EvoMem-MCP
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AlicenseNot gradedqualityCmaintenanceEnables AI agents to log decisions with their constraints, retrieve relevant precedents through token-efficient pointer suggestions and expansion, and supersede outdated decisions, building persistent institutional memory backed by local SQLite.4AGPL 3.0- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to store decisions, preferences, and lessons in a local SQLite store, then recall the most relevant ones on demand. Runs fully local — no cloud, API keys, or per-query cost — and also exposes forget and consolidate operations.2Apache 2.0
- AlicenseAqualityCmaintenanceEnables coding agents to save facts, decisions and conventions into a single SQLite file and search them again in later sessions. Recall is ranked by BM25 relevance boosted by recency and scoped per project, with zero native dependencies.41MIT
- AlicenseAqualityBmaintenanceProvides 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.1165 PyPI4MIT

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.7468 npm15MIT- AlicenseNot gradedqualityBmaintenanceLocal 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 Facts carry a validity window and a confidence score, so an outdated one is superseded and downweighted rather than deleted, and retrieval fuses BM25, vector similarity and a knowledge graph.4Business Source 1.1
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: remember writes, recall searches by free-text, get_node fetches by id, forget removes, and stats reports storage. The recall/get_node boundary is well explained (query-based with history chain vs single-id lookup), leaving no realistic misselection.
All names are lowercase snake_case and the core operations use bare verbs (remember, recall, forget), but get_node mixes verb_noun and stats is a bare noun. The style is slightly mixed yet entirely predictable and readable.
Five tools is well-scoped for a memory server: one per core operation (write, search, read, delete, inspect). Nothing appears redundant or missing at this size.
The surface covers the full memory lifecycle—create, query, read, soft-delete/purge, and stats—with updates modeled through supersedes chains rather than a separate update tool. Minor gaps remain, such as no listing/enumeration or relation-traversal operation, but agents can work around these via recall and get_node.