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Autorag

OrganizationPopular
Marker-Inc-Korea
autorag

Search, summarize, compare, and answer questions from an already configured AutoRAG librarian over local documents and configured datasources. Use when the user asks AutoRAG to search PDFs, wikis, notes, or a knowledge base. Use autorag-setup for install, model, roots, indexing, or datasource changes.

Overview

PublisherMarker-Inc-Korea
RepositoryAutoRAG
Skill nameautorag
Stars
5.1K
Forks
446
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Marker-Inc-Korea on GitHub. Read the source before you install it.

Installation

Install the Autorag AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Marker-Inc-Korea/AutoRAG/tree/main/skills/autorag
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Marker-Inc-Korea/AutoRAG.git /tmp/AutoRAG
mkdir -p .claude/skills
cp -r /tmp/AutoRAG/skills/autorag .claude/skills/autorag
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autorag in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Autorag on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Autorag is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AutoRAG Librarian Skill

Use this skill when AutoRAG is already configured and the user asks to search, summarize, compare, or answer questions from local PDFs, wikis, notes, research papers, knowledge bases, or configured datasources.

AutoRAG is the specialized librarian agent. One configured model plans the search, calls MinSync, Jikji, datasource, and filesystem tools, reads sources, judges evidence, and curates the final answer. There is no subagent or separate model role.

AutoRAG reads source documents and writes indexes only under the configured workspace .autorag/ directory and Jikji's per-source .jikji/ caches. Never move, rename, edit, or delete source files.

Preflight

Confirm a config exists at --config, AUTORAG_CONFIG, $AUTORAG_HOME/config.json, or ~/.autorag/config.json. Inspect only non-secret searchPaths and model metadata, then run:

bash
autorag duplicates --json
autorag status --json
autorag health --json

Duplicate-file review

Use autorag duplicates when the user asks to find duplicate files, choose likely latest copies, or reduce corpus/index space. The command and the scan_duplicate_documents Agent tool are read-only and never delete or move source files. Exact means dupey's canonical extracted-text hash matches; near/contains families require human review. Exact duplicate exclusion during refresh is enabled by default and can be disabled with "excludeExactDuplicates": false in config.json.

Excluding local files from the index

"excludePaths" in config.json lists files or folders (absolute, or relative to workspacePath) that refresh keeps out of the parsed mirror and MinSync. A folder entry excludes everything under it. Removing an entry and running autorag refresh --method minsync indexes the file again. Excluded sources are recorded as user-excluded skips, so they are not reported as stale. Jikji indexes the source folders directly and is not refreshed here, so AutoRAG also drops excluded paths from the jikji_find answer pack and the baseline prefetch at retrieval time; the on-disk .jikji_agent_map.md stays complete, and direct file reads remain available.

status is model-free and path-opaque. health resolves the single model, checks credential presence, and normally probes one live completion. If the model, authentication, configuration, or indexes are unhealthy, use autorag-setup rather than guessing private provider details.

MinSync and Jikji should normally be healthy. Answering a question never builds or installs them: autorag refresh auto-installs both by default and builds their indexes incrementally. If they are missing or stale, run a full autorag refresh or return to setup rather than silently degrading to lexical-only search.

Search

Prefer --json --debug when another agent will consume the result. --json alone omits sessionId. --debug adds session/diagnostics fields and does not print filesystem paths.

bash
autorag search "what were the key findings in the Q3 report" --top-k 5 --json --debug

--json --debug includes answer, numbered results (number, title, summary, optional source), and sessionId. --json without --debug is only answer plus results. Every bracketed [n] citation in answer resolves to a results[].number of the same response; an unmatched citation is removed and reported as a citation-without-result diagnostic.

To inspect the exact persisted evidence behind numbered results, use:

bash
autorag evidence <sessionId> --result 1 --json

The response includes the original source, retrieval method, stable evidence ID, raw excerpt/content, and any available chunkIndex, lineNumber, retrievalResultId, and metadata. Omit --result to inspect every result in the session. Prefer this command whenever the caller wants detailed chunk text rather than only the curated summary.

