How do I install Llm Wiki?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Llm Wiki need an API key?

No required environment variable is listed on this record.

What transport does Llm Wiki use?

Llm Wiki runs locally over stdio.

When was Llm Wiki last checked?

Last checked 2026-10-27. Git-backed wiki engine with MCP server — build knowledge that compounds

Llm Wiki MCP server

llm-wiki

A headless wiki engine for agents. 24 MCP tools. One Rust binary. No LLM inside.

Build knowledge that compounds — not answers that evaporate.

A git-backed Markdown wiki — searchable, typed, graph-linked. Accessible from the command line, from any MCP-compatible agent, or from any IDE via ACP.

llm-wiki

The problem with RAG

Most AI knowledge tools retrieve and generate on every query. Each answer is disposable — nothing is learned, nothing is kept. Ask the same question twice and the LLM reasons from scratch.

llm-wiki implements a different pattern — the Dynamic Knowledge Repository (DKR), introduced by Andrej Karpathy:

Process sources at ingest time, not query time. The LLM integrates each source into the wiki — updating concept pages, creating source summaries, flagging contradictions — and commits the result. Knowledge compounds with every addition.

	Traditional RAG	llm-wiki (DKR)
When knowledge is built	At query time	At ingest time
Cross-references	Ad hoc or missed	Pre-built, typed graph
Knowledge accumulation	Resets each query	Compounds over time
Audit trail	None	Git history per page
Data ownership	Provider systems	Your files, your git repo

How it works

The engine is pure infrastructure. It manages files, git, full-text search, and graph structure. The LLM is always external — it calls the engine's tools via MCP, reads pages, writes pages, and commits knowledge. Intelligence flows through skills, not the binary.

LLM agent
  │
  ├── wiki_list(format: "llms")             → all pages grouped by type
  ├── wiki_search("mixture of experts")     → ranked results + facets
  ├── wiki_content_read("concepts/moe")     → full page + backlinks
  ├── wiki_graph(root: "concepts/moe")      → typed graph in Mermaid/DOT
  ├── wiki_suggest("concepts/moe")          → pages worth linking
  ├── wiki_content_new("concepts/new-page") → scaffold + returns local path
  ├── [write directly to path]              → no MCP round-trip
  └── wiki_ingest(path: "concepts/")        → validate, index, commit

A wiki page is a plain Markdown file with typed frontmatter:

---
type: concept
title: Mixture of Experts
status: active
confidence: 0.9
tags: [routing, scaling, efficiency]
sources:
  - sources/switch-transformer-2021
  - sources/mixtral-2024
concepts:
  - concepts/sparse-routing
  - concepts/scaling-laws
---

Sparse routing of tokens to expert subnetworks...

The engine validates frontmatter against a JSON Schema, extracts typed graph edges from sources and concepts, and indexes everything in tantivy. The graph is live the moment a page is committed.

Install

# Homebrew (macOS / Linux) — recommended
brew tap geronimo-iia/tap
brew install llm-wiki

# macOS / Linux — curl installer
curl -fsSL https://raw.githubusercontent.com/geronimo-iia/llm-wiki/main/install.sh | bash

# Windows (PowerShell)
irm https://raw.githubusercontent.com/geronimo-iia/llm-wiki/main/install.ps1 | iex

# Cargo
cargo install llm-wiki

→ All installation options

Quick start

# Create a wiki space
llm-wiki spaces create ~/wikis/research --name research

# Start the MCP server
llm-wiki serve

Connect your agent or editor — VS Code, Cursor, Windsurf, Zed, Claude Code — via the MCP config. The 24 tools are immediately available.

→ Getting started guide · IDE integration

MCP configuration

Add to your agent or IDE's MCP config:

{
  "mcpServers": {
    "llm-wiki": {
      "command": "llm-wiki",
      "args": ["serve"]
    }
  }
}

Works with Claude Code, VS Code (Copilot), Cursor, Windsurf, and any MCP-compatible client. For Zed (ACP over stdio), see the IDE integration guide.

