Add DevRag - Lightweight Local RAG MCP Server

README.md

@@ -913,6 +913,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
 - [shinpr/mcp-local-rag](https://github.com/shinpr/mcp-local-rag) 📇 🏠 - Privacy-first document search server running entirely locally. Supports semantic search over PDFs, DOCX, TXT, and Markdown files with LanceDB vector storage and local embeddings - no API keys or cloud services required.
 - [Smart-AI-Memory/empathy-framework](https://github.com/Smart-AI-Memory/empathy-framework) 🐍 🏠 - Five-level AI collaboration system with persistent memory and anticipatory capabilities. MCP-native integration for Claude and other LLMs with local-first architecture via MemDocs.
 - [TechDocsStudio/biel-mcp](https://github.com/TechDocsStudio/biel-mcp) 📇 ☁️ - Let AI tools like Cursor, VS Code, or Claude Desktop answer questions using your product docs. Biel.ai provides the RAG system and MCP server.
+- [tomohiro-owada/devrag](https://github.com/tomohiro-owada/devrag) 🏎️ 🏠 🍎 🪟 🐧 - Lightweight local RAG MCP server for semantic vector search over markdown documents. Reduces token consumption by 40x with sqlite-vec and multilingual-e5-small embeddings. Supports filtered search by directory and filename patterns.
 - [topoteretes/cognee](https://github.com/topoteretes/cognee/tree/dev/cognee-mcp) 📇 🏠 - Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources
 - [unibaseio/membase-mcp](https://github.com/unibaseio/membase-mcp) 📇 ☁️ - Save and query your agent memory in distributed way by Membase
 - [upstash/context7](https://github.com/upstash/context7) 📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors.