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qurio

by irahardianto·16·Score 41

A self-hosted RAG engine for AI coding assistants that ingests technical docs and code repositories locally, serving grounded context via MCP to prevent hallucinations.

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Overview

Qurio is a local knowledge engine that runs on your machine to ingest heterogeneous documentation (web crawls, PDFs, Markdown) and serve it directly to AI coding assistants via MCP. It features a custom structural chunker that respects code blocks, API definitions, and config files. With hybrid search combining BM25 and vector embeddings, configurable reranking, and an intuitive Vue.js admin dashboard, Qurio ensures your AI writes better code faster using only the context you trust.

Try asking AI

After installing, here are 3 things you can ask your AI assistant:

you:Providing accurate, context-aware responses for AI coding assistants like Cursor, Claude Code, or Gemini CLI
you:Creating a private knowledge base for proprietary documentation and internal code repositories
you:Enhancing AI productivity by reducing hallucinations through grounded context retrieval

When to choose this

Choose Qurio when you need a self-hosted RAG solution for AI coding assistants that respects your privacy and processes technical documentation with structure-aware chunking.

When NOT to choose this

Don't choose Qurio if you need cloud-based deployment or require authentication/authorization for multi-user access as it's designed for localhost-only usage.

Tools this server exposes

4 tools extracted from the README
  • qurio_search

    Search your knowledge base using hybrid search (keywords + vectors).

  • qurio_list_sources

    List all available data sources in the knowledge base.

  • qurio_list_pages

    List pages within a specific source.

  • qurio_read_page

    Read the complete content of a specific document or web page.

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Installation

Installation

Prerequisites

  • [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/)
  • A [Google Gemini API Key](https://aistudio.google.com/app/apikey) (for embeddings)

Steps

  1. Clone the repository:

``bash git clone https://github.com/irahardianto/qurio.git cd qurio ``

  1. Configure environment:

``bash cp .env.example .env # Add your Gemini API key to .env ``

  1. Start the system:

``bash docker-compose up -d ``

  1. Access the dashboard at http://localhost:3000
  2. Add additional API keys (Jina AI/Cohere) in the settings page

MCP Configuration

Add to your MCP settings:

{
  "mcpServers": {
    "qurio": {
      "httpUrl": "http://localhost:8081/mcp"
    }
  }
}

Compare qurio with

GitHub →

Last updated · Auto-generated from public README + GitHub signals.