IDE integration via ACP

In addition to MCP, llm-wiki speaks ACP (Agent Client Protocol) — a session-oriented streaming protocol over stdio. Connect from Zed or any ACP-compatible editor and trigger built-in workflows directly from the IDE panel:

Prompt	What runs
`llm-wiki:research <query>`	Search + read top results, stream summaries
`llm-wiki:lint [rules]`	Run structural lint rules, stream findings
`llm-wiki:graph [root]`	Build and stream the concept graph
`llm-wiki:ingest [path]`	Ingest a path, stream the report
`llm-wiki:use <slug>`	Stream a page body directly into the IDE
`llm-wiki:help`	List all available workflows

Start with --acp alongside --http to give ACP exclusive stdio:

llm-wiki serve --acp --http :18765

→ IDE integration guide · ACP configuration

What agents can do

Tool	What it does
`wiki_search`	BM25 full-text search across one or all wikis, with type/status/tag facets
`wiki_list`	Paginated page listing with filters; `format: "llms"` for LLM-readable output
`wiki_content_read`	Read a page with optional backlinks
`wiki_content_write`	Write a page (validates frontmatter against type schema)
`wiki_content_new`	Scaffold a new page; returns local `path` for direct writes
`wiki_resolve`	Resolve a slug or `wiki://` URI to its local filesystem path
`wiki_ingest`	Validate a path, update the index, commit to git
`wiki_graph`	Typed concept graph — Mermaid, DOT, LLM-readable, or `summary` (aggregate metrics + community stats, no render)
`wiki_suggest`	Find pages worth linking by tag overlap, graph distance, BM25 similarity
`wiki_stats`	Wiki health: page counts, type distribution, staleness, graph density; `detail: "full"` adds community centre slugs
`wiki_lint`	Deterministic quality rules: orphans, broken links, missing fields, stale pages; scalable via `summary`, `path_prefix`, `page_size`, `cursor`
`wiki_export`	Write full wiki to `llms.txt`, `llms-full`, or JSON — JSON includes custom frontmatter fields per page
`wiki_history`	Git commit history for a page, with rename following
`wiki_schema`	Show, validate, or template a type schema
`wiki_info`	Server metadata: version, config path, registered spaces, default wiki, index status
`wiki_config`	Read or update config values without editing `config.toml` directly
`wiki_content_commit`	Stage and commit specific pages or all pending changes to git
`wiki_index_rebuild`	Rebuild the tantivy index for a wiki space
`wiki_index_status`	Check whether the index is up to date
`wiki_spaces_create`	Create a new wiki space at a given path
`wiki_spaces_register`	Register an existing wiki directory as a space
`wiki_spaces_list`	List all registered spaces with status
`wiki_spaces_remove`	Remove a registered space (does not delete files)
`wiki_spaces_set_default`	Set the default wiki for tool calls that omit a wiki name

Full tool reference: docs/specifications/tools/

Skills

The engine exposes tools. Skills tell agents how to use them.

llm-wiki-skills is a Claude Code plugin that ships ready-to-use workflows:

Skill	What it does
`crystallize`	Distil a session into durable wiki pages — decisions, findings, open questions
`ingest`	Process source files from `inbox/` into synthesized, cross-referenced pages
`research`	Search the wiki and synthesize an answer from existing knowledge
`lint`	Structural audit — orphans, broken links, schema issues; uses `summary: true` first for large wikis
`graph`	Explore and interpret the concept graph
`migrate`	Clean up built-in schema copies left by the old copy-on-create model; runs `wiki migrate --all`

Skills are plain Markdown files — readable by the LLM, replaceable, forkable. Write your own for your own workflows.

