llm-wiki

LLM Wiki — a second brain for AI agents

Turn PDFs, articles, transcripts, and notes into a shared wiki that your AI agents can search, cite, and keep up to date. Add a source once. Ask questions later. Keep the useful answers.

Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode, OpenClaw, Pi, and OMP. Read the documentation.

What is LLM Wiki?

AI agents are good at the task in front of them, but a new session starts with limited context. LLM Wiki gives them a shared memory that lives inside your project.

When you add a source, the agent turns it into linked Markdown pages. Later, it can find the right section and answer with citations. Useful answers can be saved back into the wiki, so the knowledge grows instead of being rebuilt from scratch.

Everything canonical stays in readable Markdown. Default semantic search is local—no hosted vector database or embedding service.

What's new in v3.0.0

• Local semantic search by default. FastEmbed runs BAAI/bge-small-en-v1.5 on-device; sqlite-vec stores derived vectors in each wiki's cache.
• Hybrid precision and recall. BM25 exact-term ranks and semantic ranks are fused with RRF.
• Incremental indexing. Content hashes limit re-embedding to new or changed sections and remove deleted sections.
• Zero provider surface. No OpenAI-compatible endpoint, API key, remote text transfer, provider consent, or per-query charge.
• Safe lexical escape hatch. --no-embed stays pure-Python BM25; local backend failures fall back to it automatically.
• No content migration. Existing Markdown remains valid and old embeddings.jsonl caches are simply ignored.

Why use it?

• Stop repeating project context. Your agent can read the knowledge you already collected.
• Use the same wiki across agents. The files are plain Markdown, not tied to one model or tool.
• Trace every answer. Citations point back to the wiki page and original source.
• Keep the wiki healthy. The agent updates links, summaries, and contradictions as new sources arrive.
• Stay in control. Your pages remain readable, editable, and versionable.

LLM Wiki works best for knowledge that grows over time: research, meeting notes, customer calls, papers, articles, and project decisions. Use a regular database when your main problem is structured records and transactions.

Installation

uv is the only prerequisite: it creates the pinned script environments on every supported platform. Install it using the official uv instructions before running /wiki:init or the natural-language equivalent.

Plugin/skill installation copies all agent-facing commands and bundled tools. Wiki initialization then performs mandatory runtime setup: FastEmbed 0.8.0, sqlite-vec 0.1.9, PyYAML 6.0.3, the local BAAI/bge-small-en-v1.5 model, the parse cache, and vectors for every existing section. Upgrade runs the same setup again, incrementally synchronizing changed and deleted sections.

Claude Code — full plugin

The native path: the skill, the seven /wiki:* slash commands, and the marketplace manifest all ship in one install.

/plugin marketplace add praneybehl/llm-wiki-plugin
/plugin install llm-wiki@llm-wiki

Once installed, the plugin works in any project — the wiki itself lives in the project's working directory, not in the plugin.

Other coding agents — skill only

The llm-wiki skill uses the standard agentskills.io format, so it installs cleanly into any agent supported by the skills CLI. Pick the --agent flag that matches your setup:

# Install globally so the skill is available across all projects
npx skills add praneybehl/llm-wiki-plugin -a <agent> -g

# Or install into the current project only
npx skills add praneybehl/llm-wiki-plugin -a <agent>

Agent	--agent value	Invoke via	Scripts run
Claude Code	claude-code	/wiki:* slash commands (bundled) or natural language	✅
Codex (OpenAI)	codex	/skills or $llm-wiki / natural language	✅
Cursor	cursor	/llm-wiki or natural language	✅
Gemini CLI	gemini-cli	/skills management commands / natural language	✅
OpenCode	opencode	natural language (agent invokes the native skill tool)	✅
OpenClaw	openclaw	auto-exposed as a user command	✅
Pi Agent	pi	/skill:llm-wiki or natural language	✅
OMP ("Oh My Pi")	manual (see below)	natural language (skills auto-surface via skill://)	✅

OpenCode also reads .claude/skills/ and ~/.claude/skills/, so if you already installed the skill for Claude Code you can use it in OpenCode without a second install.