🧠 Karpathy LLM Wiki Plugin for Obsidian

An Obsidian plugin that turns your notes into a connected, queryable knowledge base — the Karpathy LLM Wiki idea, built into the editor where you already write.

Obsidian Review Perfect Score • Zero-embedding graph retrieval • 11-language native • Native PDF + images + Office ingest • Works with every provider • Local-first • No backend • GDPR-Friendly

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English | 简体中文 | 繁體中文 | 日本語 | 한국어 | Deutsch | Français | Español | Português | Italiano | Русский

Official Site | Obsidian Marketplace | Blog | Discussions

🤔 Why this plugin? | 🚀 Quick Start | ✨ Features | 🌐 Ecosystem | 🔍 How retrieval works | 🤖 Models | ❓ FAQ | 🔒 Privacy

Karpathy LLM Wiki is free, self-hosted, and has no backend of its own. If it saves you time, a one-off coffee keeps it maintained — and a ⭐ helps just as much.

🤔 Why this plugin?

You write notes. They sit in folders. Finding what relates to what means remembering threads you forgot months ago.

Other open-source reimplementations of Karpathy's LLM Wiki idea exist — but none ships as a one-click Obsidian plugin. Most are CLI tools, Claude Code skills, or separate desktop apps; this one runs inside Obsidian — Graph View, ribbons, command palette included.

How we compare

Three things we chose on purpose, not by accident

• 🪟 Obsidian is the runtime. No terminal, no separate app, no Docker, no Python. Install from Community Plugins, click Ingest, the wiki lives in your vault from the first second. Obsidian's native Graph View renders your [[wiki-link]] graph — built in, zero extra bundle size.
• 🧭 Clean and self-contained. Zero dependencies: no embedding model, no vector database, no pip package. One plugin reads your notes, talks to an LLM, and writes pages into your vault.
• 🔌 Any model you already pay for. Anthropic, Bedrock, OpenAI, ChatGPT Plan (Codex OAuth), Gemini, DeepSeek, Qwen, Grok, Kimi, GLM, MiniMax, Step, Hunyuan, MiMo, Gemma, Ollama, LM Studio, OpenRouter, Anthropic-compatible, custom endpoint — fifteen-plus providers, none of them required to have an embedding endpoint.

Who it is for

• You want a 5-minute setup. Install from Community Plugins → pick a provider → ingest one note. Wiki pages appear in wiki/ within seconds, with no CLI, no Python and no vector database to stand up first.
• You want answers from your own notes, with every answer citing the pages it came from. Ollama or LM Studio keep the whole call on your machine.
• You write in or read from any of 11 languages. Interface and wiki output are independent: a Chinese wiki behind an English interface is a supported setup.
• You want one-click maintenance. Lint health scan and Smart Fix All keep duplicates, dead links and orphans in check without hand-curating.

Not for you if you want a general-purpose ChatGPT replacement, RAG over external corpora such as Confluence or arXiv, or a hosted service with team collaboration. Answers come from your vault, ingest covers your vault plus standalone files, and there is no backend to share.

🚀 Quick Start

Requirements: Obsidian 1.11.4 or newer, desktop or mobile. Nothing else — no Node, no Python, no container. Your own API key, or a local model with no key at all; credentials live in Obsidian SecretStorage, which is why 1.11.4 is the floor.

1. Install. Obsidian → Settings → Community plugins → Browse → search "Karpathy LLM Wiki" → Install → Enable. Or visit the Community Plugin page and click Add to Obsidian.

   

2. Configure a provider. Open Settings → Karpathy LLM Wiki → pick a provider (OpenAI, Anthropic, Ollama, ChatGPT Plan (Codex OAuth), etc.) → enter API key (not needed for local) → click Test Connection → Save.

3. Ingest one note. Two ways:

   • ⌨️ Keyboard: Cmd+P/Ctrl+P → "Ingest single source" → pick any Markdown (or PDF, v1.25.0+) file.
   • 🖱️ Toolbar icon: Click the sticker icon in Obsidian's left ribbon to instantly ingest the currently-open note — no menu hunting.