Architecture

llm-wiki-engine          pure Rust binary — tools, git, index, graph
llm-wiki-skills          Claude Code plugin — workflow skills (Markdown)
llm-wiki-hugo-cms        Hugo scaffold — render the wiki as a website

The engine has no opinions about workflows, LLM providers, or interfaces. Every LLM call happens outside the binary. Every workflow lives in a skill. The separation means skills ship independently, the engine stays stable, and nothing is coupled to a specific AI provider.

Technology

The file format is Markdown. The history store is git. Both predate llm-wiki and will outlive it — your wiki is readable, diffable, and portable with zero dependency on this tool. The engine itself is a single Rust binary with no runtime, no database, and nothing to keep running between sessions.

Single Rust binary. No runtime, no database, no Docker.

Component	Technology
Search	tantivy — BM25, Lucene-class performance
Git	git2 — libgit2 bindings
Graph	petgraph — typed DiGraph
MCP	rmcp — stdio + Streamable HTTP
ACP	agent-client-protocol

Documentation


Getting started	End-to-end walkthrough
Guides	Installation, IDE, custom types, CI/CD, multi-wiki
Specifications	Formal tool and model contracts
Architecture	Core concepts, project map
Roadmap	What shipped, what's next
Decisions	Architectural decision records

Related Projects

Project	Roadmap
llm-wiki-skills	`docs/roadmap.md`
llm-wiki-hugo-cms	`docs/roadmap.md`
homebrew-tap	Formula updates per release
asdf-llm-wiki	Plugin updates per release

Why I built this

Like many of you, I've been exploring agents, LLMs, and all that comes with it. This project started after Andrej Karpathy's post — he put into words something I was already practicing: plain Markdown files with structured frontmatter as a practical knowledge base, for work and for the messier explorations.

The technical direction reflects years of SRE-minded practice: minimize dependencies, use proven tools, keep the binary dumb. Written in Rust with Claude as a pair programmer — a language I enjoy exploring more and more.

I have "a few" years of experience, but if you spot bad practices, call them out — I'm doing this to learn together too. And if you're using it, personally or at work, I'd love to hear about it :)

Acknowledgments

Andrej Karpathy — for the LLM Wiki gist that defined the Dynamic Knowledge Repository pattern.
vanillaflava — for llm-wiki-claude-skills, which turned the pattern into a practical skill architecture.

llm-wiki is a continuation of agent-foundation.

Contributing

Contributing guide · Code of conduct · Security policy

License

MIT OR Apache-2.0

llm-wiki

A headless wiki engine for agents. 24 MCP tools. One Rust binary. No LLM inside.

Build knowledge that compounds — not answers that evaporate.

A git-backed Markdown wiki — searchable, typed, graph-linked. Accessible from the command line, from any MCP-compatible agent, or from any IDE via ACP.

llm-wiki

The problem with RAG

Most AI knowledge tools retrieve and generate on every query. Each answer is disposable — nothing is learned, nothing is kept. Ask the same question twice and the LLM reasons from scratch.

llm-wiki implements a different pattern — the Dynamic Knowledge Repository (DKR), introduced by Andrej Karpathy:

Process sources at ingest time, not query time. The LLM integrates each source into the wiki — updating concept pages, creating source summaries, flagging contradictions — and commits the result. Knowledge compounds with every addition.

	Traditional RAG	llm-wiki (DKR)
When knowledge is built	At query time	At ingest time
Cross-references	Ad hoc or missed	Pre-built, typed graph
Knowledge accumulation	Resets each query	Compounds over time
Audit trail	None	Git history per page
Data ownership	Provider systems	Your files, your git repo

How it works

LLM agent
  │
  ├── wiki_list(format: "llms")             → all pages grouped by type
  ├── wiki_search("mixture of experts")     → ranked results + facets
  ├── wiki_content_read("concepts/moe")     → full page + backlinks
  ├── wiki_graph(root: "concepts/moe")      → typed graph in Mermaid/DOT
  ├── wiki_suggest("concepts/moe")          → pages worth linking
  ├── wiki_content_new("concepts/new-page") → scaffold + returns local path
  ├── [write directly to path]              → no MCP round-trip
  └── wiki_ingest(path: "concepts/")        → validate, index, commit

