Qdrant MCP Servers

Qdrant

27,119

Provides a high-performance, massive-scale vector database and search engine for AI applications.

Qdrant

1,066

Connects language models to a Qdrant vector database for storing and retrieving information.

Qdrant Memory

17

Implements a knowledge graph with semantic search capabilities, leveraging the Qdrant vector database for persistence.

Adk Rag

17

Implements a Retrieval-Augmented Generation (RAG) system leveraging Google's Agent Development Kit (ADK) and Qdrant vector database for enhanced LLM responses.

Qdrant Loader

15

Builds searchable knowledge bases from multiple data sources by loading data into a Qdrant vector database with MCP server support.

BuildAutomata Memory

Provides AI agents with persistent, searchable, and versioned long-term memory that endures across conversations and tools.

Cardea Agentic Search

Provides agentic search capabilities with support for vector search using Qdrant, full-text search using TiDB, or both combined within a Model Context Protocol (MCP) server.

Qdrant-OpenAI

Enables semantic search in Qdrant vector databases using OpenAI embeddings.

WisdomForge

Forges wisdom from experiences and insights using a vector database for efficient knowledge management.

Workspace Qdrant

Provides intelligent project-scoped vector database operations for AI assistants, featuring hybrid search and configurable collection management with scratchbook functionality.

Qdrant Neo4j Crawl4AI

Combines vector search, knowledge graphs, and web intelligence to enhance AI coordination and optimize performance.

Qdrant

Manages vectors, performs similarity searches, and provides automatic text-to-vector embedding for the Qdrant vector database.

RAGDB

Enables fast natural language search of vectorized Markdown documents within an MCP-compatible environment.

Qdrant Enhanced

Provides an enhanced Model Context Protocol server for Qdrant, enabling robust vector memory management with advanced performance and deployment features.

Better Qdrant

Manages Qdrant vector database collections and performs semantic searches using various embedding services.

Qdrant

Stores and retrieves information from a Qdrant vector database using the Machine Control Protocol.

Vito

Implements a Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) applications, supporting Qdrant and Chroma vector databases.

Qdrant Retrieve

Performs semantic searches across multiple Qdrant vector store collections using user-provided queries.