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"Designing a memory system for persona-based agents to improve user experience" matching MCP servers:

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    Central user-state database MCP server with durable profile facts and live virtual sensors (stress, room intensity) with configurable smoothing, readable by any agent or smart-home service.
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    MIT
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    MCP server providing governed access to board meeting data, with tools to query meetings, motions, and documents, automatically applying anonymity policies before returning answers.
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    Exposes user, membership, and role administration tools via MCP, acting as a thin passthrough to a coordinator API with RBAC enforced by the coordinator using caller bearer tokens.
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    All-in-one Feishu/Lark MCP for Claude Code & Codex. 84 tools, 3 auth layers, send-as-user (cookie + protobuf protocol path). Covers IM, docs, bitable, wiki, drive, calendar, tasks v2, OKR, realtime WS events.
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    Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.
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    MIT
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    A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep
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    A local-first MCP server for durable agent memory using SQLite and FTS5, enabling knowledge graph storage, search, and recall for AI agents.
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    Provides persistent memory for Claude with hierarchical categorization, cross-corpus recall, session journals, and customizable persona, enabling memory continuity across sessions.
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    MIT
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    MCP server for persistent, cross-session, local-first memory for AI agents, storing memories as Markdown files with SQLite indexing for hybrid search.
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    Apache 2.0
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    Exposes Azure AI Foundry agents, workflows, and AI Search vector-database capabilities as MCP tools, enabling natural language interaction with agents, semantic search, and index management.
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    Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.
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    Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most useful information across sessions using persistent memory storage.
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    8
    MIT