Autorag Lite Setup logo

Autorag Lite Setup

OrganizationPopular
Marker-Inc-Korea
autorag-lite-setup

Install and register the model-free AutoRAG Lite MCP server, configure approved roots and datasources, build indexes, and verify MCP discovery and search. Use when autorag-mcp is missing, MCP connection fails, indexes are stale, or the user wants document search without configuring a search model.

Overview

PublisherMarker-Inc-Korea
RepositoryAutoRAG
Skill nameautorag-lite-setup
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 Lite Setup 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-lite-setup
  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-lite-setup .claude/skills/autorag-lite-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Autorag Lite Setup 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 Lite Setup 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 Lite Setup 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 Lite setup

Use this skill only to bootstrap, register, or repair AutoRAG Lite MCP: initialize trusted config, connect the host, build indexes, and verify search. Normal Lite use is MCP tool calling, not a search skill or shell command. Use autorag-setup instead when the model-backed librarian must be configured.

Safety

  • Inspect only non-secret config metadata: searchPaths, workspacePath, memoryPath, minSync, jikji, datasources.
  • Never print, copy, migrate, compare, or persist credential values. Store only environment-variable names such as tokenEnv or apiKeyEnv.
  • Never move, rename, edit, or delete source documents. Lite commands write indexes only under the configured workspace .autorag/ directory and Jikji's per-source .jikji/ caches.
  • Do not index system trees, app bundles, caches, credential stores, node_modules, .git, dist, build, target, .cache, .autorag, or .jikji.

Install the package if needed

@autorag/librarian ships both autorag (bootstrap/maintenance) and autorag-mcp (stdio server). The executable requires Node.js >= 24 on PATH.

bash
command -v autorag-mcp >/dev/null || bun install -g @autorag/librarian
command -v autorag
command -v autorag-mcp
autorag lite --help

If Bun is unavailable, npm install -g @autorag/librarian is acceptable. If only autorag exists, upgrade the package rather than substituting CLI retrieval for MCP. For a source checkout, run bun run build and register the absolute dist/mcp/index.js path instead of the installed executable.

Initialize a model-free config

bash
autorag lite init \
  --search-paths "/path/to/documents,/path/to/notes" \
  --workspace "/path/to/workspace" \
  --memory-path "/path/to/memory.json"

lite init writes the configured model-free lifecycle config. Use --force only when intentionally replacing an existing config, and target it with an explicit path (--config or AUTORAG_CONFIG); --force refuses to replace the implicit ~/.autorag/config.json. Explicit user paths win; otherwise propose one to three document-dense roots and get approval before indexing. Supported parsed formats are md, markdown, txt, text, pdf, docx, pptx, xlsx, xls, hwp, hwpx, and eml. Legacy .doc is not a supported parsed format.

Config resolution follows the usual order: --config, AUTORAG_CONFIG, $AUTORAG_HOME/config.json, or ~/.autorag/config.json. Environment overrides include AUTORAG_HOME, AUTORAG_CONFIG, AUTORAG_SEARCH_PATHS, AUTORAG_WORKSPACE, and AUTORAG_MEMORY_PATH.

Register and verify the MCP connection

After config and datasource setup, register the server with the host. Use absolute config, search root, workspace, and executable paths: hosts may start the server from a different working directory or with a restricted PATH. Resolve command -v autorag-mcp and substitute its absolute path below. Inspect an existing autorag registration first; keep a working registration and update only an outdated command or config path.

bash
# Claude Code: project-local registration
claude mcp add --transport stdio --scope local autorag \
  --env AUTORAG_CONFIG=/absolute/path/to/.autorag/config.json \
  -- /absolute/path/to/autorag-mcp

# Codex: user registration
codex mcp add autorag \
  --env AUTORAG_CONFIG=/absolute/path/to/.autorag/config.json \
  -- /absolute/path/to/autorag-mcp

For other hosts, use their stdio MCP configuration with the same command, empty arguments, and AUTORAG_CONFIG environment variable. The host starts the subprocess; do not run it as a background HTTP service. Pass any required credential environment-variable names through the host's secret mechanism; never put credential values in registration examples or logs.

