RagCode FAQs

Question 1

Which programming languages and IDEs are supported by RagCode?

Accepted Answer

RagCode provides full semantic support for Go, PHP (including Laravel), and Python, with planned support for JavaScript/TypeScript. It integrates automatically with popular AI-powered IDEs such as Cursor, Windsurf, VS Code (Copilot), and Claude Desktop upon installation.

Question 2

What performance benefits does RagCode offer for AI assistants?

Accepted Answer

RagCode delivers 5-10x faster code understanding and up to 98% token savings for AI assistants. By providing precise, context-rich code snippets through its 9 powerful MCP tools, it significantly enhances AI's ability to answer questions and navigate complex codebases efficiently.

Question 3

What is RagCode and how does it make my codebase AI-ready?

Accepted Answer

RagCode is a privacy-first Model Context Protocol (MCP) server that transforms any code repository into an AI-ready asset. It enables semantic search and Retrieval-Augmented Generation (RAG) for AI assistants, helping them understand your entire codebase deeply and instantly.

Question 4

How does RagCode ensure privacy and security?

Accepted Answer

RagCode operates 100% locally on your infrastructure with zero cloud dependencies. It uses local AI models (Ollama) and a local vector database (Qdrant), meaning your code and data never leave your machine, ensuring complete privacy, no API costs, and offline capability.

Question 5

Is RagCode difficult to install or configure?

Accepted Answer

No, RagCode is designed for ease of use with a one-command installation that automatically sets up required components like Ollama and Qdrant in Docker, downloads AI models, and configures your IDE. Once installed, it's zero-configuration usage – just open your project and ask your AI assistant.

RagCode

RagCode

Características Principales

Casos de Uso

Características Principales

Casos de Uso