regret-mcp
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., "@regret-mcpsearch lessons about deployment failures"
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.
regret-mcp
A tiny local MCP memory server that stores lessons learned and resurfaces them before your AI agent repeats the same mistake.
🎯 Why?
Trending repos focus on agent memory, agent skills, and autonomous coding agents, but most memory systems are vector stores, hosted services, or heavyweight frameworks. Developers need a zero-dependency local memory primitive for explicit post-mortems: task, mistake, correction, tags. regret-mcp fills that gap with a small SQLite-backed MCP server and CLI that can be attached to Claude Code or any MCP client in seconds.
Target audience: Developers using Claude Code, Cursor, or custom MCP agents who want persistent local memory; agent framework authors needing a simple lesson-memory tool; DevOps and platform teams capturing operational lessons.
Related MCP server: apex-memory
✨ Features
✨ MCP stdio server exposing add_lesson, search_lessons, list_lessons, forget_lesson, and stats tools
✨ Local SQLite storage with tag normalization, severity levels, and keyword search
✨ CLI for adding, searching, listing, forgetting, and inspecting lessons without an MCP client
🚀 Quick Start
# Install
pip install regret-mcp
# Run
regret-mcp --help📦 Installation
From Source
git clone https://github.com/YOUR_USERNAME/regret-mcp.git
cd regret-mcp# Create virtual environment
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
# Install in development mode
pip install -e ".[dev]"
# Run tests
pytest -v🎬 Demo
The GIF above was recorded using Charm VHS:
vhs < demo.tape📖 Usage
# Show help
regret-mcp --help
# Common usage examples
regret-mcp --example🏗️ Architecture
graph LR
A[Input] --> B[Core Engine]
B --> C[Output]
B --> D[Plugins]
D --> E[Extensions]🤝 Contributing
Contributions are welcome! Please:
Fork the repo
Create a feature branch (
git checkout -b feature/amazing-feature)Commit your changes (
git commit -m 'Add amazing feature')Push to the branch (
git push origin feature/amazing-feature)Open a Pull Request
📄 License
MIT © 2026 — See LICENSE for details.
If this project helped you, please ⭐ star it!
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