LLM Wiki Kit
Provides tools for OpenAI Codex to process and ingest raw documents, write and update structured wiki pages, query the knowledge base via search, and manage persistent markdown documentation with cross-references.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LLM Wiki Kitingest the papers from raw/ and update the knowledge base"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
📚 llm-wiki-kit
Stop re-explaining your research to your AI agent every session.
llm-wiki-kit gives your AI agent a persistent, structured memory that compounds over time. Drop PDFs, URLs, YouTube videos — your agent builds a wiki, connects the dots, and remembers everything across sessions.
Based on Karpathy's LLM Wiki pattern. Works with Claude, Codex, Cursor, Windsurf, and any MCP-compatible agent.
https://github.com/user-attachments/assets/8814a581-1832-4e94-a5df-9e9b6b041507
The Problem
Every time you start a new chat:
You: "Remember that paper on speculative decoding I shared last week?"
Agent: "I don't have access to previous conversations..."
You: *sighs, re-uploads PDF, re-explains context*You're constantly re-teaching your agent things it should already know.
The Solution
With llm-wiki-kit, your agent maintains its own knowledge base:
You: "What did we learn about speculative decoding?"
Agent: *searches wiki* "Based on the 3 papers you've shared, the Eagle
architecture shows the best efficiency tradeoffs because..."The wiki persists. Cross-references build up. Your agent gets smarter with every source you add.
⚡ Quickstart (2 minutes)
1. Install
pip install "llm-wiki-kit[all] @ git+https://github.com/iamsashank09/llm-wiki-kit.git"2. Initialize a wiki
mkdir my-research && cd my-research
llm-wiki-kit init --agent claude3. Connect your agent
Add to Claude Desktop config (claude_desktop_config.json):
{
"mcpServers": {
"llm-wiki-kit": {
"command": "llm-wiki-kit",
"args": ["serve", "--root", "/path/to/my-research"]
}
}
}OpenAI Codex
codex mcp add llm-wiki-kit -- llm-wiki-kit serve --root /path/to/my-researchCursor
Add to .cursor/mcp.json:
{
"mcpServers": {
"llm-wiki-kit": {
"command": "llm-wiki-kit",
"args": ["serve", "--root", "/path/to/my-research"]
}
}
}Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"llm-wiki-kit": {
"command": "llm-wiki-kit",
"args": ["serve", "--root", "/path/to/my-research"]
}
}
}4. Use it
You: "Ingest this paper: raw/attention-is-all-you-need.pdf"
Agent: *creates wiki pages, cross-references concepts, updates index*
You: "Now ingest https://youtube.com/watch?v=kCc8FmEb1nY"
Agent: *extracts transcript, links to existing transformer concepts*
You: "How does the attention mechanism in the paper relate to Karpathy's explanation?"
Agent: *searches wiki, synthesizes answer from both sources*Your agent now has persistent memory that survives across sessions.
🔥 What Makes This Different
Feature | Why It Matters |
Multi-format ingest | PDFs, URLs, YouTube, markdown — just drop it in |
Auto cross-referencing | Agent builds |
Persistent across sessions | Start fresh chats without losing context |
Full-text search | Agent finds relevant pages instantly (SQLite FTS5) |
Health checks |
|
Graph visualization |
|
Zero lock-in | It's just markdown files in a folder — view in Obsidian, VS Code, anywhere |
Works with any MCP agent | Claude, Codex, Cursor, Windsurf, and more |
📥 Supported Sources
Your agent can ingest anything:
Drop this... | Get this... |
| Extracted text, page markers, metadata |
| Clean article content, auto-saved to |
| Full transcript with timestamps |
| Direct markdown ingestion |
Install what you need:
pip install "llm-wiki-kit[pdf]" # PDF support
pip install "llm-wiki-kit[web]" # URL extraction
pip install "llm-wiki-kit[youtube]" # YouTube transcripts
pip install "llm-wiki-kit[all]" # Everything🧠 How It Works
┌─────────────────────────────────────────────────────────┐
│ YOU │
│ "Ingest this paper. How does it relate to X?" │
└───────────────────────┬─────────────────────────────────┘
│
┌───────────────────────▼─────────────────────────────────┐
│ WIKI (agent-maintained) │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ concepts/ │ │ sources/ │ │ synthesis/ │ │
│ │ attention.md │◄─┤ paper-1.md │──► cache.md │ │
│ │ [[linked]] │ │ [[linked]] │ │ [[linked]] │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │
│ + index.md (table of contents) │
│ + log.md (what happened when) │
└───────────────────────┬─────────────────────────────────┘
│
┌───────────────────────▼─────────────────────────────────┐
│ RAW SOURCES (immutable) │
│ paper.pdf, article.html, transcript.md │
└─────────────────────────────────────────────────────────┘The agent reads raw sources, writes wiki pages, and maintains the connections. You never touch the wiki directly — the agent does all the work.
📊 Knowledge Graph
wiki_graph generates an interactive HTML visualization of your wiki's structure:
Nodes are color-coded by type (sources, concepts, synthesis). Click and drag to explore connections.
🛠 Available Tools
Your agent gets these MCP tools:
Tool | What it does |
| Process any source (file, URL, YouTube) |
| Create or update a wiki page |
| Read a specific page |
| Full-text search across all pages |
| Find broken links, orphans, empty pages |
| Overview: page count, sources, recent activity |
| Append to the operation log |
| Generate interactive HTML graph visualization |
💡 Use Cases
Research: Feed papers into your wiki over weeks. Ask synthesis questions that span all your reading.
Technical onboarding: Ingest a codebase's docs. Your agent answers architecture questions from accumulated context.
Competitive intel: Add market reports, earnings calls, news. Agent maintains a living landscape that updates as you add more.
Learning: Watch YouTube tutorials, read blog posts. Agent builds a personalized wiki of everything you've studied.
Book notes: Ingest chapters as you read. Agent tracks characters, themes, plot threads, and connections.
🔍 Pro Tips
Use Obsidian to visualize your wiki's graph — it's just a folder of markdown files
Git init your wiki directory — get version history for free
Let the agent link aggressively — the value compounds in the connections
Run lint periodically — catches contradictions and gaps in your knowledge base
Start small — even 5-10 sources produce a surprisingly useful wiki
📦 Development
git clone https://github.com/iamsashank09/llm-wiki-kit
cd llm-wiki-kit
uv venv && source .venv/bin/activate
uv pip install -e ".[all]"🙏 Credits
Based on the LLM Wiki idea by Andrej Karpathy.
📄 License
MIT — do whatever you want with it.
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