Quillrag

Name: Quillrag
Author: LLM Mart

No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.

One file. Zero dependencies. Ready before your editor finishes loading.

A local RAG engine in a single static binary — MiniLM embeddings compiled inside, hybrid dense + BM25 retrieval, MCP-native. No Node, no Python, no model download on first query.

quillrag terminal demo: serve, index, search

Why quillrag


~20 ms to ready	MCP handshake completes before the model even loads
Zero runtime deps	no Node, no Python, no pip/npm, no model downloads — ever
Hybrid retrieval	dense cosine ⊕ BM25 fused with Reciprocal Rank Fusion
Private by construction	no network code path after installation
One file, three OSes	~105 MB (the model lives inside), CI-built for linux/macOS/Windows

Quick start

# 1. grab a prebuilt binary (or cargo install --path .)
gh release download --repo Ayush-yadav11/quillrag -p '*linux*'
tar xzf quillrag-x86_64-linux.tar.gz && chmod +x quillrag

# 2. point it at any folder of notes/docs/code
./quillrag index ~/notes          # incremental walk

# 3. ask it something
./quillrag search "how does backpropagation work"

Or wire it straight into Claude Desktop / Cursor and let the AI search your notes mid-conversation — config below.

$ ./quillrag serve --data-dir ~/.local/share/quillrag
2026-08-26 INFO quillrag 0.1.2 ready in 41ms      <- handshake-ready before the model loads

Why it's fast

Stage	Cost
Binary start + MCP initialize	~20 ms (measured: store open + tool registration only)
First `rag_search` / `rag_index` call	+~300 ms one-time (mmap safetensors, build BERT graph)
Subsequent searches	~25 ms per query (2-core CPU, small corpus)
Re-indexing unchanged corpus	near-zero (FNV content hash skip)

The embedding model is lazy: the MCP handshake and rag_status never touch it, so editors see an instant server.

Install

Download a prebuilt archive from the latest release — Windows x86_64, macOS Apple Silicon, and Linux x86_64 are all built by CI on every version tag:

# linux/macOS example: fetch + extract the latest release
gh release download --repo Ayush-yadav11/quillrag -p '*linux*' | tar xz
chmod +x quillrag && ./quillrag --version

Linux requirement: v0.1.6 binaries are built on Ubuntu 24.04 runners and link glibc 2.39 (pidfd_spawnp/pidfd_getpid). On older distros (glibc < 2.39, e.g. Ubuntu 22.04), build from source with your local toolchain instead: cargo install --path ..

Or build from source:

cargo install --path .

Cross-compile targets used by CI: x86_64-unknown-linux-gnu, aarch64-apple-darwin, x86_64-pc-windows-msvc.

Wire it into your editor

Claude Desktop / Cursor / any MCP client:

{
  "mcpServers": {
    "quillrag": {
      "command": "/usr/local/bin/quillrag",
      "args": ["serve"],
      "env": { "QUILLRAG_DATA": "~/.local/share/quillrag" }
    }
  }
}

Or just run ./quillrag serve and point any stdio client at it.

Why quillrag

Quick start

Why it's fast

Install

Wire it into your editor

Tools