   Your first wiki pages appear in wiki/sources/, wiki/entities/, wiki/concepts/ within seconds.

   

4. Query your wiki. Two ways:

   • ⌨️ Keyboard: Cmd+P/Ctrl+P → "Query wiki".
   • 🖱️ Toolbar icon: Click the message-circle icon in Obsidian's left ribbon.

   A right-docked side panel opens (Copilot-style) where you can chat with your wiki. Answers carry [[wiki-links]] back into your knowledge graph.

That's it. The plugin modifies nothing in your original notes — only creates new pages under wiki/. Both Ingest and Query wiki are pinned to the left ribbon for one-click access anytime. (Cmd on macOS, Ctrl on Windows/Linux.)

Core commands

📖 Walkthroughs in GitHub Discussions → Guides. Found it useful? Star the repo to follow releases.

✨ Features

📚 Knowledge quality

• 🔍 Entity & concept extraction — LLM extracts entities (people, orgs, products, events) and concepts (theories, methods, terms) into standalone pages. Granularity is configurable (Minimal → Fine, plus Custom) so you trade cost vs. depth.
• 🏷️ Mandatory aliases — every page ships with at least one alias (translation, abbreviation, variant) so cross-language duplicate detection works.
• 🔄 Tiered duplicate detection — Tier 1 (direct name match: cross-language, abbreviation, high-similarity titles) is always verified; Tier 2 (shared links, medium similarity) fills remaining token budget.
• 🧩 Smart merge & contradiction state — duplicates merge while preserving aliases; contradictions are flagged with source attribution; reviewed: true pages are protected from overwrite.
• 🎨 One tag vocabulary, yours — the tags a page may carry come from your notes' nested tags, the tags already on wiki pages, and the Custom list in Settings → Wiki → Tag Vocabulary. Prompt, write gate, lint and retag all read that one list, so what the model is offered is what lands on disk; a value outside it is dropped, never written. Small models still drift (about one run in ten returns the model's own taxonomy) — the gate catches it and Lint reports the page. Design anchor: Issue #328.

📄 Document / PDF / Image ingest

Six on-ramps, switchable per ingest:

1. 🧾 Built-in MinerU backend — Settings → Wiki Configuration → Markdown Conversion Backend → MinerU. PDF, images (PNG/JPG/WebP/GIF/BMP and more) and Office (DOC/DOCX/PPT/PPTX/XLS/XLSX) through MinerU's Precise parser. Token in SecretStorage; it expires after about 90 days, so a 401 says the token has probably expired and names the page to renew it. The caps are 200 MB / 200 pages per PDF and 256 MB / 10,000 files per archive. The path for scanned papers and Office files where layout matters.
2. ☁️ Cloud providers with native PDF — Anthropic, OpenAI, Google Gemini, and AWS Bedrock (Anthropic + OpenAI variants) read PDFs as file parts out of the box. No setup beyond provider selection.
3. 🖥️ Local OCR on Apple Silicon — oMLX bundles Microsoft Markitdown as a built-in PDF→Markdown backend. Enable Markitdown in oMLX, load Baidu Unlimited-OCR (3B / 570M-active, open-sourced 2026-06) as the vision model, point the plugin at oMLX as a Custom OpenAI-Compatible provider, turn on Force PDF Support, pick the multimodal model oMLX is serving. The PDF never leaves your machine.
4. 🛠️ Third-party extractor (MinerU online UI) — use the MinerU Extractor online service for a quick manual UI when you don't want to wire up an API token. Download the converted .md, drop it in your vault outside the wiki folder, and ingest as a regular Markdown note.
5. 🔌 Force PDF Support — for any other OpenAI/Anthropic-compatible endpoint that accepts file parts, the plugin attempts the call (Settings → LLM Configuration → Advanced). The endpoint decides; failures surface as a localized Notice.
6. 🖼️ Embedded Markdown images — turn on Analyze embedded images during Markdown ingestion in Advanced settings to analyze every eligible local ![[image.png]] and ![alt](image.png) embed. Each image is analyzed with its nearest Markdown paragraphs, sent in 20 MiB visual-evidence packages, and capped at 10 MiB; remote URLs are never downloaded. For testable per-image output, optionally enable Save embedded image visual evidence to source page to add a collapsible audit section to the generated source page.

