khwan-mcp
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Alternatives to khwan-mcp
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AlicenseNot gradedqualityBmaintenanceEnables persistent memory storage and retrieval for MCP clients, allowing AI assistants to remember facts and context across conversations.15 npmMIT- AlicenseNot gradedqualityBmaintenanceEnables coding agents to persistently remember, recall, and forget facts, preferences, and decisions through MCP, keeping project-scoped and personal memory available across sessions and clients.MIT
- AlicenseAqualityBmaintenanceEnables AI agents to persist memory across sessions through MCP, supporting semantic retrieval, source-backed context, knowledge graph relationships, supersession lineage, and topic digests.431MIT
- AlicenseAqualityCmaintenanceProvides AI agents with persistent long-term memory over MCP, extracting durable facts from conversations, storing them as plain files, and injecting ranked memories into later sessions via recall and search tools.10MIT
- AlicenseNot gradedqualityAmaintenanceProvides AI agents with persistent, human-like memory infrastructure via MCP, enabling them to store, search, summarize, and forget episodic, semantic, procedural, and working memories across sessions.1,099 npmMIT
- AlicenseAqualityDmaintenanceProvides persistent memory for AI assistants via MCP, enabling them to store and recall facts, preferences, and tasks across conversations using either local file storage or a cloud backend with semantic search.58 npmMIT
TDQS
Scored across 6 tools
The tools split into two semantically overlapping pairs—prepare/recall for retrieval and record/remember for persistence—but the descriptions clearly separate turn-by-turn context from session seeding, and structured recording from standalone fact storage. khwan_memory and khwan_cores are unambiguous. An agent must read carefully, but misselection risk is low.
All tools share the khwan_ prefix and use lowercase snake_case, with most names being imperative verbs: prepare, record, recall, remember. khwan_memory and khwan_cores break the verb pattern by being plain nouns, making the set slightly inconsistent but still readable and predictable.
Six tools is a well-scoped size for a memory/context server: two for the turn loop, one for session seeding, one for direct persistence, one for debugging, and one for core isolation. Each tool has a clear job and none feels redundant.
The core memory lifecycle is covered: prepare/record for turns, remember for durable facts, recall for retrieval, and memory for inspection. However, there is no explicit forget/update or memory-editing tool, which is a notable gap for a persistent brain, though it does not break the main workflows.