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    An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.
    5
    Apache 2.0
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    Local-first memory engine for AI-agent teams: private/team/project ACL, associative recall, and federated sync across nodes. One SQLite file, no LLM required.
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    Apache 2.0
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    MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.
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    Apache 2.0
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    Persistent operational state for AI agents. Decisions, plans, priorities, and patterns that survive across sessions. Not conversation logs; structured project state with enforcement.
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    MIT
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    A personal memory MCP server that ingests AI agent conversation logs from multiple platforms into a searchable PostgreSQL+pgvector database, enabling cross-session recall of past reasoning and decisions.
    6
    MIT
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    Hierarchical markdown-based memory system for AI agents. Enables efficient context management by loading only relevant rooms (directories) instead of full memory.
    5
    MIT
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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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    Cross-agent memory server: hybrid (vector + full-text + knowledge-graph) recall, per-user private/shared visibility, write-side secret/PII redaction, and as-of-date bi-temporal fact queries. Auto-injection hooks for Claude Code / Cursor / Codex; self-hostable on PostgreSQL or SQLite.
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    MIT
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    A project-agnostic Open Knowledge Format MCP server that indexes Markdown concepts with YAML frontmatter and provides CLI and MCP tools for search, validation, and graph navigation of structured knowledge.
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    MIT
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    AoT MCP server enables AI models to solve complex reasoning problems by decomposing them into independent, reusable atomic units of thought, featuring a powerful decomposition-contraction mechanism that allows for deep exploration of problem spaces while maintaining high confidence in conclusions.
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    MIT