Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI agent builders, researchers, and self-hosters who want a local-first, markdown-on-disk persistent memory layer for AI agents with no database lock-in, so agents can remember, forget, and re-learn across sessions without uploading the knowledge graph to a vendor cloud or depending on a hosted memory SaaS.
Who should use it
Who should skip it
Skip aayoawoyemi/Ori-Mnemos if the source link, documentation, or setup requirements do not align with your current workflow or stack.
About this signal
aayoawoyemi/Ori-Mnemos is tracked by RepoRadar as a library in the Radar section. First seen 2026-06-21; the source record was last checked on 2026-06-21. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. Across RepoRadar's eight signals, aayoawoyemi/Ori-Mnemos is strongest on workflow potential (9.3) and novelty (9.0) and weakest on maturity (5.8) — 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.
How this item is evaluated
The aayoawoyemi/Ori-Mnemos record combines a 7.8/10 composite score with separate popularity (7.6), risk (low), and setup (moderate) signals. See the scoring methodology for the current weights and evidence definitions.
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 is a local-first persistent memory layer where every byte of the user's knowledge graph lives on disk in markdown, so audit what is committed to the knowledge graph, review the activation decay + spreading activation parameters for any sensitive content before enabling auto-retrieval, and back up the markdown knowledge graph before any schema-level migration.