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Pentago Swap AI Agent implements an intelligent opponent for the Pentago Swap game by leveraging a Monte Carlo Tree Search (MCTS) algorithm to explore and evaluate potential game states. At each turn, the agent simulates numerous playouts, scoring resulting board positions to identify moves that maximize win probability. It supports customization of search parameters like simulation count, exploration constant, and playout policy, enabling users to fine-tune performance. The agent includes a command-line interface for head-to-head matches, self-play to generate training data, and a Python API for integration into larger game environments or tournaments. Built with modular code, it facilitates extension with alternative heuristics or neural network evaluators for advanced research and development.