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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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    MCP-native persistent memory layer for AI agents across Claude Code, Cursor, VS Code, and OpenClaw. Powered by hybrid vector search, BM25, and cross-encoder reranking with a published 73.1% LoCoMo benchmark accuracy.
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    Apache 2.0
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    MCP server for searching French and European law, providing natural language access to jurisprudence, consolidated legal texts, and related legal information via tools like search_decisions and search_legal_texts.
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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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    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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    Provides AI assistants with access to Civitai's collection of AI models, enabling users to browse, search, and discover AI models through MCP-compatible AI assistants.
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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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    287 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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    23 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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    414 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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    222 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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    615 PyPI
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    MIT
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    A 100% local MCP server for semantic and lexical search over your code, library docs, and PDFs, featuring hybrid BM25 and dense retrieval, syntax aware chunking, and an optional code knowledge graph. It also ships a Coral integration, so you can expose your code search as SQL and join it with live data, all without anything leaving your machine.
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    116 PyPI
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    Apache 2.0
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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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    4,084 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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    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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    41 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
    MIT
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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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    Local-first MCP server that gives AI agents token-efficient context from any codebase: project tree, search, and budgeted packs. Secret redaction is enforced by the server, Git scopes and syntax-aware compression keep packs small. The same engine ships as a GUI, TUI, and CLI.
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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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    Shared fleet memory for AI agents — what one agent learns, the whole fleet knows. Nightly self-correction: stale facts rewritten in place, duplicates merged. Recall pushed into every prompt in supported coding agents. Self-hosted.
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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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    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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    660 npm
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    Enables searching documents in a Tropicalia project using hybrid, neural, or keyword strategies, with optional AI-generated answers and source inclusion.
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    5 npm
    MIT

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