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    Ratary is an AI Brain Platform — infrastructure that gives AI: Persistent memory — durable, owner-scoped, versioned Structured knowledge — metadata, relations, graph traversal Intelligent retrieval — hybrid search + bounded context assembly Protocol access — Ratary MCP, REST, optional gRPC It sits between AI clients and storage. One brain, many surfaces — Cursor, Claude Code, custom agents, enter
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    A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.
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    Self-hosted personal knowledge graph for Claude that persists across sessions, devices, and tools. Built on Neo4j with local semantic embeddings; OAuth 2.1 lets Claude Code, Claude Desktop, and claude.ai web all hit the same graph.
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    MCP server that exposes agent-memory-daemon to any MCP-compatible client — Kiro (CLI & IDE), Claude Desktop, Cursor, and others. The daemon does the thinking (consolidation + extraction); this server is a thin filesystem bridge so agents can read, append, and search memory through the Model Context Protocol.
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    Gives AI coding agents persistent, evolving knowledge about a codebase, enabling them to store and retrieve observations about architecture, conventions, gotchas, and recent work context.
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    Hand a project over — to your next session, to Cursor/Codex/Gemini, to your successor — with the reasons attached. Local-first memory MCP for coding agents: decisions re-injected before the agent acts, drift detection across every project you run. One SQLite file, nothing leaves your machine.
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    Gives coding assistants a local-first SQLite store for project and global lessons with evidence, immutable replacements, retrieval tracking, and agent feedback, searched by keyword out of the box or by optional local semantic search. It runs over MCP stdio with launch-bound workspace scopes and write permissions disabled by default, so assistants can save, supersede, archive, retrieve, and rate memories entirely on the local machine.
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    Provides 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.
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    Local-first persistent memory MCP: shared SQLite key/value store, searchable, TTL-aware and secret-safe.
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    Enables MCP clients to store, recall, correct, and forget knowledge with per-sentence source bindings and a replayable retrieval traceId, so every answer can be traced to a specific document passage. Adds document-level version chains that retire superseded content from default retrieval, tombstone-based forgetting, three-layer user/namespace/scope isolation with full operation auditing, and deterministic calculator/date tools.
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    An implementation of a classification-based persistent memory system that enables Claude to store and retrieve classified memory information across chat sessions
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    Local-first agentic knowledge layer over Obsidian notes, enabling MCP-aware agents to search, retrieve, and compile knowledge with provenance and task contracts.
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    An MCP server that provides AI assistants with persistent, semantic memory using Turso for storage and OpenAI for vector search. It enables natural language operations to store, retrieve, and refine information with automatic duplicate detection and quality validation.
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    An MCP server that lets users capture quick phone thoughts via Android and later query them from AI assistants like Grok or Claude, functioning as a simple task memory store without notifications or geolocation.
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    Personal memory MCP server for Claude Code with an Obsidian-style graph. Enables persistent, queryable memories across sessions using Markdown files with bidirectional wiki-links and a D3.js force-directed graph UI.
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