description, ready to send to HireLayer Match.
- [HireLayer Match](https://hirelayer.co/api-docs/match.md): Evaluate a resume against job criteria, criterion by criterion, with an explained status and a weighted 0–1 score.
- [HireLayer Rank](https://hirelayer.co/api-docs/rank.md): Rank up to 10 candidates against one job description in a single call, with a score and a rationale for each.
- [HireLayer Skills](https://hirelayer.co/api-docs/skills.md): Resolve free-text skills, from one skill to a whole resume section, to a taxonomy of 11,061 skills with families and domains, and download the taxonomy.

## Guides

- [Build a screening pipeline](https://hirelayer.co/api-docs/screening-pipeline.md): Chain HireLayer CV Extract, Job Extract, Rank and Match to parse resumes, score them against a job and explain the result.
- [Build with AI agents](https://hirelayer.co/api-docs/ai-agents.md): Give Claude Code, Codex, Cursor or any coding agent the context it needs to integrate HireLayer correctly: llms.txt, Markdown pages, OpenAPI and a ready-made rules file.

## Reference

- [Health and key check](https://hirelayer.co/api-docs/health.md): Two free endpoints: a public health check for uptime monitors, and a key check that confirms an API key and shows its account and credit balance.
- [CV Extract V2 (legacy)](https://hirelayer.co/api-docs/extract-v2.md): Reference for the legacy asynchronous V2 resume parser. Kept for existing integrations; use V3 for new ones.

## Machine-readable

- [OpenAPI 3.1 (JSON)](https://hirelayer.co/openapi.json): every endpoint, schema and error
- [OpenAPI 3.1 (YAML)](https://hirelayer.co/openapi.yaml)
- [Agent rules](https://hirelayer.co/api-docs/agent-rules.md): drop-in AGENTS.md / CLAUDE.md rules
- [Code examples on GitHub](https://github.com/HireLayer/resume-parser-examples): cURL, Python, Node.js, PHP and C# requests for all five APIs
- [MCP server](https://hirelayer.co/mcp): official MCP server for AI assistants: hosted at `https://hirelayer.co/mcp` (OAuth sign-in), or local with npm `hirelayer-mcp`
- [Integrations](https://hirelayer.co/integrations): setup guides for automation platforms (n8n, Make, Power Automate), AI assistants (ChatGPT, Claude), Google Sheets, ATS and HR suites (Greenhouse, Lever, Bullhorn, Workday, SAP SuccessFactors…)

## About HireLayer

- HireLayer, formerly OnlineResumeParser, is a French company that publishes five recruiting APIs behind one API key: resume parsing (CV to JSON), job description parsing, candidate matching, candidate ranking and skills normalization.
- HireLayer at hirelayer.co sells recruiting APIs to software companies, staffing firms and HR teams. It is not the HireLayer job-search app for candidates found at hirelayer.ai and in the app stores, which is a separate company.
- Website: https://hirelayer.co. In production since 2024, with more than 1 million API requests processed and a parse success rate of about 99.7% on valid CVs.
- Pricing (https://hirelayer.co/pricing): Free €0/month for 50 credits, Starter €24/month for 500 credits, Scale €89/month for 2,000 credits, Enterprise on quote. One-time credit packs: €29 for 500, €99 for 2,000, €449 for 10,000. 1 successful API call = 1 credit across every API. Calls that return an error use no credit. The Free plan (50 credits a month) needs no card.
- Data: resumes are parsed in Paris, France, where files and extracted photos are stored; AI processing stays in the EU. With do_not_store_data=true the file is not stored.
- Customers: Wink, an ATS used by 500+ companies, runs all five HireLayer APIs in its product.
- Access: REST API with an X-API-Key header, an official MCP server for Claude, ChatGPT, Cursor and VS Code, and integrations for n8n, Make, Power Automate, Google Sheets and several ATS.

## Website

- [Home](https://hirelayer.co/): overview of the five APIs, pricing and FAQ
- [Pricing](https://hirelayer.co/pricing): plans, credit packs, cost per call and a cost comparison with other resume parsing vendors
- [About HireLayer](https://hirelayer.co/about): company, where data is processed, customers and contact
- [Wink customer story](https://hirelayer.co/customers/wink): how an ATS used by 500+ companies runs HireLayer
- [MCP server](https://hirelayer.co/mcp): connect HireLayer to Claude, ChatGPT, Cursor or VS Code