Caveat for Office formats: Obsidian does not natively render .docx / .xlsx / .pptx (file-formats), so the practical workflow for Office files is: MinerU converts to .md, the plugin ingests that .md into wiki pages, and the original Office file is kept around for reference. Use a community plugin like Pandoc Plugin / Docxer / Md Importer / Office Reader if you need to inline-preview Office files.

Plumbing shared across all paths:

• 🗄️ Bounded cache — .obsidian/plugins/karpathywiki/pdf-cache/ stores converted Markdown keyed by content hash + model + converter version; 100 MB total / 1000 entries / 10 MB single-entry caps with LRU-by-mtime eviction.
• 📝 Optional vault sidecar — Settings → Wiki Configuration → Wiki Folder → Write PDF Markdown to Vault writes <basename>.pdf.md next to the source PDF (off by default — cache-only is the default).
• 🛡️ Verbatim transcriber prompt — OCR-style conversion with [illegible] / [figure: ...] anti-hallucination markers; markdown-fence-wrapping from small local models is auto-cleaned before cache write.
• 🔁 Source-page verbatim quotes (v1.27.0, #496) — every generated sources/<slug>.md page now carries a Mentions in Source section built from the same verbatim quotes the extraction captured per entity/concept (the prose the model already proved it could see), so the underlying document is the one wiki page with a real, grounded trail back to its source text.

📖 Full setup walkthroughs for all paths (cloud providers, oMLX hardware tiers, MinerU installation, cache housekeeping) → docs/PDF-OCR-GUIDE.md

💬 Query & maintenance

Retrieval and answers

• 🧭 5-stage PPR cascade — see How retrieval works. Personalized PageRank over [[wiki-link]] gives graph-aware multi-hop context.

• 🔤 Body-aware lexical scoring — the fast path scores title, aliases, summary and body text with BM25F, so the longest text on a page reaches the scorer. It is the single largest recall change the project has made.

• 🧩 Related-page shaping — one hub page can no longer drag every other page toward it. Related lists are recomputed on every write from the names that survived, ranked, and capped.

• 🔁 Legacy query scorer switch — Settings → Advanced → Legacy query scorer runs the previous hand-tuned linear ranking instead of the new one. Off by default; the new ranking is the default and the toggle is the way back.

• 📏 The answer budget is 16,000 tokens, and the pipeline says when it runs out — the answer no longer stops silently at the budget. Context is fitted to a page budget before assembly, so 20 pages are in the prompt by plan rather than by luck.

• 🔖 Citations read in academic form — the composer grows with the content, and clear history clears the record.

• 🪟 Right-docked side panel — Query Wiki opens in a Copilot-style right sidebar leaf (v1.22.1+) instead of a centered modal. Maintenance and repair

• 🔍 Lint health scan — single command catches: duplicates, dead links, empty pages, orphans, missing aliases, contradictions. It also lists hub pages whose neighbourhood has crystallized — a hub standing in for connections that content can no longer make.

• ⚡ Smart Fix All — one-click causal-order repair: fill aliases → merge duplicates → fix dead links → link orphans → expand empty pages, with per-phase report.

  

• 🚧 Fix Dead Links leave-it outcome — Settings → Advanced → Create Stubs for Unresolvable Links (on by default). Turn it off and a dead link stays visible in every lint report until a real source defines it, instead of becoming an empty placeholder page. The never-expand-without-a-source gate from #197 is unchanged; this control decides only whether the stub is written at all.

• 📊 Operation history panel — searchable, filterable UI for past ingestions, lint reports, and maintenance runs. Gates that run before a call is paid for

• 🛡️ Pre-ingest gate — empty / whitespace / frontmatter-only notes are rejected before any LLM call; content-hash dedup catches identical files across paths.

• 🚦 Ingest candidate gate — opt-in (skipMentionOnlyCandidates, off by default). A candidate named only inside parentheses, an enumeration or a short list item is pruned before it costs a generation and a dedup call. It applies only to languages with a measured profile; a language without one is never gated silently, and cross-language notes are not gated at all.

