(S)AGE
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- AlicenseNot gradedqualityAmaintenanceEnables AI agents to safely write to and read from persistent memory by validating every write through three independent consensus nodes, storing memories only on 2/3 agreement and quarantining poisoning attempts.Apache 2.0

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- FlicenseNot gradedqualityBmaintenanceA long-term memory system built for AI Agents. Agent wakes up already knowing who he is, not querying "who am I?" every session. Every turn calling back accurate memory context. Achieving accurate memory hits while also preventing memory from expanding at scale. No compression, no forgetting.35-
- AlicenseNot gradedqualityCmaintenanceGives AI agents persistent memory, handoffs, and shared context across sessions, enabling seamless continuity and multi-agent collaboration.20 npm69-
- AlicenseNot gradedqualityAmaintenanceOpen-source persistent memory infrastructure for AI agents.92 npm323Apache 2.0
- AlicenseCqualityAmaintenanceA vendor-agnostic cognitive persistence layer for AI agents. Eliminate the "repetition tax" by transporting your context, preferences, and history across sessions. Features an auto-adaptation engine that syncs global instructions to ensure operational cohesion and optimize token usage across any LLM or multi-agent workflow.386Apache 2.0
TDQS
Scored across 34 tools
Each tool targets a distinct operation across memory, messaging, governance, and identity, and the descriptions clearly separate near neighbors like sage_inbox, sage_messages_receive, and sage_message_history. A few pairs (sage_remember vs sage_reflect vs sage_turn, sage_message_replies vs sage_message_status) could be confused at a glance, but the detailed descriptions resolve most ambiguity.
All 34 tools share the sage_ prefix and snake_case, and most follow a verb_noun pattern like sage_remember, sage_recall, and sage_message_send. Deviations include several noun-style commands (sage_backlog, sage_inbox, sage_directory) and a singular/plural mismatch between sage_message_* and sage_messages_receive.
At 34 tools, the surface is above the comfortable range and spans four distinct subsystems: memory, messaging, identity/governance, and task tracking. Each tool is individually justified, but the sheer number makes discovery and tool selection heavier than ideal for an agent.
The surface covers the core lifecycle well: memory create/read/list/link/deprecate, task create/update/backlog, full messaging send/receive/reply/history, and registration/governance operations. Minor gaps exist, such as no explicit non-replacement memory update and no deregistration tool, but agents can work around these.