Reload/reconnect the host, then use MCP tools/list to discover the actual tools and schemas. Verify autorag.status, autorag.refresh, autorag.search, autorag.search.files, and autorag.datasources.list are available. Configured integrated datasources add their own search tools; do not assume a fixed count. Run autorag.status and autorag.datasources.list through MCP, then refresh and search a known phrase from an approved document. Registration alone is not proof of a connected or searchable server. After config changes, restart the server so datasource tools are rebuilt.

AUTORAG_MCP_READ_ONLY=1 omits refresh; build indexes with the CLI before connecting that mode. AUTORAG_MCP_TOOLS is an optional comma-separated exact tool allowlist; omitted tools cannot be called. Use unrestricted tools for initial setup unless the user intentionally requests a restricted connection.

Probe and configure datasources (setup wizard)

A datasource setup UI is not shipped in this build — do not recommend it for datasource setup. Configure datasources directly in trusted config, wizard-style:

  1. Probe every datasource for setup feasibility before asking the user anything: the backing CLI exists (lazykatok, discrawl, slacrawl, wacrawl, telecrawl, notcrawl, qmd, mailcrawl, rclone) and its local store or archive is present. CLI-backed datasources own their own archive, index, and authentication, so environment credentials (such as bot tokens) are never required or checked for them. Non-CLI connectors (such as github) require their credential environment variable (GITHUB_TOKEN).
  2. Auto-configure every datasource that probes feasible — write its trusted datasources entries without asking. For example, when Slack (slacrawl) and Discord (discrawl) are installed with local stores present, set both up automatically. Discord uses discrawl's local desktop wiretap archive; no Discord bot token is configured or needed.
  3. Skip every datasource that probes infeasible (for example Notion or Telegram when their CLIs are not installed) and always report the skipped list to the user, with what is missing for each.
  4. Set up a skipped datasource only when the user explicitly asks for it: install or authenticate the backing CLI first, then configure it.
  5. E-mail datasources (mail-export, mailcrawl) matter to most users — always probe them and report their status, even when they end up skipped.

Datasource skills belong in trusted config.

Config keys may be builtin template names (kakao, whatsapp, telegram, slack, discord, clawgallery, notion, github, cloud-drive, mail-export, mailcrawl, obsidian, rss, spotlight, lark) or connection aliases with "type": "<template>". Unknown names are skipped with an unknown-datasource-skill warning; they do not fail config resolution. scope narrows a query to a sub-path as ordinary filtering. Tags are descriptive metadata only, not search filters. Use MCP datasourceIds to select configured connections before retrieval; discover their IDs with autorag.datasources.list. Store only env-var names such as tokenEnv or apiKeyEnv, never credential values.

Build and refresh indexes

After connecting, call MCP autorag.refresh with {} for an incremental run. Use {"methods":["parsed","minsync"]} to narrow indexing, or {"force":true} only when a full resync is needed. Omit methods to run all configured methods; MCP does not accept "all" as a method value.

CLI refresh remains available for bootstrap, read-only deployments, and repair:

bash
AUTORAG_CONFIG=/absolute/path/to/.autorag/config.json autorag lite refresh --json
  • Refresh syncs parsed mirrors, MinSync, Jikji, and configured datasources.
  • CLI --full and --force request a full resync; MCP uses force: true.
  • CLI --method <csv> accepts parsed, minsync, datasources, jikji, everything (Windows), fsearch (macOS/Linux), and all. MCP methods accepts the same individual methods, not all. Unknown values are rejected.
  • Indexing never runs while answering: retrieval only reads what refresh built. MinSync and Jikji auto-install during refresh (never during a query). If they are missing or broken, run a full refresh or return to setup rather than silently degrading to lexical-only search.
  • MinSync's default embedder is in-process native Qwen3 embeddings (native:Qwen/Qwen3-Embedding-0.6B, 1024 dimensions, MinSync 0.4.6): no embedder flags, no API key, and no external daemon (such as Ollama) are needed, and no corpus text leaves the machine. For gateway-profiled embeddings, prefetch with autorag models prefetch --profile qwen3-embedding-0.6b. The legacy Ollama/TEI adapter path (EmbeddingGemma, 768 dimensions) is for manual QA only. Override the embedder only when intentionally using a different provider.
  • Exact duplicate exclusion during refresh is enabled by default via the external dupey CLI. Install dupey during setup when it is missing (command -v dupey || cargo install dupey --locked) and tell the user the feature is available; when installation is impossible, refresh continues without it and the user is told duplicate exclusion is off. Set "excludeExactDuplicates": false to index every copy.

