Lorekeeper
About
Self-improving MCP memory server for AI agents. One command, no cloud, no config. Hybrid search, feedback loop quality system, dashboard UI, auto-linking knowledge graph. Gets better the more you use it.
Details
- Author
- Jessinra
- GitHub stars
- 3
- Downloads
- 414
- Categories
- Other, AI, Knowledge Base
Jump to
- Hybrid semantic + keyword search with time-decay and usage scores
- Self-improving quality loop: feedback adjusts memory scores
- Auto-linking: new memories linked to closest semantic neighbors
- Duplicate detection: blocks near-identical content
- Full web dashboard with seven tabs for management
- Universal MCP support: works with many agents
- Local-first: data stays on your machine, no cloud
- Namespaces: multiple agents share one store isolated
- Reflection: agents auto-extract learnings from sessions
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
LorekeeperCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install with pip install lorekeeper-mcp, then run lorekeeper setup to configure your agents, and start the server with lorekeeper. Use lorekeeper-dashboard to open a web UI for managing memories. Agents can then call tools like lore_remember and lore_search to store and retrieve memories.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"lorekeeper": {
"lorekeeper": {
"command": "lorekeeper"
}
}
}
}McpServers
{
"lorekeeper": {
"command": "lorekeeper"
}
}Why Lorekeeper
Every AI agent session starts blank. You re-explain context, re-state preferences, re-teach patterns — every single time.
Files like CLAUDE.md and .cursorrules help, but they're hand-maintained, can't search themselves, and grow stale. Cloud services work, but your session data leaves your machine and you're paying per API call. Libraries are powerful, but you're writing the integration yourself.
Lorekeeper is a different shape: a local MCP server you pip install once. It connects to your existing agents, stores memories in SQLite on your own disk, and starts improving with every session:
Agent uses a memory → rates it useful or not →
scores adjust automatically → weak memories decay →
strong memories surface more often → search gets sharper
A fresh install and a six-month-old install are genuinely different products. The longer you use it, the less noise you get — and the more your agents feel like they actually know your codebase.
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