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π€ AI Agent Architecture
Date: December 23, 2025
Status: π In Planning
π― Goal
Build an LLM-based AI agent that can play Settlers of Catan autonomously and intelligently.
ποΈ Overall Architecture
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β GameManager β
β (Game coordination & decisions) β
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β β’ game_loop() - Main game loop β
β β’ handle_turn_rules() - Turn management β
β β’ coordinate_interactions() - Player coordination β
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β β β
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β Game β β Users β β Viz β
β (Core) β β(Players)β β(Display)β
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β β
βΌ βΌ
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β Human β β AI Agent β β NEW!
β User β β (LLM) β
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β
π§
π¦ Existing Components
1οΈβ£ Core - The Game (game.py)
What exists:
- Complete and tested game rules
- Board, player, and resource management
- Action validation
Status: β Ready and stable
2οΈβ£ GameManager - Game Manager (management/game_manager.py)
What exists:
- Turn and phase management
- Player coordination
- Rule enforcement
Status: β Ready and stable
3οΈβ£ Actions - Action System (management/actions.py)
What exists:
- Definition of all possible game actions
- Unified data structure for each action
- Validation and permissions
Example:
class Action:
def __init__(self, action_type, player_num, **kwargs):
self.action_type = action_type # BUILD_SETTLEMENT, BUILD_ROAD, etc.
self.player_num = player_num
self.details = kwargs # point, edge, resources, etc.
Status: β Ready and stable
4οΈβ£ HumanUser - Human User (players/human_user.py)
What exists:
- CLI interaction
- Getting decisions from user
- Executing actions through GameManager
Status: β Ready and tested