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# `regret-mcp`

### A tiny local MCP memory server that stores lessons learned and resurfaces them before your AI agent repeats the same mistake.

![License](https://img.shields.io/badge/license-MIT-blue.svg) ![Language](https://img.shields.io/badge/language-python-green.svg) ![Status](https://img.shields.io/badge/status-active-success.svg) [![PyPI](https://img.shields.io/pypi/v/regret-mcp)](https://pypi.org/project/regret-mcp/) [![Python](https://img.shields.io/badge/python-3.9%2B-blue)](https://www.python.org/) [![MCP](https://img.shields.io/badge/MCP-stdio-orange)](https://modelcontextprotocol.io/)

<img src="demo.gif" alt="Demo" width="700" />

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## 🎯 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.

## ✨ 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

```bash
# Install
pip install regret-mcp

# Run
regret-mcp --help
```

## 📦 Installation

### From Source

```bash
git clone https://github.com/YOUR_USERNAME/regret-mcp.git
cd regret-mcp
```

```bash
# 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](https://github.com/charmbracelet/vhs):

```bash
vhs < demo.tape
```

## 📖 Usage

```bash
# Show help
regret-mcp --help

# Common usage examples
regret-mcp --example
```

## 🏗️ Architecture

```mermaid
graph LR
    A[Input] --> B[Core Engine]
    B --> C[Output]
    B --> D[Plugins]
    D --> E[Extensions]
```

## 🤝 Contributing

Contributions are welcome! Please:

1. Fork the repo
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## 📄 License

MIT © 2026 — See [LICENSE](LICENSE) for details.

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