Content search requires a completed refresh. MCP autorag.search returns isError: true with errorCode: "index-not-ready" until refresh completes. Always refresh first, and refresh again when roots change. Stale indexes can still return results with stale: true and diagnostics; use strict: true to reject stale answers, or call autorag.refresh before searching. Sources deliberately skipped during refresh do not count as stale. Jikji is a discovery/indexing preparer, not a lite retrieval method.

Watch and scheduled freshness

bash
autorag lite watch --once --json
autorag lite watch

Prefer non-daemon autorag lite watch --once from cron, launchd, a systemd user timer, or Task Scheduler, hourly by default (every 1 hour; shorten only when the user asks for fresher indexes). Use the same config as retrieval, avoid overlapping runs, and keep logs outside source trees. --immediate triggers a first refresh on start and --debounce-ms N tunes change coalescing. Once the schedule is installed, tell the user right away that hourly freshness is set up.

Status and index maintenance

Use MCP autorag.status for health and autorag.duplicates for read-only duplicate-family inspection. No search model is required. CLI-only repair:

bash
AUTORAG_CONFIG=/absolute/path/to/.autorag/config.json autorag lite index rebuild --yes --json
AUTORAG_CONFIG=/absolute/path/to/.autorag/config.json autorag lite index reset --method parsed --yes --json
  • index reset and index rebuild remove or rebuild only workspace .autorag indexes selected by --method. They never target source documents.
  • duplicates scans duplicate document families read-only and never deletes or moves files.

Unavailable components and failure handling

Inspect MCP isError, errorCode, diagnostics, and unsearched before trusting results. ok: true with skipped surfaces is partial coverage, not proof that the whole corpus was searched. Preserve the underlying error text when reporting unavailable components such as retrieval-method-failed. MinSync is required: when its binary is missing the call fails with an MinSync is required ... error rather than returning partial results, so install it (cargo install minsync) instead of working around it. Repair the component rather than silently accepting degraded search.

Normal MCP workflow and curation lifecycle

MCP results carry source, method, and content directly. After a search, the optional curation lifecycle is also exposed through MCP: autorag.report records a curated answer, and autorag.evidence returns the exact chunks behind numbered results. These accept and return JSON matching the CLI's report/evidence contracts; discover the exact schemas with MCP tools/list rather than restating them here. Every bracketed [n] citation in a report answer must be a results[].number; an unmatched citation is removed from the persisted answer and returned as a citation-without-result diagnostic. The CLI autorag report and autorag evidence commands remain available for terminal maintenance.

Completion condition

Setup is complete only when the package and stdio executable are installed, roots are approved, trusted model-free config is written, every datasource has been probed and configured/skipped lists reported, and dupey is installed or its absence reported. The host must connect, discover tools, refresh requested indexes, return acceptable MCP status, and retrieve known source content via MCP. Any requested watch schedule must use the same config and be verified. Remove any previously copied Lite search skill from the host's skill directory so it no longer routes routine search through shell commands.

Frequently asked questions

What does the Autorag Lite Setup AI skill do?

Install and register the model-free AutoRAG Lite MCP server, configure approved roots and datasources, build indexes, and verify MCP discovery and search. Use when autorag-mcp is missing, MCP connection fails, indexes are stale, or the user wants document search without configuring a search model.

Why use Autorag Lite Setup on TypingMind?

Because you install it once and use it with any model. Autorag Lite Setup 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 Lite Setup in TypingMind?

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

Which AI models can use Autorag Lite Setup?

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 Lite Setup?

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

Is the Autorag Lite Setup 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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