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    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
    3
    1
    MIT
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    A local-first personal RAG memory system that turns AI conversation history into a searchable, retrievable knowledge base via MCP, enabling LLMs to semantically search past conversations.
    2
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    AGPL 3.0
  • F
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    Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.
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    A local-first knowledge base server that enables AI clients to store, retrieve, and manage documents using semantic search. Provides privacy-focused, offline-capable memory for AI assistants with tools for ingesting, querying, updating, and deleting knowledge.
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    Local-first knowledge retrieval MCP server that turns private text documents into a source-backed knowledge base, enabling retrieval, comparison, summaries, and review outlines for any local MCP client while keeping source paths and index operations visible.
    12
    MIT
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    A nested MCP system that demonstrates server composition by using an orchestrator to manage an internal vector store for semantic search. It enables complex, multi-hop retrieval and reasoning over a knowledge base through an agentic reasoning loop.
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  • A
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    Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.
    115
    MIT
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    A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.
    MIT
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    Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.
    MIT
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    Enables AI agents to query and manage a document knowledge base via MCP, with RAG-powered search and grounded answers with citations.
    MIT
  • A
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    A local-first knowledge base for LLM coding agents that indexes repository documentation, concept ontology, and build targets into Qdrant and exposes retrieval as MCP tools (search, get, list sources, reindex).
    2
    MIT
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    Enables Claude Desktop to perform local-first semantic search, ingest documents, and manage a private knowledge base with hybrid search, PII redaction, and multi-format support.
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    MIT
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    A personal knowledge base MCP server with semantic search, storing thoughts in PostgreSQL with pgvector embeddings and providing 8 tools for capture, search, browse, stats, relations, traces, and hydration.
    Apache 2.0