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librarian agent on Pi framework with pluggable retrieval methods","maintainers":[{"name":"vkehfdl1","email":"vkehfdl1@gmail.com"}],"readme":"# AutoRAG Agent\n\n**Now your agent can find anything in your computer.**\n\n<p align=\"left\">\n  <a href=\"https://www.npmjs.com/package/@autorag/librarian\"><img src=\"https://img.shields.io/npm/v/@autorag/librarian.svg?style=flat-square&color=38bdf8\" alt=\"npm version\" /></a>\n  <a href=\"https://www.npmjs.com/package/@autorag/librarian\"><img src=\"https://img.shields.io/npm/dm/@autorag/librarian.svg?style=flat-square&color=818cf8\" alt=\"npm downloads\" /></a>\n  <a href=\"https://github.com/Marker-Inc-Korea/AutoRAG/stargazers\"><img src=\"https://img.shields.io/github/stars/Marker-Inc-Korea/AutoRAG?style=flat-square&logo=github&color=f59e0b\" alt=\"GitHub stars\" /></a>\n  <a href=\"https://github.com/Marker-Inc-Korea/AutoRAG/actions/workflows/ci.yml\"><img src=\"https://img.shields.io/github/actions/workflow/status/Marker-Inc-Korea/AutoRAG/ci.yml?branch=main&label=CI&style=flat-square\" alt=\"CI Status\" /></a>\n  <a href=\"LICENSE\"><img src=\"https://img.shields.io/badge/license-MIT-green.svg?style=flat-square\" alt=\"License: MIT\" /></a>\n  <img src=\"https://img.shields.io/badge/node-%3E%3D24.0.0-informational.svg?style=flat-square\" alt=\"Node >= 24\" />\n  <img src=\"https://img.shields.io/badge/bun-%3E%3D1.2-black.svg?style=flat-square&logo=bun\" alt=\"Bun >= 1.2\" />\n</p>\n\n<p align=\"center\">\n  <img src=\"assets/autorag-agent-social-preview.png\" alt=\"AutoRAG Agent\" width=\"100%\" />\n</p>\n\n> [!IMPORTANT]\n> **Looking for the original AutoRAG (RAG AutoML / pipeline optimization tool)?**\n> 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/`](legacy/) directory of this repository.\n>\n> **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](legacy/README.md) for its documentation, and file issues in this repository as usual. New feature development is focused on AutoRAG Agent (2.0).\n\n---\n\n## What is AutoRAG Agent?\n\nSearch tools dump file paths and matching lines — forcing you to open files, read context, and synthesize answers yourself.\n\n**AutoRAG Agent is a self-evolving librarian agent.** Built on the [Pi](https://github.com/earendil-works/pi-mono) 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:\n\n```text\nYou ask:  \"What changes were made to the cloud infrastructure contract in Q3?\"\n\nAutoRAG Agent:\n[1] Enterprise Compute Discount — AWS annual discount tier increased to 28% based on commitment volume. (contracts/cloud-2026.pdf:p.4)\n[2] Regional Data Residency — Explicit compliance clause pinning customer data storage to the Seoul AWS region. (/slack/legal-ops/chunks/318)\n[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)\n```\n\n---\n\n## Core Values\n\n<p align=\"center\">\n  <img src=\"assets/autorag-agent-principles.png\" alt=\"AutoRAG Agent Core Principles\" width=\"100%\" />\n</p>\n\nThree principles drive every design decision in AutoRAG Agent:\n\n1. **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](docs/competitive-landscape-2026-09.md) on why in-place federation is the durable differentiator).*\n\n2. **Just works — no RAG degree required.** No pipeline tuning, no vector-DB maintenance, and no remote embedding API keys. AutoRAG Agent automatically manages [MinSync](docs/minsync-setup.md) for incremental Change Data Capture (CDC) chunking and provides a local [embedding gateway](docs/embedding-runtime.md) out of the box with zero external telemetry.\n\n3. **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.\n\n---\n\n## Architecture & How It Works\n\nAutoRAG Agent orchestrates five integrated subsystems:\n\n1. **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.\n2. **Pluggable Multi-Method Retrieval:**\n   - **BM25 Lexical Search:** Fast keyword ranking via MinSync over parsed markdown mirrors.\n   - **Semantic Vector Search:** Dense vector retrieval over CDC chunks via the built-in embedding gateway.