Karpathy LLM Wiki Plugin for Obsidian

AI-powered structured knowledge base that ingests your notes and generates a connected Wiki — based on Andrej Karpathy's LLM Wiki concept.

Author: Greener-Dalii | Version: 1.7.13

English | 中文文档 | 日本語 | 한국어 | Deutsch | Français | Español | Português | Official Site | Discussions

What is LLM-Wiki?

You write. AI organizes. You ask. That's it.

The problem. Your notes are a goldmine — people, concepts, ideas, connections. But right now they're just files in folders. Finding what relates to what means searching, tagging, and hoping you remember the thread.

The fix. Andrej Karpathy suggested something elegant: treat your notes as raw material, and let an LLM do the architect work. It reads what you write, pulls out entities and concepts, and weaves them into a structured Wiki — complete with [[bidirectional links]], an auto-generated index, and a chat interface that answers questions from your knowledge.

So you don't have to be the librarian. No deciding what deserves a page. No maintaining cross-links. No wondering if something is out of date. Drop notes into sources/ and the LLM reads, extracts, writes, links, and even flags contradictions — while you stay in flow.

And it's not another chatbot. ChatGPT knows the internet. LLM-Wiki knows you — or rather, what you've taught it. Every answer carries [[wiki-links]] back into your knowledge graph. Every response is a trailhead, not a dead end.

Why Obsidian + LLM-Wiki?

Obsidian is brilliant at linked thinking. But there's a catch: you're the one doing all the linking.

LLM-Wiki flips that. Instead of you building the graph by hand, the AI grows it with you. Add a note about a new concept — it finds the connections you'd miss. Ask a question — it walks your own knowledge graph and brings back answers with citations.

• Your Graph View comes alive. New notes don't just sit there — they sprout links to entities, concepts, and sources. The graph grows organically, and the plugin maintains it: detecting duplicates, fixing dead links, bridging languages with aliases.
• Your notes learn to talk back. Search becomes conversation. "What did I write about X?" becomes a dialogue, with streaming responses and [[wiki-links]] as breadcrumbs. Every answer is a path deeper into your own knowledge.
• Obsidian becomes a thinking partner. It stops being a cabinet for notes and starts being something that helps you think — surfacing hidden connections, flagging contradictions, remembering what you forgot you knew.

Quick Start

Installation

Recommended — Obsidian Community Plugin Market:

1. In Obsidian, go to Settings → Community plugins
2. Click Browse and search for "Karpathy LLM Wiki"
3. Click Install, then Enable

Or from the Community Plugin website — visit community.obsidian.md/plugins/karpathywiki and click Add to Obsidian to install directly.

Manual (alternative):

1. Download main.js, manifest.json, styles.css from Releases
2. In Obsidian, go to Settings → Community plugins. On the Installed plugins tab, click the folder icon to open your plugins directory
3. Create a folder named karpathywiki, drop the three files inside
4. Back in Obsidian, click the refresh icon — Karpathy LLM Wiki will appear under Installed plugins
5. Toggle it on to enable

Development: git clone, pnpm install, pnpm build.

Configure an LLM Provider

1. Open Settings → Karpathy LLM Wiki
2. Pick a provider from the dropdown (Anthropic, Anthropic Compatible, Google Gemini, OpenAI, DeepSeek, Kimi, GLM, Ollama, OpenRouter, or custom)
3. Enter your API key (not needed for Ollama)
4. Click Fetch Models to populate the model dropdown, or type a model name manually
5. Click Test Connection, then Save Settings

Ollama (local, no API key): Install Ollama, pull a model (ollama pull gemma4), select "Ollama (Local)" in the provider dropdown.

See README_CN.md for provider-specific instructions in Chinese.

Usage

Re-ingesting the same source does incremental updates on entity/concept pages (new info merged in). Summary pages are regenerated.

Smart Batch Skip: When ingesting a folder, the plugin automatically detects already-processed files and skips them to save time and API costs. The batch report shows skipped count.

Upgrading from an earlier version? Run Cmd+P → "Regenerate index" to rebuild your Wiki index with aliases included — this enables alias-aware search in Query (e.g., searching "DSA" will find "DeepSeek-Sparse-Attention").

Ingestion Acceleration: For sources with many entities (20+), enable parallel page generation in Settings → Ingestion Acceleration:

• Page Generation Concurrency: 1 (serial, safest) to 5 (parallel, fastest). Start with 3 for most providers.
• Batch Delay: 100–2000ms between parallel batches. Increase to 500ms+ for rate-limited providers.

Safety: Parallel generation uses Promise.allSettled — if one page fails, others continue. Failed pages are retried individually with exponential backoff.

