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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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    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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    Enables AI agents to convert files, render web pages to markdown/PDF/screenshots, search the live web, extract structured data, ingest RAG-ready chunks, and monitor pages for changes through a single API key.
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    Keyless, local MCP server bringing ISTQB / OWASP / IEEE / ISO / EU AI Act QA standards into your AI client. Standards-grounded retrieval, deterministic QA effort estimation, automated QA document quality review (0-100 rubric), and JUnit/CSV test-results flakiness analysis.
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    Apache 2.0
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    An MCP server for searching and downloading academic papers from multiple sources including arXiv, PubMed, bioRxiv, and Sci-Hub, designed for seamless integration with large language models like Claude Desktop.
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    A universal interface that enables AI Agents to seamlessly communicate with Adb MySQL databases, allowing them to retrieve database metadata and execute SQL operations.
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    Apache 2.0
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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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    Embedded memory and retrieval engine for AI agents, providing local-first memory with MCP support for multi-agent access control.
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    Provides persistent memory for AI coding agents via MCP, enabling teams to share and recall facts across sessions. Automatically captures, classifies, and curates knowledge from supported transcript sources.
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    AGPL 3.0
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    Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.
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    Apache 2.0
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    Hybrid search over your local documents, with file and page citations. One binary, fully local.
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    A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
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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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    Dynamic semantic memory for AI assistants that persists across sessions and clients, enabling natural language recall and project-scoped isolation.
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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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    A Model Context Protocol server that provides real-time hot trending topics from major Chinese social platforms and news sites.
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    This server converts webpages into clean, structured Markdown optimized for language model consumption, removing unnecessary content and supporting JavaScript rendering.
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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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    Cross-agent memory server: hybrid (vector + full-text + knowledge-graph) recall, per-user private/shared visibility, write-side secret/PII redaction, and as-of-date bi-temporal fact queries. Auto-injection hooks for Claude Code / Cursor / Codex; self-hostable on PostgreSQL or SQLite.
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    A production-ready Model Context Protocol server that enables language models to leverage AI-powered web scraping capabilities, offering tools for transforming webpages to markdown, extracting structured data, and executing AI-powered web searches.
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    Verifiable document intelligence for AI agents. Extract text, tables, and structured data from PDFs and URLs. Summarize, answer questions, check claims, and translate — all with cited evidence. Store tamper-evident evidence bundles with cryptographic signatures and on-chain attestation via Base L2. Cross-document semantic search and Q&A across named collections. Pay per call with USDC
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