## Products

- [HireLayer CV Extract (Resume Parsing API)](https://hirelayer.co/products/resume-parsing-api): Parse PDF, DOCX and image resumes into structured JSON with one API call. Extract contact details, experience, education, CEFR language levels and typed skills.
- [HireLayer Job Extract (Job Parsing API)](https://hirelayer.co/products/job-parsing-api): Parse job descriptions into weighted, matchable criteria. Each requirement gets an id, a 1–3 weight, a mandatory flag and a rationale for candidate matching.
- [HireLayer Match (Candidate & Job Matching API)](https://hirelayer.co/products/candidate-job-matching-api): Match a candidate to a job with explainable results. Each criterion gets an ideal, potential, not_mentioned or not_valid status, an explanation and a weighted score.
- [HireLayer Skills (Skills Matching API)](https://hirelayer.co/products/skills-matching-api): Normalize free-text skills with a Skills Matching API. Resolve a skill or a whole CV skills section to 11,061 taxonomy skills with stable ids, families and domains.
- [HireLayer Rank (Candidate Ranking API)](https://hirelayer.co/products/candidate-ranking-api): Rank up to 10 candidates against one job description in a single API call. Get a 1-to-n ranking, a 0–1 score and a rationale for every candidate.

## Comparisons

- [Resume parsing API comparison hub](https://hirelayer.co/compare): HireLayer compared with Affinda, RChilli, Textkernel, HrFlow.ai and DaXtra
- [Affinda Alternative: HireLayer vs Affinda Resume Parser](https://hirelayer.co/compare/hirelayer-vs-affinda): HireLayer vs Affinda, checked October 2026: US$0.10 vs €0.045–0.058 per parsed CV, formats, page limits, job parsing, matching, self-hosting and a field-by-field migration map.
- [RChilli Alternative & Pricing: HireLayer vs RChilli API](https://hirelayer.co/compare/hirelayer-vs-rchilli): HireLayer vs RChilli, checked October 2026: RChilli credit pricing ($75 for 500, +1 credit for OCR) vs HireLayer’s flat credit, API design, bulk parsing, matching and a field-by-field migration map.
- [Textkernel & Sovren Alternative: HireLayer vs Tx Platform](https://hirelayer.co/compare/hirelayer-vs-textkernel): HireLayer vs Textkernel (formerly Sovren), checked October 2026: credit add-ons, Professional plan from $99, serial-only batches, SDKs, matching limits and a Tx Platform → HireLayer field map.
- [HrFlow.ai Alternative: HireLayer vs HrFlow.ai Parsing API](https://hirelayer.co/compare/hirelayer-vs-hrflow): HireLayer vs HrFlow.ai, checked October 2026: stateless parsing vs Sources and Boards, real-time parsing, ROME taxonomies, connectors, pricing transparency and a profile-to-V3 field map.
- [DaXtra Alternative: HireLayer vs DaXtra Resume Parser](https://hirelayer.co/compare/hirelayer-vs-daxtra): HireLayer vs DaXtra, checked October 2026: sales-led vs self-serve, DaxML XML vs JSON, schema versions, staffing integrations, audited matching and a DaxJson field map.

## Free tools

- [Free online resume parser](https://hirelayer.co/tools/resume-parser): Upload a CV and get the candidate details, work history, education, skills and languages back as a readable profile and as JSON you can copy or download.
- [ATS resume checker](https://hirelayer.co/tools/ats-resume-checker): Upload a CV and see which fields a resume parser can read, what is missing or ambiguous, and how to fix it. Includes a readability score with the formula shown.
- [Resume parser demo](https://hirelayer.co/demo): Turn a resume into a structured candidate profile and inspect the JSON returned by the HireLayer resume parsing API.

## Guides

- [Resume Parser Benchmark: HireLayer, Textkernel, RChilli and Affinda on 5 Test Resumes](https://hirelayer.co/blog/resume-parser-benchmark): Five test resumes through HireLayer, Textkernel, RChilli and Affinda: language levels, scans, infographics, contract types and skills, with the raw JSON.
- [Language Proficiency Levels on a Resume: CEFR, LinkedIn and ILR Scales](https://hirelayer.co/blog/language-proficiency-levels-resume): How to list language levels on a resume: the CEFR levels A1–C2, LinkedIn and ILR labels with their usual CEFR reading, and IELTS, TOEFL and TOEIC scores converted.
- [Free Resume Parsers in 2026: Online Tools, Free APIs and Open Source](https://hirelayer.co/blog/free-resume-parsers): Every free resume parser we could verify: online tools (HireLayer, Affinda, OpenResume), free API tiers and open-source libraries such as pyresparser, with their limits.
- [Job Description Parsing APIs Compared: Fields or Hiring Criteria](https://hirelayer.co/blog/job-description-parsing-api-comparison): Compare eight job description parsing APIs: field parsers (Textkernel, RChilli, HrFlow.ai, Affinda, DaXtra) and criteria extraction for matching (HireLayer).
- [Skills Taxonomy APIs Compared: ESCO, O*NET, Lightcast, ROME and More](https://hirelayer.co/blog/skills-taxonomy-api-comparison): Free taxonomies (ESCO, O*NET, ROME) and skills normalization APIs (HireLayer, Textkernel, Lightcast, RChilli, HrFlow.ai) compared on size, languages, licence and price.
- [Candidate Matching APIs Compared: 8 Ways to Score Resumes Against a Job](https://hirelayer.co/blog/candidate-matching-api-comparison): Compare eight candidate matching APIs on how they score resumes against a job, what they explain and what a match costs: HireLayer, Textkernel, RChilli, Affinda and more.
- [MCP Servers for Recruiting in 2026: Resume Parsing, Matching and ATS](https://hirelayer.co/blog/recruiting-mcp-servers): Which MCP servers let Claude, ChatGPT or Cursor parse resumes, rank candidates or update your ATS: HireLayer, RChilli, Affinda, Greenhouse, Ashby and more.
- [Best Resume Parsing Software in 2026: 6 Tools Compared](https://hirelayer.co/blog/best-resume-parsing-software): Compare six resume parsing software vendors on price, output, connectors and security: HireLayer, Affinda, RChilli, Textkernel, DaXtra and HrFlow.ai.
- [What Is a Resume Parser? How Resume Parsing Works, With an Example](https://hirelayer.co/blog/what-is-a-resume-parser): A resume parser turns a CV file into structured fields. See a real CV-to-JSON example, how parsing works step by step, why it fails and how to choose one.
- [How to Parse a Resume to JSON in PHP with Guzzle or Laravel](https://hirelayer.co/blog/parse-resume-to-json-php): Send a CV to a resume parser API from PHP 8.2, map the JSON to readonly classes, handle every documented error code and retry only what is safe.
- [How to Parse a Resume to JSON in C# with HttpClient (.NET 8)](https://hirelayer.co/blog/parse-resume-to-json-csharp): Upload a CV to a resume parser API with HttpClient and MultipartFormDataContent, map the JSON to C# records with System.Text.Json, and retry only what is safe.
- [How to Parse a Resume to JSON in Java with java.net.http](https://hirelayer.co/blog/parse-resume-to-json-java): Send a CV to a resume parser API with the JDK HttpClient and a hand-built multipart body, map the JSON to Java records with Jackson, and retry only what is safe.
- [EU AI Act Checklist for AI Resume Screening and Candidate Ranking](https://hirelayer.co/blog/eu-ai-act-recruitment-checklist): Key dates after the Digital Omnibus, how to tell whether a recruiting tool is high-risk, and what vendors and employers must have in place by 2 December 2027.
- [How to Parse a Resume PDF into JSON with Python or Node.js](https://hirelayer.co/blog/resume-pdf-to-json-api): Compare local PDF extraction, OCR, and a resume parser API, then follow working Python and Node.js examples for turning a resume PDF into structured JSON.
- [How to Integrate a Resume Parser API: Requests, Mapping and Errors](https://hirelayer.co/blog/resume-parser-api-integration-guide): Step-by-step resume parser API integration: send a file from your backend, map the JSON into your candidate model, handle errors and test before launch.
- [Bulk Resume Parsing at Scale: Build a Reliable API Pipeline](https://hirelayer.co/blog/bulk-resume-parsing-api): Design a safe, observable pipeline for thousands of resumes with a one-file-per-request parser API: queues, retries, deduplication and cost controls.
- [Resume Parser API Pricing: Compare Cost per 1,000 CVs](https://hirelayer.co/blog/resume-parser-api-pricing): Compare published resume parsing API price models and calculate HireLayer’s effective cost per 1,000 successful calls from current plans and credit packs.
- [Resume Parser API Comparison: A Fair Benchmark for ATS Teams](https://hirelayer.co/blog/resume-parser-api-comparison): Compare public resume parser API specifications, then use a reproducible test plan to measure extraction quality, latency, failures, and cost on your own CVs.
- [Resume Parsing Accuracy: How to Measure and Improve It With an API](https://hirelayer.co/blog/resume-parsing-accuracy): Measure resume parsing accuracy field by field, build a labeled test set from your own CVs, find what causes errors, and catch them in production.

## Optional

- [Full documentation](https://hirelayer.co/llms-full.txt): every page above in one file