Is DevRag truly local and free?
Yes. It runs entirely on your machine, with no API calls or external services, so there are no usage costs.
@tomohiro-owada
基本情報
Lightweight local RAG MCP server for semantic vector
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"devrag": {
"type": "stdio",
"command": "/usr/local/bin/devrag"
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
DevRag is a lightweight, local-only MCP server that provides Retrieval-Augmented Generation (RAG) capabilities to MCP hosts (e.g., Claude Desktop). It performs semantic vector search over indexed markdown files, dramatically reducing token usage and search latency—all without any API dependencies or costs.
DevRag is distributed as a single binary with no dependencies. To use it, launch the binary and configure your MCP host to point to it. Once running, the server exposes tools such as search, index_markdown, list_documents, delete_document, and reindex_document for managing and querying your local knowledge base.
Yes. It runs entirely on your machine, with no API calls or external services, so there are no usage costs.
DevRag ships as a single binary with no dependencies—just download and execute.
Indexing and search are available in both Japanese and English.
Use the `index_markdown` tool to add a markdown file to the index, and `list_documents` to review indexed content.
The server follows the Model Context Protocol, which typically uses STDIO transport for host–server communication.
The README does not specify explicit limits; performance and index capacity depend on the local environment.

Open-source Python SDK & MCP interface for Priostack Agent Context Network (ACN). Zero-setup, autonomous memory orchestration for AI agents.
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors

MCP connector for Snipara, the Project Brain for AI coding agents

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 162 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
コメント