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

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    Remote MCP server for auditing app surfaces in AI agent ecosystems, checking recommendation status, and creating distribution plans.
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    MIT
  • A
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    A persistent, project-scoped memory layer for AI coding agents, providing 39 tools for capturing, searching, deduplicating, relating, and maintaining memory records across long-running coding sessions.
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    MIT
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    Provides GitHub Copilot with 160+ AI agent personas and 1,400+ specialized skills, enabling developers to activate expert roles like backend architect or security engineer through natural language.
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    MIT
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    Enables AI assistants to remember and retrieve user information across conversations using vector search technology. Built on Cloudflare infrastructure with isolated user namespaces for secure, persistent memory storage.
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    Local MCP server giving AI coding agents (Claude Code, Cursor, VS Code/JetBrains Copilot) a shared, persistent memory of your projects and every bug/issue faced during development. Stateless, plain-file storage (AGENTS.md + issues.jsonl) — no database.
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    16
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    MIT
  • A
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    MARM MCP provides persistent memory and structured session context beneath any AI tool, so your agents learn, remember, and collaborate across all your workflows.
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    Apache 2.0
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    40-tool colour intelligence API for AI agents. Cultural risk, archive-verified colour naming, palette generation, WCAG accessibility, material physics, ecommerce copy, and image generation briefs. 16,682+ named colours across 43 cultural archives.
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    66
    MIT
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    Persistent, local, cross-IDE memory for AI agents — markdown source of truth, LanceDB-powered semantic search, zero cloud dependency
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    MIT
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    Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.
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    Apache 2.0
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    Enables agents to maintain persistent memory through three-tiered architecture: short-term session context with TTL, long-term user profiles and preferences, and searchable episodic event history with sentiment analysis. Provides comprehensive memory management for personalized AI interactions.
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    A 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.
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    Apache 2.0
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    Enables persistent, graph-based memory for AI agents, allowing them to store, traverse, and recall relationships between facts, decisions, and context across sessions for efficient reasoning and reduced token usage.
    Last updated
    MIT