Features

Knowledge Quality

• Entity/Concept Extraction — LLM extracts entities (people, orgs, products, events) and concepts (theories, methods, terms) from your notes
• Mandatory Page Aliases — Every generated page includes at least 1 alias (translation, acronym, alternate name), enabling cross-language duplicate detection
• Duplicate Detection & Merge — Semantic tiering catches true duplicates (cross-language translations, abbreviations, spelling variants); intelligent LLM merge fuses content and preserves aliases
• Smart Knowledge Fusion — Multi-source updates merge new info without redundancy, contradictions preserved with attribution, reviewed: true pages protected from overwrite
• Content Truncation Protection — 8000 max_tokens with automatic stop_reason detection and retry at 2× tokens across all providers
• Verbatim Source Mentions — Original language quotes preserved with optional translation for traceability

Maintenance

• Lint Health Scan — Detects duplicates, dead links, empty pages, orphans, missing aliases, and contradictions in one comprehensive report
• Semantic-Tier Duplicate Detection — Tier 1 (direct name matches: cross-language, abbreviations, high-similarity titles) always verified; Tier 2 (indirect signals: shared links, moderate similarity) fills token budget
• Smart Fix All — Causality-ordered batch fix: duplicates merged → dead links resolved → orphans linked → empty pages expanded
• Alias Completion — One-click parallel batch generation of missing aliases, improving future duplicate detection
• Auto-Maintenance — Multi-folder file watcher, periodic lint, startup health check (all optional)
• Contradiction State Machine — detected → review_ok → resolved (AI fix) or detected → pending_fix (manual)

Query & Feedback

• Conversational Query — ChatGPT-style dialog with streaming Markdown and [[wiki-links]], multi-turn history
• Query-to-Wiki Feedback — Save valuable conversations to Wiki with entity/concept extraction, semantic dedup before save
• Duplicate Save Prevention — Hash tracking prevents re-evaluation of unchanged conversations

LLM & Language

• Multi-Provider — Anthropic, Anthropic Compatible (Coding Plan), Gemini, OpenAI, DeepSeek, Kimi, GLM, OpenRouter, Ollama, custom endpoints
• 5xx Retry — Automatic exponential backoff retry (max 2) on HTTP 5xx/429 errors across all clients
• Dynamic Model List — Real-time fetching from provider APIs
• Wiki Output Language — 8 languages independent of UI (EN/ZH/JA/KO/DE/FR/ES/PT), with custom input
• Internationalization — English and Chinese UI (default: English)

Architecture & Performance

• Parallel Page Generation — Configurable 1–5 concurrent pages, 3× faster for large sources, error isolation per page
• Iterative Batch Extraction — Adaptive batch sizing eliminates max_tokens bottleneck for long documents
• Three-Layer Architecture — sources/ (read-only) → wiki/ (LLM-generated) → schema/ (co-evolved config)
• Modular Codebase — 13 focused modules in src/

Commands

Example

Input: sources/machine-learning.md

# Machine Learning
Machine learning uses algorithms to learn from data.

## Types
- Supervised learning
- Unsupervised learning
- Reinforcement learning

Output — Entity page: wiki/entities/supervised-learning.md

---
type: entity
created: 2026-05-15
updated: 2026-05-15
sources: ["[[sources/machine-learning]]"]
tags: [method]
aliases: ["监督学习", "Supervised Learning"]
---

# Supervised Learning

## Basic Information
- Type: method
- Source: [[sources/machine-learning]]

## Description
Supervised learning is a machine learning paradigm where models learn
from labeled training data to make predictions on unseen data...

## Related Concepts
- [[concepts/Machine-Learning|Machine Learning]]
- [[concepts/Unsupervised-Learning|Unsupervised Learning]]

## Related Entities
- [[entities/Arthur-Samuel|Arthur Samuel]]

## Mentions in Source
- "Supervised learning uses labeled data to train predictive models..."

Model Selection Guide

This plugin follows Karpathy's philosophy: feed the LLM full Wiki context, not chunked RAG retrieval. Long-context models are strongly recommended — the larger your Wiki grows, the more context the LLM needs.

Why not RAG? Karpathy's original critique argues that RAG fragments knowledge and breaks the LLM's ability to reason across the full knowledge graph.

Top recommendations:

For local models (Ollama): context windows are typically smaller (8K–128K). Consider using a cloud provider for ingestion + local model for query.

Anthropic Compatible (Coding Plan): If your provider offers an Anthropic-compatible API endpoint, select "Anthropic Compatible" and enter your provider's Base URL and API Key.

Architecture

Karpathy's three-layer separation design:

sources/     # Your source documents (read-only)
  ↓ ingest
wiki/        # LLM-generated Wiki pages
  ↓ query / maintain
schema/      # Wiki structure configuration (naming, templates, categories)

Codebase (src/):

wiki/               # Wiki engine modules
  wiki-engine.ts    # Orchestrator
  query-engine.ts   # Conversational query
  source-analyzer.ts # Iterative batch extraction
  page-factory.ts   # Entity/concept CRUD + merge
  lint-controller.ts # Lint orchestration
  lint-fixes.ts     # Fix logic + duplicate candidate generation
  contradictions.ts # Contradiction detection
  system-prompts.ts # Language directive + section labels
schema/             # Schema co-evolution
  schema-manager.ts # Schema CRUD + suggestions
  auto-maintain.ts  # File watcher + periodic lint
ui/                 # User interface
  settings.ts       # Settings panel
  modals.ts         # Lint/Ingest/Query modals
+ shared modules: llm-client.ts, prompts.ts, texts.ts, utils.ts, types.ts

Generated pages:

• wiki/sources/filename.md — Source summary
• wiki/entities/entity-name.md — Entity pages (people, orgs, projects, etc.)
• wiki/concepts/concept-name.md — Concept pages (theories, methods, terms, etc.)
• wiki/index.md — Auto-generated index
• wiki/log.md — Operation log

License

MIT License — see LICENSE.

Acknowledgments

• Concept: Andrej Karpathy's LLM Wiki — the original vision that inspired this plugin
• Platform: Obsidian Plugin API
• LLM SDKs: Anthropic SDK, OpenAI SDK