JSPM

OpenAPI client for Qdrant

Give Claude perfect memory of all your conversations - Installation wizard for Python MCP server

Qdrant vector search engine client library

MCP server for semantic search using local Qdrant and Ollama (default) with support for OpenAI, Cohere, and Voyage AI

Gatsby plugin to recommend articles based on OpenAI embeddings and Qdrant vector search.

Qdrant module for Testcontainers

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AI-powered MCP server for codebase navigation and LLM prompt optimization

A TypeScript client library for the Vectors Gateway API

MCP server for AutoMem: AI memory storage and recall

MCP server for memory management using Qdrant vector database

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

Qdrant vector search engine client library for node

A service hub for AI services

Modern JavaScript-first RAG framework with contextual embeddings, professional CLI, and one-command deployment

Developer-friendly TypeScript client for the Catalyst RAG Server.

Simplified advanced memory engine - no tiers, just powerful semantic search with persistence

Retrieval Augmented Generation (RAG) and local GPT (text generation LLM - large language models) toolkit for machine learning (ML) apps with node-red

An MCP server for semantic documentation search and retrieval using vector databases to augment LLM capabilities.

Genkit AI framework plugin for the Qdrant vector database.

MCP server for enhanced Qdrant vector database functionality

This is a WebAssembly (WASM) port of the modified Barnes-Hut t-SNE algorithm that works with pre-computed distance vector. The original Rust implementation can be found [here](https://github.com/frjnn/bhtsne).

An MCP server for semantic documentation search and retrieval using vector databases to augment LLM capabilities.

A code indexing service using MCP, Ollama, and Qdrant.

Semantic knowledge infrastructure for Claude Code agents - AI-powered messaging with vector search and intelligent ranking

CLI tool to create modern Node.js projects with TypeScript, AI capabilities, and more

Adis CLI for interacting with Qdrant and Milvus databases.

Een MCP server voor interactie met Qdrant vectordatabase

An MCP server for semantic documentation search and retrieval using vector databases to augment LLM capabilities.

CLI tool to load german snippets vectors to Qdrant Cloud.

TypeScript SDK for turbopuffer vector database API

A robust and optimized JavaScript library for integrating Google's Teachable Machine models, supporting various image sources and providing efficient classification capabilities.

Qdrant client for NestJS with gRPC and REST support

A TypeScript/JavaScript module for implementing Retrieval-Augmented Generation (RAG) using Qdrant vector database, Google's Generative AI embeddings, and Groq LLM.

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

A code context tool with vector search and real-time monitoring, with optional Git integration.

Javascript API for qdrant

Busca de dados de controle de acesso em condomínios usando Qdrant e rastreamento integrado com Firestore.

⚡ Instantly create and manage local databases with one command

Pipedream Qdrant Components

Advanced Qdrant community node for n8n — full Qdrant HTTP API support with $fromAI expressions

MCP server to search qdrant database

MCP server for hybrid search using Meilisearch and Qdrant

Web Application powered by Go and Qdrant to search through 2500+ common German words and sentences.

One-command installer to set up and run Toq via Docker Compose

A powerful memory stack for AI applications with vector embeddings

One-command installer to set up and run Torqbit via Docker Compose

Hukuki kararlar için özel Qdrant tabanlı MCP sunucusu

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

A NodeJS RAG framework to easily work with LLMs and custom datasets

一个MCP服务器实现，提供通过向量搜索检索和处理文档的工具，使得AI助手可以在其响应中增加相关文档内容。

MCP server for codebase indexing with Voyage AI embeddings and Qdrant vector storage

A powerful and flexible Retrieval-Augmented Generation (RAG) library for Node.js and TypeScript

A RAG-based document question answering system

Semantic caching SDK for LLM cost reduction using Qdrant

AI-powered MCP server for codebase navigation and LLM prompt optimization

MCP server for enhanced Qdrant vector database functionality

Qdrant MCP server with OpenAI Compatible API embedding support