Autorag Setup

AutoRAG setup

Use this skill when AutoRAG is unconfigured, the autorag CLI is missing, model resolution fails, indexes are missing or stale, or the user wants to change the document collection or datasources.

For model-free external-agent search, use autorag-lite-setup to configure and register autorag-mcp; do not configure a search model or install a search skill for Lite. This skill's model setup and CLI search apply to the full librarian, not the Lite MCP server.

Safety

• Inspect only non-secret provider/model metadata and credential availability.
• Never print, copy, migrate, compare, or persist credential values. Store only environment-variable names such as apiKeyEnv.
• Do not scan the whole filesystem or home directory without explicit approval.
• Never move, rename, edit, or delete source documents.
• Do not index system trees, app bundles, caches, credential stores, node_modules, .git, dist, build, target, .cache, .autorag, or .jikji.

Install the CLI if needed

The CLI is @autorag/librarian (autorag). Runtime is Node.js ≥ 24 or Bun.

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

If Bun is unavailable, npm install -g @autorag/librarian is acceptable.

Inspect existing configuration

Check --config, AUTORAG_CONFIG, $AUTORAG_HOME/config.json, or ~/.autorag/config.json. Relevant fields are:

• searchPaths, workspacePath, and memoryPath
• model.provider, model.id, model.api, model.baseUrl, model.apiKeyEnv
• bm25, minSync, and jikji
• limits (retrieval, baseline-prefetch, and model-facing candidate caps)
• datasources and ui

Preserve explicit user choices and a working config unless the user asks to replace them or health checks fail.

Configure one search model

AutoRAG is the specialized librarian agent. One configured model plans the search, calls retrieval and filesystem tools, reads sources, judges evidence, and curates the answer in one loop. There are no orchestrator/explorer roles.

Prefer a model with reliable tool calling and structured output, enough context for source excerpts, high output TPS, and low first-token latency. Use a larger reasoning model only when difficult synthesis or domain judgment matters more than latency.

Start from a provider/model the current runtime can actually call. A Pi-usable ChatGPT, Claude, Gemini, or other authenticated subscription is valid; an installed CLI or subscription that Pi cannot invoke is not. For custom OpenAI-compatible endpoints, record the real wire API, base URL, and credential environment-variable name.

Allowed api values:

• openai-completions
• openai-responses
• anthropic-messages
• openai-codex-responses
• azure-openai-responses

If no callable setup can be established, ask the user for the provider, model id, API protocol, base URL when custom, and credential environment-variable name. Do not invent provider identities or model ids.

Propose and approve document roots

Explicit user paths win. Otherwise inspect only these likely document-dense candidates for existence and approximate supported-file counts, then ask for approval before indexing:

Supported parsed formats are md, markdown, txt, text, pdf, docx, pptx, xlsx, xls, hwp, hwpx, and eml. OCR for jpg, jpeg, png, bmp, and tiff is optional (parserOptions.ocr.enabled). Do not present legacy .doc as a supported parsed format.

Keep the first-run set small, usually one to three roots. Present a concrete proposal and require yes, a narrowed keep-list, a custom list, or skip before running refresh.

Initialize

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autorag init \
  --search-paths "/path/to/documents,/path/to/notes" \
  --workspace "/path/to/workspace" \
  --model-provider PROVIDER \
  --model-id MODEL

Model resolution runs through the pi runtime. A configured provider/id is resolved against the pi runtime catalog — the built-in providers plus any ~/.pi/agent/models.json, custom, or extension providers — and credentials come from stored pi auth (~/.pi/agent/auth.json, API key or OAuth established with pi /login), then provider environment variables. When no model is configured, AutoRAG uses pi's defaultProvider/defaultModel when that provider has configured credentials; otherwise it falls back to the authenticated local codex runtime. So model flags may be omitted when either pi or the local runtime already supplies the intended model. Inspect what pi can resolve with autorag models list (--available shows only providers with configured credentials); it never prints credential values.

