chat-memory
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- -licenseNot gradedqualityNot gradedmaintenanceProvides persistent local memory functionality for AI assistants, enabling them to store, retrieve, and search contextual information across conversations with SQLite-based full-text search. All data stays private on your machine while dramatically improving context retention and personalized assistance.3-

fimemoryofficial
FlicenseNot gradedqualityCmaintenanceEnables AI assistants to read and write a local, encrypted-by-default memory store, so they can share persistent notes and context across sessions and models on your machine.48 npm-- AlicenseNot gradedqualityDmaintenanceProvides AI assistants with persistent memory across sessions using local SQLite and keyword search, allowing storage and retrieval of user preferences, project context, and decisions.27 npm9MIT
- FlicenseNot gradedqualityBmaintenanceEnables AI assistants to keep persistent external memory in a local SQLite database, with semantic search, short summaries, full-text recall, priority categories, and memory links. It helps assistants retrieve only the few relevant memories for each question instead of loading all notes into context.-
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to save, recall, and forget persistent memories stored entirely on the local machine in SQLite, using hybrid semantic and keyword search with on-device embeddings, plus spaces and profile context injection.MIT
- AlicenseNot gradedqualityBmaintenanceGives an AI agent persistent memory across sessions: it wakes each session with what it previously chose to keep in its own words, recalls relevant memories as conversations unfold, and lets memories fade, link, and consolidate into lessons between sessions. Everything is stored locally in a single SQLite file on the user's machine, with tools for keeping, correcting, and recalling memories plus a journal and notes to the user.1Apache 2.0
TDQS
Scored across 9 tools
Each tool maps to a fairly distinct memory lifecycle operation, and descriptions clarify the subtle cases (remember's duplicate/needs_review outcomes, supersede vs forget both hiding but for different reasons). The main soft spot is the boundary between remember (which can update on same topic_key) and update_memory, plus forget vs supersede, which an agent could occasionally misselect.
Half the tools follow verb_noun (get_memory, update_memory, list_recent, get_profile) while the other half are bare verbs (remember, recall, supersede, forget, review). Each name is readable and reasonably self-describing, but the two conventions are mixed without a clear rule for which applies.
Nine tools is well within the sweet spot and every one earns its place in a memory lifecycle (save, search, read, edit, supersede, forget, list, profile, review). No filler tools and no obviously merged concerns.
The surface covers full lifecycle: create (remember), read (recall/get_memory), update (update_memory/supersede), soft-delete (forget), plus listing, profile briefing, and cleanup review. Minor gaps remain: no hard/permanent delete or purge, and no batch operations, but core workflows are fully served.