From 308f738c37a3dbd1f1e479a54d1b0cd38e5e173b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=A1=D0=BD=D0=B5=D1=81=D0=B0=D1=80=D0=B5=D0=B2=20=D0=9C?=
 =?UTF-8?q?=D0=B0=D0=BA=D1=81=D0=B8=D0=BC?= <m.snesarev@cognitivepilot.com>
Date: Mon, 19 Jan 2026 20:45:35 +0300
Subject: [PATCH] [new] add goap agents

---
 backend/api/routes.py                | 124 +++-
 backend/config.py                    |  68 ++-
 backend/core/ai.py                   | 472 ++++++++-------
 backend/core/engine.py               |  28 +-
 backend/core/goap/__init__.py        |  39 ++
 backend/core/goap/action.py          | 381 +++++++++++++
 backend/core/goap/actions.py         | 362 ++++++++++++
 backend/core/goap/debug.py           | 258 +++++++++
 backend/core/goap/goal.py            | 185 ++++++
 backend/core/goap/goals.py           | 411 ++++++++++++++
 backend/core/goap/goap_ai.py         | 411 ++++++++++++++
 backend/core/goap/planner.py         | 335 +++++++++++
 backend/core/goap/world_state.py     | 303 ++++++++++
 backend/core/market.py               | 123 ++--
 config.json                          |  31 +-
 config_goap_optimized.json           |  79 +++
 tools/optimize_goap.py               | 496 ++++++++++++++++
 web_frontend/goap_debug.html         | 820 +++++++++++++++++++++++++++
 web_frontend/index.html              |  36 ++
 web_frontend/src/api.js              |  15 +
 web_frontend/src/scenes/GameScene.js | 304 ++++++++++
 web_frontend/styles.css              | 348 ++++++++++++
 22 files changed, 5310 insertions(+), 319 deletions(-)
 create mode 100644 backend/core/goap/__init__.py
 create mode 100644 backend/core/goap/action.py
 create mode 100644 backend/core/goap/actions.py
 create mode 100644 backend/core/goap/debug.py
 create mode 100644 backend/core/goap/goal.py
 create mode 100644 backend/core/goap/goals.py
 create mode 100644 backend/core/goap/goap_ai.py
 create mode 100644 backend/core/goap/planner.py
 create mode 100644 backend/core/goap/world_state.py
 create mode 100644 config_goap_optimized.json
 create mode 100644 tools/optimize_goap.py
 create mode 100644 web_frontend/goap_debug.html

diff --git a/backend/api/routes.py b/backend/api/routes.py
index b193444..c7870b6 100644
--- a/backend/api/routes.py
+++ b/backend/api/routes.py
@@ -93,12 +93,12 @@ def get_market_prices():
 )
 def initialize_simulation(request: InitializeRequest = None):
     """Initialize or reset the simulation.
-    
+
     If request is provided with specific values, use those.
     Otherwise, use values from config.json.
     """
     engine = get_engine()
-    
+
     if request and (request.num_agents != 8 or request.world_width != 20 or request.world_height != 20):
         # Custom values provided - use them
         from backend.core.world import WorldConfig
@@ -113,7 +113,7 @@ def initialize_simulation(request: InitializeRequest = None):
         # Use values from config.json
         engine.reset()  # This now loads from config.json automatically
         num_agents = engine.world.config.initial_agents
-    
+
     return ControlResponse(
         success=True,
         message=f"Simulation initialized with {num_agents} agents",
@@ -131,21 +131,21 @@ def initialize_simulation(request: InitializeRequest = None):
 def next_step():
     """Advance the simulation by one step."""
     engine = get_engine()
-    
+
     if engine.mode == SimulationMode.AUTO:
         raise HTTPException(
             status_code=400,
             detail="Cannot manually advance while in AUTO mode. Switch to MANUAL first.",
         )
-    
+
     if not engine.is_running:
         raise HTTPException(
             status_code=400,
             detail="Simulation is not running. Initialize first.",
         )
-    
+
     turn_log = engine.next_step()
-    
+
     return ControlResponse(
         success=True,
         message=f"Advanced to turn {engine.world.current_turn}",
@@ -163,7 +163,7 @@ def next_step():
 def set_mode(request: SetModeRequest):
     """Set the simulation mode."""
     engine = get_engine()
-    
+
     try:
         mode = SimulationMode(request.mode)
         engine.set_mode(mode)
@@ -223,11 +223,11 @@ def get_logs(limit: int = 10):
 def get_turn_log(turn: int):
     """Get log for a specific turn."""
     engine = get_engine()
-    
+
     for log in engine.turn_logs:
         if log.turn == turn:
             return log.to_dict()
-    
+
     raise HTTPException(status_code=404, detail=f"Log for turn {turn} not found")
 
 
@@ -315,3 +315,107 @@ def load_config_from_file():
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"Failed to load config: {str(e)}")
 
+
+# ============== GOAP Debug Endpoints ==============
+
+@router.get(
+    "/goap/debug/{agent_id}",
+    summary="Get GOAP debug info for an agent",
+    description="Returns detailed GOAP decision-making info including goals, actions, and plans.",
+)
+def get_agent_goap_debug(agent_id: str):
+    """Get GOAP debug information for a specific agent."""
+    engine = get_engine()
+    agent = engine.world.get_agent(agent_id)
+
+    if agent is None:
+        raise HTTPException(status_code=404, detail=f"Agent {agent_id} not found")
+
+    if not agent.is_alive():
+        raise HTTPException(status_code=400, detail=f"Agent {agent_id} is not alive")
+
+    from backend.core.goap.debug import get_goap_debug_info
+
+    debug_info = get_goap_debug_info(
+        agent=agent,
+        market=engine.market,
+        step_in_day=engine.world.step_in_day,
+        day_steps=engine.world.config.day_steps,
+        is_night=engine.world.is_night(),
+    )
+
+    return debug_info.to_dict()
+
+
+@router.get(
+    "/goap/debug",
+    summary="Get GOAP debug info for all agents",
+    description="Returns GOAP decision-making info for all living agents.",
+)
+def get_all_goap_debug():
+    """Get GOAP debug information for all living agents."""
+    engine = get_engine()
+
+    from backend.core.goap.debug import get_all_agents_goap_debug
+
+    debug_infos = get_all_agents_goap_debug(
+        agents=engine.world.agents,
+        market=engine.market,
+        step_in_day=engine.world.step_in_day,
+        day_steps=engine.world.config.day_steps,
+        is_night=engine.world.is_night(),
+    )
+
+    return {
+        "agents": [info.to_dict() for info in debug_infos],
+        "count": len(debug_infos),
+        "current_turn": engine.world.current_turn,
+        "is_night": engine.world.is_night(),
+    }
+
+
+@router.get(
+    "/goap/goals",
+    summary="Get all GOAP goals",
+    description="Returns a list of all available GOAP goals.",
+)
+def get_goap_goals():
+    """Get all available GOAP goals."""
+    from backend.core.goap import get_all_goals
+
+    goals = get_all_goals()
+    return {
+        "goals": [
+            {
+                "name": g.name,
+                "type": g.goal_type.value,
+                "max_plan_depth": g.max_plan_depth,
+            }
+            for g in goals
+        ],
+        "count": len(goals),
+    }
+
+
+@router.get(
+    "/goap/actions",
+    summary="Get all GOAP actions",
+    description="Returns a list of all available GOAP actions.",
+)
+def get_goap_actions():
+    """Get all available GOAP actions."""
+    from backend.core.goap import get_all_actions
+
+    actions = get_all_actions()
+    return {
+        "actions": [
+            {
+                "name": a.name,
+                "action_type": a.action_type.value,
+                "target_resource": a.target_resource.value if a.target_resource else None,
+            }
+            for a in actions
+        ],
+        "count": len(actions),
+    }
+
diff --git a/backend/config.py b/backend/config.py
index 424ebaf..ee4e02c 100644
--- a/backend/config.py
+++ b/backend/config.py
@@ -14,19 +14,19 @@ class AgentStatsConfig:
     max_hunger: int = 100
     max_thirst: int = 100  # Increased from 50 to give more buffer
     max_heat: int = 100
-    
+
     # Starting values
     start_energy: int = 50
     start_hunger: int = 80
     start_thirst: int = 80  # Increased from 40 to start with more buffer
     start_heat: int = 100
-    
+
     # Decay rates per turn
     energy_decay: int = 2
     hunger_decay: int = 2
     thirst_decay: int = 2  # Reduced from 3 to match hunger decay rate
     heat_decay: int = 2
-    
+
     # Thresholds
     critical_threshold: float = 0.25  # 25% triggers survival mode
     low_energy_threshold: int = 15    # Minimum energy to work
@@ -39,7 +39,7 @@ class ResourceConfig:
     meat_decay: int = 8   # Increased from 5 to give more time to use
     berries_decay: int = 25
     clothes_decay: int = 50
-    
+
     # Resource effects
     meat_hunger: int = 30
     meat_energy: int = 5
@@ -62,11 +62,11 @@ class ActionConfig:
     weave_energy: int = -8
     build_fire_energy: int = -5
     trade_energy: int = -1
-    
+
     # Success chances (0.0 to 1.0)
     hunt_success: float = 0.7
     chop_wood_success: float = 0.9
-    
+
     # Output quantities
     hunt_meat_min: int = 1
     hunt_meat_max: int = 3
@@ -86,7 +86,7 @@ class WorldConfig:
     initial_agents: int = 8
     day_steps: int = 10
     night_steps: int = 1
-    
+
     # Agent configuration
     inventory_slots: int = 10
     starting_money: int = 100
@@ -103,31 +103,54 @@ class MarketConfig:
 @dataclass
 class EconomyConfig:
     """Configuration for economic behavior and agent trading.
-    
+
     These values control how agents perceive the value of money and trading.
     Higher values make agents more trade-oriented.
     """
     # How much agents value money vs energy
     # Higher = agents see money as more valuable (trade more)
-    energy_to_money_ratio: float = 1.5  # 1 energy ≈ 1.5 coins
-    
+    energy_to_money_ratio: float = 150  # 1 energy ≈ 150 coins
+
+    # Minimum price floor for any market transaction
+    min_price: int = 100
+
     # How strongly agents desire wealth (0-1)
     # Higher = agents will prioritize building wealth
     wealth_desire: float = 0.3
-    
+
     # Buy efficiency threshold (0-1)
     # If market price < (threshold * fair_value), buy instead of gather
     # 0.7 means: buy if price is 70% or less of the fair value
     buy_efficiency_threshold: float = 0.7
-    
+
     # Minimum wealth target - agents want at least this much money
-    min_wealth_target: int = 50
-    
+    min_wealth_target: int = 5000
+
     # Price adjustment limits
     max_price_markup: float = 2.0      # Maximum price = 2x base value
     min_price_discount: float = 0.5    # Minimum price = 50% of base value
 
 
+@dataclass
+class AIConfig:
+    """Configuration for AI decision-making system.
+
+    Controls whether to use GOAP (Goal-Oriented Action Planning) or
+    the legacy priority-based system.
+    """
+    # Use GOAP-based AI (True) or legacy priority-based AI (False)
+    use_goap: bool = True
+
+    # Maximum A* iterations for GOAP planner
+    goap_max_iterations: int = 50
+
+    # Maximum plan depth (number of actions in a plan)
+    goap_max_plan_depth: int = 3
+
+    # Fall back to reactive planning if GOAP fails to find a plan
+    reactive_fallback: bool = True
+
+
 @dataclass
 class SimulationConfig:
     """Master configuration containing all sub-configs."""