For a custom endpoint, add api, baseUrl, and apiKeyEnv to the single model object in the trusted config:

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{
  "searchPaths": ["/path/to/documents"],
  "model": {
    "provider": "openrouter",
    "id": "anthropic/claude-sonnet-5.5",
    "api": "openai-completions",
    "baseUrl": "https://openrouter.ai/api/v1",
    "apiKeyEnv": "OPENROUTER_API_KEY"
  }
}

When provider/id names a pi runtime catalog model, the catalog entry stays the base: baseUrl, api, and any declared reasoning, input, contextWindow, or maxTokens override only those fields, and the catalog's reasoning, thinking, and compat settings are kept. Only an id outside the catalog (private proxy, Ollama, LiteLLM) gets a generic text model with a 128k context window unless those fields are declared.

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. Legacy cwd autorag.config.json is a migration source only and is never deleted by init.

Retrieval defaults

MinSync and Jikji are enabled by default. Leave them enabled unless the user explicitly asks otherwise. Indexing never happens while answering: a question only reads indexes that autorag refresh (or autorag watch) built, so run a refresh after setup and whenever documents change. A never-refreshed workspace still answers, but without MinSync/Jikji evidence.

Refresh (never a query) auto-installs the binaries: MinSync installs a verified GitHub release into <workspace>/.autorag/bin (minSync.autoInstall defaults to true), and Jikji installs jikji-cli through cargo (jikji.autoInstall defaults to true; requires the Rust toolchain). Set "autoInstall": false only when managing the binary yourself. Refresh is incremental: MinSync syncs only changed parsed mirrors, and jikji prepare reuses unchanged documents. Roots prepare in parallel.

Jikji stores its prepared corpus metadata in a hidden .jikji directory inside each indexed source root — that is Jikji's native index layout and intended behavior, not a misplaced artifact. Tell the user before the first refresh that <root>/.jikji will be created inside every approved document root (one per root, alongside the documents), and never delete or edit its contents; removing it only forces a full Jikji re-prepare on the next refresh.

Exact duplicate exclusion is enabled by default. AutoRAG invokes the external dupey CLI before parsed-mirror indexing, keeps the newest filesystem copy for each exact canonical-text hash, and excludes older copies from the mirror. 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 this optimization and the user is told duplicate exclusion is off. Set "excludeExactDuplicates": false to index every copy.

MinSync's default embedder is in-process native Qwen3 embeddings (native:Qwen/Qwen3-Embedding-0.6B, 1024 dimensions, MinSync 0.4.6). The default needs no embedder flags, no API key, and no external daemon (such as Ollama) — all embeddings run in-process locally and privately. For workspaces using the loopback llama-server gateway, prefetch the verified model with autorag models prefetch --profile qwen3-embedding-0.6b (or verify with autorag models verify --profile qwen3-embedding-0.6b).

The legacy Ollama/TEI adapter path (EmbeddingGemma, 768 dimensions) is supported only for existing legacy workspaces and manual QA; do not use it as a fresh install default.

Override the embedder only when intentionally using a different, for example remote, provider:

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autorag init \
  --embedder-id "voyageai/voyage-4-lite" \
  --embedder-base-url "https://openrouter.ai/api/v1" \
  --embedder-api-key-env "OPENROUTER_API_KEY" \
  --embedder-dimension 1024 \
  --embedder-batch-size 64

Only store the environment-variable name, never its value. Dimension and batch size must be positive integers, and the dimension must match the embedder (default Qwen3 is 1024; legacy EmbeddingGemma is 768; voyageai/voyage-4-lite via OpenRouter is 1024).

Jev routing and question decomposition (on by default)

Leave both enabled. They are the recommended setup: they make simple questions fast and multi-part questions thorough.