\n   - **Hybrid Search:** Combines BM25 and vector ranking via Reciprocal Rank Fusion (RRF).\n   - **Jikji Find-First Discovery:** Local CLI-backed fast discovery answer packs.\n   - **Datasource Skills:** Server-authorized federated retrieval across external applications.\n3. **Result Merger & Scoped Access Gate:** Cross-method deduplication, score normalization, and default-deny permission checks.\n4. **Direct Evidence Reading (`bash`):** The agent directly opens and inspects promising files with `cat`, `grep`, or `find` to verify facts against ground truth.\n5. **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.\n\n---\n\n## 🤖 For AI Agents & LLMs: Setup & Skills\n\nIf 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/`](skills/) directory:\n\n| Skill | Directory | When to Use |\n|---|---|---|\n| **`autorag`** | [`skills/autorag/`](skills/autorag/SKILL.md) | Querying, searching, comparing, and summarizing documents with an already configured AutoRAG librarian. |\n| **`autorag-setup`** | [`skills/autorag-setup/`](skills/autorag-setup/SKILL.md) | Installing AutoRAG, configuring models, adding document roots/datasources, running health checks, and repairing indexes. |\n| **`autorag-lite-setup`** | [`skills/autorag-lite-setup/`](skills/autorag-lite-setup/SKILL.md) | Initializing and maintaining the model-free AutoRAG Lite lifecycle without an LLM. |\n| **`autorag-lite-search`** | [`skills/autorag-lite-search/`](skills/autorag-lite-search/SKILL.md) | Performing model-free retrieval, reporting evidence, and recording feedback without an LLM. |\n\n### Install the skills into your coding agent\n\nSkills are not auto-discovered — copy the folders you want into the agent's skill directory. For **Claude Code** that directory is `.claude/skills/` in the project (or `~/.claude/skills/` to enable them everywhere). Once `autorag-lite-setup` and `autorag-lite-search` are in place, Claude Code drives `autorag lite retrieve` as a search tool:\n\n```bash\n# From a clone of this repository\nmkdir -p .claude/skills\ncp -R skills/autorag-lite-setup skills/autorag-lite-search .claude/skills/\n```\n\n```bash\n# From a global install — the npm package ships the same skills/ folder\nAUTORAG_SKILLS=\"$(npm root -g)/@autorag/librarian/skills\"\n# Bun global installs live at ~/.bun/install/global/node_modules/@autorag/librarian/skills\nmkdir -p .claude/skills\ncp -R \"$AUTORAG_SKILLS/autorag-lite-setup\" \"$AUTORAG_SKILLS/autorag-lite-search\" .claude/skills/\n```\n\nCopy `skills/autorag` and `skills/autorag-setup` the same way when the agent should also drive the model-backed librarian, and `skills/autorag-doctor` for diagnostics. Other agents read their own directories (for example `~/.agents/skills/`) — copy the same folders there and reload the agent session so it picks them up.\n\n### Quick Agent Workflow\n\n1. **Install CLI:**\n   ```bash\n   command -v autorag >/dev/null || bun install -g @autorag/librarian\n   ```\n2. **Inspect & Preflight:**\n   ```bash\n   autorag status --json        # check corpus freshness and index readiness\n   autorag duplicates --json    # scan for exact or near-duplicate document families\n   ```\n3. **Perform Curated Search:**\n   ```bash\n   autorag search \"your question\" --json\n   ```\n4. **Safety Guidelines for Agents:**\n   - **Never delete, move, or modify original user documents.**\n   - AutoRAG writes index artifacts strictly into `<workspace>/.autorag/` and `.jikji/` caches.\n   - Never print or leak API keys, tokens, or credential values into stdout or logs.\n\n---\n\n## ⚡ AutoRAG Lite: Model-Free Retrieval Engine\n\nNeed blazing fast local search without configuring an LLM or paying for API tokens? Use **AutoRAG Lite**.\n\nAutoRAG 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**:\n\n- **Zero LLM Token Usage:** Run purely local BM25 and vector search offline.\n- **Agent Integration Ready:** Use `autorag lite retrieve` inside your own agentic workflows to supply raw context chunks to an external model.\n- **Fast Local CLI:** Instant responses directly from your terminal.