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    A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.
    MIT
  • A
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    Enables semantic search across conversation archives via MCP, allowing AI clients to retrieve relevant past conversations using vector embeddings and text fallback.
    0
    4
    ISC
  • F
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    A federation MCP server that sits in front of multiple memory backends and presents a unified search surface to AI agents, allowing a single query to search across knowledge graphs, session history, and web search.
  • F
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    Full-text search over Claude Code conversation history using SQLite FTS5, exposing indexed transcripts as MCP tools for searching, browsing, and reading turns.
    3
  • A
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    quality
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    Enables keyword search, filtering, and retrieval of Claude Code memory files, reducing context overhead by fetching only relevant memories on demand.
    4
    MIT
  • A
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    quality
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    Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.
    3
    2
    BSD 2-Clause "Simplified"
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    MCP bridge for PDF Content Search — full-text PDF search with Apple Vision OCR across thousands of documents in under a second from Claude, Cursor, or any MCP client. Advanced filters (date, category, sender, amount), wildcards, boolean operators. Bridge open-source (MIT), PDF Content Search app is commercial with free iOS+Android companion scanner apps.
    84
    1
    MIT
  • A
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    Enables Claude to search and analyze Obsidian vault content using ripgrep with support for wiki links, backlinks, frontmatter properties, orphaned notes, and intelligent context detection showing which heading or property contains each match.
    7
    MIT
  • A
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    quality
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    A zero-config personal knowledge management MCP server that enables Claude Desktop to automatically classify, search, and organize local Markdown notes using hybrid search and Graph RAG.
    4
    MIT
  • F
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    Enables AI agents to search through Memex conversation history and local project files to retrieve specific commands, code snippets, and technology overviews. It utilizes smart context management and faceted filtering to provide relevant search results without causing context overload.
  • F
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    Enables semantic search over local notes and documents using natural language queries. Supports multiple file types (Markdown, Python, HTML, JSON, CSV, text) with fast local embeddings and persistent ChromaDB vector storage.
    1