Free Resume Parsers in 2026: Online Tools, Free APIs and Open Source

There are three kinds of free resume parser. Free online tools (HireLayer, Affinda, OpenResume) read a few CVs in the browser with no sign-up. Free API tiers (HireLayer with 50 credits a month, APILayer with 100 requests a month) let you parse from code. Open-source libraries cost nothing to license, but the best-known Python one has not had a release since 2019 and most others are resume builders or tailoring tools rather than parsers.

This guide lists the options we could verify on 7 October 2026, with the limits each one puts on you. HireLayer is our product and it is on the list; every other fact comes from the project's repository or the vendor's own page, linked at the end.

The short list

Free online resume parsers

An online parser is the quickest way to see what a CV turns into, or to check a handful of files without writing code. All three below are free and need no account.

HireLayer free online resume parser

Our free online resume parser takes a PDF, Word, ODT, RTF, TXT, slide or image file up to 5 MB and returns the contact details, work history with dates, education, skills and languages. You read the result as a profile, or copy and download the JSON, which is the same JSON our API returns. It parses one CV at a time, three every 15 minutes, so its meant for spot checks, not for a backlog.

Affinda free resume parser

Affinda's free tool accepts up to 100 files at once with no sign-up, and reads PDF, Word, text, HTML, spreadsheets and images, scans and photos included, in more than 50 languages. It is the most generous free online option for a batch. To call it from code you move to Affinda's API, where the first 14 days include 1,000 free documents and the self-serve price is then US$0.10 per document (see HireLayer vs Affinda).

OpenResume parser

OpenResume is an open-source resume builder whose parser page runs entirely in the browser: the file never leaves your computer. Its own page says the parser is designed for single-column resumes in English and it only accepts PDF. The tool exists to check how readable a resume is, so it suits job seekers testing their CV better than a recruiter extracting data.

Free resume parser APIs

If you need the data inside your own software, you need an API, and a free tier is enough to build and test the integration.

• HireLayer: the free plan gives 50 credits a month with no card. One parsed CV is one credit, scans included, and the same credits work on job description parsing, candidate matching, ranking and skills normalization. The resume parsing API reads 13 file formats and resumes in 70 languages.
• APILayer Resume Parser: a free plan with 100 requests a month, then US$29.99 for 1,500 requests. It reads PDF, DOC and DOCX and works best on English resumes.
• Trials from the large vendors: RChilli gives 100 free credits, Textkernel 500 credits and Affinda 1,000 documents over 14 days. They are trials rather than free plans, but they are enough to compare output on your own files.

A free API tier is the honest middle ground: nothing to host, a documented response schema, and you only pay once real volume arrives. Our resume PDF to JSON guide has working Python and Node.js code for the first call.

Open-source resume parsers

Searching GitHub for "resume parser" returns hundreds of repositories, but very few are maintained parsers you could put in production. Here is what the best-known ones are, checked on their repositories on 7 October 2026.

pyresparser

pyresparser is the library most tutorials still point to. It extracts name, email, phone, skills, degrees and companies from PDF and DOCX files. The catch is age: its last release dates from December 2019, its requirements pin spaCy 2.1, and several open issues report it failing to load on current installs. Expect to fork it before it runs on a modern Python stack.

ResumeParser by Omkar Pathak

The same author rebuilt his older ResumeParser project in 2026 around Qwen2.5-1.5B, a small language model that runs locally through llama.cpp and returns JSON. It needs about 4 GB of RAM and a model download of about 1 GB. The README states the limit plainly: the context window is 2,048 tokens, so the input is cut to roughly 1,500 characters. On a two-page CV that means most of the work history is never read.

Tools that are not really parsers

Two of the most starred projects come up in every search but solve another problem. Resume-Matcher (more than 28,000 stars) describes itself as a tool to build tailored resumes for each job application, and ResuLLMe converts a CV into the JSON Resume format only to rewrite it with an LLM. Both are usefull for job seekers; neither is a component you would call from an ATS.

If you write your own parser, the open JSON Resume schema (MIT) is a sensible target format: it has basics, work, education and skills sections that most tools can read.

Building your own parser with an LLM

The other free-ish route is to send the CV text to a general-purpose language model with a JSON schema and ask it to fill the fields. It works surprisingly well on clean, text-based PDFs, and the model cost per CV is small. What takes time is everything around it: extracting text from scans and images, normalizing dates and language levels, catching invented values, handling 30-page uploads and keeping the output stable when you change models. Our article on what a resume parser does walks through those steps, and measuring parsing accuracy shows how to test the result before you trust it.

Which free option to pick

• You want to see how one CV reads: use an online parser. OpenResume if the file must stay on your computer, HireLayer or Affinda if you also want the JSON.
• You have a folder of CVs to process once: Affinda's free tool takes 100 files at a time.
• You are building a product: start on a free API tier and keep the integration when volume grows. Self-hosting an open-source parser makes sense mainly when no CV may leave your servers, and then budget for the maintenance.
• You are learning NLP: pyresparser and the open-source projects above are good material to read, even if you would not ship them as they are.

Free stops being free once you parse real volume. At a few hundred CVs a month, a paid plan usualy costs less than the engineering time spent keeping a self-hosted parser running; our resume parser API pricing guide does the math per 1,000 CVs, and best resume parsing software compares the paid vendors.

Frequently asked questions

Sources and further reading

1. HireLayer free online resume parser
2. Affinda free resume parser
3. OpenResume resume parser
4. APILayer Resume Parser API plans
5. pyresparser on GitHub
6. pyresparser on PyPI
7. ResumeParser by Omkar Pathak on GitHub
8. OpenResume on GitHub
9. Resume-Matcher on GitHub
10. ResuLLMe on GitHub
11. JSON Resume schema