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Vector Databases

Specialized database systems designed for storing, indexing, and querying high-dimensional vector embeddings. Enables similarity search, semantic retrieval, and powering features like AI context retrieval, nearest neighbor search, and RAG workflows for LLMs.

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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    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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    2,884 PyPI
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    MIT
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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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    24,959 PyPI
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    Apache 2.0
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    A Model Context Protocol server that allows Large Language Models to interact with Astra DB databases, providing tools for managing collections and records through natural language commands.
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    9 npm
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    Apache 2.0
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    Persistent AI memory server with 3-layer hybrid search (vector + FTS5 + keyword), confidence scoring via Reciprocal Rank Fusion, episodic/profile memory, and 16 tools. Zero LLM dependency. Works standalone with Claude Desktop and Claude Code. MIT licensed.
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    545 PyPI
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    Local-first knowledge retrieval MCP server that turns private text documents into a source-backed knowledge base, enabling retrieval, comparison, summaries, and review outlines for any local MCP client while keeping source paths and index operations visible.
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    260 npm
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    Self-hosted Mem0 MCP server integrating Qdrant, Neo4j, and Ollama for semantic memory search, graph entity relationships, and memory management via OpenMemory API.
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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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    Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.
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    A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.
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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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    15 PyPI
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    Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.
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    ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.
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    An MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.
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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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    Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.
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    214 PyPI
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    Apache 2.0
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    Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.
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    264 PyPI
    279
    MIT
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    A persistent semantic memory system for Claude Code, using vector search and a judgment ledger to surface prior decisions and calibrate predictions.
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    MIT
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    Memory for facts that change. A retrieved record is marked SUPERSEDED, with when a later record replaced it, so an assistant does not answer with a value that is no longer true. Single file, numpy the only dependency, runs with a local embedding model or none at all.
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    A lightweight server implementation of the Model Context Protocol that connects Memgraph database with LLMs, allowing users to interact with graph databases through natural language.
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    An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.
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    A local-first personal RAG memory system that turns AI conversation history into a searchable, retrievable knowledge base via MCP, enabling LLMs to semantically search past conversations.
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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.
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    MIT

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