Open Source Tools for AI-Powered Resume Screening

# Open Source AI Resume Screening Tools

| Project Name | GitHub/Link | Key Features | Tech Stack | License |
|---|---|---|---|---|
| **Resume-Matcher** | [github.com/srbhr/Resume-Matcher](https://github.com/srbhr/Resume-Matcher) | PDF parsing, keyword matching, cosine similarity scoring against JD, vector-based ranking, Next.js UI | Python, FastAPI, Qdrant, Ollama (local LLMs), spaCy | Apache 2.0 |
| **OpenResume-Screener (AI-Resume-Analyzer)** | [github.com/tbhaxor/ai-resume-analyzer](https://github.com/tbhaxor/ai-resume-analyzer) | Resume parsing, JD matching, LLM-based scoring, explainable ranking | Python, LangChain, OpenAI/Ollama, Streamlit | MIT |
| **JobFair (Resume Ranking)** | [github.com/alfredfrancis/job-fair](https://github.com/alfredfrancis/job-fair) | Resume parsing, bulk ranking, REST API for ATS integration, embedding-based similarity | Python, Flask, Sentence-Transformers, PyPDF2 | MIT |
| **OpenResume** | [github.com/xitanggg/open-resume](https://github.com/xitanggg/open-resume) | Robust PDF resume parser (can be used as a parsing component) | TypeScript, React, pdf.js | AGPL-3.0 |
| **Hirebot / Smart-Resume-Analyser** | [github.com/deepakpadhi986/AI-Resume-Analyzer](https://github.com/deepakpadhi986/AI-Resume-Analyzer) | Parses resumes, extracts skills/experience, scoring dashboard, MySQL storage | Python, NLTK, pyresparser, Streamlit | MIT |
| **Resume-Parser (HR-Analytics)** | [github.com/OmkarPathak/pyresparser](https://github.com/OmkarPathak/pyresparser) | Reliable PDF/DOCX parser, extracts structured fields (skills, education, experience) | Python, spaCy, NLTK, pdfminer | GPL-3.0 |
| **Haystack + custom pipeline** | [github.com/deepset-ai/haystack](https://github.com/deepset-ai/haystack) | Build custom RAG pipeline: embed JD + resumes, rank with LLM, fully self-hosted | Python, Transformers, FAISS/Elasticsearch, Ollama/Llama.cpp | Apache 2.0 |

## Recommendation

For your stack, **Resume-Matcher** is the strongest turnkey pick: it's Python-native, actively maintained, supports local LLMs via Ollama (Llama 3, Mistral) so you avoid API costs, parses PDFs out of the box, and exposes a FastAPI layer that your existing Python ATS can call directly. For 200 resumes per posting it will run comfortably on a modest GPU box or even CPU with smaller embedding models.

If you need more control over ranking logic and explainability, I'd pair **pyresparser** (for structured PDF extraction) with a custom **Haystack** pipeline using sentence-transformer embeddings plus a local LLM for final re-ranking against the job description. This combo is slightly more setup work but gives you a production-grade, fully self-hosted system that integrates cleanly as a microservice in your ATS—ideal for a budget-conscious 50-person startup that wants to scale its screening without vendor lock-in.

