llmwiki

Compile raw sources into an interlinked markdown wiki.

Inspired by Karpathy's LLM Wiki pattern: instead of re-discovering knowledge at query time, compile it once into a persistent, browsable artifact that compounds over time.

Who this is for

AI researchers and engineers building persistent knowledge from papers, docs, and notes
Technical writers compiling scattered sources into a structured, interlinked reference
Anyone with too many bookmarks who wants a wiki instead of a graveyard of tabs

Quick start

npm install -g llm-wiki-compiler
export ANTHROPIC_API_KEY=sk-...
# Or use ANTHROPIC_AUTH_TOKEN if your Anthropic-compatible gateway expects it.
# Or use a different provider:
# export LLMWIKI_PROVIDER=openai
# export OPENAI_API_KEY=sk-...

llmwiki ingest https://some-article.com
llmwiki compile
llmwiki query "what is X?"

Configuration

llmwiki configures providers via environment variables. The default provider is Anthropic.

Configuration precedence for Anthropic values:

Shell env / local .env
Claude Code settings fallback (~/.claude/settings.json → env block)
Built-in provider defaults (where applicable)

LLMWIKI_PROVIDER: The provider to use (e.g., anthropic, openai).
LLMWIKI_MODEL: The model name to override the provider default.

Anthropic (Default)

ANTHROPIC_API_KEY or ANTHROPIC_AUTH_TOKEN: Required. Either one can satisfy Anthropic authentication.
ANTHROPIC_BASE_URL: Optional. Custom endpoint for proxies. Valid HTTP(S) URLs are accepted, including Claude-style path endpoints such as https://api.kimi.com/coding/.

Example using an Anthropic or cc-switch custom proxy:

export LLMWIKI_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-...
export ANTHROPIC_BASE_URL=https://proxy.example.com

If those values are not set in shell env or .env, llmwiki will try Anthropic-compatible values from ~/.claude/settings.json (env block) for:

ANTHROPIC_API_KEY
ANTHROPIC_AUTH_TOKEN
ANTHROPIC_BASE_URL
ANTHROPIC_MODEL

Example with zero exports (Claude Code already configured):

llmwiki compile

OpenAI-Compatible Local Servers

Use the OpenAI provider for local OpenAI-compatible servers such as llama-server. OPENAI_BASE_URL is used for chat/tool calls, and OPENAI_EMBEDDINGS_BASE_URL is optional. Set it only when embeddings are served from a different endpoint; when unset, embeddings use the same client and base URL as chat. Include /v1 in custom URLs.

Split endpoint example:

export LLMWIKI_PROVIDER=openai
export LLMWIKI_MODEL=qwen3.6-35b
export LLMWIKI_EMBEDDING_MODEL=text-embedding-model
export OPENAI_API_KEY=sk-local
export OPENAI_BASE_URL=http://host_url:port/v1
export OPENAI_EMBEDDINGS_BASE_URL=http://host_url:port/v1

OPENAI_API_KEY is still required by the CLI and OpenAI SDK. For local servers that do not check authentication, any dummy value is sufficient.

Ollama

Ollama uses its OpenAI-compatible endpoint. Set OLLAMA_HOST for chat and optionally set OLLAMA_EMBEDDINGS_HOST only when embeddings are served from a different endpoint. When unset, embeddings use OLLAMA_HOST. Include /v1 in custom URLs.

export LLMWIKI_PROVIDER=ollama
export LLMWIKI_MODEL=llama3.1
export LLMWIKI_EMBEDDING_MODEL=nomic-embed-text
export OLLAMA_HOST=http://ollama_host:11434/v1
export OLLAMA_EMBEDDINGS_HOST=http://ollama_host:11435/v1

Why not just RAG?

RAG retrieves chunks at query time. Every question re-discovers the same relationships from scratch. Nothing accumulates.

llmwiki compiles your sources into a wiki. Concepts get their own pages. Pages link to each other. When you ask a question with --save, the answer becomes a new page, and future queries use it as context. Your explorations compound.

This is complementary to RAG, not a replacement. RAG is great for ad-hoc retrieval over large corpora. llmwiki gives you a persistent, structured artifact to retrieve from.

RAG:     query → search chunks → answer → forget
llmwiki: sources → compile → wiki → query → save → richer wiki → better answers

How it works

sources/  →  SHA-256 hash check  →  LLM concept extraction  →  wiki page generation  →  [[wikilink]] resolution  →  index.md

Two-phase pipeline. Phase 1 extracts all concepts from all sources. Phase 2 generates pages. This eliminates order-dependence, catches failures before writing anything, and merges concepts shared across multiple sources into single pages.

Incremental. Only changed sources go through the LLM. Everything else is skipped via hash-based change detection.

Compounding queries. llmwiki query --save writes the answer as a wiki page and immediately rebuilds the index. Saved answers show up in future queries as context.

