agentchat-memory
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AlicenseBqualityBmaintenanceProvides persistent memory for AI agents, including context storage, facts, plans, RAG search, code snippets, and conversation compaction, enabling state to survive across sessions and processes.1411 npm2MIT- AlicenseNot gradedqualityAmaintenanceProvides persistent, shared memory for AI agents by capturing conversations verbatim, distilling facts and summaries, and enabling retrieval through search, timeline, details, and explicit remember tools.MIT
- FlicenseNot gradedqualityDmaintenanceProvides AI agents with persistent, searchable memory that survives across conversations using semantic search, temporal versioning, and smart organization. Enables long-term context retention and cross-session continuity for AI assistants.14-
- AlicenseAqualityAmaintenanceProvides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.191MIT

Mnemexa MCPofficial
AlicenseAqualityDmaintenanceProvides persistent, self-optimizing memory for AI agents, enabling them to remember preferences and context across sessions and share knowledge across multiple agents.410 npmISC- AlicenseNot gradedqualityCmaintenanceGives AI agents persistent memory, handoffs, and shared context across sessions, enabling seamless continuity and multi-agent collaboration.14 npm69-
TDQS
Scored across 10 tools
Each tool has a clearly distinct purpose: load/save manage persistence, add_message feeds buffer, get_context renders full context, get_lane pulls from a lane, apply_summary writes summaries, get_recent fetches recent messages for persona, apply_persona updates persona, status reports health, and set_normative sets policy. No two tools appear to overlap in function.
All tools follow the prefix memory_ followed by a descriptive verb (load, save, add_message, get_context, get_lane, apply_summary, get_recent, apply_persona, status, set_normative). The pattern is consistent, with the only minor deviation being 'status' (a noun) but this is a common exception and does not break the overall predictability.
10 tools is well within the ideal range for a memory management server. Each tool addresses a specific lifecycle or action (persistence, message ingestion, context retrieval, summarization, persona, status, policy) without redundancy, making the set appropriately scoped.
The tool surface covers the core memory lifecycle: load/save, add messages, get context, manage lanes and summaries, handle personas, and set normative policy. Minor gaps exist, such as no explicit clear/delete operations or direct retrieval of a single message, but these are easily worked around and do not break typical workflows.