• Jev (jev, default { "backend": "openrouter" }, model typesafe/jev-1.13) runs before the fast answer. It routes each question to local search, web search, a direct answer (general knowledge or small talk skips retrieval entirely), or the config branch, and decides whether to decompose it. On local search it also decides, per registered datasource, whether to search it before the fast answer, using each datasource's description and where similar past questions were answered (retrieval memory). When setting up a datasource, always write a description from what it actually holds: channels or rooms, people, topics, time range (for example "Team Slack, 2024-2026: #release and #on-call channels, dependabot notifications"). Jev is told descriptions are short, non-exhaustive summaries, so list the main content and do not try to list everything. After the fast answer it decides whether verification is needed, so a complete, evidence-backed fast answer ends the run.
• Question decomposition (queryDecomposition, default model openrouter/qwen/qwen3.7-flash) splits a multi-part question into at most five search queries that run in parallel.

Both use the user's OPENROUTER_API_KEY; confirm it is set (test -n "$OPENROUTER_API_KEY", never print it) and tell the user Jev routing is on. Without the key, routing falls back to a single local search and the run always verifies, so searches still work. A query-route-fallback diagnostic (autorag search --debug) shows that state.

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{
  "jev": { "backend": "openrouter" },
  "queryDecomposition": { "model": { "provider": "openrouter", "id": "qwen/qwen3.7-flash" } }
}

autorag init writes these defaults into new configs. To change them:

• Jev backend: "backend": "typesafe" (TYPESAFE_API_KEY) or "vercel" (AI_GATEWAY_API_KEY).
• Decomposition model: any catalog provider/id, with the same fields as the top-level model.
• "queryDecomposition": false decomposes with the search model itself.
• "jev": false turns routing off entirely. Do this only when the user explicitly opts out, for example because questions must never leave the machine (Jev and decomposition send the question text to OpenRouter).

When a user asks the running agent itself to change its settings (switch the default model, add a provider, check that a provider works), Jev's config branch loads this whole skill into that turn. The agent edits only the active config file (and models.json for a custom provider), verifies with autorag health --json and autorag models list --available, and reports each change as old → new in its final message. It never prints a credential value and never uses init --force.

Retrieval and ingest caps

limits bounds retrieval, baseline prefetch, and the candidate lists handed to the model. Every field is optional — an omitted field keeps the shipped default, so add the section only to tighten or widen a specific cap. Values must be positive integers; unknown keys (and unknown prefetch keys) fail config resolution.

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{
  "limits": {
    "mergedEvidenceCeiling": 1000,
    "prefetch": { "jikjiTopK": 12, "sectionLimit": 30 }
  }
}

The baseline renders each prefetched result's full chunk content (not a per-result excerpt) so the model can see where the hit came from. Bound baseline size with prefetch.sectionLimit / prefetch.minSyncTopK / prefetch.jikjiPathLimit, not with a per-result content truncation.

Ingest caps are trusted connector options under datasources.<name>.connector and are never settable from model/tool arguments: maxDocuments, maxItemsPerFeed, and maxContentChars (RSS); maxDocuments, maxContentChars, maxResultsPerQuery, and maxBytesPerFile (Spotlight); maxDocuments, maxContentChars, maxBytesPerFile, concurrency, bandwidthLimit, and dryRun (cloud-drive/rclone). maxContentChars defaults to 20000 for RSS and 100000 for Spotlight and cloud-drive.

Probe and configure datasource skills (setup wizard)

Use autorag setup to probe the local runtime, model profile, and known datasources automatically:

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autorag setup --format json

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, lark-cli) 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 or native stores are not present) 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. Builtin template names are kakao, whatsapp, telegram, slack, discord, clawgallery, notion, github, github-gist, cloud-drive, mail-export, mailcrawl, obsidian, rss, spotlight, and lark. Config keys may be 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. MCP datasourceIds selects configured connections before retrieval; discover their IDs with autorag.datasources.list.

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{
  "datasources": {
    "github": { "connector": { "repos": ["owner/repo"], "tokenEnv": "GITHUB_TOKEN" } },
    "github-gist": { "connector": { "tokenEnv": "GITHUB_TOKEN" } },
    "google-drive": { "type": "cloud-drive", "connector": { "provider": "google-drive", "remote": "gdrive:" } },
    "archive-drive": { "type": "cloud-drive", "connector": { "remote": "archive:" } },
    "mailcrawl": { "instanceId": "personal", "connector": { "account": "personal", "mailbox": "INBOX", "binaryPath": "mailcrawl" } },
    "obsidian": { "connector": { "vaultPath": "/path/to/vault" } },
    "rss": { "connector": { "feeds": [{ "url": "https://example.com/feed.xml" }] } }
  }
}

Tokens are environment-variable names, not raw secrets. CLI-backed connectors keep authentication in their external tool configuration.