• 🎛️ Per-step task policies — LLM Advanced → Task Policies field; override the per-step text-mode/thinking setting without code changes. Built-in baseline stays intact for steps you don't list.

🦙 Local-first

• 🖥️ Ollama, LM Studio, OpenRouter, custom endpoint — out-of-the-box. Local models work for query (smaller context windows); ingest on a 2,000-page vault usually needs a long-context cloud model.
• 📄 PDF OCR path is fully local on Apple Silicon — see Document / PDF / Image ingest above.
• 🔐 ChatGPT Plan (Codex OAuth) — desktop loopback or mobile device-code; credentials live only in Obsidian SecretStorage. (See Anthropic vs OpenAI vs Codex OAuth below for the full provider-boundary explanation.)

🌐 Language

• 🌍 11 UI languages — English, 简体中文, 繁體中文, 日本語, 한국어, Deutsch, Français, Español, Português, Italiano, Русский. UI and wiki-output language are independent — your wiki can be Chinese while the interface is English.

  

• 📚 11 wiki-output languages — same set; pick in Settings → Wiki Configuration. Custom input option for ad-hoc prompts.

• All UI strings translated per locale — every label, modal, and notice. Adding a 12th language is contributor-driven (PR #159 pattern).

• 🔧 Custom Headers per provider — a free-text field directly below the API Base URL. One Name: value per line, sent with every request. Ships with an OpenCode preset and a (Responses) variant for endpoints that speak /v1/responses.

• 📡 A stream that survives a blocked origin — when a cross-origin request is refused, the stream is retried over a desktop node:https transport instead of degrading to a buffered answer, and a failed fallback is recorded rather than swallowed.

⚙️ Settings that shape a run

Most of what the plugin does is decided for you. These eight settings change that, and each one is the answer to a failure someone hit.

🌐 Ecosystem

The plugin composes with the rest of your Obsidian stack — each tool below plugs into the [[wiki-link]] graph without code changes.

• 📄 MinerU multi-format backend (built-in since v1.27.0) — what used to be a separate CLI/UI step is now a plugin switch; see Document / PDF / Image ingest for the full path table. The MinerU online service remains available for users who prefer a quick UI over an API token; self-host MinerU is also an option.

• 🕸️ Obsidian Graph View — open the native graph on any wiki page; every [[wiki-link]] becomes a node, every back-link an edge. Built in, zero extra bundle size.

  

• ✂️ Obsidian Web Clipper — official browser extension. Save web pages (articles, blog posts, Reddit threads, Hacker News, recipes, research papers, YouTube transcripts via Interpreter) into any folder of your vault, then run the plugin's Ingest from folder command to batch-extract entities and concepts.

• 📊 Dataview — query the wiki like a database with DQL (LIST FROM "wiki/entities" WHERE contains(tags, "person")) or JS API. The plugin writes standard frontmatter (tags:, type:, aliases:) on every page, so Dataview queries work out of the box.

• 🌿 Git — version your vault (any Git client). The plugin never rewrites your source files; only creates new pages under wiki/, so git diff cleanly separates your edits from LLM-generated content.

• 🎞️ Marp Slides — turn any Obsidian note into slide decks via Marp frontmatter (marp: true). Wiki pages are pure Markdown, so they render as slides without extra conversion.

• 🖼️ Canvas — Obsidian's native infinite canvas. Drag wiki cards onto a Canvas to assemble study guides, mind maps, or research overviews from [[wiki-links]] without leaving the vault.

🔍 How retrieval works

Most "AI search" plugins fragment your notes into chunks and embed them in a vector DB. We don't. Karpathy's argument against RAG is that chunking breaks the LLM's ability to reason across your whole knowledge graph — and that argument holds up in practice. Instead, we walk the graph you already maintain by writing [[wiki-links]].

