Power your agent
Install from the publisher. Not a PlugMCP endpoint.
Host a copy with our keys and logs.
No packaged release in the registry yet. Follow the repository README.
Remote URL or a command on your machine? How the two installs differ
Run on PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
LaunchFrom the publisher
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.
Single-binary local RAG MCP server in Rust. MiniLM compiled in, hybrid search, zero deps.
# 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
The embedding model is lazy: the MCP handshake and rag_status never touch
it, so editors see an instant server.
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.
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.
CLI equivalents (same engine):
quillrag index ~/notes # incremental walk
quillrag search "auth flow" -k 5 # one-shot search
quillrag status # stats
quillrag clear # wipe
sentence-transformers/all-MiniLM-L6-v2 — masked mean pooling +
L2 norm, numerically matching sentence-transformers on CPU. Weights are
include_bytes!-ed into the binary and mmap'd from a materialized cache on
first load.Σ 1/(60+rank)) — no score-scale tuning,
robust to heterogeneous rankings.md markdown txt rst json yaml yml toml csv tsv html htm xml log rs py js jsx ts tsx go c h cpp hpp java rb sh bash zsh sql proto graphql dockerfile makefile ini cfg conf env — extend with -e ext1,ext2 / "extensions": [...].
Ignored dirs: every dot-directory (.git .obsidian .vscode …) plus
node_modules target dist build venv __pycache__ vendor.
Applications embedding the engine (default-features = false) can index more
through IndexHooks extractors:
Files that are readable but contain no text are recorded as empty documents,
so unchanged ones are not re-OCR'd on every pass (IndexOptions::retry_empty
re-checks them).
quillrag index ~/notes # incremental walk of one root
quillrag index ~/notes --no-prune # add/update without deleting anything
quillrag index ~/notes --force # re-embed this root, preserve other roots
~/work-docs
survive a later quillrag index ~/personal-notes against the same data
dir. Only files that lived under the directory being walked and have since
disappeared get pruned. Existing files excluded by extensions or size stay
indexed. --no-prune disables deletion, including through MCP's no_prune.
--force re-embeds only the selected sources. Use clear explicitly to wipe
a store. An empty successful scan warns and may prune that root's last file.Older batched indexing could associate document paths with the wrong chunks.
The fix cannot reconstruct those associations from the stored index alone.
Build a fresh data directory from your source files, verify search results,
then switch your client to it. Keep the old directory until verification passes.
This also removes legacy relative-path keys, which the new indexer preserves
rather than guessing their original working directory. New keys are absolute.
Paths containing .. resolve through the filesystem; use a stable spelling
for symlink-based roots. Re-indexing refreshes the BM25 sidecar as well.
Everything runs locally: embeddings, storage, search. Nothing leaves the machine — there is no network code path at all after installation.
Binary ≈ 105 MB (the model lives inside). RAM ≈ 120 MB resident while idle, spiking to ~250 MB during batch embedding.
quillrag stores everything in a single redb file and runs dense retrieval as
an exact, single-threaded linear scan over all vectors — no ANN index yet.
That makes the relevant limit query latency, not storage. Storage scales to
millions of chunks; retrieval speed is O(N) per query.
Verified on a corpus of 1K chunks (5/5 tests including real JSON-RPC-over-stdio
e2e); figures above 1K are extrapolated from the O(N) dense-scan cost, not
measured. A synthetic scale probe (src/bin/quillbench.rs) exists to measure
the curve on your own hardware — run cargo build --release && ./target/release/quillbench.
What this means in practice:
How it compares to common alternatives on the relevance axis:
all-MiniLM-L6-v2 ceiling as quillrag's dense path, but quillrag adds BM25 +
RRF fusion, which wins on keyword-heavy queries (error codes, IDs, exact
tokens). quillrag has no reranker or metadata filtering, which llama-index
offers on top.quillrag is deliberately minimal today. The big unlock is an approximate nearest-neighbor index:
Track the ANN work here: issue #1 — "ANN index for <1M chunks."
Is it really one file? Yes. The MiniLM weights + tokenizer are compiled in
via include_bytes!. No npm install, no Python, no model download on first
query. The binary is ~105 MB because the model lives inside it.
Why is startup so fast? The embedding model is lazy. The MCP handshake and
rag_status never touch it — editors see a ready server in ~20 ms. The model
only loads on the first rag_search / rag_index (~300 ms one-time).
What's the largest corpus it handles? Verified at 1K chunks (~25 ms/query). The architecture scales to millions of stored chunks; interactive retrieval holds up to low-tens-of-thousands today, and an ANN index (Roadmap) extends that to 1M+.
How is this different from llama-index? Similar hybrid retrieval quality, but quillrag is a single static binary with no runtime/dependency footprint and instant startup. llama-index adds rerankers, sophisticated chunking, and query expansion that quillrag doesn't have yet.
What file types are indexed? md markdown txt rst json yaml yml toml csv tsv html htm xml log rs py js jsx ts tsx go c h cpp hpp java rb sh bash zsh sql proto graphql dockerfile makefile ini cfg conf env — extend with -e.
Does it phone home? No. There is no network code path after installation.
--no-prune, source-scoped
--force, symlink traversal with failure-safe discovery, absolute source
keys, and correct partial-batch progress. Fixes shuffled document/chunk
associations in batched writes and missing BM25 result paths. Rebuild old
indexes into a fresh directory as described above..obsidian plugin configs
no longer pollute results); first fully automated 3-platform CI release.
Upgrade note: run quillrag clear once and re-index.cargo test --release # unit + end-to-end with real model inference
cargo run -- serve # dev server
RUST_LOG=debug cargo run ... # verbose logs (stderr only)
License: MIT. The optional OCR models under assets/ocr/ are CC BY-SA 4.0;
see assets/ocr/NOTICE.md.
© 2026 MCP Directory.
Single-binary local RAG MCP server in Rust. MiniLM compiled in, hybrid search, zero deps.
Tools
No tool list yet. Remote servers get one after the first health check.
FAQ
Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
No required environment variable is listed on this record.
Quillrag runs locally over stdio.
Last checked 2026-11-08. Single-binary local RAG MCP server in Rust. MiniLM compiled in, hybrid search, zero deps.
