AutoRAG Agent

Now your agent can find anything in your computer.

= 24" /> = 1.2" />

[!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 self-evolving librarian agent. 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).

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 self-evolving librarian agent. Built on the Pi agent framework, a single configured model searches across multiple retrieval methods, opens source documents directly via bash to verify ground truth, and curates answers into clean, numbered knowledge units:

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, rclone, qmd) 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 gateway out of the box with zero external telemetry. A remote rerank model (OpenRouter, voyageai/rerank-3-lite by default) is an opt-in extra.

3. Fast by design. Rather than coordinating slow multi-agent hierarchies, a single configured model owns the entire retrieval, direct-read, and curation loop. Local CDC chunks (BM25, vector, and hybrid modes) deliver rapid, low-latency turnaround across multi-turn research queries.

Architecture & How It Works

AutoRAG Agent orchestrates five integrated subsystems:

1. Self-Evolving Memory System (check_memory): Before querying, the agent consults historical outcomes stored in ~/.autorag/memory.json to prioritize retrieval methods that have proven successful for similar queries.
2. Pluggable Multi-Method Retrieval:
   • BM25 Lexical Search: Fast keyword ranking via MinSync over parsed markdown mirrors.
   • Semantic Vector Search: Dense vector retrieval over CDC chunks via the built-in embedding gateway.
   • Hybrid Search: Combines BM25 and vector ranking via Reciprocal Rank Fusion (RRF).
   • Jikji Find-First Discovery: Local CLI-backed fast discovery answer packs.
   • Everything File-Name Search (Windows): The bundled voidtools Everything indexes file and folder names under your search roots so the agent finds files by name, extension, path, size, or date instantly (everything_search).
   • FSearch File-Name Search (macOS/Linux): fsearch-cli (FSearch) keeps a live, per-workspace name index of your search roots so the agent finds files by name, extension, path, size, or date instantly (fsearch_search); without fsearch-cli installed it degrades to a slow filesystem walk.
   • Datasource Skills: Server-authorized federated retrieval across external applications.
3. Result Merger & Scoped Access Gate: Cross-method deduplication, score normalization, and default-deny permission checks.
4. Direct Evidence Reading (bash): The agent directly opens and inspects promising files with cat, grep, or find to verify facts against ground truth.
5. Curation & Active Feedback: Structured findings are returned via emit_autorag_results. When callers provide feedback on which items were useful, AutoRAG records this to optimize future queries.

Pi host boundary

AutoRAG uses @earendil-works/pi-coding-agent as the runtime host for model sessions. Pi owns provider credentials and OAuth storage, model-runtime dispatch, session JSONL persistence/resume, extension loading, lifecycle events, and the built-in read/bash/edit/write/grep/find/ls tools. AutoRAG registers only its domain tools and keeps orchestration outside the host: datasource authorization, MinSync/Jikji preparation, memory hints, fast-to-verification two-phase search, structured result emission, and remote-session filtering.

The CLI TUI (autorag tui) is Pi's interactive mode hosted on the AutoRAG librarian: there is no separate AutoRAG renderer. Pi owns the terminal UI and its native in-session commands — /login//logout, /model, /resume, /new, /tree, /compact, and /settings — so provider sign-in and model selection work on first launch even when no AutoRAG model is configured. AutoRAG registers its retrieval tools and streams progress, preliminary answers, and final results into the same Pi session, so resume and two-phase search keep working together.

Update notices are AutoRAG's, not Pi's: autorag tui suppresses Pi's "run pi update" banner (Pi is a bundled host, not a separate install) and instead checks the published @autorag/librarian release, showing a one-line notice in the session when a newer version exists. Set AUTORAG_NO_UPDATE_CHECK=1 to skip the lookup; AUTORAG_UPDATE_CHECK_URL overrides the registry endpoint.

🤖 For AI Agents & LLMs: Setup & Skills

If you are an AI coding agent or LLM (Claude Code, Cursor, Windsurf, Codex, Senpi, Pi, etc.) tasked with installing, configuring, or querying AutoRAG Agent, use our built-in skills located in the skills/ directory:

Install the skills into your coding agent

Skills are not auto-discovered — copy only the setup skill when an agent needs to bootstrap AutoRAG. Routine AutoRAG Lite retrieval is provided by MCP tools, not by a autorag-lite-search skill or shell command.

