nanomem
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Alternatives to nanomem
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- FlicenseNot gradedqualityAmaintenanceLocal memory for AI agents in one SQLite file on your own machine. Facts carry a validity window and a confidence score, so an outdated one is superseded and downweighted rather than deleted, and retrieval fuses BM25, vector similarity and a knowledge graph.2-
- AlicenseAqualityBmaintenanceLets AI clients store, retrieve, update, and forget long-term facts in a local SQLite database with deterministic lexical search, sharing the same memory across multiple clients without relying on model APIs or embedding services.7MIT
- AlicenseAqualityAmaintenanceEmbedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.742Apache 2.0
- AlicenseNot gradedqualityBmaintenanceGives AI agents a long-term memory layer over plain Markdown notes, letting them run ranked, citable searches, record new facts, and inspect conflicts and stale entries. Results carry freshness labels, supersession history, and audit fields like search timestamp and no-match status so agents can prove a search actually ran.MIT
- AlicenseAqualityBmaintenanceEnables AI agents to persist facts with tags, sources, and importance in a local SQLite database, retrieve them through hybrid BM25 plus embedding search weighted by retention decay, link or supersede related entries, and review everything in a read-only visual dashboard showing the memory graph and timeline.6MIT
- AlicenseBqualityAmaintenanceProvides AI agents with a persistent long-term memory over a single SQLite file, letting them write, screen, consolidate, and recall notes across sessions without any generative LLM in the loop. Recall judges relevance, follows links between notes, and signals when memory is insufficient, so agents get one to three useful facts per question instead of a generic top-k list.111MIT
TDQS
Scored across 7 tools
Each tool has a distinct primary purpose (write, search, temporal history, as-of query, stats, change log, volatility stats), but the temporal tools (history, as_of, changes) overlap in scope and could be confused without careful reading of descriptions. The descriptions do clarify boundaries, so misselection is unlikely but possible.
All tools share the 'nanomem_' prefix, but the second part is inconsistent: some are verbs (add, search), some are nouns (history, stats, changes, volatility), and one is a prepositional phrase (as_of). The pattern is not uniform, though it remains readable and predictable in the prefix.
Seven tools is a well-scoped set for a memory server. Each tool covers a distinct aspect of storing, retrieving, analyzing, and inspecting memory, with no unnecessary bloat or obvious missing core functionality.
The surface covers the core lifecycle: add, search, and temporal/analytical views. There is no explicit update or delete tool, but the memory model appears to handle changes via superseding writes, and search handles superseded values. Minor gap: no direct tool to remove a fact entirely, but this may be intentional.