@@ -137,13 +160,15 @@ class SimulationConfig:
     world: WorldConfig = field(default_factory=WorldConfig)
     market: MarketConfig = field(default_factory=MarketConfig)
     economy: EconomyConfig = field(default_factory=EconomyConfig)
-    
+    ai: AIConfig = field(default_factory=AIConfig)
+
     # Simulation control
     auto_step_interval: float = 1.0  # Seconds between auto steps
-    
+
     def to_dict(self) -> dict:
         """Convert to dictionary."""
         return {
+            "ai": asdict(self.ai),
             "agent_stats": asdict(self.agent_stats),
             "resources": asdict(self.resources),
             "actions": asdict(self.actions),
@@ -152,11 +177,12 @@ class SimulationConfig:
             "economy": asdict(self.economy),
             "auto_step_interval": self.auto_step_interval,
         }
-    
+
     @classmethod
     def from_dict(cls, data: dict) -> "SimulationConfig":
         """Create from dictionary."""
         return cls(
+            ai=AIConfig(**data.get("ai", {})),
             agent_stats=AgentStatsConfig(**data.get("agent_stats", {})),
             resources=ResourceConfig(**data.get("resources", {})),
             actions=ActionConfig(**data.get("actions", {})),
@@ -165,12 +191,12 @@ class SimulationConfig:
             economy=EconomyConfig(**data.get("economy", {})),
             auto_step_interval=data.get("auto_step_interval", 1.0),
         )
-    
+
     def save(self, path: str = "config.json") -> None:
         """Save configuration to JSON file."""
         with open(path, "w") as f:
             json.dump(self.to_dict(), f, indent=2)
-    
+
     @classmethod
     def load(cls, path: str = "config.json") -> "SimulationConfig":
         """Load configuration from JSON file."""
@@ -188,7 +214,7 @@ _config: Optional[SimulationConfig] = None
 
 def get_config() -> SimulationConfig:
     """Get the global configuration instance.
-    
+
     Loads from config.json if not already loaded.
     """
     global _config
@@ -246,7 +272,7 @@ def _reset_all_caches() -> None:
         reset_action_config_cache()
     except ImportError:
         pass
-    
+
     try:
         from backend.domain.resources import reset_resource_cache
         reset_resource_cache()
diff --git a/backend/core/ai.py b/backend/core/ai.py
index e714b48..e13e204 100644
--- a/backend/core/ai.py
+++ b/backend/core/ai.py
@@ -1,16 +1,21 @@
 """AI decision system for agents in the Village Simulation.
 
-Major rework to create diverse, personality-driven economy:
+This module provides two AI systems:
+1. GOAP (Goal-Oriented Action Planning) - The default, modern approach
+2. Legacy priority-based system - Kept for comparison/fallback
+
+GOAP Benefits:
+- Agents plan multi-step sequences to achieve goals
+- Goals are dynamically prioritized based on state
+- More emergent and adaptive behavior
+- Easier to extend with new goals and actions
+
+Major features:
 - Each agent has unique personality traits affecting all decisions
 - Emergent professions: Hunters, Gatherers, Traders, Generalists
 - Class inequality through varied strategies and skills
-- Traders focus on arbitrage (buy low, sell high) 
+- Traders focus on arbitrage (buy low, sell high)
 - Personality affects: risk tolerance, hoarding, market participation
-
-Key insight: Different personalities lead to different strategies.
-Traders don't gather - they profit from others' labor.
-Hunters take risks for bigger rewards.
-Gatherers play it safe.
 """
 
 import random
@@ -49,7 +54,7 @@ class AIDecision:
     # For price adjustments
     adjust_order_id: Optional[str] = None
     new_price: Optional[int] = None
-    
+
     def to_dict(self) -> dict:
         return {
             "action": self.action.value,
@@ -112,9 +117,9 @@ def _get_economy_config():
 
 class AgentAI:
     """AI decision maker with personality-driven economy behavior.
-    
+
     Core philosophy: Each agent has a unique strategy based on personality.
-    
+
     Personality effects:
     1. wealth_desire: How aggressively to accumulate money
     2. hoarding_rate: How much to keep vs. sell on market
@@ -122,106 +127,114 @@ class AgentAI:
     4. market_affinity: How often to engage with market
     5. trade_preference: High = trader profession (arbitrage focus)
     6. price_sensitivity: How picky about deals
-    
+
     Emergent professions:
     - Traders: High trade_preference + market_affinity = buy low, sell high
     - Hunters: High hunt_preference + risk_tolerance = meat production
     - Gatherers: High gather_preference, low risk = safe resource collection
     - Generalists: Balanced approach to all activities
     """
-    
+
     # Thresholds for stat management
     LOW_THRESHOLD = 0.45         # 45% - proactive action trigger
     COMFORT_THRESHOLD = 0.60     # 60% - aim for comfort
-    
+
     # Energy thresholds
     REST_ENERGY_THRESHOLD = 18   # Rest when below this if no urgent needs
     WORK_ENERGY_MINIMUM = 20     # Prefer to have this much for work
-    
+
     # Resource stockpile targets (modified by personality.hoarding_rate)
     BASE_WATER_STOCK = 2
     BASE_FOOD_STOCK = 3
     BASE_WOOD_STOCK = 2
-    
+
     # Heat thresholds
     HEAT_PROACTIVE_THRESHOLD = 0.50
-    
-    # Base economy settings (modified by personality)
-    ENERGY_TO_MONEY_RATIO = 1.5  # 1 energy = ~1.5 coins in perceived value
+
+    # Base economy settings (loaded from config, modified by personality)
+    # These are default fallbacks; actual values come from config
     MAX_PRICE_MARKUP = 2.0       # Maximum markup over base price
     MIN_PRICE_DISCOUNT = 0.5     # Minimum discount (50% of base price)
-    
+
     def __init__(self, agent: Agent, market: "OrderBook", step_in_day: int = 1, day_steps: int = 10, current_turn: int = 0):
         self.agent = agent
         self.market = market
         self.step_in_day = step_in_day
         self.day_steps = day_steps
         self.current_turn = current_turn
-        
+
         # Personality shortcuts
         self.p = agent.personality  # Convenience shortcut
         self.skills = agent.skills
-        
+
         # Load thresholds from config
         config = _get_ai_config()
         self.CRITICAL_THRESHOLD = config.critical_threshold
         self.LOW_ENERGY_THRESHOLD = config.low_energy_threshold
-        
+
         # Personality-adjusted values
         # Wealth desire from personality (0.1 to 0.9)
         self.WEALTH_DESIRE = self.p.wealth_desire
-        
+
+        # Load economy config
+        economy = _get_economy_config()
+
+        # Energy to money ratio (how much 1 energy is worth in coins)
+        self.ENERGY_TO_MONEY_RATIO = getattr(economy, 'energy_to_money_ratio', 150) if economy else 150
+
+        # Minimum price floor
+        self.MIN_PRICE = getattr(economy, 'min_price', 100) if economy else 100
+
         # Buy efficiency threshold adjusted by price sensitivity
         # High sensitivity = only buy very good deals
-        economy = _get_economy_config()
         base_threshold = getattr(economy, 'buy_efficiency_threshold', 0.7) if economy else 0.7
         self.BUY_EFFICIENCY_THRESHOLD = base_threshold / self.p.price_sensitivity
-        
+
         # Wealth target scaled by wealth desire
-        base_target = getattr(economy, 'min_wealth_target', 50) if economy else 50
+        base_target = getattr(economy, 'min_wealth_target', 5000) if economy else 5000
         self.MIN_WEALTH_TARGET = int(base_target * (0.5 + self.p.wealth_desire))
-        
+
         # Resource stockpile targets modified by hoarding rate
         # High hoarders keep more in reserve
         hoarding_mult = 0.5 + self.p.hoarding_rate  # 0.6 to 1.4
         self.MIN_WATER_STOCK = max(1, int(self.BASE_WATER_STOCK * hoarding_mult))
         self.MIN_FOOD_STOCK = max(2, int(self.BASE_FOOD_STOCK * hoarding_mult))
         self.MIN_WOOD_STOCK = max(1, int(self.BASE_WOOD_STOCK * hoarding_mult))
-        
+
         # Trader mode: agents with high trade preference become market-focused
         self.is_trader = self.p.trade_preference > 1.3 and self.p.market_affinity > 0.5
-        
+
     @property
     def is_evening(self) -> bool:
         """Check if it's getting close to night (last 2 day steps)."""
         return self.step_in_day >= self.day_steps - 1
-    
+
     @property
     def is_late_day(self) -> bool:
         """Check if it's past midday (preparation time)."""
         return self.step_in_day >= self.day_steps // 2
-    
+
     def _get_resource_fair_value(self, resource_type: ResourceType) -> int:
         """Calculate the 'fair value' of a resource based on energy cost to produce.
-        
+
         This is the theoretical minimum price an agent should sell for,
         and the maximum they should pay before just gathering themselves.
         """
         energy_cost = get_energy_cost(resource_type)
-        return max(1, int(energy_cost * self.ENERGY_TO_MONEY_RATIO))
-    
+        return max(self.MIN_PRICE, int(energy_cost * self.ENERGY_TO_MONEY_RATIO))
+
     def _is_good_buy(self, resource_type: ResourceType, price: int) -> bool:
         """Check if a market price is a good deal (cheaper than gathering)."""
         fair_value = self._get_resource_fair_value(resource_type)
         return price <= fair_value * self.BUY_EFFICIENCY_THRESHOLD
-    
+
     def _is_wealthy(self) -> bool:
         """Check if agent has comfortable wealth."""
         return self.agent.money >= self.MIN_WEALTH_TARGET
-    
+
     def decide(self) -> AIDecision:
         """Make a decision based on survival, personality, and economic goals.
-        
+
         Decision flow varies by personality:
         - Traders prioritize market operations (arbitrage)
         - Hunters prefer hunting when possible
@@ -232,54 +245,54 @@ class AgentAI:
         decision = self._check_critical_needs()
         if decision:
             return decision
-        
+
         # Priority 2: Proactive survival (prevent problems before they happen)
         decision = self._check_proactive_needs()
         if decision:
             return decision
-        
+
         # Priority 3: Trader-specific behavior (high trade_preference agents)
         # Traders focus on market operations when survival is secured
         if self.is_trader:
             decision = self._do_trader_behavior()
             if decision:
                 return decision
-        
+
         # Priority 4: Price adjustment - respond to market conditions
         decision = self._check_price_adjustments()
         if decision:
             return decision
-        
+
         # Priority 5: Smart shopping - buy good deals on the market!
         # Frequency affected by market_affinity
         if random.random() < self.p.market_affinity:
             decision = self._check_market_opportunities()
             if decision:
                 return decision
-        
+
         # Priority 6: Craft clothes if we have hide
         decision = self._check_clothes_crafting()
         if decision:
             return decision
-        
+
         # Priority 7: Energy management
         decision = self._check_energy()
         if decision:
             return decision
-        
+
         # Priority 8: Economic activities (sell excess, build wealth)
         # Frequency affected by hoarding_rate (low hoarding = sell more)
         if random.random() > self.p.hoarding_rate * 0.5:
             decision = self._check_economic()
             if decision:
                 return decision
-        
+
         # Priority 9: Routine survival work (gather resources we need)
         return self._do_survival_work()
-    
+
     def _do_trader_behavior(self) -> Optional[AIDecision]:
         """Trader-specific behavior: focus on arbitrage and market operations.
-        
+
         Traders don't gather much - they profit from buying low and selling high.