The 5-stage seed-selection cascade

When you ask "Who founded Microsoft?", Query Wiki runs five stages before any answer generation:

1. Lex fast path — BM25F over every entity/concept title, aliases, summary and body text, with per-field saturation and IDF over a real document-frequency table. Free, instant, and the gating step for everything that follows.
2. LLM keyword generation — the LLM proposes 8–12 cross-language keywords from your query (handles synonyms, abbreviations, and token-overlap-resistant terms in one LLM call).
3. Local substring scan — every generated keyword is re-matched locally against page titles, aliases, and body snippets. No extra LLM call; rounds out noise-tolerant recall.
4. LLM KB fallback — when lex + keyword scan returns weak signals, the LLM re-seeds the top-N candidates against the full wiki for one semantic pass.
5. PPR graph expansion — Personalized PageRank (Haveliwala 2002) over the [[wiki-link]] graph starting from the candidate seed set. This is what gives graph-aware multi-hop context: "Bill Gates" → "Microsoft" → "competitors", not just literal title overlap.

The cascade truncates at whichever stage returned enough signal — no fixed 5-stage cost; no LLM calls when lex is sufficient; semantic fallback only when lex + keyword scan alone isn't enough.

Query expansion, and a way back

When the cascade reaches the graph stage, the query is expanded twice before the seeds are chosen: once with the wiki's own terms, then again with LLM-proposed terms. Each pass is weighted, and the gain is measured on the project's own vaults rather than assumed — on the two vaults written by the current release the expansion is worth +0.086 and +0.113 at r@10, with the interval excluding zero in both cases. The claim is deliberately narrow: the weighted form is not measurably better than the simple concatenation, and the switch that runs the previous scorer is there for a reader whose results got worse.

Settings → Advanced → Legacy query scorer runs the previous hand-tuned linear scorer instead. The new ranking is the default; the toggle is the way back.

Personalized PageRank at scale

We use Monte Carlo PPR (Fogaras 2005) — 3,000 random walks × 50 steps each — with the dead-end rule from Haveliwala 2002. Cost is O(K × L) (K = walks, L = steps per walk), independent of the number of pages, so a 2,000-page vault sees the same expansion latency as a 200-page one.

PPR @5 = 27.1% vs pure-kNN baseline 24.1% on a project vault. The corpus is not public, so read the number as the comparison the project measured for itself rather than a claim about your vault; tools/dev-instrument/run-recall-harness.mjs is the harness that measures recall against a vault.

Why no embeddings

We deliberately rejected the embedding path in Issue #175. The graph signal is already there — every [[wiki-link]] is a hand-curated "these are related" edge, and most providers we support (Ollama, LM Studio, Anthropic, Bedrock, Kimi, GLM, MiniMax) don't ship a /v1/embeddings endpoint at all. Adding an embedding model would mean a per-page download, a per-provider adapter, and zero benefit on retrieval quality.

🤖 Models

Fifteen named providers, plus any OpenAI- or Anthropic-compatible endpoint. The pick-by-tier table — cloud, local, OCR, quantization, hardware — lives in docs/MODEL-GUIDE.md, last verified 2026-11-06 against models.dev.

• Frontier cloud — Anthropic, OpenAI, Google Gemini, xAI Grok, Moonshot Kimi, Zhipu GLM, MiniMax. Native PDF on Anthropic, OpenAI and Gemini.
• Budget and open-weight — DeepSeek, Alibaba Qwen, Step, Tencent Hunyuan, Xiaomi MiMo, Google Gemma.
• Enterprise — AWS Bedrock (Anthropic and OpenAI variants, with API key, SSO or IAM auth) and ChatGPT Plan through Codex OAuth.
• Local and gateways — Ollama, LM Studio, OpenRouter, Anthropic-compatible gateways.

This plugin feeds the LLM your full wiki context per query, so long-context models win.

What matters

• 🧠 Context window ≥ 200K tokens for vaults over ~500 pages. Below 200K the cascade may drop earlier stages to fit.
• ⚖️ Instruction-following quality matters more than raw IQ for the extraction task — pick a model that follows the schema template, not the biggest leaderboard number.
• 🔌 Embedding endpoint is irrelevant — we don't use embeddings. A provider that lacks /v1/embeddings is fine (most of our 15+ providers don't ship one).
• 🦙 Local works for query, cloud for ingest — ingest on a 2,000-page vault usually needs a long-context cloud model; a 262K local model covers most queries.

