lflabs-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-
- 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
- AlicenseAqualityBmaintenanceGives AI agents persistent, local-first memory using SQLite and on-device embeddings, enabling semantic search and recall across sessions with no cloud calls.89MIT

Make0 Brainofficial
AlicenseAqualityBmaintenanceGives every AI agent you use one shared, versioned memory in a local SQLite file, so a fact saved from one app is found by all the others. Runs fully offline with no API key, records which agent wrote each change, and keeps every prior version so nothing is silently overwritten.7964 npm8MIT- AlicenseNot gradedqualityDmaintenanceProvides persistent long-term memory for LLMs via local SQLite storage and semantic search, enabling recall across sessions without external APIs.15 npm5MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to keep durable long-term memory on the user's own machine, writing sessions and messages into a local SQLite database and retrieving them via keyword, exact, or optional semantic search with surrounding context. Runs entirely offline over stdio, so no cloud service or API key is required to give an agent recall across sessions.1MIT
TDQS
Scored across 10 tools
The memory CRUD tools (remember, recall, recall_full, forget, recent) are clearly separable, but handoff and whats_up overlap heavily: both instruct the agent to 'call this FIRST when you start work in a project' and both return the last session's notes (decided/done/next/open). Only the live-state extras distinguish whats_up, so an agent may pick either or both. recall vs search_chats is a milder overlap (stored memory vs past chat excerpts) but the descriptions do enough to separate them.
Mostly snake_case, but conventions are mixed: bare verbs (remember, recall, forget), a noun/adverb (recent, handoff, whats_up), a qualified variant (recall_full), and a prefixed pair (access_status/access_change). It is readable and nothing is cryptic, but there is no single predictable verb_noun pattern across the set.
Ten tools is a well-scoped surface for a personal memory/context server: five cover memory lifecycle, one covers chat history, two cover project continuity, and two cover access control. Each tool has a defensible place with no filler.
Core lifecycle is covered: write (remember, which also updates near-duplicates), read/search (recall, recall_full, recent), delete (forget), plus chat search, project continuity, and read/write access control. Minor gaps exist: no explicit edit/append to an existing memory id, no list of sections/tags or bulk export, and the restore path for forgotten memories is described but not exposed as a tool.