         Core trader strategy:
         1. Look for arbitrage opportunities (price differences)
@@ -295,37 +308,37 @@ class AgentAI:
                 return decision
             # If nothing to sell, do some quick gathering
             return None  # Fall through to normal behavior
-        
+
         # Look for arbitrage opportunities
         # Find resources being sold below fair value
         best_deal = None
         best_margin = 0
-        
+
         for resource_type in [ResourceType.MEAT, ResourceType.BERRIES, ResourceType.WATER, ResourceType.WOOD]:
             order = self.market.get_cheapest_order(resource_type)
             if not order or order.seller_id == self.agent.id:
                 continue
-            
+
             fair_value = self._get_resource_fair_value(resource_type)
-            
+
             # Check if we can profit by buying and reselling
             buy_price = order.price_per_unit
             # Apply trading skill to get better prices
             sell_modifier = get_trade_price_modifier(self.skills.trading, is_buying=False)
             potential_sell_price = int(fair_value * sell_modifier * 1.1)  # 10% markup target
-            
+
             margin = potential_sell_price - buy_price
             if margin > best_margin and self.agent.money >= buy_price:
                 best_margin = margin
                 best_deal = (resource_type, order, buy_price, potential_sell_price)
-        
+
         # Execute arbitrage if profitable
         if best_deal and best_margin >= 2:  # At least 2 coins profit
             resource_type, order, buy_price, sell_price = best_deal
             safe_price = max(1, buy_price)  # Prevent division by zero
-            quantity = min(3, self.agent.inventory_space(), order.quantity, 
+            quantity = min(3, self.agent.inventory_space(), order.quantity,
                           self.agent.money // safe_price)
-            
+
             if quantity > 0:
                 return AIDecision(
                     action=ActionType.TRADE,
@@ -335,49 +348,49 @@ class AgentAI:
                     price=buy_price,
                     reason=f"Trader: buying {resource_type.value} @ {buy_price}c (resell @ {sell_price}c)",
                 )
-        
+
         # If holding inventory, try to sell at markup
         decision = self._try_trader_sell()
         if decision:
             return decision
-        
+
         # Adjust prices on existing orders
         decision = self._check_price_adjustments()
         if decision:
             return decision
-        
+
         return None
-    
+
     def _try_trader_sell(self) -> Optional[AIDecision]:
         """Trader sells inventory at markup prices."""
         for resource in self.agent.inventory:
             if resource.type == ResourceType.CLOTHES:
                 continue
-            
+
             # Traders sell everything except minimal survival reserves
             if resource.quantity <= 1:
                 continue
-            
+
             fair_value = self._get_resource_fair_value(resource.type)
-            
+
             # Apply trading skill for better sell prices
             sell_modifier = get_trade_price_modifier(self.skills.trading, is_buying=False)
-            
+
             # Check market conditions
             signal = self.market.get_market_signal(resource.type)
             if signal == "sell":  # Scarcity - high markup
                 price = int(fair_value * sell_modifier * 1.4)
             else:
                 price = int(fair_value * sell_modifier * 1.15)
-            
+
             # Don't undercut ourselves if we have active orders
-            my_orders = [o for o in self.market.get_orders_by_seller(self.agent.id) 
+            my_orders = [o for o in self.market.get_orders_by_seller(self.agent.id)
                         if o.resource_type == resource.type]
             if my_orders:
                 continue  # Wait for existing order to sell
-            
+
             quantity = resource.quantity - 1  # Keep 1 for emergencies
-            
+
             return AIDecision(
                 action=ActionType.TRADE,
                 target_resource=resource.type,
@@ -385,53 +398,53 @@ class AgentAI:
                 price=price,
                 reason=f"Trader: selling {resource.type.value} @ {price}c (markup)",
             )
-        
+
         return None
-    
+
     def _check_critical_needs(self) -> Optional[AIDecision]:
         """Check if any vital stat is critical and act accordingly."""
         stats = self.agent.stats
-        
+
         # Check thirst first (depletes fastest and kills quickly)
         if stats.thirst < stats.MAX_THIRST * self.CRITICAL_THRESHOLD:
             return self._address_thirst(critical=True)
-        
+
         # Check hunger
         if stats.hunger < stats.MAX_HUNGER * self.CRITICAL_THRESHOLD:
             return self._address_hunger(critical=True)
-        
+
         # Check heat - critical level
         if stats.heat < stats.MAX_HEAT * self.CRITICAL_THRESHOLD:
             return self._address_heat(critical=True)
-        
+
         return None
-    
+
     def _check_proactive_needs(self) -> Optional[AIDecision]:
         """Proactively address needs before they become critical.
-        
+
         IMPORTANT CHANGE: Now considers buying as a first option, not last!
         """
         stats = self.agent.stats
-        
+
         # Proactive thirst management
         if stats.thirst < stats.MAX_THIRST * self.LOW_THRESHOLD:
             return self._address_thirst(critical=False)
-        
+
         # Proactive hunger management
         if stats.hunger < stats.MAX_HUNGER * self.LOW_THRESHOLD:
             return self._address_hunger(critical=False)
-        
+
         # Proactive heat management
         if stats.heat < stats.MAX_HEAT * self.HEAT_PROACTIVE_THRESHOLD:
             decision = self._address_heat(critical=False)
             if decision:
                 return decision
-        
+
         return None
-    
+
     def _check_price_adjustments(self) -> Optional[AIDecision]:
         """Check if we should adjust prices on our market orders.
-        
+
         Smart pricing strategy:
         - If order is stale (not selling), lower price
         - If demand is high (scarcity), raise price
@@ -440,13 +453,13 @@ class AgentAI:
         my_orders = self.market.get_orders_by_seller(self.agent.id)
         if not my_orders:
             return None
-        
+
         for order in my_orders:
             resource_type = order.resource_type
             signal = self.market.get_market_signal(resource_type)
             current_price = order.price_per_unit
             fair_value = self._get_resource_fair_value(resource_type)
-            
+
             # If demand is high and we've waited, consider raising price
             if signal == "sell" and order.can_raise_price(self.current_turn, min_turns=3):
                 # Scarcity - raise price (but not too high)
@@ -462,7 +475,7 @@ class AgentAI:
                         new_price=new_price,
                         reason=f"Scarcity: raising {resource_type.value} price to {new_price}c",
                     )
-            
+
             # If order is getting stale (sitting too long), lower price
             if order.turns_without_sale >= 5:
                 # Calculate competitive price
@@ -479,7 +492,7 @@ class AgentAI:
                         int(current_price * 0.85),
                         int(fair_value * self.MIN_PRICE_DISCOUNT)
                     )
-                
+
                 if new_price < current_price:
                     return AIDecision(
                         action=ActionType.TRADE,
@@ -488,15 +501,15 @@ class AgentAI:
                         new_price=new_price,
                         reason=f"Stale order: lowering {resource_type.value} price to {new_price}c",
                     )
-        
+
         return None
-    
+
     def _check_market_opportunities(self) -> Optional[AIDecision]:
         """Look for good buying opportunities on the market.
-        
+
         KEY INSIGHT: If market price < energy cost to gather, ALWAYS BUY!
         This is the core of smart trading behavior.
-        
+
         Buying is smart because:
         - Trade costs only 1 energy
         - Gathering costs 4-8 energy
@@ -505,40 +518,40 @@ class AgentAI:
         # Don't shop if we're low on money and not wealthy
         if self.agent.money < 10:
             return None
-        
+
         # Resources we might want to buy
         shopping_list = []
-        
+
         # Check each resource type for good deals
         for resource_type in [ResourceType.WATER, ResourceType.BERRIES, ResourceType.MEAT, ResourceType.WOOD]:
             order = self.market.get_cheapest_order(resource_type)
             if not order or order.seller_id == self.agent.id:
                 continue
-            
+
             # Skip invalid orders (price <= 0)
             if order.price_per_unit <= 0:
                 continue
-            
+
             if self.agent.money < order.price_per_unit:
                 continue
-            
+
             # Calculate if this is a good deal
             fair_value = self._get_resource_fair_value(resource_type)
             is_good_deal = self._is_good_buy(resource_type, order.price_per_unit)
-            
+
             # Calculate our current need for this resource
             current_stock = self.agent.get_resource_count(resource_type)
             need_score = 0
-            
+
             if resource_type == ResourceType.WATER:
                 need_score = max(0, self.MIN_WATER_STOCK - current_stock) * 3
             elif resource_type in [ResourceType.BERRIES, ResourceType.MEAT]:
-                food_stock = (self.agent.get_resource_count(ResourceType.BERRIES) + 
+                food_stock = (self.agent.get_resource_count(ResourceType.BERRIES) +
                              self.agent.get_resource_count(ResourceType.MEAT))
                 need_score = max(0, self.MIN_FOOD_STOCK - food_stock) * 2
             elif resource_type == ResourceType.WOOD:
                 need_score = max(0, self.MIN_WOOD_STOCK - current_stock) * 1
-            
+
             # Score this opportunity
             if is_good_deal:
                 # Good deal - definitely consider buying
@@ -550,27 +563,27 @@ class AgentAI:
                 # Not a great deal, but we need it and have money
                 total_score = need_score * 0.5
                 shopping_list.append((resource_type, order, total_score))
-        
+
         if not shopping_list:
             return None
-        
+
         # Sort by score and pick the best opportunity
         shopping_list.sort(key=lambda x: x[2], reverse=True)
         resource_type, order, score = shopping_list[0]
-        
+
         # Only act if the opportunity is worth it
         if score < 1:
             return None
-        
+
         # Calculate how much to buy
         price = max(1, order.price_per_unit)  # Prevent division by zero
         can_afford = self.agent.money // price
         space = self.agent.inventory_space()
         want_quantity = min(2, can_afford, space, order.quantity)  # Buy up to 2 at a time
-        
+
         if want_quantity <= 0:
             return None
-        
+
         return AIDecision(
             action=ActionType.TRADE,
             target_resource=resource_type,
@@ -579,42 +592,42 @@ class AgentAI:
             price=order.price_per_unit,
             reason=f"Good deal: buying {resource_type.value} @ {order.price_per_unit}c (fair value: {self._get_resource_fair_value(resource_type)}c)",
         )
-    
+
     def _check_clothes_crafting(self) -> Optional[AIDecision]:
         """Check if we should craft clothes for heat efficiency."""
         # Only craft if we don't already have clothes and have hide
         if self.agent.has_clothes():
             return None
-        
+
         # Need hide to craft
         if not self.agent.has_resource(ResourceType.HIDE):
             return None
-        
+
         # Need energy to craft
         weave_config = ACTION_CONFIG[ActionType.WEAVE]
         if not self.agent.stats.can_work(abs(weave_config.energy_cost)):
             return None
-        
+
         # Only craft if we're not in survival mode
         stats = self.agent.stats
         if (stats.thirst < stats.MAX_THIRST * self.LOW_THRESHOLD or
             stats.hunger < stats.MAX_HUNGER * self.LOW_THRESHOLD):
             return None
-        
+
         return AIDecision(
             action=ActionType.WEAVE,
             target_resource=ResourceType.CLOTHES,
             reason="Crafting clothes for heat protection",
         )
-    
+
     def _address_thirst(self, critical: bool = False) -> AIDecision:
         """Address thirst - water is the primary solution.
-        
+
         NEW PRIORITY: Try buying FIRST if it's efficient!
         Trading uses only 1 energy vs 3 for getting water.
         """
         prefix = "Critical" if critical else "Low"
-        
+
         # Step 1: Consume water from inventory (best - immediate, free)
         if self.agent.has_resource(ResourceType.WATER):
             return AIDecision(
@@ -622,21 +635,21 @@ class AgentAI:
                 target_resource=ResourceType.WATER,
                 reason=f"{prefix} thirst: consuming water",
             )
-        
+
         # Step 2: Check if buying is more efficient than gathering
         # Trade = 1 energy, Get water = 3 energy. If price is reasonable, BUY!
         water_order = self.market.get_cheapest_order(ResourceType.WATER)
         if water_order and water_order.seller_id != self.agent.id:
             if self.agent.money >= water_order.price_per_unit:
                 fair_value = self._get_resource_fair_value(ResourceType.WATER)
-                
+
                 # Buy if: good deal OR critical situation OR we're wealthy
                 should_buy = (
                     self._is_good_buy(ResourceType.WATER, water_order.price_per_unit) or
                     critical or
                     (self._is_wealthy() and water_order.price_per_unit <= fair_value * 1.5)
                 )
-                
+
                 if should_buy:
                     return AIDecision(
                         action=ActionType.TRADE,
@@ -646,7 +659,7 @@ class AgentAI:
                         price=water_order.price_per_unit,
                         reason=f"{prefix} thirst: buying water @ {water_order.price_per_unit}c",
                     )
-        
+
         # Step 3: Get water ourselves
         water_config = ACTION_CONFIG[ActionType.GET_WATER]
         if self.agent.stats.can_work(abs(water_config.energy_cost)):
@@ -655,7 +668,7 @@ class AgentAI:
                 target_resource=ResourceType.WATER,
                 reason=f"{prefix} thirst: getting water from river",
             )
-        
+
         # Step 4: Emergency - consume berries (gives +3 thirst)
         if self.agent.has_resource(ResourceType.BERRIES):
             return AIDecision(
@@ -663,21 +676,21 @@ class AgentAI:
                 target_resource=ResourceType.BERRIES,
                 reason=f"{prefix} thirst: consuming berries (emergency)",
             )
-        
+
         # No energy to get water - rest
         return AIDecision(
             action=ActionType.REST,
             reason=f"{prefix} thirst: too tired, resting",
         )
-    
+
     def _address_hunger(self, critical: bool = False) -> AIDecision:
         """Address hunger - meat is best, berries are backup.