A wiki page is a plain Markdown file with typed frontmatter:

---
type: concept
title: Mixture of Experts
status: active
confidence: 0.9
tags: [routing, scaling, efficiency]
sources:
  - sources/switch-transformer-2021
  - sources/mixtral-2024
concepts:
  - concepts/sparse-routing
  - concepts/scaling-laws
---

Sparse routing of tokens to expert subnetworks...

The engine validates frontmatter against a JSON Schema, extracts typed graph edges from sources and concepts, and indexes everything in tantivy. The graph is live the moment a page is committed.

Install

# Homebrew (macOS / Linux) — recommended
brew tap geronimo-iia/tap
brew install llm-wiki

# macOS / Linux — curl installer
curl -fsSL https://raw.githubusercontent.com/geronimo-iia/llm-wiki/main/install.sh | bash

# Windows (PowerShell)
irm https://raw.githubusercontent.com/geronimo-iia/llm-wiki/main/install.ps1 | iex

# Cargo
cargo install llm-wiki

→ All installation options

Quick start

# Create a wiki space
llm-wiki spaces create ~/wikis/research --name research

# Start the MCP server
llm-wiki serve

Connect your agent or editor — VS Code, Cursor, Windsurf, Zed, Claude Code — via the MCP config. The 24 tools are immediately available.

→ Getting started guide · IDE integration

MCP configuration

Add to your agent or IDE's MCP config:

{
  "mcpServers": {
    "llm-wiki": {
      "command": "llm-wiki",
      "args": ["serve"]
    }
  }
}

Works with Claude Code, VS Code (Copilot), Cursor, Windsurf, and any MCP-compatible client. For Zed (ACP over stdio), see the IDE integration guide.

IDE integration via ACP

Prompt	What runs
`llm-wiki:research <query>`	Search + read top results, stream summaries
`llm-wiki:lint [rules]`	Run structural lint rules, stream findings
`llm-wiki:graph [root]`	Build and stream the concept graph
`llm-wiki:ingest [path]`	Ingest a path, stream the report
`llm-wiki:use <slug>`	Stream a page body directly into the IDE
`llm-wiki:help`	List all available workflows

Start with --acp alongside --http to give ACP exclusive stdio:

llm-wiki serve --acp --http :18765

→ IDE integration guide · ACP configuration

What agents can do

Tool	What it does
`wiki_search`	BM25 full-text search across one or all wikis, with type/status/tag facets
`wiki_list`	Paginated page listing with filters; `format: "llms"` for LLM-readable output
`wiki_content_read`	Read a page with optional backlinks
`wiki_content_write`	Write a page (validates frontmatter against type schema)
`wiki_content_new`	Scaffold a new page; returns local `path` for direct writes
`wiki_resolve`	Resolve a slug or `wiki://` URI to its local filesystem path
`wiki_ingest`	Validate a path, update the index, commit to git
`wiki_graph`	Typed concept graph — Mermaid, DOT, LLM-readable, or `summary` (aggregate metrics + community stats, no render)
`wiki_suggest`	Find pages worth linking by tag overlap, graph distance, BM25 similarity
`wiki_stats`	Wiki health: page counts, type distribution, staleness, graph density; `detail: "full"` adds community centre slugs
`wiki_lint`	Deterministic quality rules: orphans, broken links, missing fields, stale pages; scalable via `summary`, `path_prefix`, `page_size`, `cursor`
`wiki_export`	Write full wiki to `llms.txt`, `llms-full`, or JSON — JSON includes custom frontmatter fields per page
`wiki_history`	Git commit history for a page, with rename following
`wiki_schema`	Show, validate, or template a type schema
`wiki_info`	Server metadata: version, config path, registered spaces, default wiki, index status
`wiki_config`	Read or update config values without editing `config.toml` directly
`wiki_content_commit`	Stage and commit specific pages or all pending changes to git
`wiki_index_rebuild`	Rebuild the tantivy index for a wiki space
`wiki_index_status`	Check whether the index is up to date
`wiki_spaces_create`	Create a new wiki space at a given path
`wiki_spaces_register`	Register an existing wiki directory as a space
`wiki_spaces_list`	List all registered spaces with status
`wiki_spaces_remove`	Remove a registered space (does not delete files)
`wiki_spaces_set_default`	Set the default wiki for tool calls that omit a wiki name