What it produces

A raw source like a Wikipedia article on knowledge compilation becomes a structured wiki page:

---
title: Knowledge Compilation
summary: Techniques for converting knowledge representations into forms that support efficient reasoning.
sources:
  - knowledge-compilation.md
createdAt: "2026-04-05T12:00:00Z"
updatedAt: "2026-04-05T12:00:00Z"
---

Knowledge compilation refers to a family of techniques for pre-processing
a knowledge base into a target language that supports efficient queries.

Related concepts: [[Propositional Logic]], [[Model Counting]]

Pages include source attribution in frontmatter. Paragraphs are annotated with ^[filename.md] markers pointing back to the source file that contributed the content.

Commands

Command	What it does
`llmwiki ingest <url\|file>`	Fetch a URL or copy a local file into `sources/`
`llmwiki compile`	Incremental compile: extract concepts, generate wiki pages
`llmwiki compile --review`	Write candidate pages to `.llmwiki/candidates/` instead of `wiki/` so you can review before they land
`llmwiki review list`	List pending candidate pages
`llmwiki review show <id>`	Print a candidate's title, summary, and body
`llmwiki review approve <id>`	Promote a candidate into `wiki/` and refresh index/MOC/embeddings
`llmwiki review reject <id>`	Archive a candidate without touching `wiki/`
`llmwiki query "question"`	Ask questions against your compiled wiki
`llmwiki query "question" --save`	Answer and save the result as a wiki page
`llmwiki lint`	Check wiki quality (broken links, orphans, empty pages, low confidence, contradictions, etc.)
`llmwiki watch`	Auto-recompile when `sources/` changes
`llmwiki serve [--root <dir>]`	Start an MCP server exposing wiki tools to AI agents

Output

wiki/
  concepts/         one .md file per concept, with YAML frontmatter
  queries/          saved query answers, included in index and retrieval
  index.md          auto-generated table of contents
.llmwiki/
  candidates/       pending review candidates from `compile --review`
  candidates/archive/  rejected candidates kept for audit

Obsidian-compatible. [[wikilinks]] resolve to concept titles.

Review queue

By default, compile writes pages directly to wiki/. Add --review to write candidate JSON records to .llmwiki/candidates/ instead, so you can inspect each generated page before it lands.

llmwiki compile --review     # produces candidates, leaves wiki/ untouched
llmwiki review list          # see what's pending
llmwiki review show <id>     # inspect a single candidate
llmwiki review approve <id>  # write into wiki/ + refresh index/MOC/embeddings
llmwiki review reject <id>   # archive to .llmwiki/candidates/archive/

A few things to know:

Approve and reject acquire .llmwiki/lock so they serialize cleanly against each other and against any concurrent compile.
Source state is deferred per-source. When one source produces multiple candidates, the source isn't marked compiled until the last candidate is approved — so unresolved siblings stay re-detectable on the next compile --review.
Deletion bookkeeping is deferred. compile --review does not orphan-mark deleted sources; the next non-review compile does that. The --review help text advertises this.
MCP wiki_status exposes pendingCandidates so agents can see the queue depth.

Page metadata

Compiled pages can carry epistemic metadata in frontmatter so consumers know how trustworthy each page is. All fields are optional and existing pages without them continue to work.

---
title: Knowledge Compilation
summary: Techniques for converting knowledge representations...
sources:
  - knowledge-compilation.md
confidence: 0.82           # 0–1, LLM-reported confidence in the synthesized page
provenanceState: merged    # extracted | merged | inferred | ambiguous
contradictedBy:
  - slug: probabilistic-reasoning
inferredParagraphs: 1      # paragraphs the LLM marked as inferred (vs cited)
---

When multiple sources merge into one slug, metadata is reconciled: min confidence, provenanceState = 'merged', union of contradictedBy (deduped by slug), max inferredParagraphs.

llmwiki lint adds three rules that surface this metadata:

low-confidence — flags pages with confidence below a threshold
contradicted-page — flags pages with non-empty contradictedBy
excess-inferred-paragraphs — flags pages with too many inferred paragraphs without citations

Demo

Try it on any article or document:

mkdir my-wiki && cd my-wiki
llmwiki ingest https://en.wikipedia.org/wiki/Andrej_Karpathy
llmwiki compile
llmwiki query "What terms did Andrej coin?"

See examples/basic/ in the repo for pre-generated output you can browse without an API key.

MCP Server

llmwiki ships an MCP (Model Context Protocol) server so AI agents (Claude Desktop, Cursor, Claude Code, etc.) can drive the full pipeline directly: ingest sources, compile, query, search, lint, and read pages — without scraping CLI output.

Where llm-wiki-kit gives agents raw CRUD against wiki pages, llmwiki exposes the automated pipelines: agents get intelligent compilation, incremental change detection, and semantic query routing built in.

