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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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    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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    Provides access to Leadpipe's intent and data APIs for topic discovery, audience management, and visitor data lookup. It enables agents to automate lead generation workflows, analyze website topics, and manage tracking pixels through natural language.
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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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    Apache 2.0
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    Provides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.
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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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    MIT
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    Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build strategies in plain English directly from your favorite LLM tools and IDEs
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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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    Provides managed Apache Solr search as MCP tools, enabling hybrid retrieval, server-side GPU embeddings, document indexing, and grounded RAG answers without managing infrastructure.
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    Connect AI assistants to Manticore Search. Execute SQL queries, list tables, get schemas, and fetch documentation. Perfect for building RAG applications and search-powered AI agents.
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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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    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 open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.
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    AGPL 3.0
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    An MCP server that connects AI assistants to 10,000+ SaaS integrations through Platoona Connect, enabling search, connection, and execution of integration actions via natural language.
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    MIT
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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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    A Model Context Protocol server that enables agent applications like Cursor and Cline to integrate with Alibaba Cloud Function Compute, allowing them to deploy and manage serverless functions through natural language interactions.
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    MIT
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    Unified MCP server combining dev, graph, docs, and kit tools for Lamatic.ai. Enables managing orgs/projects/flows, executing flows, querying docs via RAG, and browsing AgentKit contributions.
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    Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.
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    Apache 2.0
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    Provides LLMs with up-to-date, version-specific documentation and code examples directly from library sources, eliminating outdated training data and hallucinated APIs by fetching current documentation at prompt time.
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