For PDF / image / Office ingest, see Document / PDF / Image ingest in Features — Anthropic, OpenAI, Bedrock, and Gemini read PDFs as file parts natively; the built-in MinerU backend (v1.27.0+) and Force PDF Support cover everything else.

Anthropic vs OpenAI vs Codex OAuth — they are distinct providers

• Anthropic (and its Bedrock variant) — separately billed Anthropic Platform API key.
• OpenAI — separately billed OpenAI Platform API key.
• ChatGPT Plan (Codex OAuth) — experimental, distinct provider that uses eligible Codex allowance after browser or device-code sign-in; availability follows OpenAI Codex authentication and allowance policies, not plan name. Third-party Codex compatibility, not an OpenAI partnership or a general ChatGPT API.

AWS Bedrock — three auth modes (v1.27.0, #425)

Settings → Provider → Bedrock (Anthropic / OpenAI) now picks one of three auth modes; the provider row then asks for the inputs that mode actually needs:

• API key — the original Stage-1 bearer path; behavior is byte-for-byte identical to v1.26.4 and the recommended pick for users who already pay for a Bedrock API key.
• SSO — IAM Identity Center device flow. Click Sign in with AWS SSO, paste the verification URL code in the browser, the plugin receives an SSO token via karpathywiki-bedrock-sso in SecretStorage, exchanges it for temporary role credentials, and signs every request with hand-rolled SigV4 (no AWS SDK added). Account ID and role name are auto-detected when the SSO identity exposes exactly one of each; otherwise enter them in the provider settings.
• IAM — static access keys for environments without SSO (CI, scheduled batch jobs). Stored in karpathywiki-bedrock-iam in SecretStorage; the in-memory cache memoizes per access-key to keep SigV4 signing within expiry.

All three modes share the same Obsidian SecretStorage discipline (no credentials in data.json, logs, or docs) and the same zero-AWS-SDK hand-rolled OIDC + SigV4 path. Bedrock region is independent of auth mode and is configured in the same provider row.

📖 Full pick table (cloud + local + PDF OCR + Codex OAuth + quantization + hardware tiers) → docs/MODEL-GUIDE.md

❓ FAQ

What does the plugin actually do?

Pick a note, folder or selection: the LLM extracts entities and concepts and writes an interlinked wiki with [[bidirectional links]]. Ask a question and the answer comes from those pages, with citations. Your original notes are never modified.

Is my existing wiki safe?

✅ Backward compatible since v1.0.0. Set reviewed: true on any page to protect it from overwrite. Upgrading from v1.24.x doesn't rewrite your vault; v1.25.0's PDF ingest is cache-only by default, and v1.27.0 adds native PDF + images + Office ingest without changing the on-disk wiki layout.

Can I ingest PDFs, images, and Office documents?

✅ Yes. Anthropic, OpenAI, Bedrock, and Gemini read PDFs natively; the built-in MinerU backend (v1.27.0) covers everything else (PDF + images + Office). Full walkthrough — cloud providers, Apple Silicon OCR, Force PDF Support, cache housekeeping — in docs/PDF-OCR-GUIDE.md.

Is my data sent anywhere?

🚫 No backend, no analytics — the plugin runs entirely inside Obsidian. Only text you explicitly send for ingest/query leaves your device, and only to the LLM provider you configure. For complete data locality, use Ollama or LM Studio.

Can I use the plugin in my language?

🌍 11 languages for both interface and wiki output, set independently. A 12th is contributor-driven (PR #159 is the pattern).

How is this different from a RAG chatbot?

No chunking, no embeddings, no vector database. Personalized PageRank runs over the [[wiki-link]] graph you already maintain, which gives multi-hop context at zero embedding cost.

Which LLM should I use?

A long-context model: ≥200K tokens for vaults over ~500 pages. The tiered table is in docs/MODEL-GUIDE.md.

Is there a published benchmark?

