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SPECIFICTION
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CandiSift

github.com

Free plan WebAPISelf-hosted
Visit github.com
Spec score 8.0 #6 of 36 in Candidate Screening Software

What it is

CandiSift is an open-source applicant tracking system for parsing resumes, matching candidates to job descriptions, and ranking applicants through a bias-audit workflow. Recruiters can create roles, upload PDF, DOCX, or plain-text resumes in batches, and review ranked results with accept or reject decisions. The screening sequence strips personally identifiable information, applies hard filters, ranks candidates, then sends those who remain to persona agents and human review. Token-cosine ranking, technical and risk personas, and synthesis contribute to that process. Before confirming a staged batch, users can view estimated processing costs; deterministic filters run before the paid LLM stage. CandiSift connects to Anthropic Claude through Agno persona agents, with an offline stub LLM available when there is no API key. It can run in Docker, uses SQLite by default, and supports a Postgres database URL. The project describes HTTP Basic authentication, security headers, rate limiting, upload validation, request limits, prompt-injection defenses, and an append-only audit log. The API includes a bias-audit endpoint. The project has no shipped accuracy benchmark; OCR, embeddings/FAISS, and skillNER are described as swaps rather than included defaults. The software fee is $0.00 per free; LLM processing may incur per-token costs.

Spec sheet

Product
CandiSift is an open-source applicant tracking system for AI resume screening, CV parsing, job description and candidate matching, and bias-audited ranking.Read 4 Oct 2026 · github.com
Intended users
The README describes a recruiter workflow for creating roles, screening uploaded resumes, reviewing ranked results, and accepting or rejecting candidates.Read 4 Oct 2026 · github.com
Security
The repository describes HTTP Basic authentication, security headers, rate limiting, request body limits, upload validation, and prompt-injection defenses.Read 4 Oct 2026 · github.com
Bias audit
The API includes a bias-audit endpoint covering pass-through and recommendation mix.Read 4 Oct 2026 · github.com
Cost controls
Users can see an estimated processing cost before confirming a staged batch, and deterministic hard filters run before the paid LLM stage.Read 4 Oct 2026 · github.com
Deployment and storage
The app can run in Docker, stores state in SQLite by default, and supports configuring a Postgres database URL.Read 4 Oct 2026 · github.com
File formats
Resume ingestion supports PDF, DOCX, and plain-text files, including multi-file uploads.Read 4 Oct 2026 · github.com
LLM integration
CandiSift integrates with Anthropic Claude through Agno persona agents; without an API key, it can use an offline stub LLM.Read 4 Oct 2026 · github.com
Notable limits
The project says it has no shipped accuracy benchmark and that OCR, embeddings/FAISS, and skillNER are documented swaps rather than included default capabilities.Read 4 Oct 2026 · github.com
Privacy and audit
CandiSift says it strips PII before a model sees candidate data and keeps an append-only audit log of scores, rationales, decisions, and versions.Read 4 Oct 2026 · github.com
Ranking and review
The product provides token-cosine ranking, technical and risk persona agents, synthesis, and a recruiter interface with ranked results and accept or reject decisions.Read 4 Oct 2026 · github.com
Screening flow
Its workflow parses resumes, strips personally identifiable information, applies hard filters, ranks candidates, and sends survivors to persona agents and human review.Read 4 Oct 2026 · github.com

Plans and prices

Open-source self-hosted Free No software fee stated; LLM processing can incur per-token costs Read 4 Oct 2026 · github.com

Every CandiSift plan and price →

Strong points and limits

Strong points

  • Open-source with no software fee stated.
  • Accepts PDF, DOCX, and plain-text resumes.
  • Removes personally identifying details before model processing.
  • Shows estimated processing costs before confirming batches.
  • Keeps an append-only log of scores and decisions.

Limits

  • LLM processing can incur per-token costs.
  • No shipped accuracy benchmark.
  • OCR, embeddings/FAISS, and skillNER are not default capabilities.

Who it suits

It is aimed at recruiters who want a self-hosted workflow for screening uploaded resumes and reviewing ranked candidates. Teams should be prepared to account for possible LLM processing costs and the absence of a shipped accuracy benchmark.

Verdict

CandiSift provides a defined screening and review flow with cost estimates and audit logging. Its stated limits matter: processing may cost per token, and the project does not ship an accuracy benchmark.

Where Specifiction places it

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