Mnemosyne OS
Related Servers
Alternatives to Mnemosyne OS
- AlicenseNot gradedqualityAmaintenanceDescription: Persistent local memory for Claude, Cursor and Codex. 13 MCP tools, SQLite + FTS5 + Knowledge Graph. No cloud, no API keys. One command: npx @studiomeyer/local-memory-mcp.82 npm14MIT
- AlicenseNot gradedqualityAmaintenanceLocal-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.2MIT
- AlicenseNot gradedqualityAmaintenanceLocal-first, source-grounded memory for AI agents, with citations, bitemporal history, review-gated corrections, and MCP tools for search and recall.40 PyPI3Apache 2.0
- AlicenseNot gradedqualityCmaintenanceProvides a local-first, source-cited memory layer for AI agents, with MCP tools to search, read, explain sources, and propose/apply memory updates.70 npm13Apache 2.0
- FlicenseNot gradedqualityAmaintenanceLocal 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 Facts carry a validity window and a confidence score, so an outdated one is superseded and downweighted rather than deleted, and retrieval fuses BM25, vector similarity and a knowledge graph.2-
Related Servers
- AlicenseNot gradedqualityAmaintenanceProvides coding agents with persistent local memory by exposing tools to save, search, and retrieve decisions, bugs, and context as Markdown with hybrid keyword and semantic search.4MIT
- FlicenseNot gradedqualityCmaintenanceProvides AI agents with persistent, local cross-session shared memory by combining vector semantic retrieval with knowledge graph relationships, and supports short/long-term memory management and local backups.-
- AlicenseBqualityAmaintenanceEnables local AI coding agents and web agents to store, retrieve, and search memories using hybrid semantic, lexical, and knowledge-graph ranking with cognitive decay modeling in a local on-device database.15MIT
- FlicenseNot gradedqualityCmaintenanceEnables AI agents to automatically capture durable knowledge and retrieve only relevant, token-bounded context from a secure local-first long-term memory, with support for progressive disclosure, snapshots, health diagnostics, and background tasks.-
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to search and retrieve a locally stored archive of past conversations, notes, and captured Codex events, preserving full original text and provenance in SQLite full-text search. It offers read-only access through two tools, search_memory and get_memory, so context from earlier chats can be reused without pasting it again.MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to capture, structure, remember, and retrieve source-backed memory as local Markdown files, with reviewable writes and no cloud dependency.190MIT
TDQS
Scored across 25 tools
Domains are cleanly partitioned by prefix (agenda, todo, memory, agent, pheme), and the one genuinely overlapping pair — memory_query vs memory_ask — is explicitly disambiguated (quote the source vs find/re-rank). The main wrinkle is the resonance domain: the entity is read via resonance_list but written/read via position_get and position_update, so the vocabulary shifts mid-domain. Otherwise each tool has a clearly distinct action+resource.
Consistent mnemosyne_ prefix with snake_case domain_action naming (agenda_add/list/update/remove, todo_add/list/update, agent_list/collisions/files). Minor deviations: position_* tools stand in for the resonance domain instead of resonance_get/resonance_update, and mnemosyne_about and mnemosyne_git_log carry no resource-action pair. Still readable and predictable overall.
25 tools is at the heavy end, but they distribute across ~10 well-scoped sub-domains (calendar, todo, memory, vault, resonance, agents, Pheme, cockpit, git, diagnostics), and each tool earns its place with no redundant variants. It sits just under the 'too many' threshold but never feels padded.
Core lifecycles are complete: calendar and todo both have add/list/update/remove (todo even splits list-shape management into todo_categories), memory has ingest/query/ask, and agents/Pheme/vault have read and write sides. Gaps are minor: no memory update or delete (memories are permanent by design), no vault creation, and git is read-only (log only). An agent can work around all of these.