PPR @5 = 27.1% against a pure-kNN baseline of 24.1%, measured on a project vault — How retrieval works says what that does and does not prove.

How do I control API costs?

Use Coarse or Minimal extraction granularity for batch ingest. Smart Batch Skip auto-detects already-ingested files. Auto-Maintenance is OFF by default. Lint shows counts before running fixes — nothing is charged without your approval.

How do I cancel a running operation?

Click the status bar (shows "Ingesting… click to cancel") or Cmd+P/Ctrl+P → "Cancel current ingestion". Stops cleanly at the next batch boundary.

Where do I get help?

GitHub Issues for bug reports · GitHub Discussions for questions and feature requests · Developer Console (Ctrl+Shift+I / Cmd+Option+I) for plugin logs.

🔒 Privacy

This plugin is listed on the Obsidian Community Plugin Market and undergoes automated review for security and permissions.

• 🚫 No backend of its own, no telemetry, no data collection. Pure local software running inside Obsidian. The plugin operates no server and reports nothing anywhere.
• 🔐 Network access is opt-in and explicit. It exists only to reach services you configure: your LLM provider, and — if you turn on the MinerU document backend — mineru.net. That second one is the exception worth naming: it uploads the document to a jurisdiction you did not choose and that publishes no retention statement. Where your document is processed lists every path and where the file goes.
• 📁 Vault file access is used for wiki management (reading notes, generating pages, scanning dead links, detecting duplicates). The plugin never modifies your source files.
• 📋 Clipboard access is used exclusively by the "Copy" button in the Query modal — and only when you click it.

For complete data locality, use Ollama or LM Studio. With a local provider, your data never leaves your machine.

💖 Support

If Karpathy LLM Wiki has become a meaningful part of your knowledge workflow:

• ☕ Buy me a Ko-fi — one-time or monthly
• 💳 Tip via PayPal — one-time tip

Thanks to the following for supporting the project:

@jameses-cyber, @issaqua, Dikson Choi

Contributing

Bug reports and pull requests are both useful. CONTRIBUTING.md covers the build, the test loop and the checks a change must pass; CODE_OF_CONDUCT.md is short. ROADMAP.md shows what is planned, and CHANGELOG.md records what shipped.

Building from source: pnpm install, pnpm build:dev, pnpm gate:1 — the last one is lint, architecture check, type check, build, test and CSS lint in one command. tools/dev-instrument/ is the dev-only measurement instrument: it runs the real WikiEngine.ingestSource against a folder on disk with no Obsidian runtime and prints per-task token and wall-clock accounting, which is where the performance numbers in the changelog come from. The user-facing CLI is a sibling repo — npm i -g karpathywiki-cli.

🔭 Other projects

• obsidian-llm-wiki-cli — the same WikiEngine with no renderer: npm i -g karpathywiki-cli, then karpathywiki-cli ingest --sources <path> --wiki <path> --provider <id> --key <key>.
• pi-shift-router — a task-level router for pi-coding-agent: a small judge marks each turn routine or consequential, and the tier it picks drives the model, the tools and the fallback chain. MIT. → shiftrouter.greenerai.top
• dsh-shift-router — the same routing design for the dsh-coding-agent runtime. MIT.
• dsh-plugin-dev-skill — the DSH-side Obsidian plugin workflow: scaffolds a workspace, drives a Red→Green loop, runs the same quality gates this repository uses, and leaves a release-ready feat/* or fix/* branch.

📜 License & Credits

Apache License, Version 2.0 — see LICENSE, NOTICE and THIRD-PARTY-NOTICES.md.

Built on:

• 💡 Andrej Karpathy's LLM Wiki — the original concept
• 🛠️ Obsidian Plugin API
• 🔌 Vercel AI SDK v7 (@ai-sdk/openai, @ai-sdk/anthropic, @ai-sdk/openai-compatible) via Obsidian requestUrl
• 🧮 Personalized PageRank (Haveliwala 2002) and Monte Carlo PPR (Fogaras 2005) — retrieval algorithms

Maintainers: @green-dalii (author) · @DocTpoint (co-maintainer since September 2026)