-        
+
         NEW PRIORITY: Consider buying FIRST if market has good prices!
         Trading = 1 energy vs 4-7 for gathering/hunting.
         """
         prefix = "Critical" if critical else "Low"
-        
+
         # Step 1: Consume meat from inventory (best for hunger - +40)
         if self.agent.has_resource(ResourceType.MEAT):
             return AIDecision(
@@ -685,7 +698,7 @@ class AgentAI:
                 target_resource=ResourceType.MEAT,
                 reason=f"{prefix} hunger: consuming meat",
             )
-        
+
         # Step 2: Consume berries if we have them (+10 hunger)
         if self.agent.has_resource(ResourceType.BERRIES):
             return AIDecision(
@@ -693,20 +706,20 @@ class AgentAI:
                 target_resource=ResourceType.BERRIES,
                 reason=f"{prefix} hunger: consuming berries",
             )
-        
+
         # Step 3: Check if buying food is more efficient than gathering
         for resource_type in [ResourceType.MEAT, ResourceType.BERRIES]:
             order = self.market.get_cheapest_order(resource_type)
             if order and order.seller_id != self.agent.id and self.agent.money >= order.price_per_unit:
                 fair_value = self._get_resource_fair_value(resource_type)
-                
+
                 # Buy if: good deal OR critical OR wealthy
                 should_buy = (
                     self._is_good_buy(resource_type, order.price_per_unit) or
                     critical or
                     (self._is_wealthy() and order.price_per_unit <= fair_value * 1.5)
                 )
-                
+
                 if should_buy:
                     return AIDecision(
                         action=ActionType.TRADE,
@@ -716,7 +729,7 @@ class AgentAI:
                         price=order.price_per_unit,
                         reason=f"{prefix} hunger: buying {resource_type.value} @ {order.price_per_unit}c",
                     )
-        
+
         # Step 4: Gather berries ourselves (easy, 100% success)
         gather_config = ACTION_CONFIG[ActionType.GATHER]
         if self.agent.stats.can_work(abs(gather_config.energy_cost)):
@@ -725,21 +738,21 @@ class AgentAI:
                 target_resource=ResourceType.BERRIES,
                 reason=f"{prefix} hunger: gathering berries",
             )
-        
+
         # No energy - rest
         return AIDecision(
             action=ActionType.REST,
             reason=f"{prefix} hunger: too tired, resting",
         )
-    
+
     def _address_heat(self, critical: bool = False) -> Optional[AIDecision]:
         """Address heat by building fire or getting wood.
-        
+
         NOW: Always considers buying wood if it's a good deal!
         Trade = 1 energy vs 8 energy for chopping.
         """
         prefix = "Critical" if critical else "Low"
-        
+
         # Step 1: Build fire if we have wood
         if self.agent.has_resource(ResourceType.WOOD):
             fire_config = ACTION_CONFIG[ActionType.BUILD_FIRE]
@@ -749,19 +762,19 @@ class AgentAI:
                     target_resource=ResourceType.WOOD,
                     reason=f"{prefix} heat: building fire",
                 )
-        
+
         # Step 2: Buy wood if available and efficient
         cheapest = self.market.get_cheapest_order(ResourceType.WOOD)
         if cheapest and cheapest.seller_id != self.agent.id and self.agent.money >= cheapest.price_per_unit:
             fair_value = self._get_resource_fair_value(ResourceType.WOOD)
-            
+
             # Buy if: good deal OR critical OR wealthy
             should_buy = (
                 self._is_good_buy(ResourceType.WOOD, cheapest.price_per_unit) or
                 critical or
                 (self._is_wealthy() and cheapest.price_per_unit <= fair_value * 1.5)
             )
-            
+
             if should_buy:
                 return AIDecision(
                     action=ActionType.TRADE,
@@ -771,7 +784,7 @@ class AgentAI:
                     price=cheapest.price_per_unit,
                     reason=f"{prefix} heat: buying wood @ {cheapest.price_per_unit}c",
                 )
-        
+
         # Step 3: Chop wood ourselves
         chop_config = ACTION_CONFIG[ActionType.CHOP_WOOD]
         if self.agent.stats.can_work(abs(chop_config.energy_cost)):
@@ -780,31 +793,31 @@ class AgentAI:
                 target_resource=ResourceType.WOOD,
                 reason=f"{prefix} heat: chopping wood for fire",
             )
-        
+
         # If not critical, return None to let other priorities take over
         if not critical:
             return None
-        
+
         return AIDecision(
             action=ActionType.REST,
             reason=f"{prefix} heat: too tired to get wood, resting",
         )
-    
+
     def _check_energy(self) -> Optional[AIDecision]:
         """Check if energy management is needed.
-        
+
         Improved logic: Don't rest at 13-14 energy just to rest.
         Instead, rest only if we truly can't do essential work.
         """
         stats = self.agent.stats
-        
+
         # Only rest if energy is very low
         if stats.energy < self.LOW_ENERGY_THRESHOLD:
             return AIDecision(
                 action=ActionType.REST,
                 reason=f"Energy critically low ({stats.energy}), must rest",
             )
-        
+
         # If it's evening and energy is moderate, rest to prepare for night
         if self.is_evening and stats.energy < self.REST_ENERGY_THRESHOLD:
             # Only if we have enough supplies
@@ -818,12 +831,12 @@ class AgentAI:
                     action=ActionType.REST,
                     reason=f"Evening: resting to prepare for night",
                 )
-        
+
         return None
-    
+
     def _check_economic(self) -> Optional[AIDecision]:
         """Economic activities: selling, wealth building, market participation.
-        
+
         NEW PHILOSOPHY: Actively participate in the market!
         - Sell excess resources to build wealth
         - Price based on supply/demand, not just to clear inventory
@@ -834,18 +847,18 @@ class AgentAI:
         decision = self._try_proactive_sell()
         if decision:
             return decision
-        
+
         # If inventory is getting full, must sell
         if self.agent.inventory_space() <= 2:
             decision = self._try_to_sell(urgent=True)
             if decision:
                 return decision
-        
+
         return None
-    
+
     def _try_proactive_sell(self) -> Optional[AIDecision]:
         """Proactively sell when market conditions are good.
-        
+
         Affected by personality:
         - hoarding_rate: High hoarders keep more, sell less
         - wealth_desire: High wealth desire = more aggressive selling
@@ -854,11 +867,11 @@ class AgentAI:
         if self._is_wealthy() and self.agent.inventory_space() > 3:
             # Already rich and have space, no rush to sell
             return None
-        
+
         # Hoarders are reluctant to sell
         if random.random() < self.p.hoarding_rate * 0.7:
             return None
-        
+
         # Survival minimums scaled by hoarding rate
         base_min = {
             ResourceType.WATER: 2,
@@ -867,32 +880,32 @@ class AgentAI:
             ResourceType.WOOD: 2,
             ResourceType.HIDE: 0,
         }
-        
+
         # High hoarders keep more
         hoarding_mult = 0.5 + self.p.hoarding_rate
         survival_minimums = {k: int(v * hoarding_mult) for k, v in base_min.items()}
-        
+
         # Look for profitable sales
         best_opportunity = None
         best_score = 0
-        
+
         for resource in self.agent.inventory:
             if resource.type == ResourceType.CLOTHES:
                 continue  # Don't sell clothes
-            
+
             min_keep = survival_minimums.get(resource.type, 1)
             excess = resource.quantity - min_keep
-            
+
             if excess <= 0:
                 continue
-            
+
             # Check market conditions
             signal = self.market.get_market_signal(resource.type)
             fair_value = self._get_resource_fair_value(resource.type)
-            
+
             # Apply trading skill for better sell prices
             sell_modifier = get_trade_price_modifier(self.skills.trading, is_buying=False)
-            
+
             # Calculate optimal price
             if signal == "sell":  # Scarcity - we can charge more
                 price = int(fair_value * 1.3 * sell_modifier)
@@ -904,18 +917,18 @@ class AgentAI:
                 # Find cheapest competitor
                 cheapest = self.market.get_cheapest_order(resource.type)
                 if cheapest and cheapest.seller_id != self.agent.id:
-                    price = max(1, cheapest.price_per_unit - 1)
+                    price = max(self.MIN_PRICE, cheapest.price_per_unit - 1)
                 else:
                     price = int(fair_value * 0.8 * sell_modifier)
                 score = 0.5  # Not a great time to sell
-            
+
             # Wealth desire increases sell motivation
             score *= (0.7 + self.p.wealth_desire * 0.6)
-            
+
             if score > best_score:
                 best_score = score
                 best_opportunity = (resource.type, min(excess, 3), price)  # Sell up to 3 at a time
-        
+
         if best_opportunity and best_score >= 1:
             resource_type, quantity, price = best_opportunity
             return AIDecision(
@@ -925,9 +938,9 @@ class AgentAI:
                 price=price,
                 reason=f"Selling {resource_type.value} @ {price}c",
             )
-        
+
         return None
-    
+
     def _try_to_sell(self, urgent: bool = False) -> Optional[AIDecision]:
         """Sell excess resources, keeping enough for survival."""
         survival_minimums = {
@@ -937,11 +950,11 @@ class AgentAI:
             ResourceType.WOOD: 1 if urgent else 2,
             ResourceType.HIDE: 0,
         }
-        
+
         for resource in self.agent.inventory:
             if resource.type == ResourceType.CLOTHES:
                 continue
-            
+
             min_keep = survival_minimums.get(resource.type, 1)
             if resource.quantity > min_keep:
                 quantity_to_sell = resource.quantity - min_keep
@@ -955,14 +968,14 @@ class AgentAI:
                     reason=reason,
                 )
         return None
-    
+
     def _calculate_sell_price(self, resource_type: ResourceType) -> int:
         """Calculate sell price based on fair value and market conditions."""
         fair_value = self._get_resource_fair_value(resource_type)
-        
+
         # Get market suggestion
         suggested = self.market.get_suggested_price(resource_type, fair_value)
-        
+
         # Check competition
         cheapest = self.market.get_cheapest_order(resource_type)
         if cheapest and cheapest.seller_id != self.agent.id:
@@ -971,12 +984,12 @@ class AgentAI:
             if signal != "sell":
                 # Match or undercut
                 suggested = min(suggested, cheapest.price_per_unit)
-        
-        return max(1, suggested)
-    
+
+        return max(self.MIN_PRICE, suggested)
+
     def _do_survival_work(self) -> AIDecision:
         """Perform work based on survival needs AND personality preferences.
-        
+
         Personality effects:
         - hunt_preference: Likelihood of choosing to hunt
         - gather_preference: Likelihood of choosing to gather
@@ -984,17 +997,17 @@ class AgentAI:
         - market_affinity: Likelihood of buying vs gathering
         """
         stats = self.agent.stats
-        
+
         # Count current resources
         water_count = self.agent.get_resource_count(ResourceType.WATER)
         meat_count = self.agent.get_resource_count(ResourceType.MEAT)
         berry_count = self.agent.get_resource_count(ResourceType.BERRIES)
         wood_count = self.agent.get_resource_count(ResourceType.WOOD)
         food_count = meat_count + berry_count
-        
+
         # Urgency calculations
         heat_urgency = 1 - (stats.heat / stats.MAX_HEAT)
-        
+
         # Helper to decide: buy or gather?
         def get_resource_decision(resource_type: ResourceType, gather_action: ActionType, reason: str) -> AIDecision:
             """Decide whether to buy or gather a resource."""
