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
Basic information
Lightweight local RAG MCP server for semantic vector
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"devrag": {
"type": "stdio",
"command": "/usr/local/bin/devrag"
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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.
Memory for AI agents that stays correct as facts change. Open source and local first: one memory for Claude Code, Codex, Cursor and your own agents.

Mess in, magic out: pour in your D&D and TTRPG notes and LoreKeeper keeps them as a private store for your world. Ask Claude or ChatGPT about it, or build a sourcebook.

Serve your docs to AI agents over SSH and MCP. Agents use ls, cat, grep, find, and pipes against a virtual filesystem backed by your documentation. Identity-scoped access, governed writing with human approval, and portab

Persistent memory for AI agents. Hosted MCP server with capsule-scoped key/value storage, tags, TTL expiration, bulk ops, and RBAC.

One permissioned folder your team and your AI agents both log into.
Comments