Full tool reference: docs/specifications/tools/

Skills

The engine exposes tools. Skills tell agents how to use them.

llm-wiki-skills is a Claude Code plugin that ships ready-to-use workflows:

Skill	What it does
`crystallize`	Distil a session into durable wiki pages — decisions, findings, open questions
`ingest`	Process source files from `inbox/` into synthesized, cross-referenced pages
`research`	Search the wiki and synthesize an answer from existing knowledge
`lint`	Structural audit — orphans, broken links, schema issues; uses `summary: true` first for large wikis
`graph`	Explore and interpret the concept graph
`migrate`	Clean up built-in schema copies left by the old copy-on-create model; runs `wiki migrate --all`

Skills are plain Markdown files — readable by the LLM, replaceable, forkable. Write your own for your own workflows.

Architecture

llm-wiki-engine          pure Rust binary — tools, git, index, graph
llm-wiki-skills          Claude Code plugin — workflow skills (Markdown)
llm-wiki-hugo-cms        Hugo scaffold — render the wiki as a website

Technology

Single Rust binary. No runtime, no database, no Docker.

Component	Technology
Search	tantivy — BM25, Lucene-class performance
Git	git2 — libgit2 bindings
Graph	petgraph — typed DiGraph
MCP	rmcp — stdio + Streamable HTTP
ACP	agent-client-protocol

Documentation


Getting started	End-to-end walkthrough
Guides	Installation, IDE, custom types, CI/CD, multi-wiki
Specifications	Formal tool and model contracts
Architecture	Core concepts, project map
Roadmap	What shipped, what's next
Decisions	Architectural decision records

Related Projects

Project	Roadmap
llm-wiki-skills	`docs/roadmap.md`
llm-wiki-hugo-cms	`docs/roadmap.md`
homebrew-tap	Formula updates per release
asdf-llm-wiki	Plugin updates per release

Why I built this

I have "a few" years of experience, but if you spot bad practices, call them out — I'm doing this to learn together too. And if you're using it, personally or at work, I'd love to hear about it :)

Acknowledgments

Andrej Karpathy — for the LLM Wiki gist that defined the Dynamic Knowledge Repository pattern.
vanillaflava — for llm-wiki-claude-skills, which turned the pattern into a practical skill architecture.

llm-wiki is a continuation of agent-foundation.

Contributing

Contributing guide · Code of conduct · Security policy

License

MIT OR Apache-2.0

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Llm Wiki?

Does Llm Wiki need an API key?

What transport does Llm Wiki use?

When was Llm Wiki last checked?

Documentation

llm-wiki

The problem with RAG

How it works

Install

Quick start

MCP configuration

IDE integration via ACP

What agents can do

Skills

Architecture

Technology

Documentation

Related Projects

Why I built this

Acknowledgments

Contributing

License

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Llm Wiki?

Does Llm Wiki need an API key?

What transport does Llm Wiki use?

When was Llm Wiki last checked?

Documentation

llm-wiki

The problem with RAG

How it works

Install

Quick start

MCP configuration

IDE integration via ACP

What agents can do

Skills

Architecture

Technology

Documentation

Related Projects

Why I built this

Acknowledgments

Contributing

License