@@ -1005,7 +1018,7 @@ class AgentAI:
                     # Apply trading skill for better buy prices
                     buy_modifier = get_trade_price_modifier(self.skills.trading, is_buying=True)
                     effective_price = order.price_per_unit  # Skill affects perceived value
-                    
+
                     if self._is_good_buy(resource_type, effective_price):
                         return AIDecision(
                             action=ActionType.TRADE,
@@ -1015,7 +1028,7 @@ class AgentAI:
                             price=order.price_per_unit,
                             reason=f"{reason} (buying @ {order.price_per_unit}c)",
                         )
-            
+
             # Gather it ourselves
             config = ACTION_CONFIG[gather_action]
             if self.agent.stats.can_work(abs(config.energy_cost)):
@@ -1025,17 +1038,17 @@ class AgentAI:
                     reason=f"{reason} (gathering)",
                 )
             return None
-        
+
         # Priority: Stock up on water if low
         if water_count < self.MIN_WATER_STOCK:
             decision = get_resource_decision(
-                ResourceType.WATER, 
-                ActionType.GET_WATER, 
+                ResourceType.WATER,
+                ActionType.GET_WATER,
                 f"Stocking water ({water_count} < {self.MIN_WATER_STOCK})"
             )
             if decision:
                 return decision
-        
+
         # Priority: Stock up on wood if low (for heat)
         # Affected by woodcut_preference
         if wood_count < self.MIN_WOOD_STOCK and heat_urgency > 0.3:
@@ -1046,30 +1059,30 @@ class AgentAI:
             )
             if decision:
                 return decision
-        
+
         # Priority: Stock up on food if low
         if food_count < self.MIN_FOOD_STOCK:
             hunt_config = ACTION_CONFIG[ActionType.HUNT]
-            
+
             # Personality-driven choice between hunting and gathering
             # risk_tolerance and hunt_preference affect this choice
             can_hunt = stats.energy >= abs(hunt_config.energy_cost) + 5
-            
+
             # Calculate hunt probability based on personality
             # High risk_tolerance + high hunt_preference = more hunting
             hunt_score = self.p.hunt_preference * self.p.risk_tolerance
             gather_score = self.p.gather_preference * (1.5 - self.p.risk_tolerance)
-            
+
             # Normalize to probability
             total = hunt_score + gather_score
             hunt_prob = hunt_score / total if total > 0 else 0.3
-            
+
             # Also prefer hunting if we have no meat
             if meat_count == 0:
                 hunt_prob = min(0.8, hunt_prob + 0.3)
-            
+
             prefer_hunt = can_hunt and random.random() < hunt_prob
-            
+
             if prefer_hunt:
                 decision = get_resource_decision(
                     ResourceType.MEAT,
@@ -1078,7 +1091,7 @@ class AgentAI:
                 )
                 if decision:
                     return decision
-            
+
             # Otherwise try berries
             decision = get_resource_decision(
                 ResourceType.BERRIES,
@@ -1087,7 +1100,7 @@ class AgentAI:
             )
             if decision:
                 return decision
-        
+
         # Evening preparation
         if self.is_late_day:
             if water_count < self.MIN_WATER_STOCK + 1:
@@ -1102,35 +1115,35 @@ class AgentAI:
                 decision = get_resource_decision(ResourceType.WOOD, ActionType.CHOP_WOOD, "Evening: stocking wood")
                 if decision:
                     return decision
-        
+
         # Default: varied work based on need AND personality preferences
         needs = []
-        
+
         if water_count < self.MIN_WATER_STOCK + 2:
             needs.append((ResourceType.WATER, ActionType.GET_WATER, "Getting water", 2.0))
-        
+
         if food_count < self.MIN_FOOD_STOCK + 2:
             # Weight by personality preferences
-            needs.append((ResourceType.BERRIES, ActionType.GATHER, "Gathering berries", 
+            needs.append((ResourceType.BERRIES, ActionType.GATHER, "Gathering berries",
                          2.0 * self.p.gather_preference))
-            
+
             # Add hunting weighted by personality
             hunt_config = ACTION_CONFIG[ActionType.HUNT]
             if stats.energy >= abs(hunt_config.energy_cost) + 3:
                 hunt_weight = 2.0 * self.p.hunt_preference * self.p.risk_tolerance
                 needs.append((ResourceType.MEAT, ActionType.HUNT, "Hunting for meat", hunt_weight))
-        
+
         if wood_count < self.MIN_WOOD_STOCK + 2:
-            needs.append((ResourceType.WOOD, ActionType.CHOP_WOOD, "Chopping wood", 
+            needs.append((ResourceType.WOOD, ActionType.CHOP_WOOD, "Chopping wood",
                          1.0 * self.p.woodcut_preference))
-        
+
         if not needs:
             # We have good reserves, maybe sell excess or rest
             if self.agent.inventory_space() <= 4:
                 decision = self._try_proactive_sell()
                 if decision:
                     return decision
-            
+
             # Default activity based on personality
             # High hunt_preference = hunt, else gather
             if self.p.hunt_preference > self.p.gather_preference and stats.energy >= 10:
@@ -1144,7 +1157,7 @@ class AgentAI:
                 target_resource=ResourceType.BERRIES,
                 reason="Default: gathering (personality)",
             )
-        
+
         # For each need, check if we can buy cheaply (market_affinity affects this)
         if random.random() < self.p.market_affinity:
             for resource_type, action, reason, weight in needs:
@@ -1159,7 +1172,7 @@ class AgentAI:
                             price=order.price_per_unit,
                             reason=f"{reason} (buying cheap!)",
                         )
-        
+
         # Weighted random selection for gathering
         total_weight = sum(weight for _, _, _, weight in needs)
         r = random.random() * total_weight
@@ -1172,7 +1185,7 @@ class AgentAI:
                     target_resource=resource,
                     reason=reason,
                 )
-        
+
         # Fallback
         resource, action, reason, _ = needs[0]
         return AIDecision(
@@ -1182,7 +1195,58 @@ class AgentAI:
         )
 
 
-def get_ai_decision(agent: Agent, market: "OrderBook", step_in_day: int = 1, day_steps: int = 10, current_turn: int = 0) -> AIDecision:
-    """Convenience function to get an AI decision for an agent."""
+def get_ai_decision(
+    agent: Agent,
+    market: "OrderBook",
+    step_in_day: int = 1,
+    day_steps: int = 10,
+    current_turn: int = 0,
+    use_goap: bool = True,
+    is_night: bool = False,
+) -> AIDecision:
+    """Get an AI decision for an agent.
+
+    By default, uses the GOAP (Goal-Oriented Action Planning) system.
+    Set use_goap=False to use the legacy priority-based system.
+
+    Args:
+        agent: The agent to make a decision for
+        market: The market order book
+        step_in_day: Current step within the day
+        day_steps: Total steps per day
+        current_turn: Current simulation turn
+        use_goap: Whether to use GOAP (default True) or legacy system
+        is_night: Whether it's currently night time
+
+    Returns:
+        AIDecision with the chosen action and parameters
+    """
+    if use_goap:
+        from backend.core.goap.goap_ai import get_goap_decision
+        return get_goap_decision(
+            agent=agent,
+            market=market,
+            step_in_day=step_in_day,
+            day_steps=day_steps,
+            current_turn=current_turn,
+            is_night=is_night,
+        )
+    else:
+        # Legacy priority-based system
+        ai = AgentAI(agent, market, step_in_day, day_steps, current_turn)
+        return ai.decide()
+
+
+def get_legacy_ai_decision(
+    agent: Agent,
+    market: "OrderBook",
+    step_in_day: int = 1,
+    day_steps: int = 10,
+    current_turn: int = 0,
+) -> AIDecision:
+    """Get an AI decision using the legacy priority-based system.
+
+    This is kept for comparison and testing purposes.
+    """
     ai = AgentAI(agent, market, step_in_day, day_steps, current_turn)
     return ai.decide()
diff --git a/backend/core/engine.py b/backend/core/engine.py
index df7b892..d50711a 100644
--- a/backend/core/engine.py
+++ b/backend/core/engine.py
@@ -171,21 +171,19 @@ class GameEngine:
                 money=agent.money,
             )
 
-            if self.world.is_night():
-                # Force sleep at night
-                decision = AIDecision(
-                    action=ActionType.SLEEP,
-                    reason="Night time: sleeping",
-                )
-            else:
-                # Pass time info so AI can prepare for night
-                decision = get_ai_decision(
-                    agent,
-                    self.market,
-                    step_in_day=self.world.step_in_day,
-                    day_steps=self.world.config.day_steps,
-                    current_turn=current_turn,
-                )
+            # Get AI config to determine which system to use
+            ai_config = get_config().ai
+
+            # GOAP AI handles night time automatically
+            decision = get_ai_decision(
+                agent,
+                self.market,
+                step_in_day=self.world.step_in_day,
+                day_steps=self.world.config.day_steps,
+                current_turn=current_turn,
+                use_goap=ai_config.use_goap,
+                is_night=self.world.is_night(),
+            )
 
             decisions.append((agent, decision))
 
diff --git a/backend/core/goap/__init__.py b/backend/core/goap/__init__.py
new file mode 100644
index 0000000..efebbd7
--- /dev/null
+++ b/backend/core/goap/__init__.py
@@ -0,0 +1,39 @@
+"""GOAP (Goal-Oriented Action Planning) module for agent decision making.
+
+This module provides a GOAP-based AI system where agents:
+1. Evaluate their current world state
+2. Select the most relevant goal based on priorities
+3. Plan a sequence of actions to achieve that goal
+4. Execute the first action in the plan
+
+Key components:
+- WorldState: Dictionary-like representation of agent/world state
+- Goal: Goals with dynamic priority calculation
+- GOAPAction: Actions with preconditions and effects
+- Planner: A* search for finding optimal action sequences
+"""
+
+from .world_state import WorldState
+from .goal import Goal, GoalType
+from .action import GOAPAction
+from .planner import GOAPPlanner
+from .goals import SURVIVAL_GOALS, ECONOMIC_GOALS, get_all_goals
+from .actions import get_all_actions, get_action_by_type
+from .debug import GOAPDebugInfo, get_goap_debug_info, get_all_agents_goap_debug
+
+__all__ = [
+    'WorldState',
+    'Goal',
+    'GoalType',
+    'GOAPAction',
+    'GOAPPlanner',
+    'SURVIVAL_GOALS',
+    'ECONOMIC_GOALS',
+    'get_all_goals',
+    'get_all_actions',
+    'get_action_by_type',
+    'GOAPDebugInfo',
+    'get_goap_debug_info',
+    'get_all_agents_goap_debug',
+]
+
diff --git a/backend/core/goap/action.py b/backend/core/goap/action.py
new file mode 100644
index 0000000..2429cb2
--- /dev/null
+++ b/backend/core/goap/action.py
@@ -0,0 +1,381 @@
+"""GOAP Action definitions.
+
+Actions are the building blocks of plans. Each action has:
+- Preconditions: What must be true for the action to be valid
+- Effects: How the action changes the world state
+- Cost: How expensive the action is (for planning)
+"""
+
+from dataclasses import dataclass, field
+from typing import Callable, Optional, TYPE_CHECKING
+
+from backend.domain.action import ActionType
+from backend.domain.resources import ResourceType
+
+from .world_state import WorldState
+
+if TYPE_CHECKING:
+    from backend.domain.agent import Agent
+    from backend.core.market import OrderBook
+
+
+@dataclass
+class GOAPAction:
+    """A GOAP action that can be part of a plan.
+
+    Actions transform the world state. The planner uses preconditions
+    and effects to search for valid action sequences.
+
+    Attributes:
+        name: Human-readable name
+        action_type: The underlying ActionType to execute
+        target_resource: Optional resource this action targets
+        preconditions: Function that checks if action is valid in a state
+        effects: Function that returns the expected effects on state
+        cost: Function that calculates action cost (lower = preferred)
+        get_decision_params: Function to get parameters for AIDecision
+    """
+    name: str
+    action_type: ActionType
+    target_resource: Optional[ResourceType] = None
+
+    # Functions that evaluate in context of world state
+    preconditions: Callable[[WorldState], bool] = field(default=lambda s: True)
+    effects: Callable[[WorldState], dict] = field(default=lambda s: {})
+    cost: Callable[[WorldState], float] = field(default=lambda s: 1.0)
+
+    # For generating the actual decision
+    get_decision_params: Optional[Callable[[WorldState, "Agent", "OrderBook"], dict]] = None
+
+    def is_valid(self, state: WorldState) -> bool:
+        """Check if this action can be performed in the given state."""
