Comparison

Shared heritage. A different architecture bet.

CareerOS owes a real debt to Career Ops. The comparison below is about design choices, not a ranking — both are open-source, MIT-licensed, and local-first.

Attribution

The Final Evaluation rubric and matching methodology in CareerOS are adapted from Career Ops. CareerOS deliberately diverges on architecture (host-CLI-driven, not a standalone bot), output format (structured JSON, not a long report for every job), and cost model (gate before evaluate, cache everything).

Dimension	Career Ops	CareerOS
Runtime model	Mode files run inside your AI coding CLI, which drives the whole flow.	A Python package (careeros) does the deterministic work; a host coding CLI performs only the reasoning steps.
Where AI is spent	The evaluation and tailoring are model-driven, per its A–F scoring across weighted dimensions.	AI is limited to a cheap gate and one structured evaluation per surviving job; discovery, filtering, and reporting spend no tokens.
Evaluation output	An A–F grade with tailored, ATS-oriented CV documents.	A compact JSON judgment (score, recommendation, strengths, weaknesses, fit paragraph) that every artifact reuses.
Cost model	Processes offers, including in batch, to evaluate many in parallel.	Gate before evaluate, plus fingerprinted caching keyed on job, profile version, and prompt version — an unchanged re-run costs zero calls.
Where results land	A tracked single source of truth with integrity checks.	A Google Sheet (newest on top) with a hand-editable status column, plus local run files as the source of truth.
Truthfulness enforcement	Generates tailored CVs customized per job description.	A deterministic verify step refuses to cache a resume whose lines don't verbatim-match your profile; a lint enforces voice.

Career Ops descriptions reflect its own public documentation and may change as that project evolves. If anything here is out of date, the correction belongs upstream — please open an issue.