Setup

Start the server (stdio transport, no API key required at startup):

llmwiki serve --root /path/to/your/wiki-project

Claude Desktop / Cursor configuration

Add to your client's MCP config (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "llmwiki": {
      "command": "npx",
      "args": ["llm-wiki-compiler", "serve", "--root", "/path/to/wiki-project"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Tools that need an LLM (compile_wiki, query_wiki, search_pages) check for a configured provider on each call. Read-only tools (read_page, lint_wiki, wiki_status) and ingest_source work without any credentials.

Tools

Tool	What it does
`ingest_source`	Fetch a URL or local file into `sources/`.
`compile_wiki`	Run the incremental compile pipeline; returns counts, slugs, errors.
`query_wiki`	Two-step grounded answer with optional `--save`.
`search_pages`	Return full content of pages relevant to a question.
`read_page`	Read a single page by slug (concepts/ then queries/).
`lint_wiki`	Run quality checks; returns structured diagnostics.
`wiki_status`	Page count, source count, orphans, pending changes (read-only).

Resources

URI	Returns
`llmwiki://index`	Full `wiki/index.md` content.
`llmwiki://concept/{slug}`	A single concept page (frontmatter + body).
`llmwiki://query/{slug}`	A single saved query page.
`llmwiki://sources`	List of ingested source files with metadata.
`llmwiki://state`	Compilation state (per-source hashes, last compile times).

Limitations

Early software. Best for small, high-signal corpora (a few dozen sources). Query routing is index-based.

Honest about truncation. Sources that exceed the character limit are truncated on ingest with truncated: true and the original character count recorded in frontmatter, so downstream consumers know they're working with partial content.

Karpathy's LLM Wiki pattern vs this compiler

Karpathy describes an abstract pattern for turning raw data into compiled knowledge. Here's how llmwiki maps to it:

Karpathy's concept	llmwiki	Status
Data ingest	`llmwiki ingest`	Implemented
Compile wiki	`llmwiki compile`	Implemented
Q&A	`llmwiki query`	Implemented
Output filing (save answers back)	`llmwiki query --save`	Implemented
Auto-recompile	`llmwiki watch`	Implemented
Linting / health-check pass	`llmwiki lint`	Implemented
Agent integration	`llmwiki serve` (MCP server)	Implemented
Image support	—	Not yet implemented
Marp slides	—	Not yet implemented
Fine-tuning	—	Not yet implemented

Roadmap

Shipped in 0.3.0:

✅ Candidate review queue (approve compile output before pages are written)
✅ Confidence and contradiction metadata on compiled pages

Shipped in 0.2.0:

✅ Better provenance (paragraph-level source attribution)
✅ Linting pass for wiki quality checks
✅ Multi-provider support (OpenAI, Ollama, MiniMax)
✅ Larger-corpus query strategy (semantic search, embeddings)
✅ Deeper Obsidian integration (tags, aliases, Map of Content)
✅ MCP server for agent integration

Next up:

Claim-level provenance with source ranges
First-class schema layer with typed page kinds (concept, entity, comparison, overview)
Multimodal ingest (images, PDFs, transcripts)
Chunked retrieval with reranking
Export bundle (llms.txt, JSON, JSON-LD, GraphML, Marp)
Session-history adapters (Claude, Codex, Cursor exports)

If you like ambitious problems: schema layer + typed page kinds, claim-level provenance, and chunked retrieval with reranking are the meatiest. Open an issue to claim one or kick off a design discussion.

Requirements

Node.js >= 18, plus provider credentials (for Anthropic: ANTHROPIC_API_KEY or ANTHROPIC_AUTH_TOKEN).

License

MIT

Disclaimer

No LLMs were harmed in the making of this repo.

Name		Name	Last commit message	Last commit date
Latest commit History 35 Commits
.github/workflows		.github/workflows
.husky		.husky
docs/images		docs/images
examples/basic		examples/basic
scripts		scripts
src		src
test		test
.fallowrc.json		.fallowrc.json
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
package-lock.json		package-lock.json
package.json		package.json
tsconfig.json		tsconfig.json
tsup.config.ts		tsup.config.ts
vitest.config.ts		vitest.config.ts

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llmwiki

Who this is for

Quick start

Configuration

Anthropic (Default)

OpenAI-Compatible Local Servers

Ollama

Why not just RAG?

How it works

What it produces

Commands

Output

Review queue

Page metadata

Demo

MCP Server

Setup

Claude Desktop / Cursor configuration

Tools

Resources

Limitations

Karpathy's LLM Wiki pattern vs this compiler

Roadmap

Requirements

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llmwiki

Who this is for

Quick start

Configuration

Anthropic (Default)

OpenAI-Compatible Local Servers

Ollama

Why not just RAG?

How it works

What it produces

Commands

Output

Review queue

Page metadata

Demo

MCP Server

Setup

Claude Desktop / Cursor configuration

Tools

Resources

Limitations

Karpathy's LLM Wiki pattern vs this compiler

Roadmap

Requirements

License

Disclaimer

About

Resources

License

Contributing

Uh oh!

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