+        return self.preconditions(state)
+
+    def apply(self, state: WorldState) -> WorldState:
+        """Apply this action's effects to a state, returning a new state.
+
+        This is used by the planner for forward search.
+        """
+        new_state = state.copy()
+        effects = self.effects(state)
+
+        for key, value in effects.items():
+            if hasattr(new_state, key):
+                if isinstance(value, (int, float)):
+                    # For numeric values, handle both absolute and relative changes
+                    current = getattr(new_state, key)
+                    if isinstance(current, bool):
+                        setattr(new_state, key, bool(value))
+                    else:
+                        setattr(new_state, key, value)
+                else:
+                    setattr(new_state, key, value)
+
+        # Recalculate urgencies
+        new_state._calculate_urgencies()
+
+        return new_state
+
+    def get_cost(self, state: WorldState) -> float:
+        """Get the cost of this action in the given state."""
+        return self.cost(state)
+
+    def __repr__(self) -> str:
+        resource = f"({self.target_resource.value})" if self.target_resource else ""
+        return f"GOAPAction({self.name}{resource})"
+
+    def __hash__(self) -> int:
+        return hash((self.name, self.action_type, self.target_resource))
+
+    def __eq__(self, other) -> bool:
+        if not isinstance(other, GOAPAction):
+            return False
+        return (self.name == other.name and
+                self.action_type == other.action_type and
+                self.target_resource == other.target_resource)
+
+
+def create_consume_action(
+    resource_type: ResourceType,
+    stat_name: str,
+    stat_increase: float,
+    secondary_stat: Optional[str] = None,
+    secondary_increase: float = 0.0,
+) -> GOAPAction:
+    """Factory for creating consume resource actions."""
+    count_name = f"{resource_type.value}_count" if resource_type != ResourceType.BERRIES else "berries_count"
+    if resource_type == ResourceType.MEAT:
+        count_name = "meat_count"
+    elif resource_type == ResourceType.WATER:
+        count_name = "water_count"
+
+    # Map stat name to pct name
+    pct_name = f"{stat_name}_pct"
+    secondary_pct = f"{secondary_stat}_pct" if secondary_stat else None
+
+    def preconditions(state: WorldState) -> bool:
+        count = getattr(state, count_name, 0)
+        return count > 0
+
+    def effects(state: WorldState) -> dict:
+        result = {}
+        current = getattr(state, pct_name)
+        result[pct_name] = min(1.0, current + stat_increase)
+
+        if secondary_pct:
+            current_sec = getattr(state, secondary_pct)
+            result[secondary_pct] = min(1.0, current_sec + secondary_increase)
+
+        # Reduce resource count
+        current_count = getattr(state, count_name)
+        result[count_name] = max(0, current_count - 1)
+
+        # Update food count if consuming food
+        if resource_type in [ResourceType.MEAT, ResourceType.BERRIES]:
+            result["food_count"] = max(0, state.food_count - 1)
+
+        return result
+
+    def cost(state: WorldState) -> float:
+        # Consuming is very cheap - 0 energy cost
+        return 0.5
+
+    return GOAPAction(
+        name=f"Consume {resource_type.value}",
+        action_type=ActionType.CONSUME,
+        target_resource=resource_type,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+def create_gather_action(
+    action_type: ActionType,
+    resource_type: ResourceType,
+    energy_cost: float,
+    expected_output: int,
+    success_chance: float = 1.0,
+) -> GOAPAction:
+    """Factory for creating resource gathering actions."""
+    count_name = f"{resource_type.value}_count"
+    if resource_type == ResourceType.BERRIES:
+        count_name = "berries_count"
+    elif resource_type == ResourceType.MEAT:
+        count_name = "meat_count"
+
+    def preconditions(state: WorldState) -> bool:
+        # Need enough energy and inventory space
+        energy_needed = abs(energy_cost) / 50.0  # Convert to percentage
+        return state.energy_pct >= energy_needed + 0.05 and state.inventory_space > 0
+
+    def effects(state: WorldState) -> dict:
+        result = {}
+
+        # Spend energy
+        energy_spent = abs(energy_cost) / 50.0
+        result["energy_pct"] = max(0, state.energy_pct - energy_spent)
+
+        # Gain resources (adjusted for success chance)
+        effective_output = int(expected_output * success_chance)
+        current = getattr(state, count_name)
+        result[count_name] = current + effective_output
+
+        # Update food count if gathering food
+        if resource_type in [ResourceType.MEAT, ResourceType.BERRIES]:
+            result["food_count"] = state.food_count + effective_output
+
+        # Update inventory space
+        result["inventory_space"] = max(0, state.inventory_space - effective_output)
+
+        return result
+
+    def cost(state: WorldState) -> float:
+        # Calculate cost based on efficiency (energy per unit of food)
+        food_per_action = expected_output * success_chance
+        if food_per_action > 0:
+            base_cost = abs(energy_cost) / food_per_action * 0.5
+        else:
+            base_cost = abs(energy_cost) / 5.0
+
+        # Adjust for success chance (penalize unreliable actions slightly)
+        if success_chance < 1.0:
+            base_cost *= 1.0 + (1.0 - success_chance) * 0.3
+
+        # Mild personality adjustments (shouldn't dominate the cost)
+        if action_type == ActionType.GATHER:
+            # Cautious agents slightly prefer gathering
+            base_cost *= (0.9 + state.risk_tolerance * 0.2)
+
+        return base_cost
+
+    return GOAPAction(
+        name=f"{action_type.value}",
+        action_type=action_type,
+        target_resource=resource_type,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+def create_buy_action(resource_type: ResourceType) -> GOAPAction:
+    """Factory for creating market buy actions."""
+    can_buy_name = f"can_buy_{resource_type.value}"
+    if resource_type in [ResourceType.MEAT, ResourceType.BERRIES]:
+        can_buy_name = "can_buy_food"  # Simplified - we check specific later
+
+    count_name = f"{resource_type.value}_count"
+    if resource_type == ResourceType.BERRIES:
+        count_name = "berries_count"
+    elif resource_type == ResourceType.MEAT:
+        count_name = "meat_count"
+
+    price_name = f"{resource_type.value}_market_price"
+    if resource_type in [ResourceType.MEAT, ResourceType.BERRIES]:
+        price_name = "food_market_price"
+
+    def preconditions(state: WorldState) -> bool:
+        # Check specific availability
+        if resource_type == ResourceType.MEAT:
+            can_buy = state.can_buy_meat
+        elif resource_type == ResourceType.BERRIES:
+            can_buy = state.can_buy_berries
+        else:
+            can_buy = getattr(state, f"can_buy_{resource_type.value}", False)
+
+        return can_buy and state.inventory_space > 0
+
+    def effects(state: WorldState) -> dict:
+        result = {}
+
+        # Get price
+        if resource_type == ResourceType.MEAT:
+            price = state.food_market_price
+        elif resource_type == ResourceType.BERRIES:
+            price = state.food_market_price
+        else:
+            price = getattr(state, price_name, 10)
+
+        # Spend money
+        result["money"] = state.money - price
+
+        # Gain resource
+        current = getattr(state, count_name)
+        result[count_name] = current + 1
+
+        # Update food count if buying food
+        if resource_type in [ResourceType.MEAT, ResourceType.BERRIES]:
+            result["food_count"] = state.food_count + 1
+
+        # Spend small energy
+        result["energy_pct"] = max(0, state.energy_pct - 0.02)
+
+        # Update inventory
+        result["inventory_space"] = max(0, state.inventory_space - 1)
+
+        return result
+
+    def cost(state: WorldState) -> float:
+        # Trading cost is low (1 energy)
+        base_cost = 0.5
+
+        # Market-oriented agents prefer buying
+        base_cost *= (1.5 - state.market_affinity)
+
+        # Check if it's a good deal
+        if resource_type == ResourceType.MEAT:
+            price = state.food_market_price
+        elif resource_type == ResourceType.BERRIES:
+            price = state.food_market_price
+        else:
+            price = getattr(state, price_name, 100)
+
+        # Higher price = higher cost (scaled for 100-500g price range)
+        # At fair value (~150g), multiplier is ~1.5x
+        # At min price (100g), multiplier is ~1.33x
+        base_cost *= (1.0 + price / 300.0)
+
+        return base_cost
+
+    return GOAPAction(
+        name=f"Buy {resource_type.value}",
+        action_type=ActionType.TRADE,
+        target_resource=resource_type,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+def create_rest_action() -> GOAPAction:
+    """Create the rest action."""
+    def preconditions(state: WorldState) -> bool:
+        return state.energy_pct < 0.9  # Only rest if not full
+
+    def effects(state: WorldState) -> dict:
+        # Rest restores energy (12 out of 50 = 0.24)
+        return {
+            "energy_pct": min(1.0, state.energy_pct + 0.24),
+        }
+
+    def cost(state: WorldState) -> float:
+        # Resting is cheap but we prefer productive actions
+        return 2.0
+
+    return GOAPAction(
+        name="Rest",
+        action_type=ActionType.REST,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+def create_build_fire_action() -> GOAPAction:
+    """Create the build fire action."""
+    def preconditions(state: WorldState) -> bool:
+        return state.wood_count > 0 and state.energy_pct >= 0.1
+
+    def effects(state: WorldState) -> dict:
+        return {
+            "heat_pct": min(1.0, state.heat_pct + 0.20),  # Fire gives 20 heat out of 100
+            "wood_count": max(0, state.wood_count - 1),
+            "energy_pct": max(0, state.energy_pct - 0.08),  # 4 energy cost
+        }
+
+    def cost(state: WorldState) -> float:
+        # Building fire is relatively cheap when we have wood
+        return 1.5
+
+    return GOAPAction(
+        name="Build Fire",
+        action_type=ActionType.BUILD_FIRE,
+        target_resource=ResourceType.WOOD,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+def create_sleep_action() -> GOAPAction:
+    """Create the sleep action (for night)."""
+    def preconditions(state: WorldState) -> bool:
+        return state.is_night
+
+    def effects(state: WorldState) -> dict:
+        return {
+            "energy_pct": 1.0,  # Full energy restore
+        }
+
+    def cost(state: WorldState) -> float:
+        return 0.0  # Sleep is mandatory at night
+
+    return GOAPAction(
+        name="Sleep",
+        action_type=ActionType.SLEEP,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
diff --git a/backend/core/goap/actions.py b/backend/core/goap/actions.py
new file mode 100644
index 0000000..f20e7a6
--- /dev/null
+++ b/backend/core/goap/actions.py
@@ -0,0 +1,362 @@
+"""Predefined GOAP actions for agents.
+
+Actions are organized by category:
+- Consume actions: Use resources from inventory
+- Gather actions: Produce resources
+- Trade actions: Buy/sell on market
+- Utility actions: Rest, sleep, build fire
+"""
+
+from typing import Optional, TYPE_CHECKING
+
+from backend.domain.action import ActionType, ACTION_CONFIG
+from backend.domain.resources import ResourceType
+
+from .world_state import WorldState
+from .action import (
+    GOAPAction,
+    create_consume_action,
+    create_gather_action,
+    create_buy_action,
+    create_rest_action,
+    create_build_fire_action,
+    create_sleep_action,
+)
+
+if TYPE_CHECKING:
+    from backend.domain.agent import Agent
+    from backend.core.market import OrderBook
+
+
+def _get_action_configs():
+    """Get action configurations from config."""
+    return ACTION_CONFIG
+
+
+# =============================================================================
+# CONSUME ACTIONS
+# =============================================================================
+
+def _create_drink_water() -> GOAPAction:
+    """Drink water to restore thirst."""
+    return create_consume_action(
+        resource_type=ResourceType.WATER,
+        stat_name="thirst",
+        stat_increase=0.50,  # 50 thirst out of 100
+    )
+
+
+def _create_eat_meat() -> GOAPAction:
+    """Eat meat to restore hunger (primary food source)."""
