Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for engineering teams that want every AI coding agent — Claude Code, Cursor, Codex, OpenCode — to operate against the same specs, tasks, and conventions instead of each agent rediscovering them. Install via npm, point Trellis at a repo, and let it record the spec/task/memory so successive runs inherit context.
Who should use it
Who should skip it
Skip mindfold-ai/Trellis if the source repository or demo is inactive, unmaintained, or no longer matches the description shown here.
About this signal
mindfold-ai/Trellis is tracked by RepoRadar as a developer tool in the Agent Frameworks section. It was first seen on 2026-06-17 and last updated on 2026-06-17. The current verdict is 'try now' with a Gold tier and easy setup difficulty. Across RepoRadar's eight signals, mindfold-ai/Trellis is strongest on workflow potential (9.8) and momentum (9.0) and weakest on evidence quality (7.2) — a profile worth weighing against your own priorities. This page summarizes the public evidence on the linked source page and states where additional review is still needed. 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 mindfold-ai/Trellis a composite score of 8.3 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 88.0 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
AGPL-3.0 license is a real consideration for closed-source commercial products — internal and open-source use is fine; persists specs/tasks/memory inside the repo, so contributors will see (and can edit) the harness state.