\n\n```bash\n# Initialize a model-free workspace\nautorag lite init --search-paths ./documents\n\n# Index local files and datasources\nautorag lite refresh\n\n# Retrieve ranked document chunks (returns JSON candidates with scores)\nautorag lite retrieve \"compliance policy exception process\" --top-k 5 --json\n\n# Check index status and freshness\nautorag lite status\n```\n\n---\n\n## 🔌 Supported Datasources\n\nAutoRAG 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:\n\n| Datasource | Skill Alias | Backend / Driver | Storage & Privacy Model | Search Capabilities |\n|---|---|---|---|---|\n| **Local Documents** | `local` | Native filesystem & MinSync | Workspace-local parsed mirrors (`.autorag/`) | BM25, Semantic, Hybrid |\n| **KakaoTalk** | `kakao` | [`lazykatok`](https://github.com/changeroa/lazykatok) CLI | Native KakaoTalk archive; zero direct DB access | BM25, Semantic, Hybrid |\n| **Discord** | `discord` | [`discrawl`](https://github.com/openclaw/discrawl) CLI | Native SQLite archive; token-free wiretap mode | BM25, Semantic, Hybrid |\n| **WhatsApp** | `whatsapp` | [`wacrawl`](https://github.com/openclaw/wacrawl) CLI | Local-first incremental archive + FTS5 | Lexical FTS5 |\n| **Telegram** | `telegram` | [`telecrawl`](https://github.com/openclaw/telecrawl) CLI | Local-first desktop archive + FTS5 | Lexical FTS5 |\n| **Slack** | `slack` | [`slacrawl`](https://github.com/openclaw/slacrawl) CLI | Local workspace/channel/thread archive + FTS5 | Lexical FTS5 |\n| **Notion** | `notion` | [`notcrawl`](https://github.com/openclaw/notcrawl) CLI | Local page/database/block archive + FTS5 | Lexical FTS5 |\n| **Email Archives** | `mailcrawl` | [`mailcrawl`](https://github.com/NomaDamas/mailcrawl) CLI | Local Gmail, IMAP, and Maildir storage | BM25, Semantic, Hybrid |\n| **Local Mail Export**| `mail-export` | Built-in `.mbox` / `.eml` parser | Local filesystem mailboxes | Lexical |\n| **Obsidian Vaults** | `obsidian` | [`qmd`](https://github.com/tobi/qmd) CLI | Direct markdown vault indexing | BM25, Semantic |\n| **GitHub** | `github` | GitHub REST API | In-memory fetched Issues and Pull Requests | Lexical, Scoped |\n| **Cloud Drives** | `cloud-drive` | [`rclone`](https://rclone.org) CLI | Google Drive (Tier-1), OneDrive, Dropbox, etc. | Incremental Mirror + BM25 |\n| **Photos & Shots** | `clawgallery` | `clawgallery` CLI | Local screenshot and photo store | Hybrid OCR/Visual Search |\n| **RSS / News** | `rss` | Native HTTP Poller | RSS 2.0 & Atom feeds (24h deduplication) | Lexical |\n| **macOS Spotlight** | `spotlight` | Native `mdfind` CLI | macOS system metadata and content index | System Native |\n\nFor configuration syntax and connector details, see [docs/datasource-skills.md](docs/datasource-skills.md).\n\n---\n\n## Installation & Setup\n\nAutoRAG Agent is published as `@autorag/librarian` (requires Node.js ≥ 24 or Bun):\n\n```bash\n# Install CLI globally\nbun install -g @autorag/librarian\n# or with npm:\nnpm install -g @autorag/librarian\n\n# Add as a TypeScript/JavaScript library\nbun add @autorag/librarian\n```\n\n### System Prerequisites\n\n- **No Java required:** Document parsing (HWP/HWPX/HWPML, PDF, DOCX, XLSX/XLS) runs in-process through [`kordoc`](https://github.com/chrisryugj/kordoc).\n- **Rust Toolchain (Optional):** Automatically compiles Jikji (`jikji-cli`) if installed.\n- **MinSync:** Automatically downloaded and installed into `<workspace>/.autorag/bin` on first run.\n\n---\n\n## Quick Start\n\n### 1. Initialize and Index\n\n```bash\n# Initialize configuration for your documents folder\nautorag init --search-paths ~/Documents/research\n\n# Korean + English is the default; set it explicitly for other corpora\nautorag init --search-paths ~/Documents/research --languages ja,en\n\n# Index documents (parses PDFs/Markdown, builds BM25 and MinSync vectors)\nautorag refresh\n\n# Check indexing health\nautorag status\n```\n\n### Document languages and parsers\n\nOne global `languages` setting describes the corpus. It is resolved from\n`--languages`, then `AUTORAG_LANGUAGES`, then `languages` in the config file,\nfalling back to `[\"ko\", \"en\"]`. Supported tags: `ko`, `en`, `ja`, `zh-hans`,\n`zh-hant`, `fr`, `de`, `es`, `ru`, `it`, `pt`, `vi`, `th`, `ar`, `hi`.