  • --scope narrows datasource retrieval to a requested sub-path (ordinary per-query filtering).
  • --json is required for programmatic consumption.
  • --debug is required for sessionId and diagnostics in search output.
  • autorag evidence is the detailed source/chunk inspection path.

Do not bypass the librarian with ad hoc raw search when the user requested AutoRAG. The search loop can use Jikji, MinSync lexical/vector/hybrid retrieval, datasource retrieval, and direct source reading as appropriate. If search fails because of model, provider, auth, or timeout problems, diagnose with autorag health --json.

Every search is two-phase: a fast answer, then verification. With Jev on (the default, OpenRouter), Jev first routes the question:

  • General knowledge or small talk is answered directly.
  • A request to view or change AutoRAG's own settings (model, providers, datasources) is handled by the agent itself: it loads the setup skill, edits the active config, verifies it, and reports what changed. The running agent keeps its startup model; changes apply to the next autorag launch.
  • Private-data questions use local search; public current facts use web search.
  • A multi-part question is split into up to five parallel search queries.
  • On local search, Jev also picks which registered datasources (Slack, Discord, KakaoTalk, email, ...) to search before the fast answer, from each datasource's description and where similar past questions were answered; their chunks are reranked together with local file evidence.

After the fast answer, Jev ends the run if the answer is complete and evidence-backed. So results may come straight from the fast answer, with no verification phase. --debug diagnostics show the decision: query-routed (branch and queries), datasources-selected (datasources searched or skipped, with probabilities), follow-up-skipped (fast answer final), or query-route-fallback (Jev unavailable, single local search).

Maintenance

bash
autorag setup --format json
autorag status --json
autorag health --json
autorag refresh --json
autorag refresh --method minsync,jikji --json
autorag watch --once --json
autorag watch
autorag refresh --force --json
autorag index rebuild --yes --json
autorag index reset --method parsed --yes --json
autorag memory inspect --json
autorag tui
autorag serve --force
autorag p2p policy list --json

Prefer a full refresh so parsed mirrors, MinSync, Jikji, configured datasources, and (on Windows) the bundled Everything file-name index stay aligned. --method accepts parsed,minsync,datasources,jikji,everything,all. BM25 is a MinSync retrieval mode, not a --method name. Use --method only for deliberate narrowing. Scheduled maintenance should use non-daemon autorag watch --once, typically every 1 hour, with the same config used by search and no overlapping runs. autorag tui is the shipped beta terminal UI. autorag serve / autorag p2p are opt-in SimpleX peer sharing (disabled until p2p.enabled is true, unless --force).

Reset and rebuild commands remove only selected workspace .autorag indexes. Never target source documents. memory inspect is read-only and path-opaque.

Rules

  • Use only configured and approved search paths.
  • Never expose provider credentials or authentication payloads.
  • Never invent provider identities or model ids.
  • A Pi-usable subscription is valid; a subscription Pi cannot invoke is not.
  • Preserve real source mapping.
  • Prefer --json --debug when another agent consumes search output.
  • Do not invent CLI commands. autorag --help is the command list of record, including the shipped setup, gateway, models, serve, p2p, tui, and lite commands. autorag <command> --help prints that command's own flags, and autorag --version prints the installed version. See docs/embedding-runtime.md for the local embedding runtime and gateway setup.

Frequently asked questions

What does the Autorag AI skill do?

Search, summarize, compare, and answer questions from an already configured AutoRAG librarian over local documents and configured datasources. Use when the user asks AutoRAG to search PDFs, wikis, notes, or a knowledge base. Use autorag-setup for install, model, roots, indexing, or datasource changes.

Why use Autorag on TypingMind?

Because you install it once and use it with any model. Autorag is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Autorag in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Marker-Inc-Korea/AutoRAG/tree/main/skills/autorag. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Autorag?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Autorag?

As many as you like. As long as a model supports skills, you can use Autorag with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Autorag AI skill free?

Yes. It is published on GitHub by Marker-Inc-Korea under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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