Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI agent developers, automation builders, AI-curious readers, founders, creators, and any developer wiring an AI coding agent to a self-evolving memory layer with hot/cold tiered storage + hybrid retrieval (semantic + BM25 + BGE-Reranker CPU) + private GitHub backup with conflict-safe restore + stdlib-only fallback for the write-before-venv-exists case -- and who can pair kevintsai1202/
Who should use it
Who should skip it
Consider kevintsai1202/deep-memory lower priority if you already have a working solution in this category.
About this signal
kevintsai1202/deep-memory is tracked by RepoRadar as a mit open-source self-evolving kn in the Self-Evolving Memory Skill Pack with Hybrid RAG section. It was first seen on 2026-07-07 and last updated on 2026-07-07. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. Across RepoRadar's eight signals, kevintsai1202/deep-memory is strongest on workflow potential (9.0) and open-source/build quality (8.4) and weakest on maturity (5.8) — a profile worth weighing against your own priorities. 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 kevintsai1202/deep-memory a composite score of 7.9 out of 10, placing it in the Silver 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 'low' 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 evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
The 30* / 4-fork repo is brand new (days old) and at active maintenance but the consumer SHOULD review the keyword fingerprint stability + topic-switch detection accuracy before relying on the 5-step Core Loop in a production workflow; the consumer SHOULD review the cold -> hot promotion threshold before relying on the hot/cold tiered storage in a production workflow (the consumer SHOULD monitor cold-store-to-hot-store promotion cadence to avoid runaway hot-store growth); the cross-skill memory is the right cross-skill primitive but the consumer SHOULD review the proactive skill experience check (the consumer SHOULD verify the agent's skill-experience reminders are reliable; not hallucinations).