by tomohiro-owada · MCP Server · ★ 50
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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 /...
| Stars | 50 |
| Forks | 13 |
| Language | Go |
| Category | MCP Server |
| Quality Score | 30.75/100 |
| Last Updated | 2026-04-15 |
| Created | 2025-10-24 |
| Platforms | claude-code, go, mcp |
| Est. Tokens | ~851k |
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devrag is Markdown vector search MCP server for Claude Code. Natural language search for markdown files using multilingual-e5-small embeddings.. It is categorized as a MCP Server with 50 GitHub stars.
devrag is primarily written in Go. It covers topics such as claude-code, developer-tools, documentation.
You can find installation instructions and usage details in the devrag GitHub repository at github.com/tomohiro-owada/devrag. The project has 50 stars and 13 forks, indicating an active community.
The top alternatives to devrag on Agent Skills Hub include Vera, Writ, knowledge-rag. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.