hippocampus
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- AlicenseNot gradedqualityCmaintenanceEnables any MCP-capable AI client to search and write a local-first personal memory store backed by one SQLite file, fusing FTS5 keyword and locally computed vector search over records and git-tracked markdown knowledge pages. An optional authenticated HTTP mode shares the same memory across agents and devices without any cloud dependency.MIT

litopysofficial
AlicenseNot gradedqualityBmaintenancePersistent graph-based memory for AI agents, stored as plain markdown — no vector DB. Typed nodes and 11 relation types via 5 MCP tools (search, get, create, link, related), stdio and HTTP/SSE transports.3MIT- FlicenseNot gradedqualityAmaintenanceEnables AI clients to read and write a self-hosted, git-backed graph of team memory nodes — searching, creating, updating, linking, and tracing the history of notes about what exists, its state, and what was done — over MCP (stdio or HTTP) or a plain REST API. Combines lexical and semantic search in an embedded index, so it works model-agnostically and even fully offline.1-
- FlicenseNot gradedqualityAmaintenanceEnables creating, linking, searching, exploring, and synthesizing atomic notes as a knowledge graph through MCP-compatible clients such as Claude. Supports tags, note types, semantic relationships, Markdown storage, and an SQLite index for efficient traversal.-
- AlicenseAqualityAmaintenanceGives every AI agent on a machine a shared, local-first long-term memory: 16 tools to save, search, edit and organize structured notes in one SQLite file, with attribution per client. Runs over stdio or localhost HTTP, so Claude Code, omp and any other MCP client recall the same facts, decisions and entities.16MIT
- 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 21 tools
The three prefixed domains (personal/tech/conversation) are clearly separated, and most tools target a distinct resource or action. Within personal, however, personal_provenance and personal_get_node both expose raw document text, and personal_neighbors/personal_graph both return graph-like views, requiring careful reading of descriptions.
The personal_/tech_/conversation_ prefixes create clear namespaces, and most tools follow a verb_noun pattern. A few personal_ names are bare nouns (provenance, neighbors, graph) rather than action verbs, which is a minor but noticeable inconsistency.
21 tools is on the heavy side for a single MCP server, even though the load is distributed across three distinct modules. The count feels manageable but exceeds the typical well-scoped range.
Core workflows are well covered: creating and linking entities, adding documents/attachments, searching and retrieving content, updating tech observations, and importing/auditing conversations. The main gaps are the lack of a general delete/detach operation and limited batch listing, but these can usually be worked around.