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Shared, persistent memory for AI assistants, built on the Zettelkasten method.
Shared, persistent memory for AI assistants, built on the Zettelkasten method.
Valid MCP server (1 strong, 1 medium validity signals). 1 known CVE in dependencies Package registry verified. Imported from the Official MCP Registry.
4 files analyzed · 2 issues found
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mrosnerr-open-zk-kb": {
"args": [
"-y",
"open-zk-kb"
],
"command": "npx"
}
}
}From the project's GitHub README.
You open a new session and your agent has no idea who you are. Again. You re-explain your stack, your conventions, that one edge case you've corrected five times.
open-zk-kb gives your agent a memory — so corrections stick, context compounds, and every session starts smarter than the last.
Your agent starts from zero every session. No memory, no learning curve. You correct the same mistakes, re-explain the same conventions, re-teach the same context. Switch tools and it's even worse — your Cursor agent doesn't know what your Claude agent learned.
open-zk-kb fixes that.
Your agent gets sharper the longer you use it — for your specific workflow.
Your knowledge base is a fully themed Obsidian vault — homepage dashboard, kind-based folders with icons, breadcrumb navigation, and quick-add buttons. No manual setup.
See the Obsidian Guide for the full walkthrough.
Requires Bun — install with
curl -fsSL https://bun.sh/install | bash
bunx open-zk-kb@latest
That's it. The interactive installer:
~/.local/share/open-zk-kbSupported clients: OpenCode, Claude Code, Cursor, Windsurf, Zed
See the Setup Guide for manual installation and troubleshooting.
Zero configuration required. Local embeddings work out of the box with no API key.
See the Configuration Guide for embeddings, vault path, lifecycle tuning, and Obsidian scaffold options.
Built on the Zettelkasten method — atomic, linked notes with structured kinds. Each note captures one concept (a decision, a preference, a gotcha) and links to related notes, building an interconnected knowledge graph.
Search combines SQLite FTS5 full-text indexing with local vector embeddings (MiniLM-L6-v2) for semantic matching. Markdown files are the source of truth; the database is a rebuildable index.
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