AutoRAG Agent

Now your agent can find anything in your computer.

[!IMPORTANT] Looking for the original AutoRAG (RAG AutoML / pipeline optimization tool)? This repository now hosts AutoRAG 2.0, a complete reimagining of AutoRAG as a personal librarian agent for your office work. The original Python-based AutoRAG — the RAG AutoML tool for automatically finding an optimal RAG pipeline for your data — now lives in the legacy/ directory of this repository.

The legacy AutoRAG is NOT abandoned. It continues to be maintained (bug fixes, dependency updates, and PyPI releases via pip install AutoRAG) in maintenance mode. Existing users can keep using it exactly as before — see the legacy README for its documentation, and file issues in this repository as usual. New feature development is focused on AutoRAG Agent (2.0).

🖥️ The desktop app

A Finder for your computer that understands what's inside your files. The AutoRAG desktop app puts this librarian behind a real file manager: browse your folders as usual, then ask a question and get an answer that cites the files, chats, and mail it came from. Everything it searches with is the engine in this repository.

🔎 Just ask

Ask the way you'd ask a teammate. The librarian reads the files and writes back, every claim linked to the passage behind it. Drag files, folders, or links into a question to ask about just those.

🗂️ Spot the twins

proposal-final, proposal-v3, proposal (1): stacked into one row, newest on top, tagged Latest, Similar, or Identical. Powered by dupey, the same scan as autorag duplicates.

⌨️ Space to see anything

Markdown, code, PDF, video, audio, Office, and HWP/HWPX previewed instantly in Quick Look. HWP pages are drawn locally by rhwp; Hancom Office is not required.

🪟 A real file manager

Tabs and split panes: drag a tab to an edge to split, and every pane keeps its own tabs, so a folder and a conversation sit side by side. Folders update live as other apps change them.

And more: hybrid keyword + meaning search with a reranker · scheduled re-indexing that runs even while the app is closed · an Ask entry in the Finder and Explorer right-click menu · light and dark mode · Korean, English, Japanese, and Simplified Chinese.

Every place, one search

The app connects the same datasources the librarian does, and searches them right alongside your files:

Private by design

• Embedded on your machine. Semantic indexing runs on MinSync's local Qwen3-Embedding-0.6B engine (1024 dimensions), which MinSync provisions and runs; AutoRAG owns no embedding model. File contents are never sent to a remote embedder unless you explicitly configure a remote minSync.embedder.
• Keys stay encrypted. Sign-in tokens and API keys live in the system's encrypted store, never in a config file.
• Bring your own model. Use the hosted Dazzi Models plan, or connect your own provider key and pay the provider directly.

[!TIP] The Dazzi desktop app's source lives in NomaDamas/AutoRAG-app (Electron + React, Fair Source under FSL-1.1-MIT). It depends on the open-source @autorag/librarian engine (Apache-2.0) from this repository; build, run, and file app issues there.

One engine, every surface

The app is one way in. The same librarian runs everywhere else:

What is AutoRAG Agent?

Search tools dump file paths and matching lines — forcing you to open files, read context, and synthesize answers yourself.

AutoRAG Agent is a personal librarian agent that helps with your office work. Built on the Pi agent framework, a single configured model searches your files, mail, chats, and notes in place, opens source documents directly via bash to verify ground truth, and curates answers into clean, numbered knowledge units. Because it works for you rather than only on your index, it can also act on your files when you ask, such as organizing, renaming, moving, or editing them with a record of every change:

You ask:  "What changes were made to the cloud infrastructure contract in Q3?"

AutoRAG Agent:
[1] Enterprise Compute Discount — AWS annual discount tier increased to 28% based on commitment volume. (contracts/cloud-2026.pdf:p.4)
[2] Regional Data Residency — Explicit compliance clause pinning customer data storage to the Seoul AWS region. (/slack/legal-ops/chunks/318)
[3] SLA Guarantee — Guaranteed uptime threshold adjusted to 99.95% with penalty credits starting at 15 minutes downtime. (contracts/sla-addendum.md:lines 45-62)

Core Values

Three principles drive every design decision in AutoRAG Agent:

1. Never migrate your data to search it. Traditional RAG systems force you to upload, ETL, and duplicate your files into a centralized vector database. AutoRAG Agent federates your data in place, querying CLI-native stores (lazykatok, discrawl, slacrawl, mailcrawl) where your data already lives. Results retain opaque, source-native identities (/kakao/..., /slack/...) that preserve local access control and privacy. (See our Competitive Landscape Study on why in-place federation is the durable differentiator).

2. Just works — no RAG degree required. No pipeline tuning, no vector-DB maintenance, and no remote embedding keys required. AutoRAG Agent automatically manages MinSync for incremental Change Data Capture (CDC) chunking and provides a local embedding backend out of the box with zero external telemetry. A remote rerank model is an opt-in extra: it runs only when you name one in rerank.model, and otherwise the first answer is built from each source's