Claude Code MCP server for RAG and memory powered by zvec
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# zvec-mcp Claude Code MCP server that gives your AI agent a **local vector database** for RAG knowledge retrieval and long-term memory — powered by [zvec](https://github.com/alibaba/zvec). --- ## Table of contents - [What it does](#what-it-does) - [Installation](#installation) - [Option A — Desktop extension (.mcpb)](#option-a--desktop-extension-mcpb) - [Option B — pip install](#option-b--pip-install) - [Option C — From source](#option-c--from-source) - [Registering with Claude Code](#registering-with-claude-code) - [User scope](#user-scope-available-in-all-projects) - [Project scope](#project-scope-shared-via-mcpjson) - [Verify](#verify) - [Embedding backends](#embedding-backends) - [HTTP — LM Studio / Ollama / vLLM](#http--lm-studio--ollama--vllm) - [Local — sentence-transformers](#local--sentence-transformers) - [OpenAI](#openai) - [Configuration reference](#configuration-reference) - [Tools reference](#tools-reference) - [Knowledge base (RAG)](#knowledge-base-rag) - [Memory](#memory) - [Status](#status) - [Usage examples](#usage-examples) - [Architecture](#architecture) - [Data storage](#data-storage) - [Troubleshooting](#troubleshooting) - [License](#license) --- ## What it does zvec-mcp runs as a [Model Context Protocol](https://modelcontextprotocol.io/) server over stdio. Once connected, Claude Code gets **11 new tools** for storing, searching, and managing a local vector database — completely offline, no data leaves your machine. --- ## Installation ### Prerequisites - Python 3.10 or later - macOS, Linux, or Windows - An embedding source (one of): - A local server like [LM Studio](https://lmstudio.ai/), [Ollama](https://ollama.com/), or [vLLM](https://docs.vllm.ai/) (recommended) - [sentence-transformers](https://www.sbert.net/) installed locally - An [OpenAI](https://platform.openai.com/) API key ### Option A — Desktop extension (.mcpb) The fastest way to get started. Download the pre-built `.mcpb` file and drag …
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