Cloud-based vector MCP memory service for Claude.ai - TypeScript implementation
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Built-in support for popular icon fonts and the tooling to create your own Icon components from your font and glyph map. This is a wrapper around react-native-vector-icons to make it compatible with Expo.
Parses vector tiles
Upstash Vector MCP server - 为 AI 助手提供向量数据库操作能力（相似性搜索、向量管理、命名空间管理）
Isomorphic storage client for Supabase.
Serialize mapbox vector tiles to binary protobufs in javascript.
Slice GeoJSON data into vector tiles efficiently
MCP Server for querying pg vector db
Slice GeoJSON data into vector tiles efficiently
Serialize mapbox vector tiles to binary protobufs in javascript.
TypeScript definitions for react-native-vector-icons
Self-learning vector memory for AI agents — single-file .rvf cognitive container with HNSW search, episodic Reflexion memory, causal graph + Cypher, 9 RL algorithms, Thompson Sampling bandit, 41 MCP tools, hybrid (BM25 + dense) retrieval, GNN attention. 1
Customizable Icons for React Native with support for image source and full styling.
An HTTP/REST based Vector DB client built on top of Upstash REST API.
The official Pinecone TypeScript SDK for building vector search applications with AI/ML.
A WebGL interactive maps library
Find the nearest point to a sample point
AWS SDK for JavaScript S3vectors Client for Node.js, Browser and React Native
Phase 2.x — MCP (Model Context Protocol) server scaffolding for the catalog plane. Wraps the catalog plane's APIs as agent-callable tools so AI assistants (Claude, ChatGPT plugins, custom agents) connect with tenant-scoped credentials and call tools rathe
Model Context Protocol implementation for TypeScript
Creates a term vector from tokenized text.
AI Manipulation Defense System (AIMDS) with self-learning, prompt injection detection, and vector search integration
Material Design Icons font for react native vector icons
A Ruby gem implementing the Model Context Protocol (MCP) server-side specification. Provides a framework for creating MCP servers that expose tools, resources, prompts, and roots to LLM clients with comprehensive security features, structured logging, and production-ready capabilities.
MCP server that indexes codebases using AST-aware chunking and vector embeddings, providing semantic search for Claude Code and other MCP clients.
llm.rb is Ruby's most capable AI runtime. It runs on Ruby's standard library by default. loads optional pieces only when needed, and offers a single runtime for providers, agents, tools, skills, MCP, A2A (Agent2Agent), RAG (vector stores & embeddings), streaming, files, and persisted state. As a bonus, llm.rb is also available for mruby. It supports OpenAI, OpenAI-compatible endpoints, Anthropic, Google Gemini, DeepSeek, xAI, Z.ai, AWS Bedrock, Ollama, and llama.cpp. It also includes built-in ActiveRecord and Sequel support, plus concurrent tool execution through threads, tasks (via async gem), fibers, ractors, and fork (via xchan.rb gem).
RailsLLM integrates the llm.rb runtime and its features into Rails. RailsLLM extends the builtin ActiveRecord support available to the llm.rb runtime with a Rails integration that includes generators for getting set up quickly, and an engine for a stream-capable chat interface that can be extended with your own tools. The llm.rb runtime runs on Ruby's standard library by default. loads optional pieces only when needed, and offers a single runtime for providers, agents, tools, skills, MCP, A2A (Agent2Agent), RAG (vector stores & embeddings), streaming, files, and persisted state.
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