RMS Memory MCP
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- FlicenseNot gradedqualityBmaintenancePersistent memory for AI coding agents: local-first semantic search, dependency graphs, git history.1-
- AlicenseAqualityBmaintenanceLocal-first memory layer for AI coding agents — Markdown as source of truth, MCP server + CLI, human-reviewed capture, team knowledge via git PRs4131,344 npm6Apache 2.0
- AlicenseAqualityBmaintenanceLocal-first memory for AI coding agents using SQLite, enabling persistent recall of decisions, lessons, and project context without cloud or API keys.10132 PyPI284Apache 2.0
- FlicenseNot gradedqualityCmaintenanceLocal-first cross-agent memory for AI coding agents. Persistent, shared memory over MCP — what you tell one agent can be recalled by another — with all data stored in a single local SQLite file, no cloud and no API keys.-
- AlicenseNot gradedqualityBmaintenanceZero-GPU, local-first, CPU-native cognitive memory layer for autonomous AI agents with hybrid search (LanceDB + SQLite FTS5), WebUI inspector, and multi-agent shared workspaces.Apache 2.0
- FlicenseNot gradedqualityAmaintenanceProvides AI coding agents with a local-first persistent memory layer they can read and write during sessions, storing decisions, preferences, error patterns, and research per project or globally. Enables semantic and keyword search, formatted context recall, and cross-project guideline surfacing without API keys or cloud storage.-
TDQS
Scored across 20 tools
Most tools target clearly distinct operations: search, read/write, checkpoint lifecycle, sync/reindex, diagnostics, graph, history, and reminders. A few pairs need careful reading to distinguish, notably rms_doctor vs rms_hygiene (diagnostics vs duplicate/cleanup reporting), rms_prune vs rms_hygiene, and rms_sync vs rms_reindex, but their descriptions explicitly clarify the boundaries.
All tools share a predictable rms_ snake_case prefix, which is consistent throughout. Within that, some names are verb-first (rms_search, rms_read, rms_write) and others are noun-first (rms_checkpoint_query, rms_file_history, rms_wiki_pack), but the style remains readable and mostly grouped by domain.
Twenty tools is on the heavy side for a memory server, though the product genuinely spans search, persistence, checkpoints, indexing, graph, diagnostics, and reminders. Tools like rms_system_instructions and rms_remind feel somewhat peripheral, making the surface borderline heavy rather than tightly scoped.
The surface covers search (vault/code/all), read/write, checkpoint create/query/load/close, project discovery, overview, incremental sync and full reindex, graph traversal/mutation, file history, diagnostics, hygiene, pruning, wiki packing, and reminders. Lifecycle coverage is broad and coherent, with only intentionally absent operations like hard deletion, which is explicitly replaced by pruning/archiving.