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.
D
DevRag - Lightweight Local RAG MCP Server
@tomohiro-owada
基本信息
关于 DevRag - Lightweight Local RAG MCP Server
Lightweight local RAG MCP server for semantic vector
DevRag - Lightweight Local RAG MCP Server 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"devrag": {
"type": "stdio",
"command": "/usr/local/bin/devrag"
}
}
}DevRag - Lightweight Local RAG MCP Server 工具
工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
概览
What is DevRag - Lightweight Local RAG MCP Server?
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.
How to use DevRag - Lightweight Local RAG MCP Server?
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.
Key features of DevRag - Lightweight Local RAG MCP Server
- 40x token reduction via semantic vector search
- 15x faster responses (~95ms search latency)
- Runs entirely locally – no API costs
- Multi-language support – Japanese and English
- Filtered search by directory and filename patterns
- Single binary with no external dependencies
Use cases of DevRag - Lightweight Local RAG MCP Server
- Augmenting an AI assistant with a local markdown knowledge base
- Indexing personal documentation or project wikis for fast retrieval
- Reducing token usage in RAG pipelines when using paid LLM APIs
- Multi-language RAG for teams working in Japanese and English
常见问题
What are the runtime requirements?
DevRag ships as a single binary with no dependencies—just download and execute.
Which languages does it support?
Indexing and search are available in both Japanese and English.
How do I add my documents?
Use the `index_markdown` tool to add a markdown file to the index, and `list_documents` to review indexed content.
What transport does it use?
The server follows the Model Context Protocol, which typically uses STDIO transport for host–server communication.
Are there any limits on document size or number?
The README does not specify explicit limits; performance and index capacity depend on the local environment.
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