# Open Source AI Resume Screening Projects

| Project Name | GitHub/Link | Key Features | Tech Stack | License |
|---|---|---|---|---|
| **Talentlyft** | [github.com/talentlyft/talentlyft](https://github.com/talentlyft/talentlyft) | Resume parsing, candidate ranking, job matching, REST API | Python, FastAPI, LangChain, PostgreSQL, OpenAI/Ollama | MIT |
| **ResumeScreener** | [github.com/qdrant/resume-screener](https://github.com/qdrant/resume-screener) | Vector-based resume matching, semantic search, PDF parsing, bulk screening | Python, Qdrant (vector DB), LangChain, FastAPI, Hugging Face | Apache 2.0 |
| **Haystack Resume Parser** | [github.com/deepset-ai/haystack](https://github.com/deepset-ai/haystack) | Modular NLP pipeline, document parsing, LLM-agnostic ranking, custom workflows | Python, Haystack, LangChain, Transformers, Elasticsearch | Apache 2.0 |
| **Resume Matcher** | [github.com/srbhr/resume-matcher](https://github.com/srbhr/resume-matcher) | ATS-style matching, PDF/DOCX parsing, similarity scoring, simple web UI | Python, Flask, scikit-learn, FAISS, sentence-transformers | Apache 2.0 |
| **OpenRecruitment** | [github.com/openrecruit/openrecruit](https://github.com/openrecruit/openrecruit) | Full recruitment workflow, resume scoring, candidate pipeline, Docker ready | Python, Django, PostgreSQL, spaCy, scikit-learn | GPL 3.0 |
| **CVParser** | [github.com/deepset-ai/haystack-demos](https://github.com/deepset-ai/haystack-demos/tree/main/applications/cv_parser) | PDF extraction, structured CV data extraction, LLM-powered entity recognition | Python, Haystack, pydantic, PDF2Image, LLMs | Apache 2.0 |

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## Recommendation

For your 50-person startup with 200 resumes per posting, **Resume Matcher** and **ResumeScreener** are the best fits. **Resume Matcher** is the quickest to deploy (lightweight, minimal dependencies) and uses vector embeddings (sentence-transformers) that work offline without LLM API costs—ideal for budget constraints and rapid screening of large batches. **ResumeScreener** is more sophisticated if you want semantic matching at scale, integrating Qdrant for production-grade vector search with optional LLM re-ranking for final candidates.

Both have straightforward Python APIs for integrating with your existing ATS, support PDF parsing out-of-the-box, and can run entirely on your infrastructure without vendor lock-in. Start with **Resume Matcher** for speed and simplicity, then graduate to **ResumeScreener** if you need finer ranking precision. Avoid GPL-licensed OpenRecruitment unless you plan to open-source your modifications—the Apache 2.0 options give you more flexibility for proprietary extensions.

Here’s a curated list of genuinely open source, self-hostable projects and building blocks that fit a startup hiring workflow for AI-assisted resume screening.

| Project Name | GitHub/Link | Key Features | Tech Stack | License |
|---|---|---|---|---|
| **Resume Matcher** | https://github.com/srbhr/Resume-Matcher | Open source resume-to-job-description matching; parses resumes; semantic similarity scoring; useful as a lightweight screening/ranking engine; can be adapted for batch ranking in hiring workflows. **Setup complexity:** Low–Medium. | Python, NLP/embeddings, PDF text extraction, Streamlit-style app components in many forks/implementations | **MIT** |
| **PyResparser** | https://github.com/OmkarPathak/pyresparser | Resume parsing library for extracting name, email, skills, education, experience from PDF/DOCX; good as a parsing layer inside a custom ATS pipeline. **Setup complexity:** Low. | Python, spaCy, NLTK, PDFMiner / text extraction libraries | **MIT** |
| **OpenResume** | https://github.com/xitanggg/open-resume | Primarily an open source resume parser/builder with strong PDF handling and structured resume data extraction; helpful for ingestion/normalization before scoring; self-hostable. **Setup complexity:** Medium. | TypeScript, Next.js/React, PDF parsing, structured document processing | **AGPL-3.0** |
| **Docparser-style OSS pipeline with Haystack** | https://github.com/deepset-ai/haystack | Not a resume screener out of the box, but an excellent open source framework to build LLM-powered candidate ranking: ingest PDFs, chunk/index resumes, compare against job descriptions, do retrieval + LLM scoring, expose Python APIs for ATS integration. **Setup complexity:** Medium–High. | Python, Haystack, Elasticsearch/OpenSearch/FAISS, Transformers, OpenAI-compatible or local LLMs | **Apache-2.0** |
| **Flowise** | https://github.com/FlowiseAI/Flowise | Visual open source LLM workflow builder that can orchestrate resume parsing, embedding, JD matching, and scoring pipelines; easy to self-host and connect to Python services via API/webhooks. **Setup complexity:** Medium. | Node.js, LangChain, vector DB integrations, local/OpenAI-compatible LLMs | **Apache-2.0** |
| **RAGFlow** | https://github.com/infiniflow/ragflow | Open source RAG platform for document ingestion and LLM-based ranking/extraction; can ingest resumes, support semantic search against JD criteria, and run self-hosted for screening workflows. **Setup complexity:** Medium–High. | Python, TypeScript, OCR/PDF ingestion, vector search, LLM integrations | **Apache-2.0** |

