llmwiki MCP Server
lucasastorian/llmwiki
[](https://agentrank-ai.com/tool/lucasastorian--llmwiki/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Open Source Implementation of Karpathy's LLM Wiki. Upload documents, connect your Claude account via MCP, and have it write your wiki !
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
lucasastorian/llmwiki is a Python tool licensed under Apache-2.0. Open Source Implementation of Karpathy's LLM Wiki. Upload documents, connect your Claude account via MCP, and have it write your wiki ! Topics: agents, ai-agents, claude, karpathy, knowledge-base, llm, llm-wiki, mcp, mcp-server, rag, supabase.
Ranked #5632 out of 24846 indexed tools.
Has 1,684 GitHub stars.
Ecosystem
Score Breakdown
1,684 stars → well-known project
Last commit 1mo ago → moderate activity
2/35 issues closed → many open issues
5 contributors → small team
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
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From the README
# LLM Wiki **An autonomous, self-maintaining personal Wikipedia built and maintained by AI.** </div> LLM Wiki transforms your scattered reading and research into a persistent, AI-maintained second brain. Capture documents, notes, and web clippings as you work, and deploy a nightly Claude Routine to autonomously synthesize those sources into a permanent knowledge base. Because the clipper captures your highlights and margin notes alongside the source, the wiki becomes a record of not just what you read but what you *thought* about it — one that compounds over months and years, long after the original context would have faded. This architecture is heavily inspired by [Andrej Karpathy's LLM Wiki concept](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f), with an increased emphasis on autonomous maintenance. <p align="center"> </p> LLM Wiki is designed to work at three distinct scales: - **For you** — a personal Wikipedia of what you've read that you don't have toRead full README on GitHub →
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