Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for people who want a real desktop agent that can touch files and browser tasks on their own machine, while keeping the model path flexible enough to swap between local inference and external APIs.
Who should use it
Who should skip it
Move on from accomplish-ai/coworker if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
accomplish-ai/coworker is tracked by RepoRadar as a ai product in the Desktop Agents section. It was first seen on 2026-07-01 and last updated on 2026-07-01. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. accomplish-ai/coworker leads on workflow potential (10.0) and practical usefulness (9.0); its lowest signal is setup ease (6.4), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned accomplish-ai/coworker a composite score of 8.7 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
It can manipulate local files and browser tasks, so the first evaluation should stay inside scoped folders and low-risk sites; Using external model APIs or connected productivity tools can expose document content and prompts outside the local machine unless you stay on local models and tightly scoped integrations.