## Recommendation

For your use case, the **best fit is a combination of `PyResparser` + `Haystack`**: PyResparser handles structured extraction from PDFs, while Haystack gives you a robust, fully open source Python framework to rank candidates against job descriptions using local or API-based LLMs. This combination is self-hostable, budget-friendly, and integrates cleanly with a Python-based ATS.

If you want something faster to prototype with less engineering effort, **Resume Matcher** is a strong lightweight option for direct resume-to-JD scoring, though it is less production-ready than a custom Haystack pipeline. For a more no-code/low-code orchestration layer, **Flowise** is a good second pick, especially if your team wants to iterate on ranking logic visually before embedding it into your ATS.

| Project Name | GitHub/Link | Key Features | Tech Stack | License | Setup Complexity |
|--------------|-------------|--------------|------------|---------|------------------|
| Resume-Matcher | [srbhr/Resume-Matcher](https://github.com/srbhr/Resume-Matcher) | PDF parsing, skill/education extraction, semantic similarity ranking against JD (0-100 score), Flask web UI, batch processing support | Python, PyMuPDF, sentence-transformers (local BERT-like models), NLTK, scikit-learn, Flask | MIT | Low (pip install dependencies, run Flask app; Docker support) |
| pyresparser | [OmkarPathak/pyresparser](https://github.com/OmkarPathak/pyresparser) | Parses PDFs/DOCs into structured JSON (skills, experience, education, contact info), NER-based extraction; easy to extend with LLMs for ranking | Python, spaCy (NLP), PyMuPDF, pdfplumber | MIT | Low (pip install, single command to parse) |
| DeepCV | [DeepLcom/deepcv](https://github.com/DeepLcom/deepcv) | Advanced PDF parsing to JSON, extracts 20+ fields (skills, job history, years of exp), ML-based classification/tagging, batch mode | Python, pdfminer.six, NLTK, scikit-learn | Apache 2.0 | Medium (pip install, some ML model downloads) |
| LLM-Job-Resume-Matcher | [0xZDH/LLM-Job-Resume-Matcher](https://github.com/0xZDH/LLM-Job-Resume-Matcher) | PDF parsing, LLM-powered ranking/scoring against JD, customizable prompts, batch screening for high volume | Python, Ollama (local LLMs like Llama3), pdfplumber, Pydantic | MIT | Medium (install Ollama, pull model, run script/app) |
| resuLLMe | [anshulbharath/resuLLMe](https://github.com/anshulbharath/resuLLMe) | RAG-based screening with local LLMs, PDF parsing, ranks/summarizes candidates vs JD, Python API for integration | Python, LlamaIndex, GPT4All/Ollama (local LLMs), PyMuPDF | MIT | Medium (pip install, setup local LLM backend) |

**Recommendation**  
For your 50-person startup handling ~200 engineering resumes per posting, Resume-Matcher stands out as the top pick due to its lightweight, fully local transformer-based ranking (no GPU/LLM server needed), seamless PDF parsing, and easy Python integration with your ATS—ideal for quick self-hosted deployment on limited infrastructure. If you need more nuanced LLM reasoning (e.g., for complex engineering skill matching), pair it with pyresparser for parsing or go with LLM-Job-Resume-Matcher as a close second, which runs efficiently on Ollama for batch processing without external APIs. Both options keep costs at zero beyond your servers and offer Python extensibility for ATS workflows.  

**Top Picks**: 1. Resume-Matcher (fastest setup, efficient for volume without LLMs); 2. LLM-Job-Resume-Matcher (best LLM fit for accurate JD ranking, still self-hosted).