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  • A
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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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    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.
    7
    40 PyPI
    43
    Apache 2.0
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    Enables MCP clients to ingest invoice PDFs into a hybrid-chunked FAISS index, semantically search them, and extract typed invoice data with deterministic arithmetic validation. It also provides listed-company financials and current web search results so agents can research vendors.
    5
    MIT
  • F
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    Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.
    2
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  • F
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    A fully-local, three-layer memory plugin for Claude Code with Chinese support, offering hybrid FTS5/trigram and HNSW vector search, self-iterating memory management, consolidation, and a web console.
    6
    3
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  • A
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    Enables local AI coding agents and web agents to store, retrieve, and search memories using hybrid semantic, lexical, and knowledge-graph ranking with cognitive decay modeling in a local on-device database.
    15
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    MIT
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    Enables a single MCP server for interacting with multiple vector databases through pluggable backends, starting with Pinecone. It supports schema-aware index management, upserts, hybrid search, embeddings, TTL conventions, and namespace operations.
    28
    1
    MIT
  • A
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    Enables users to index and retrieve local documents through semantic, BM25, or hybrid search, with tools for chunk reads and page images accessible from MCP clients such as Codex and ChatGPT web.
    MIT
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    Provides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.
    MIT
  • A
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    Enables private, offline semantic search across local files (documents, images, videos) using OCR and vector search, and optionally performs web research with cited sources.
    AGPL 3.0
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    Enables vector search and document retrieval from Vectorize pipelines, conversion of any file into chunked Markdown text, and generation of private deep-research reports with optional web search.
    MIT
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    Enables Claude Code to perform local hybrid search over crawled web pages and personal PDF, Markdown, HTML, and text documents, returning cited passages with source, section, page, and score details. It also exposes tools to list documents, retrieve full document text, and check index status.
    1
    Apache 2.0
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    Enables vector search and document retrieval from configured pipelines, text extraction and chunking of files into Markdown, and private deep research generation with optional web search.
    MIT
  • A
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    Gives AI agents a shared, encrypted memory store accessible over MCP, with per-agent keys, per-namespace permissions, history and revert, attachments, and semantic search that handles Czech as well as English. Memories, agents, network access, and HTTPS can be managed through a self-hosted admin web on the same port, with backups being simply copies of the encrypted data file.
    1
    PolyForm Noncommercial 1.0.0