# From a clone of this repository
mkdir -p .claude/skills
cp -R skills/autorag-lite-setup .claude/skills/

# From a global install — the npm package ships the same skills/ folder
AUTORAG_SKILLS="$(npm root -g)/@autorag/librarian/skills"
# Bun global installs live at ~/.bun/install/global/node_modules/@autorag/librarian/skills
mkdir -p .claude/skills
cp -R "$AUTORAG_SKILLS/autorag-lite-setup" .claude/skills/

Run the setup skill once to register the stdio server with the host. Reload the agent, then discover the actual tool names and schemas with MCP tools/list. The normal Lite path is autorag.status → autorag.refresh when needed → autorag.search; use autorag.report, autorag.evidence, and autorag.feedback for the optional curation lifecycle. Copy skills/autorag and skills/autorag-setup only when the agent should also drive the model-backed librarian, and skills/autorag-doctor for diagnostics. Other agents read their own skill directories; copy the same setup folder there and reload the agent session so it picks up the MCP registration instructions. Do not copy or retain the removed autorag-lite-search skill.

Quick Agent Workflow

1. Install CLI:

   command -v autorag >/dev/null || bun install -g @autorag/librarian
   

2. Inspect & Preflight:

   autorag status --json        # check corpus freshness and index readiness
   autorag duplicates --json    # scan for exact or near-duplicate document families
   

3. Perform Curated Search:

   autorag search "your question" --json
   

4. Safety Guidelines for Agents:
   • Never delete, move, or modify original user documents.
   • AutoRAG writes index artifacts strictly into /.autorag/ and .jikji/ caches.
   • Never print or leak API keys, tokens, or credential values into stdout or logs.

AutoRAG Lite MCP Server

Register the package's stdio server with the host; the host owns the process lifecycle and sends MCP tool calls:

AUTORAG_CONFIG=/absolute/path/to/.autorag/config.json autorag-mcp

For Claude Code and Codex registration commands, use skills/autorag-lite-setup/SKILL.md. The MCP contract source of truth is src/mcp/server.ts (tool registration, schemas, handlers) plus src/mcp/index.ts (stdio entrypoint). Discover tools and schemas with MCP tools/list; do not hard-code a tool count.

Core Lite tools include autorag.status, autorag.search, autorag.search.files, autorag.datasources.list, autorag.datasources.get, autorag.refresh, autorag.report, autorag.evidence, and autorag.feedback. Configured integrated datasources expose additional scoped search tools. Read-only MCP mode omits mutating tools such as refresh, report, and feedback.

⚡ AutoRAG Lite: Model-Free Retrieval Engine

Need blazing fast local search without configuring an LLM or paying for API tokens? Use AutoRAG Lite.

AutoRAG Lite provides the exact same high-performance indexing, BM25 ranking, and local MinSync vector/hybrid retrieval engine as the full librarian, but runs 100% model-free:

• Zero LLM Token Usage: Run purely local BM25 and vector search offline.
• Agent Integration Ready: Use the AutoRAG Lite MCP server to supply raw context chunks to an external model; the CLI remains a bootstrap and maintenance interface.
• Fast Local CLI: The CLI remains available for terminal-only indexing and repair.

# Initialize a model-free workspace
autorag lite init --search-paths ./documents

# Index local files and datasources
autorag lite refresh

Normal Lite retrieval runs through the MCP tools (autorag.status, autorag.search); the CLI remains the bootstrap and maintenance interface for indexing and repair.

🔌 Supported Datasources

AutoRAG Agent connects to external tools and communication platforms using dedicated datasource skills. Data remains in each tool's native store — AutoRAG does not copy or ingest foreign databases into a centralized store:

For configuration syntax and connector details, see docs/datasource-skills.md. Non-interactive sync/index steps get a 30-minute per-connector budget (connector.indexTimeoutMs) because first-run imports routinely take minutes; interactive search keeps its 60-second default.

