LM-Pocket
Related Servers
Alternatives to LM-Pocket
No user-submitted related servers found.
Related Servers
AlicenseNot gradedqualityAmaintenanceGives AI apps a shared, plain-folder memory on your own machine, so whatever you tell one model the others can read, and you can inspect, review and undo every change they make. Memory stays portable across vendors, models and account changes instead of living on a provider's servers.544 PyPI17Apache 2.0
fimemoryofficial
FlicenseNot gradedqualityAmaintenanceEnables AI assistants to read and write a local, encrypted-by-default memory store, so they can share persistent notes and context across sessions and models on your machine.38 npm-- AlicenseAqualityDmaintenanceUser-owned portable memory vault as an MCP server: agents can fetch context, search entries, and propose new memories, while humans curate the vault via a web app.3MIT
- 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
- AlicenseAqualityAmaintenanceEnables multiple AI agents on one machine to write, search, fetch, link, and give feedback on a shared local store of Markdown-backed facts, preferences, and conventions that persists across sessions. Memories gain confidence as different hosts confirm their use and can be distilled into denser ones, so agents stop re-discovering the same context.5507 PyPI1MIT
- AlicenseAqualityBmaintenanceLocal-first memory engine for AI-agent teams: private/team/project ACL, associative recall, and federated sync across nodes. One SQLite file, no LLM required.127Apache 2.0
TDQS
Scored across 8 tools
Most tools have distinct targets (memory by id, spaces, policies, profile, search), but get_recent_context overlaps with search_memories (whose empty query returns most recent), and get_profile vs get_policies have a subtle boundary (permissions vs rules) that could cause misselection.
All tools follow a clean snake_case verb_noun pattern (get_context, get_recent_context, get_policies, propose_memory, get_profile, list_spaces, search_memories, get_memory) with no deviation.
Eight tools sit comfortably in the ideal 3-15 range and each maps to a distinct facet of the memory domain (retrieve, search, propose, inspect spaces/policies/profile).
Read coverage is strong (context, recent, search, get by id, spaces, policies), but the proposal lifecycle is a dead end: propose_memory creates a candidate yet there is no tool to list, review, revise, withdraw, or delete memories or pending candidates.