TinyMem
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- FlicenseNot gradedqualityAmaintenanceEnables AI coding agents to read and write persistent memory directly during sessions, storing decisions, preferences, error patterns, research, and cross-project guidelines in local SQLite databases. Supports semantic search so relevant project and global context can be recalled by meaning, fully offline and without API keys.-
- AlicenseNot gradedqualityBmaintenanceEnables AI agents and any MCP-compatible client to persist, search, update, and delete plain-text memories across sessions, using BM25 lexical ranking over SQLite FTS5 with namespace filtering, tags, free-form metadata, and JSONL export/import — entirely local, with zero external infrastructure.1MIT
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose: add/update/delete/search memories operate on individual facts while get_profile/set_profile manage static namespace data and memory_stats reports counts. The descriptions even cross-reference each other (e.g., 'use update_memory' vs re-adding), leaving little room for misselection.
Six of seven tools follow a consistent snake_case verb_noun pattern (add_memory, search_memories, update_memory, get_profile, set_profile, delete_memory). Only memory_stats deviates as a noun-only label, a minor inconsistency that is still readable.
Seven tools is well-scoped for a memory store, covering the essential lifecycle operations without redundant or filler tools. Each tool clearly earns its place.
The surface covers full lifecycle CRUD (add/search/update/delete) plus profile get/set and stats, which is strong. Minor gaps exist, such as no list-all/export or namespace management, but core agent workflows are fully supported.