+    return create_consume_action(
+        resource_type=ResourceType.MEAT,
+        stat_name="hunger",
+        stat_increase=0.35,  # 35 hunger
+        secondary_stat="energy",
+        secondary_increase=0.24,  # 12 energy
+    )
+
+
+def _create_eat_berries() -> GOAPAction:
+    """Eat berries to restore hunger and some thirst."""
+    return create_consume_action(
+        resource_type=ResourceType.BERRIES,
+        stat_name="hunger",
+        stat_increase=0.10,  # 10 hunger
+        secondary_stat="thirst",
+        secondary_increase=0.04,  # 4 thirst
+    )
+
+
+CONSUME_ACTIONS = [
+    _create_drink_water(),
+    _create_eat_meat(),
+    _create_eat_berries(),
+]
+
+
+# =============================================================================
+# GATHER ACTIONS
+# =============================================================================
+
+def _create_get_water() -> GOAPAction:
+    """Get water from the river."""
+    config = _get_action_configs()[ActionType.GET_WATER]
+    return create_gather_action(
+        action_type=ActionType.GET_WATER,
+        resource_type=ResourceType.WATER,
+        energy_cost=config.energy_cost,
+        expected_output=1,
+        success_chance=1.0,
+    )
+
+
+def _create_gather_berries() -> GOAPAction:
+    """Gather berries (safe, reliable)."""
+    config = _get_action_configs()[ActionType.GATHER]
+    expected = (config.min_output + config.max_output) // 2
+    return create_gather_action(
+        action_type=ActionType.GATHER,
+        resource_type=ResourceType.BERRIES,
+        energy_cost=config.energy_cost,
+        expected_output=expected,
+        success_chance=1.0,
+    )
+
+
+def _create_hunt() -> GOAPAction:
+    """Hunt for meat (risky, high reward).
+
+    Hunt should be attractive because:
+    - Meat gives much more hunger than berries (35 vs 10)
+    - Meat also gives energy (12)
+    - You also get hide for clothes
+
+    Cost is balanced against gathering:
+    - Hunt: -7 energy, 70% success, 2-5 meat + 0-2 hide
+    - Gather: -3 energy, 100% success, 2-4 berries
+
+    Effective food per energy:
+    - Hunt: 3.5 meat avg * 0.7 = 2.45 meat = 2.45 * 35 hunger = 85.75 hunger for 7 energy = 12.25 hunger/energy
+    - Gather: 3 berries avg * 1.0 = 3 berries = 3 * 10 hunger = 30 hunger for 3 energy = 10 hunger/energy
+
+    So hunting is actually MORE efficient per energy for hunger! The cost should reflect this.
+    """
+    config = _get_action_configs()[ActionType.HUNT]
+    expected = (config.min_output + config.max_output) // 2
+
+    # Custom preconditions for hunting
+    def preconditions(state: WorldState) -> bool:
+        # Need more energy for hunting (but not excessively so)
+        energy_needed = abs(config.energy_cost) / 50.0 + 0.05
+        return state.energy_pct >= energy_needed and state.inventory_space >= 2
+
+    def effects(state: WorldState) -> dict:
+        # Account for success chance
+        effective_meat = int(expected * config.success_chance)
+        effective_hide = int(1 * config.success_chance)  # Average hide
+
+        energy_spent = abs(config.energy_cost) / 50.0
+
+        return {
+            "energy_pct": max(0, state.energy_pct - energy_spent),
+            "meat_count": state.meat_count + effective_meat,
+            "food_count": state.food_count + effective_meat,
+            "hide_count": state.hide_count + effective_hide,
+            "inventory_space": max(0, state.inventory_space - effective_meat - effective_hide),
+        }
+
+    def cost(state: WorldState) -> float:
+        # Hunt should be comparable to gather when considering value:
+        # - Hunt gives 3.5 meat avg (35 hunger each) = 122.5 hunger value
+        # - Gather gives 3 berries avg (10 hunger each) = 30 hunger value
+        # Hunt is 4x more valuable for hunger! So cost can be higher but not 4x.
+
+        # Base cost similar to gather
+        base_cost = 0.6
+
+        # Success chance penalty (small)
+        if config.success_chance < 1.0:
+            base_cost *= 1.0 + (1.0 - config.success_chance) * 0.2
+
+        # Risk-tolerant agents prefer hunting
+        # Range: 0.85 (high risk tolerance) to 1.15 (low risk tolerance)
+        risk_modifier = 1.0 + (0.5 - state.risk_tolerance) * 0.3
+        base_cost *= risk_modifier
+
+        # Big bonus if we have no meat - prioritize getting some
+        if state.meat_count == 0:
+            base_cost *= 0.6
+
+        # Bonus if low on food in general
+        if state.food_count < 2:
+            base_cost *= 0.8
+
+        return base_cost
+
+    return GOAPAction(
+        name="Hunt",
+        action_type=ActionType.HUNT,
+        target_resource=ResourceType.MEAT,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+def _create_chop_wood() -> GOAPAction:
+    """Chop wood for fires."""
+    config = _get_action_configs()[ActionType.CHOP_WOOD]
+    expected = (config.min_output + config.max_output) // 2
+    return create_gather_action(
+        action_type=ActionType.CHOP_WOOD,
+        resource_type=ResourceType.WOOD,
+        energy_cost=config.energy_cost,
+        expected_output=expected,
+        success_chance=config.success_chance,
+    )
+
+
+def _create_weave_clothes() -> GOAPAction:
+    """Craft clothes from hide."""
+    config = _get_action_configs()[ActionType.WEAVE]
+
+    def preconditions(state: WorldState) -> bool:
+        return (
+            state.hide_count >= 1 and
+            not state.has_clothes and
+            state.energy_pct >= abs(config.energy_cost) / 50.0 + 0.05
+        )
+
+    def effects(state: WorldState) -> dict:
+        return {
+            "has_clothes": True,
+            "hide_count": state.hide_count - 1,
+            "energy_pct": max(0, state.energy_pct - abs(config.energy_cost) / 50.0),
+        }
+
+    def cost(state: WorldState) -> float:
+        return abs(config.energy_cost) / 3.0
+
+    return GOAPAction(
+        name="Weave Clothes",
+        action_type=ActionType.WEAVE,
+        target_resource=ResourceType.CLOTHES,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+GATHER_ACTIONS = [
+    _create_get_water(),
+    _create_gather_berries(),
+    _create_hunt(),
+    _create_chop_wood(),
+    _create_weave_clothes(),
+]
+
+
+# =============================================================================
+# TRADE ACTIONS
+# =============================================================================
+
+def _create_buy_water() -> GOAPAction:
+    """Buy water from the market."""
+    return create_buy_action(ResourceType.WATER)
+
+
+def _create_buy_meat() -> GOAPAction:
+    """Buy meat from the market."""
+    return create_buy_action(ResourceType.MEAT)
+
+
+def _create_buy_berries() -> GOAPAction:
+    """Buy berries from the market."""
+    return create_buy_action(ResourceType.BERRIES)
+
+
+def _create_buy_wood() -> GOAPAction:
+    """Buy wood from the market."""
+    return create_buy_action(ResourceType.WOOD)
+
+
+def _create_sell_action(resource_type: ResourceType, min_keep: int = 1) -> GOAPAction:
+    """Factory for creating sell actions."""
+    count_name = f"{resource_type.value}_count"
+    if resource_type == ResourceType.BERRIES:
+        count_name = "berries_count"
+    elif resource_type == ResourceType.MEAT:
+        count_name = "meat_count"
+
+    def preconditions(state: WorldState) -> bool:
+        current = getattr(state, count_name)
+        return current > min_keep and state.energy_pct >= 0.05
+
+    def effects(state: WorldState) -> dict:
+        # Estimate we'll get a reasonable price (around min_price from config)
+        # This is approximate - actual execution will get real prices
+        estimated_price = 100  # Base estimate (min_price from config)
+
+        current = getattr(state, count_name)
+        sell_qty = min(3, current - min_keep)  # Sell up to 3, keep minimum
+
+        result = {
+            "money": state.money + estimated_price * sell_qty,
+            count_name: current - sell_qty,
+            "inventory_space": state.inventory_space + sell_qty,
+            "energy_pct": max(0, state.energy_pct - 0.02),
+        }
+
+        # Update food count if selling food
+        if resource_type in [ResourceType.MEAT, ResourceType.BERRIES]:
+            result["food_count"] = state.food_count - sell_qty
+
+        return result
+
+    def cost(state: WorldState) -> float:
+        # Selling has low cost
+        base_cost = 1.0
+
+        # Hoarders reluctant to sell
+        base_cost *= (0.5 + state.hoarding_rate)
+
+        return base_cost
+
+    return GOAPAction(
+        name=f"Sell {resource_type.value}",
+        action_type=ActionType.TRADE,
+        target_resource=resource_type,
+        preconditions=preconditions,
+        effects=effects,
+        cost=cost,
+    )
+
+
+TRADE_ACTIONS = [
+    _create_buy_water(),
+    _create_buy_meat(),
+    _create_buy_berries(),
+    _create_buy_wood(),
+    _create_sell_action(ResourceType.WATER, min_keep=2),
+    _create_sell_action(ResourceType.MEAT, min_keep=1),
+    _create_sell_action(ResourceType.BERRIES, min_keep=2),
+    _create_sell_action(ResourceType.WOOD, min_keep=1),
+    _create_sell_action(ResourceType.HIDE, min_keep=0),
+]
+
+
+# =============================================================================
+# UTILITY ACTIONS
+# =============================================================================
+
+UTILITY_ACTIONS = [
+    create_rest_action(),
+    create_build_fire_action(),
+    create_sleep_action(),
+]
+
+
+# =============================================================================
+# ALL ACTIONS
+# =============================================================================
+
+def get_all_actions() -> list[GOAPAction]:
+    """Get all available GOAP actions."""
+    return CONSUME_ACTIONS + GATHER_ACTIONS + TRADE_ACTIONS + UTILITY_ACTIONS
+
+
+def get_action_by_type(action_type: ActionType) -> list[GOAPAction]:
+    """Get all GOAP actions of a specific type."""
+    all_actions = get_all_actions()
+    return [a for a in all_actions if a.action_type == action_type]
+
+
+def get_action_by_name(name: str) -> Optional[GOAPAction]:
+    """Get a specific action by name."""
+    all_actions = get_all_actions()
+    for action in all_actions:
+        if action.name == name:
+            return action
+    return None
+
diff --git a/backend/core/goap/debug.py b/backend/core/goap/debug.py
new file mode 100644
index 0000000..06e7ed0
--- /dev/null
+++ b/backend/core/goap/debug.py
@@ -0,0 +1,258 @@
+"""GOAP Debug utilities for visualization and analysis.
+
+Provides detailed information about GOAP decision-making for debugging
+and visualization purposes.
+"""
+
+from dataclasses import dataclass, field
+from typing import Optional, TYPE_CHECKING
+
+from .world_state import WorldState, create_world_state
+from .goal import Goal
+from .action import GOAPAction
+from .planner import GOAPPlanner, ReactivePlanner, Plan
+from .goals import get_all_goals
+from .actions import get_all_actions
+
+if TYPE_CHECKING:
+    from backend.domain.agent import Agent
+    from backend.core.market import OrderBook
+
+
+@dataclass
+class GoalDebugInfo:
+    """Debug information for a single goal."""
+    name: str
+    goal_type: str
+    priority: float
+    is_satisfied: bool
+    is_selected: bool = False
+
+    def to_dict(self) -> dict:
+        return {
+            "name": self.name,
+            "goal_type": self.goal_type,
+            "priority": round(self.priority, 2),
+            "is_satisfied": self.is_satisfied,
+            "is_selected": self.is_selected,
+        }
+
+
+@dataclass
+class ActionDebugInfo:
+    """Debug information for a single action."""