Mailcrawl must be installed separately (@nomadamas/mailcrawl@0.2.0 or newer) and configured through its own Himalaya account. AutoRAG runs its local sync and index lifecycle, then uses the mailcrawl CLI for BM25, semantic, or hybrid search. Do not use 0.1.3 or earlier: a no-op sync followed by index fails with text array must be non-empty. 0.2.0 defaults to the in-process native Qwen/Qwen3-Embedding-0.6B embedder and keeps vectors in LanceDB, so a cold cache makes the first index download ONNX weights and run for minutes. Use mailcrawl for Gmail, IMAP, and Maildir retrieval. The former Gmail REST datasource is removed.

Verify and build indexes

Configuration alone is not a successful setup:

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autorag setup --format json
autorag status --json
autorag health --json
autorag refresh --json
autorag search "summarize the collection" --top-k 3 --json --debug

• setup probes runtime health, model profile, and datasource readiness.
• status is model-free and path-opaque.
• health resolves the single model, checks credential presence, and normally performs one live completion probe.
• health --skip-probes is only for intentionally offline validation and does not prove live provider access.
• refresh syncs parsed mirrors, MinSync, Jikji, configured datasources, and on Windows the bundled Everything file-name index. --method <csv> may deliberately narrow it (parsed,minsync,datasources,jikji,everything,all).
• Use refresh --force for a full resync only when incremental refresh is not enough. Keep destructive reset/rebuild operations scoped to workspace .autorag indexes, never source documents.
• After a search, use autorag evidence <sessionId> --json to inspect the exact source chunks behind numbered results, including source, method, stable evidence ID, excerpt/content, chunk index, and line number.

Connect external agents through MCP

When setting up AutoRAG for a coding agent, register the package's autorag-mcp stdio executable with the same absolute AUTORAG_CONFIG path. Follow autorag-lite-setup's MCP registration and verification procedure: inspect existing host registration, use an absolute executable path, reload/reconnect, discover schemas with tools/list, and exercise autorag.status, autorag.datasources.list, and a known-phrase autorag.search through MCP. Restart the MCP server after config changes. The MCP server returns model-free source chunks; the calling agent curates them. It does not invoke the configured librarian model. The same server also exposes autorag.report and autorag.evidence for the curation lifecycle; the matching CLI commands remain a maintenance path. Discover the exact schemas with MCP tools/list. Keep the full autorag skill only when model-backed curated search is also wanted. For MCP-only setup, use autorag-lite-setup instead of requiring live model health.

Keep indexes fresh

For continuous freshness, create or verify an OS-appropriate scheduled autorag watch --once job, hourly by default (every 1 hour; shorten only when the user asks for fresher indexes). Prefer cron or launchd on macOS, cron or a user systemd timer on Linux, and Task Scheduler on Windows. Use the same config as search, avoid overlapping runs, and keep logs outside source trees. Once the schedule is installed, tell the user right away that hourly freshness is set up.

Environment overrides

• AUTORAG_HOME
• AUTORAG_CONFIG
• AUTORAG_SEARCH_PATHS
• AUTORAG_WORKSPACE
• AUTORAG_MEMORY_PATH
• AUTORAG_MODEL_PROVIDER
• AUTORAG_MODEL_ID

Completion condition

Setup is complete only when the CLI is installed, roots are approved, a non-secret single-model config is written, every datasource has been probed and the auto-configured and skipped lists reported to the user, dupey is installed or its absence reported, status is acceptable, live health passes, refresh builds the requested indexes, one real structured search succeeds, and any requested ongoing schedule is installed or verified with the user told it is active. For external-agent integration, also require successful MCP discovery and a known-source MCP search; CLI success alone does not prove the host connection.