\n\n| Extension | Parser |\n|---|---|\n| `.hwp` `.hwpx` `.hml` `.hwpml` `.pdf` `.docx` `.xlsx` `.xls` | `kordoc` (nested tables, per-sheet workbooks, no Java) |\n| `.pptx` | built-in PPTX reader |\n| `.eml` | built-in mail reader |\n| `.txt` `.text` `.md` `.markdown` | plain text (CP949/EUC-KR aware) |\n| `.png` `.jpg` `.jpeg` `.bmp` `.tiff` `.webp` | image OCR (opt-in) |\n\nOCR is opt-in and never runs unless enabled, so indexing downloads no model by\ndefault. When enabled, `languages` selects the recognition languages\n(`ja` → `jpn`, `zh-hans` → `chi_sim`, …) for both standalone images and scanned\nPDF pages.\n\n### 2. Search from CLI\n\n```bash\n# Perform a curated search (uses your configured reasoning model)\nautorag search \"What are our primary Q3 deliverables?\"\n\n# Launch the interactive Terminal UI (beta)\nautorag tui\n```\n\n### 3. Programmatic Usage (TypeScript API)\n\n```typescript\nimport { AutoRAGAgent } from \"@autorag/librarian\";\n\n// Initialize librarian agent\nconst agent = new AutoRAGAgent({\n  searchPaths: [\"/path/to/documents\"],\n});\n\n// Run curated search loop\nconst response = await agent.searchDocuments(\"Summarize recent compliance updates\");\n\nconsole.log(\"Answer:\", response.answer);\nfor (const result of response.results) {\n  console.log(`[${result.number}] ${result.title} (${result.source})`);\n  console.log(`    ${result.summary}`);\n}\n\n// Record feedback: Result [1] was useful, [2] was not\nagent.recordFeedbackByNumbers(response.sessionId, [1], [2]);\n```\n\n---\n\n## CLI Command Reference\n\n| Command | Description |\n|---|---|\n| `autorag init` | Initialize `~/.autorag/config.json` with search roots, document languages, and model settings |\n| `autorag refresh` | Refresh parsed mirrors, MinSync CDC chunks, datasources, and Jikji |\n| `autorag search \"<query>\"` | Run the librarian agent to curate structured answers |\n| `autorag status` | Inspect corpus freshness, indexing status, and vector readiness |\n| `autorag health` | Check model provider authentication, token validity, and API reachability |\n| `autorag tui` | Open the interactive librarian terminal UI |\n| `autorag duplicates [DIR]` | Read-only scan for exact and near-duplicate document families with `dupey` |\n| `autorag lite ...` | Model-free indexing, retrieval, report generation, and status |\n| `autorag feedback <session>` | Record useful / not-useful feedback by item number |\n| `autorag evidence <session>` | Inspect exact underlying document chunks and sources for a past query |\n| `autorag serve` | Start the P2P query server over SimpleX (opt-in) |\n| `autorag p2p ...` | Manage peer trust, query approvals, and sharing policies |\n\n---\n\n## Documentation Links\n\nDeep dive into AutoRAG Agent's architecture, security, and integration guides:\n\n- **[MinSync Setup & Embedding QA](docs/minsync-setup.md):** Automatic binary installation, CDC chunking, and EmbeddingGemma verification.\n- **[Local Embedding Runtime & Gateway](docs/embedding-runtime.md):** AutoRAG-owned local gateway, model prefetching, and zero-egress semantic search. Model cards: [`qwen3-embedding-0.6b`](docs/model-cards/qwen3-embedding-0.6b.md), [`embeddinggemma-300m`](docs/model-cards/embeddinggemma-300m.md).\n- **[Datasource Skills Reference](docs/datasource-skills.md):** Full configuration contracts, connection aliases, and connector options.\n- **[Manual QA & Datasource Test Harnesses](docs/manual-qa-datasources.md):** Real-world testing guides for Discord, KakaoTalk, Slack, Notion, and email.\n- **[P2P SimpleX Sharing & Path Standard](docs/p2p-path-standard.md):** Decentralized peer query sharing with SimpleX, PII redaction, and approval queues.\n- **[Supply Chain Security & License Audits](docs/supply-chain.md):** Software bill of materials (SBOM) and dependency gate policies.\n- **[Competitive Landscape Study](docs/competitive-landscape-2026-09.md):** In-depth analysis of why in-place federation outperforms centralized RAG.