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# Dervish MCP
<p align="left">
<p align="center">
<img src="dervish-logo.png" alt="Dervish" width="180">
</p>
<p align="center">
<img src="https://img.shields.io/badge/license-MIT-blue" alt="License">
<img src="https://img.shields.io/badge/python-3.10%2B-blue" alt="Python 3.10+">
<img src="https://ci.corentic.eu/api/badges/7/status.svg" alt="CI Pipeline Status">
<a href="https://www.buymeacoffee.com/IjonTichy85"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy me a coffee" width="120"></a>
<br>
<a href="SHOWCASE.md">Showcase</a> ·
<a href="#quick-start">Usage</a> ·
<a href="#papers">Papers</a>
<br>
<a href="https://www.buymeacoffee.com/IjonTichy85"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy me a coffee" width="140"></a>
</p>
**Dervish** infers **regular expression grammars** from example sequences using the BEX family of algorithms. Given a set of example sequences (strings over some alphabet), it learns a compact regular expression that captures the general pattern.