Installation & Setup

AutoRAG Agent is published as @autorag/librarian (requires Node.js ≥ 24 or Bun):

# Install CLI globally
bun install -g @autorag/librarian
# or with npm:
npm install -g @autorag/librarian

# Add as a TypeScript/JavaScript library
bun add @autorag/librarian

System Prerequisites

• No Java required: Document parsing (HWP/HWPX/HWPML, PDF, DOCX, XLSX/XLS) runs in-process through kordoc.
• Rust Toolchain (Optional): Automatically compiles Jikji (jikji-cli) if installed.
• MinSync: Automatically downloaded and installed into /.autorag/bin on first run.
• Everything (Windows only, bundled): The package ships the portable Everything 1.4.1.1032 and its ES 1.1.0.38 CLI (x64 and ARM64, SHA-256 pinned). On Windows, AutoRAG extracts them into /.autorag/everything/ and runs a private, user-level instance that indexes only your configured search roots: no administrator rights, no Everything service, no whole-drive scan, no HTTP/ETP server, and no change to any Everything you already run. Set "everything": false in the config to turn it off. macOS and Linux are not affected.
• FSearch (macOS/Linux only, separate install): Install fsearch-cli yourself (e.g. brew install NomaDamas/fsearch-mac/fsearch-mac (installs the fsearch-cli binary); GPL-2.0, spawned as a separate process, never bundled or linked). On macOS and Linux, AutoRAG builds a per-workspace database at /.autorag/fsearch/ indexing only your configured search roots, and keeps a fsearch-cli watch daemon live (FSEvents/inotify) for sub-second name search. It never touches the FSearch app's own database. Without fsearch-cli, name search falls back to a slow bounded filesystem walk. Set "fsearch": false in the config to turn it off. Windows is not affected (Everything covers it).

Quick Start

1. Initialize and Index

# Initialize configuration for your documents folder
autorag init --search-paths ~/Documents/research

# Korean + English is the default; set it explicitly for other corpora
autorag init --search-paths ~/Documents/research --languages ja,en

# Index documents (parses PDFs/Markdown, builds BM25 and MinSync vectors)
autorag refresh

# Check indexing health
autorag status

Document languages and parsers

One global languages setting describes the corpus. It is resolved from --languages, then AUTORAG_LANGUAGES, then languages in the config file, falling back to ["ko", "en"]. Supported tags: ko, en, ja, zh-hans, zh-hant, fr, de, es, ru, it, pt, vi, th, ar, hi.

OCR is opt-in and never runs unless enabled, so indexing downloads no model by default. When enabled, languages selects the recognition languages (ja → jpn, zh-hans → chi_sim, …) for both standalone images and scanned PDF pages.

2. Search from CLI

# Perform a curated search (uses your configured reasoning model)
autorag search "What are our primary Q3 deliverables?"

# Launch the interactive Terminal UI (Pi host: /login, /model, /resume, …)
autorag tui

3. Programmatic Usage (TypeScript API)

import { AutoRAGAgent } from "@autorag/librarian";

// Initialize librarian agent
const agent = new AutoRAGAgent({
  searchPaths: ["/path/to/documents"],
});

// Run curated search loop
const response = await agent.searchDocuments("Summarize recent compliance updates");

console.log("Answer:", response.answer);
for (const result of response.results) {
  console.log(`[${result.number}] ${result.title} (${result.source})`);
  console.log(`    ${result.summary}`);
}

// Record feedback: Result [1] was useful, [2] was not
agent.recordFeedbackByNumbers(response.sessionId, [1], [2]);

CLI Command Reference

Documentation Links

Deep dive into AutoRAG Agent's architecture, security, and integration guides:

• MinSync Setup & Embedding QA: Automatic binary installation, CDC chunking, and EmbeddingGemma verification.
• Local Embedding Runtime & Gateway: AutoRAG-owned local gateway, model prefetching, and zero-egress semantic search. Model cards: qwen3-embedding-0.6b, embeddinggemma-300m.
• Datasource Skills Reference: Full configuration contracts, connection aliases, and connector options.
• Jev Decisions: Jev query pipeline, on by default via OpenRouter: it routes each question to local search, web search, or a direct answer, splits multi-part questions into up to five parallel searches (openrouter/qwen/qwen3.7-flash), and ends the run after the fast answer when that answer is complete. Also covers the jev judgment tool, backends (OpenRouter, TypeSafe, Vercel AI Gateway), and how to opt out.
• Manual QA & Datasource Test Harnesses: Real-world testing guides for Discord, KakaoTalk, Slack, Notion, and email.
• P2P SimpleX Sharing & Path Standard: Decentralized peer query sharing with SimpleX, PII redaction, and approval queues.
• Supply Chain Security & License Audits: Software bill of materials (SBOM) and dependency gate policies.
• Competitive Landscape Study: In-depth analysis of why in-place federation outperforms centralized RAG.
• Legacy AutoRAG (AutoML): Documentation for the legacy Python AutoML pipeline optimizer (pip install AutoRAG).

