memsem
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Alternatives to memsem
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- FlicenseNot gradedqualityDmaintenancePersistent semantic memory MCP server for AI agents with hybrid search, LLM scoring, and decay engine, fully local.2-
- AlicenseNot gradedqualityBmaintenanceSemantic memory for AI agents — local-first MCP server with hybrid search, knowledge graph, contradiction detection, and plan-then-commit consolidation.403 npm4AGPL 3.0
- AlicenseAqualityBmaintenanceA self-hosted MCP memory server that gives every MCP-compatible agent a shared, persistent memory layer, so preferences, project conventions, and past decisions carry across sessions and across different agents and vendors. It combines hybrid keyword-plus-vector retrieval with automatic context injection, zero-config sync, and selective injection of memories by agent and priority.202MIT
- FlicenseNot gradedqualityDmaintenanceA local MCP server that provides semantic memory storage and retrieval for coding and AI agents, enabling durable context across chat sessions.57 npm4-
- AlicenseNot gradedqualityCmaintenanceUniversal MCP server providing adaptive semantic memory for AI agents, supporting document ingestion, semantic search, chat persistence, cross-project linking, and cloud storage overflow.151 PyPI3GPL 3.0
- FlicenseNot gradedqualityBmaintenanceA persistent memory server for AI agents using MCP protocol, enabling semantic storage and retrieval of dialogues, documents, and agent states.-
TDQS
Scored across 18 tools
Most tools have clearly distinct purposes (add vs candidate_add vs episode_add, search vs list vs themes), and descriptions clarify boundaries. However, pairings like memory_add/memory_add_many and memory_forget/memory_purge could cause initial confusion, though the descriptions resolve it.
All tools share the memory_ prefix, but the suffix pattern is mixed: verbs (add, search, list, verify, score, forget, purge) coexist with nouns (themes, stats, audit, index) and compound actions (candidate_add, episode_search). This inconsistency reduces predictability.
At 18 tools, the server is slightly above the ideal 3-15 range but each tool addresses a specific aspect of memory management, from candidate review to episode tracking to scoring. The count feels justified by the domain's complexity rather than redundant.
The tool set covers the full memory lifecycle: write (add, add_many), review (candidate_*), retrieval (search, list, episode_search), maintenance (verify, score, unsuppress), and deletion (forget, purge), plus auxiliary views (themes, stats, audit, index). No obvious gaps for a semantic memory system.