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Official description3 sourcesMCP servers3d ago
llm-wiki

llm-wiki

Build a compounding, LLM-curated personal knowledge base in any project (Karpathy's LLM Wiki pattern)
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Official descriptionBuild a compounding, LLM-curated personal knowledge base in any project (Karpathy's LLM Wiki pattern). Skill + 5 /wiki:* commands with sharded indexes, BM25 search, and a structural lint script.
Source
collected from the ccplugins/awesome-claude-code-plugins list · type: MCP server
Harvest history
first seen 2026-10-14 · last seen 2026-10-16 · seen 4× · listed by 3 sources
Score
score 303 · 3 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
first catalogued 2026-10-14;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
Third-party quality signals

LLM Wiki — a second brain for AI agents

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

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, OMP, and Hermes. 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 can live in one personal wiki across all projects or inside a specific 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 v3.2.0
- **Learn from completed work.** /wiki:learn captures verified successes/failures and consolidates cited patterns, applicability and counterexamples.
- **Turn evidence into tested procedures.** /wiki:evolve runs training, evidence consolidation, whole-skill proposals, validation, and independent final testing, retaining rejected attempts. Adapters for nine supported agent hosts provide bounded runs, durable tool traces and cross-agent transfer; see the execution requirements.
- **Explicit apply and rollback.** Only a reviewed, passing change can be applied; whole-skill snapshot checks prevent overwriting intervening edits.
- **Runnable evaluation examples.** Separate training, validation and final-test tasks cover source-grounded answers and file artifacts, with calibrated judging, paired task-level analysis, and ingestion/graph/hybrid-search checks. Fictional fixtures stay outside the installed skill.
- **Compatible upgrade.** Existing Markdown and commands remain valid. /wiki:upgrade adds optional templates and archive guidance idempotently.
See Learning and skill evolution for the full workflow, evaluation limits and inference costs. This release adds tooling; it does not claim measured model gains.
The evolution workflow adapts WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution (Google Research and Virginia Tech, 2026): persistent experience-to-pattern learning, repeated skill proposals, retained failures, validation-only selection and independent final testing. See what we adapted and changed and the measured results. Our study rejected both proposals; no quality improvement was demonstrated.
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 nine /wiki:* slash commands, and the marketplace manifest all ship in one install.
Once installed, the plugin works in any project. Installation does not decide where your wiki lives.
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:
Source
awesome · https://github.com/praneybehl/llm-wiki-plugin
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