\n- **[Legacy AutoRAG (AutoML)](legacy/README.md):** Documentation for the legacy Python AutoML pipeline optimizer (`pip install AutoRAG`).\n\n---\n\n## Contributors & Community\n\nAutoRAG Agent is an open-source project built by the community. We welcome contributions, bug reports, datasource connectors, and ideas!\n\n- **Contributing:** Feel free to open an issue or pull request. Start with [CONTRIBUTING.md](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](MAINTAINERS) lists who reviews which area. AI tools are allowed; unverified dumps are not — see [AI_POLICY.md](AI_POLICY.md).\n- **Security:** Private vulnerability reports and the response SLA live in [SECURITY.md](SECURITY.md).\n- **GitHub Contributors:** [View all contributors](https://github.com/Marker-Inc-Korea/AutoRAG/graphs/contributors) on GitHub.\n\n---\n\n## Acknowledgements\n\nAutoRAG Agent stands on the shoulders of fantastic open-source projects:\n\n- **[Pi Framework](https://github.com/earendil-works/pi-mono)** by [@earendil-works](https://github.com/earendil-works) — The foundational agent framework powering AutoRAG's reasoning loop.\n- **[MinSync](https://github.com/Marker-Inc-Korea/minsync)** — Ultra-fast incremental Change Data Capture (CDC) chunking and local BM25/vector indexing.\n- **[Jikji](https://github.com/NomaDamas/jikji)** by [NomaDamas](https://github.com/NomaDamas) — High-performance find-first local document discovery.\n- **[dupey](https://github.com/NomaDamas/dupey)** by [NomaDamas](https://github.com/NomaDamas) — Fast duplicate and near-duplicate document family detection.\n- **Federated CLI Authors:** External datasource tools [`lazykatok`](https://github.com/changeroa/lazykatok), [`discrawl`](https://github.com/openclaw/discrawl), [`mailcrawl`](https://github.com/NomaDamas/mailcrawl), [`wacrawl`](https://github.com/openclaw/wacrawl), [`telecrawl`](https://github.com/openclaw/telecrawl), [`slacrawl`](https://github.com/openclaw/slacrawl), [`notcrawl`](https://github.com/openclaw/notcrawl), [`qmd`](https://github.com/tobi/qmd), and [`rclone`](https://rclone.org).\n- **[kordoc](https://github.com/chrisryugj/kordoc)** by [chrisryugj](https://github.com/chrisryugj) — HWP/HWPX/HWPML, PDF, DOCX and XLSX parsing with nested-table fidelity, used as AutoRAG's default document parser.\n\n---\n\n## Troubleshooting\n\nWhen 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.\n\nThe first three commands cover most of it:\n\n```bash\nautorag status --json          # freshness, per-component state, diagnostics\nautorag health --json          # model resolution + one live completion probe\nautorag gateway status --format json   # on-demand embedding runtime\n```\n\nIndexing is not the same thing as searchability, so always confirm retrieval itself — this needs no model:\n\n```bash\nautorag lite retrieve 'a word that certainly appears' --top-k 3 --json\nautorag lite retrieve 'recent topic' --tags discord --top-k 3 --json\n```\n\nCommon failures and their fix:\n\n| Symptom | Diagnostic code | Fix |\n|---|---|---|\n| Results are missing recent files | `stale-index` | `autorag refresh --method parsed,minsync --json` |\n| Semantic search returns nothing after changing the embedder | `embedding-identity-mismatch` | `autorag index rebuild --method minsync` |\n| Gateway will not start, a previous run was killed | `lock-conflict` | `autorag gateway stop`, then retry |\n| A datasource is healthy in its own CLI but absent from results | — | add its tag/scope to `datasourceAccess` |\n| A datasource errors during refresh | `datasource-index-failed` | run that CLI's own `doctor` |\n| MinSync or Jikji missing | `minsync-unavailable`, `jikji-unavailable` | check the Rust toolchain, re-run refresh |\n\nNative 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`.\n\n## License\n\n- **AutoRAG 2.0 (AutoRAG Agent):** Released under the [MIT License](LICENSE).\n- **Legacy Python AutoRAG (`legacy/`):** Released under the [Apache License 2.0](legacy/LICENSE).\n- Production third-party notices and licenses are documented in [`NOTICE`](NOTICE).\n","readmeFilename":"README.md"}