DevRag

Free Local RAG for Claude Code - Save Tokens & Time

日本語版はこちら | Japanese Version

DevRag is a lightweight RAG (Retrieval-Augmented Generation) system designed specifically for developers using Claude Code. Stop wasting tokens by reading entire documents - let vector search find exactly what you need.

Why DevRag?

When using Claude Code, reading documents with the Read tool consumes massive amounts of tokens:

• ❌ Wasting Context: Reading entire docs every time (3,000+ tokens per file)
• ❌ Poor Searchability: Claude doesn't know which file contains what
• ❌ Repetitive: Same documents read multiple times across sessions

With DevRag:

• ✅ 40x Less Tokens: Vector search retrieves only relevant chunks (~200 tokens)
• ✅ 15x Faster: Search in 100ms vs 30 seconds of reading
• ✅ Auto-Discovery: Claude Code finds documents without knowing file names

Features

• 🤖 Simple RAG - Retrieval-Augmented Generation for Claude Code
• 📝 Markdown Support - Auto-indexes .md files
• 🔍 Semantic Search - Natural language queries like "JWT authentication method"
• 🚀 Single Binary - No Python, models auto-download on first run
• 💻 CLI & MCP - Use as MCP server or standalone CLI commands
• 🖥️ Cross-Platform - macOS / Linux / Windows
• ⚡ Fast - Auto GPU/CPU detection, incremental sync
• 🌐 Multilingual - Supports 100+ languages including Japanese & English

Quick Start

1. Download Binary

Get the appropriate binary from Releases:

macOS/Linux:

tar -xzf devrag-*.tar.gz
chmod +x devrag-*
sudo mv devrag-* /usr/local/bin/

Note: macOS releases include libonnxruntime.dylib for CoreML GPU acceleration. Keep it in the same directory as the devrag binary.

Windows:

• Extract the zip file
• Place in your preferred location (e.g., C:\Program Files\devrag\)

2. Configure Claude Code

Add to ~/.claude.json or .mcp.json:

{
  "mcpServers": {
    "devrag": {
      "type": "stdio",
      "command": "/usr/local/bin/devrag"
    }
  }
}

Using a custom config file:

{
  "mcpServers": {
    "devrag": {
      "type": "stdio",
      "command": "/usr/local/bin/devrag",
      "args": ["--config", "/path/to/custom-config.json"]
    }
  }
}

3. Add Your Documents

mkdir documents
cp your-notes.md documents/

That's it! Documents are automatically indexed on startup.

4. Search with Claude Code

In Claude Code:

"Search for JWT authentication methods"

Configuration

Create config.json:

{
  "document_patterns": [
    "./documents",
    "./notes/**/*.md",
    "./projects/backend/**/*.md"
  ],
  "db_path": "./vectors.db",
  "chunk_size": 500,
  "search_top_k": 5,
  "compute": {
    "device": "auto",
    "fallback_to_cpu": true
  },
  "model": {
    "name": "multilingual-e5-small",
    "dimensions": 384
  }
}

Configuration Options

• document_patterns: Array of document paths and glob patterns
  • Supports directory paths: "./documents"
  • Supports glob patterns: "./docs/**/*.md" (recursive)
  • Multiple patterns: Index files from different locations
  • Note: Old documents_dir field is still supported (automatically migrated)
• db_path: Vector database file path
• chunk_size: Document chunk size in characters
• search_top_k: Number of search results to return
• compute.device: Compute device (auto, cpu, gpu)
• compute.fallback_to_cpu: Fallback to CPU if GPU unavailable
• model.name: Embedding model name
• model.dimensions: Vector dimensions

Command-Line Options

• --config <path>:

... View full README on GitHub