Mnemosyne – Memory for AI Hermes Agents, Sub-Millisecond Recalls, Local First
Mnemosyne is a new memory system designed for AI agents, offering sub-millisecond recall times and complete local data privacy. It operates without cloud dependencies, ensuring that all data remains on the user's machine. The system features a three-tier memory architecture and supports real-time updates for efficient memory management.
- ▪Mnemosyne provides sub-millisecond query latency with zero dependencies.
- ▪The memory system is built for AI agents requiring fast and reliable persistent memory.
- ▪All data is stored locally, ensuring 100% privacy without any cloud services.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Mnemosyne |
| Canonical URL | https://mnemosyne.site/ |
| Publication time | Mon, 25 May 2026 12:38:53 +0000 |
| Retrieval time | 2026-05-25T12:52:36.700Z |
| Last seen | 2026-05-25T12:52:36.700Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | NZ7SW6nW2Het |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
v3.0.0 — MEMORIA Fact EngineMemory for HermesThe native memory system for Hermes Agent. SQLite-backed, sub-millisecond, zero dependencies. No cloud. No API keys. Just pure speed.Get StartedRead the DocsMigrate from Zep, Mem0, Honcho, or Hindsight in one command — see migration docs<1msQuery latency0Dependencies98.9%LongMemEval100%Local & privateSimpleThree lines. Infinite memory.No configuration files. No environment variables. No cloud accounts. Import, remember, recall. That is all.pip install mnemosyne-memoryZero external services requiredWorks offline, alwaysHermes Agent integration built-infrom mnemosyne import remember, recall # Store a memory remember( "User prefers dark mode", importance=0.9, scope="global" ) # Retrieve relevant context results = recall("user preferences") # =>…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Mnemosyne.
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