Qdrant Servidores MCP

Qdrant

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

Qdrant

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

Qdrant Loader

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

Qdrant Memory

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

Adk Rag

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

Directory Indexer

Transforms local directories into an AI-powered knowledge base, enabling semantic search for documents based on meaning.

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.

Coder Db

Enhances programming workflows by leveraging vector embeddings, structured databases, and knowledge graphs to intelligently store, retrieve, and analyze code patterns.

Qdrant Neo4j Crawl4AI

Combines Qdrant vector search, Neo4j knowledge graphs, and Crawl4AI web intelligence into a cohesive platform for agentic RAG and AI assistant interactions.

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.

Qdrant Neo4j Crawl4AI

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

Better Qdrant

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

RAGDB

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

Qdrant

Connects Large Language Models (LLMs) to Qdrant vector databases, enabling semantic search and retrieval through the Model Context Protocol.

Qdrant

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

Qdrant Enhanced

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

Workspace Qdrant

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

Qdrant

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