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RAG Systems

Tools and platforms for Retrieval-Augmented Generation (RAG), integrating external data sources with language models. Enables context retrieval, relevant document fetching, hybrid search, and dynamic knowledge injection into AI/LLM workflows.

MCP ServersBrowse all →

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    On-premises knowledge retrieval over the Markdown, text, PDF, Word and Excel files an organization keeps, with the rules you set enforced before anything is retrieved. Agents connect over MCP, get cited passages, and read from it and never write to it.
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    AGPL 3.0
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    It converts documents to Markdown or docling JSON for AI agents. That covers PDFs with layout analysis, table structure and OCR, plus DOCX, PPTX, XLSX, HTML, EPUB, e-mail, images and audio. It serves MarkItDown's "convert_to_markdown" tool under the same name and contract, along with "convert" and "list_input_formats".
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    MIT
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    Local-first operator MCP server: recall across your own sessions, chats and files, an encrypted vault, SSH to your own hosts, and gated shell, browser and device tools. 130 tools over stdio or HTTP; everything runs on your machine — no hosted service, no account, no API key.
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    672 PyPI
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    Apache 2.0
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    Enables any MCP-aware LLM host to filter noisy text down to query-relevant sentences via a topic-conditioned relevancy classifier, reducing token spend before the model processes the full input.
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    Turnbook records every conversation you have with your AI agents, word for word, in a store on your own PC. When a question depends on the past, the model you are using calls Turnbook over MCP and gets back a few thousand tokens of the conversations that answer it.
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    AGPL 3.0
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    A self-hosted, MIT-licensed MCP server that securely connects private documents and markdown files to LLMs. It provides a lightweight, local-first RAG infrastructure compatible with any MCP-enabled AI client or framework.
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    MIT
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    Search reusable technical experience from AI agents, inspect evidence and limitations, and discover public agent Trust Passports through seven anonymous read-only MCP tools. Apache-2.0 integrations, examples and documentation connect to the separately governed hosted Remnant service. Agent OAuth and Candy are separate surfaces.
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    Apache 2.0
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    A scraper tool that leverages the Oxylabs Web Scraper API to fetch and process web content with flexible options for parsing and rendering pages, enabling efficient content extraction from complex websites.
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    17,334 PyPI
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    MIT
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    Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.
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    26 PyPI
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    MIT
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    Provides MCP tools for web scraping, including smart fetching, proxy rotation, and browser rendering to bypass anti-bot challenges and render JavaScript-heavy sites.
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    55 npm
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    MIT
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    Local-first memory for coding agents. Discovers the memory, instructions, and rules Claude Code, Codex, and Cursor already wrote on your machine, combines them into one canonical Markdown tree, and serves grounded, cited recall through tools like ask_memory and canonical_memory.
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    236 PyPI
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    MIT
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    Official MCP server connecting hosts to a June knowledge graph for agent memory, search, and cited answers.
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    139 PyPI
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    MIT
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    Enables AI assistants to read and write a local-first SQLite memory vault from inside a chat — orienting on inherited context, searching and fetching past decisions and notes, wandering linked memories, and recording new ones on demand. All data stays on the user's machine, shared across local and cloud tools via MCP.
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    23 PyPI
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    Business Source 1.1
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    An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
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    MIT
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    The official Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis.
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    3,770 PyPI
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    MIT
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    MCP server for the Graphite Financial Knowledge Graph, enabling natural language queries about companies, supply chains, executives, regulations, and patents via MCP-compatible clients.
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    MIT
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    Enables MCP-compatible AI agents to connect to SoluCortex projects, recall relevant technical decisions, conventions, risks, and architecture before working, and record new memories afterward.
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    30 PyPI
    MIT
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    Official Telys MCP server: private, on-device AI memory and retrieval powered by native Mojo kernels. 19 tools: memory CRUD, semantic + BM25 search, single-key filters, compaction/IVF/tuning and self-refreshing repo auto-indexing. Stdio. Introspection needs no credentials; execution requires a one-time free telys login. Source: packages/telys-sdk/telys/mcp.py. Registry: io.github.thyn-ai/telys.
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    Apache 2.0
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    Gives your AI persistent memory across conversations. Stores facts automatically, finds them by meaning using hybrid search with query expansion, and organizes everything into topics without manual tagging.
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    MIT
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    An MCP server that enables web search and page content retrieval via the Keenable API, supporting search with filters and fetching clean markdown content from indexed URLs.
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    374 npm
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    Deterministic code-graph (GraphRAG) over your repo for LLM agents — local-first, git-native, zero-infra, served via MCP. Python, TS/JS, Rust, Go, Java, C#.
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    Apache 2.0
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    Self-hosted knowledge base that gives AI agents persistent memory over MCP. 18 tools covering notes, folders, tags and trash; hybrid search combining pgvector embeddings with bilingual full-text ranking via RRF; [[wikilink]] graph traversal and backlinks; document import from PDF, DOCX and EPUB. Connects over Streamable HTTP or stdio, with Bearer-token or OAuth auth.
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    AGPL 3.0
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    A fast, private, read-only search for your second brain or any folder of Markdown notes. Your AI agent finds the right note and the exact line, with the note, heading and date as the source.
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    Apache 2.0
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    Enables AI agents and MCP clients to start each session with a pre-assembled context pack of the project's Markdown notes, decisions, rules, Git and tracker state, and to write progress and decisions back as dated logs. Context is gathered locally by lookups and queries rather than model calls, so different tools like Claude Code, Codex, and Antigravity share the same memory without spending tokens on retrieval.
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    Apache 2.0
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    Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.
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    MIT

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