Contributors & Community

AutoRAG Agent is an open-source project built by the community. We welcome contributions, bug reports, datasource connectors, and ideas!

• Contributing: Feel free to open an issue or pull request. Start with CONTRIBUTING.md — development setup, the make ci check we ask for, the Signed-off-by trailer every commit needs, and when to run the live end-to-end lanes. MAINTAINERS lists who reviews which area. AI tools are allowed; unverified dumps are not — see AI_POLICY.md.
• Security: Private vulnerability reports and the response SLA live in SECURITY.md.
• GitHub Contributors: View all contributors on GitHub.

Acknowledgements

AutoRAG Agent stands on the shoulders of fantastic open-source projects:

• Pi Framework by @earendil-works — The foundational agent framework powering AutoRAG's reasoning loop.
• MinSync — Ultra-fast incremental Change Data Capture (CDC) chunking and local BM25/vector indexing.
• Jikji by NomaDamas — High-performance find-first local document discovery.
• Everything and ES by voidtools (David Carpenter) — Instant Windows file-name indexing and its command-line interface, bundled under the MIT License.
• FSearch and fsearch-cli — Everything-style file-name search for macOS/Linux (GPL-2.0-or-later). Never bundled or linked: users install fsearch-cli separately and AutoRAG spawns it as a separate process (mere aggregation per the FSF GPL FAQ).
• dupey by NomaDamas — Fast duplicate and near-duplicate document family detection.
• Federated CLI Authors: External datasource tools lazykatok, discrawl, mailcrawl, wacrawl, telecrawl, slacrawl, notcrawl, qmd, and rclone.
• kordoc by chrisryugj — HWP/HWPX/HWPML, PDF, DOCX and XLSX parsing with nested-table fidelity, used as AutoRAG's default document parser.

Troubleshooting

When search returns nothing, a datasource disappears from results, refresh hangs, or the gateway will not start, run the autorag-doctor agent skill (skills/autorag-doctor/SKILL.md). Point your coding agent at it and say "check AutoRAG" — it walks the full diagnose-and-repair procedure and ends with a per-datasource status table showing what is indexed and what actually returns hits.

The first three commands cover most of it:

autorag status --json          # freshness, per-component state, diagnostics
autorag health --json          # model resolution + one live completion probe
autorag gateway status --format json   # on-demand embedding runtime

Indexing is not the same thing as searchability, so always confirm retrieval itself through the connected Lite MCP server:

autorag.status {}
autorag.refresh {}
autorag.search {"query":"a word that certainly appears","topK":3}
autorag.search {"query":"recent topic","tags":["discord"],"topK":3}

The CLI equivalents remain available for terminal repair, but do not use them as the agent's normal Lite search path.

Common failures and their fix:

Native datasource stores stay owned by their CLIs — AutoRAG never rebuilds them. Fix a broken archive with lazykatok doctor, discrawl --json metadata, slacrawl --json doctor, wacrawl --json doctor, telecrawl --json doctor, notcrawl doctor, qmd status, or mailcrawl doctor, then re-run autorag refresh --method datasources --json.

License

• AutoRAG 2.0 (AutoRAG Agent): Released under the MIT License.
• Legacy Python AutoRAG (legacy/): Released under the Apache License 2.0.
• Production third-party notices and licenses are documented in NOTICE.
• The bundled Windows binaries keep their own licenses: Everything (MIT, plus PCRE BSD-3-Clause) in licenses/everything-MIT-and-PCRE-BSD.txt and ES (MIT) in licenses/es-MIT.txt. Everything's source code is not public; AutoRAG redistributes the unmodified voidtools portable ZIPs.
• FSearch/fsearch-cli (GPL-2.0-or-later) are not distributed with AutoRAG: no code is copied, linked, or bundled. Users install fsearch-cli themselves and AutoRAG communicates with it only through its command-line interface and daemon socket — separate programs, per the FSF mere-aggregation FAQ. Source: https://github.com/NomaDamas/fsearch-mac.