+    name: str
+    action_type: str
+    target_resource: Optional[str]
+    is_valid: bool
+    cost: float
+    is_in_plan: bool = False
+    plan_order: int = -1
+
+    def to_dict(self) -> dict:
+        return {
+            "name": self.name,
+            "action_type": self.action_type,
+            "target_resource": self.target_resource,
+            "is_valid": self.is_valid,
+            "cost": round(self.cost, 2),
+            "is_in_plan": self.is_in_plan,
+            "plan_order": self.plan_order,
+        }
+
+
+@dataclass
+class PlanDebugInfo:
+    """Debug information for the current plan."""
+    goal_name: str
+    actions: list[str]
+    total_cost: float
+    plan_length: int
+
+    def to_dict(self) -> dict:
+        return {
+            "goal_name": self.goal_name,
+            "actions": self.actions,
+            "total_cost": round(self.total_cost, 2),
+            "plan_length": self.plan_length,
+        }
+
+
+@dataclass
+class GOAPDebugInfo:
+    """Complete GOAP debug information for an agent."""
+    agent_id: str
+    agent_name: str
+    world_state: dict
+    goals: list[GoalDebugInfo]
+    actions: list[ActionDebugInfo]
+    current_plan: Optional[PlanDebugInfo]
+    selected_action: Optional[str]
+    decision_reason: str
+
+    def to_dict(self) -> dict:
+        return {
+            "agent_id": self.agent_id,
+            "agent_name": self.agent_name,
+            "world_state": self.world_state,
+            "goals": [g.to_dict() for g in self.goals],
+            "actions": [a.to_dict() for a in self.actions],
+            "current_plan": self.current_plan.to_dict() if self.current_plan else None,
+            "selected_action": self.selected_action,
+            "decision_reason": self.decision_reason,
+        }
+
+
+def get_goap_debug_info(
+    agent: "Agent",
+    market: "OrderBook",
+    step_in_day: int = 1,
+    day_steps: int = 10,
+    is_night: bool = False,
+) -> GOAPDebugInfo:
+    """Get detailed GOAP debug information for an agent.
+
+    This function performs the same planning as the actual AI,
+    but captures detailed information about the decision process.
+    """
+    # Create world state
+    state = create_world_state(
+        agent=agent,
+        market=market,
+        step_in_day=step_in_day,
+        day_steps=day_steps,
+        is_night=is_night,
+    )
+
+    # Get goals and actions
+    all_goals = get_all_goals()
+    all_actions = get_all_actions()
+
+    # Evaluate all goals
+    goal_infos = []
+    selected_goal = None
+    selected_plan = None
+
+    # Sort by priority
+    goals_with_priority = []
+    for goal in all_goals:
+        priority = goal.priority(state)
+        satisfied = goal.satisfied(state)
+        goals_with_priority.append((goal, priority, satisfied))
+
+    goals_with_priority.sort(key=lambda x: x[1], reverse=True)
+
+    # Try planning for each goal
+    planner = GOAPPlanner(max_iterations=50)
+
+    for goal, priority, satisfied in goals_with_priority:
+        if priority > 0 and not satisfied:
+            plan = planner.plan(state, goal, all_actions)
+            if plan and not plan.is_empty:
+                selected_goal = goal
+                selected_plan = plan
+                break
+
+    # Build goal debug info
+    for goal, priority, satisfied in goals_with_priority:
+        info = GoalDebugInfo(
+            name=goal.name,
+            goal_type=goal.goal_type.value,
+            priority=priority,
+            is_satisfied=satisfied,
+            is_selected=(goal == selected_goal),
+        )
+        goal_infos.append(info)
+
+    # Build action debug info
+    action_infos = []
+    plan_action_names = []
+    if selected_plan:
+        plan_action_names = [a.name for a in selected_plan.actions]
+
+    for action in all_actions:
+        is_valid = action.is_valid(state)
+        cost = action.get_cost(state) if is_valid else float('inf')
+
+        in_plan = action.name in plan_action_names
+        order = plan_action_names.index(action.name) if in_plan else -1
+
+        info = ActionDebugInfo(
+            name=action.name,
+            action_type=action.action_type.value,
+            target_resource=action.target_resource.value if action.target_resource else None,
+            is_valid=is_valid,
+            cost=cost if cost != float('inf') else -1,
+            is_in_plan=in_plan,
+            plan_order=order,
+        )
+        action_infos.append(info)
+
+    # Sort actions: plan actions first (by order), then valid actions, then invalid
+    action_infos.sort(key=lambda a: (
+        0 if a.is_in_plan else 1,
+        a.plan_order if a.is_in_plan else 999,
+        0 if a.is_valid else 1,
+        a.cost if a.cost >= 0 else 9999,
+    ))
+
+    # Build plan debug info
+    plan_info = None
+    if selected_plan:
+        plan_info = PlanDebugInfo(
+            goal_name=selected_plan.goal.name,
+            actions=[a.name for a in selected_plan.actions],
+            total_cost=selected_plan.total_cost,
+            plan_length=len(selected_plan.actions),
+        )
+
+    # Determine selected action and reason
+    selected_action = None
+    reason = "No plan found"
+
+    if is_night:
+        selected_action = "Sleep"
+        reason = "Night time: sleeping"
+    elif selected_plan and selected_plan.first_action:
+        selected_action = selected_plan.first_action.name
+        reason = f"{selected_plan.goal.name}: {selected_action}"
+    else:
+        # Fallback to reactive planning
+        reactive_planner = ReactivePlanner()
+        best_action = reactive_planner.select_best_action(state, all_goals, all_actions)
+        if best_action:
+            selected_action = best_action.name
+            reason = f"Reactive: {best_action.name}"
+
+            # Mark the reactive action in the action list
+            for action_info in action_infos:
+                if action_info.name == best_action.name:
+                    action_info.is_in_plan = True
+                    action_info.plan_order = 0
+
+    return GOAPDebugInfo(
+        agent_id=agent.id,
+        agent_name=agent.name,
+        world_state=state.to_dict(),
+        goals=goal_infos,
+        actions=action_infos,
+        current_plan=plan_info,
+        selected_action=selected_action,
+        decision_reason=reason,
+    )
+
+
+def get_all_agents_goap_debug(
+    agents: list["Agent"],
+    market: "OrderBook",
+    step_in_day: int = 1,
+    day_steps: int = 10,
+    is_night: bool = False,
+) -> list[GOAPDebugInfo]:
+    """Get GOAP debug info for all agents."""
+    return [
+        get_goap_debug_info(agent, market, step_in_day, day_steps, is_night)
+        for agent in agents
+        if agent.is_alive()
+    ]
+
diff --git a/backend/core/goap/goal.py b/backend/core/goap/goal.py
new file mode 100644
index 0000000..84f96d2
--- /dev/null
+++ b/backend/core/goap/goal.py
@@ -0,0 +1,185 @@
+"""Goal definitions for GOAP planning.
+
+Goals represent what an agent wants to achieve. Each goal has:
+- A name/type for identification
+- A condition that checks if the goal is satisfied
+- A priority function that determines how important the goal is
+- Optional target state values for the planner
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Callable, Optional
+
+from .world_state import WorldState
+
+
+class GoalType(Enum):
+    """Types of goals agents can pursue."""
+    # Survival goals - highest priority when needed
+    SATISFY_THIRST = "satisfy_thirst"
+    SATISFY_HUNGER = "satisfy_hunger"
+    MAINTAIN_HEAT = "maintain_heat"
+    RESTORE_ENERGY = "restore_energy"
+
+    # Resource goals - medium priority
+    STOCK_WATER = "stock_water"
+    STOCK_FOOD = "stock_food"
+    STOCK_WOOD = "stock_wood"
+    GET_CLOTHES = "get_clothes"
+
+    # Economic goals - lower priority but persistent
+    BUILD_WEALTH = "build_wealth"
+    SELL_EXCESS = "sell_excess"
+    FIND_DEALS = "find_deals"
+    TRADER_ARBITRAGE = "trader_arbitrage"
+
+    # Night behavior
+    SLEEP = "sleep"
+
+
+@dataclass
+class Goal:
+    """A goal that an agent can pursue.
+
+    Goals are the driving force of GOAP. The planner searches for
+    action sequences that transform the current world state into
+    one where the goal condition is satisfied.
+
+    Attributes:
+        goal_type: The type of goal (for identification)
+        name: Human-readable name
+        is_satisfied: Function that checks if goal is achieved in a state
+        get_priority: Function that calculates goal priority (higher = more important)
+        target_state: Optional dict of state values the goal aims for
+        max_plan_depth: Maximum actions to plan for this goal
+    """
+    goal_type: GoalType
+    name: str
+    is_satisfied: Callable[[WorldState], bool]
+    get_priority: Callable[[WorldState], float]
+    target_state: dict = field(default_factory=dict)
+    max_plan_depth: int = 3
+
+    def satisfied(self, state: WorldState) -> bool:
+        """Check if goal is satisfied in the given state."""
+        return self.is_satisfied(state)
+
+    def priority(self, state: WorldState) -> float:
+        """Get the priority of this goal in the given state."""
+        return self.get_priority(state)
+
+    def __repr__(self) -> str:
+        return f"Goal({self.name})"
+
+
+def create_survival_goal(
+    goal_type: GoalType,
+    name: str,
+    stat_name: str,
+    target_pct: float = 0.6,
+    base_priority: float = 10.0,
+) -> Goal:
+    """Factory for creating survival-related goals.
+
+    Survival goals have high priority when the relevant stat is low.
+    Priority scales with urgency.
+    """
+    urgency_name = f"{stat_name}_urgency"
+    pct_name = f"{stat_name}_pct"
+
+    def is_satisfied(state: WorldState) -> bool:
+        return getattr(state, pct_name) >= target_pct
+
+    def get_priority(state: WorldState) -> float:
+        urgency = getattr(state, urgency_name)
+        pct = getattr(state, pct_name)
+
+        if urgency <= 0:
+            return 0.0  # No need to pursue this goal
+
+        # Priority increases with urgency
+        # Critical urgency (>1.0) gives very high priority
+        priority = base_priority * urgency
+
+        # Extra boost when critical
+        if pct < state.critical_threshold:
+            priority *= 2.0
+
+        return priority
+
+    return Goal(
+        goal_type=goal_type,
+        name=name,
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        target_state={pct_name: target_pct},
+        max_plan_depth=2,  # Survival should be quick
+    )
+
+
+def create_resource_stock_goal(
+    goal_type: GoalType,
+    name: str,
+    resource_name: str,
+    target_count: int,
+    base_priority: float = 5.0,
+) -> Goal:
+    """Factory for creating resource stockpiling goals.
+
+    Resource goals have moderate priority and aim to maintain reserves.
+    """
+    count_name = f"{resource_name}_count"
+
+    def is_satisfied(state: WorldState) -> bool:
+        return getattr(state, count_name) >= target_count
+
+    def get_priority(state: WorldState) -> float:
+        current = getattr(state, count_name)
+
+        if current >= target_count:
+            return 0.0  # Already have enough
+
+        # Priority based on how far from target
+        deficit = target_count - current
+        priority = base_priority * (deficit / target_count)
+
+        # Lower priority if survival is urgent
+        max_urgency = max(state.thirst_urgency, state.hunger_urgency, state.heat_urgency)
+        if max_urgency > 0.5:
+            priority *= 0.5
+
+        # Hoarders prioritize stockpiling more
+        priority *= (0.8 + state.hoarding_rate * 0.4)
+
+        # Evening boost - stock up before night
+        if state.is_evening:
+            priority *= 1.5
+
+        return priority
+
+    return Goal(
+        goal_type=goal_type,
+        name=name,
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        target_state={count_name: target_count},
+        max_plan_depth=3,
+    )
+
+
+def create_economic_goal(
+    goal_type: GoalType,
+    name: str,
+    is_satisfied: Callable[[WorldState], bool],
+    get_priority: Callable[[WorldState], float],
+) -> Goal:
+    """Factory for creating economic/trading goals."""
+    return Goal(
+        goal_type=goal_type,
+        name=name,
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        max_plan_depth=2,
+    )
+
diff --git a/backend/core/goap/goals.py b/backend/core/goap/goals.py
new file mode 100644
index 0000000..da9d1af
--- /dev/null
+++ b/backend/core/goap/goals.py
@@ -0,0 +1,411 @@
+"""Predefined goals for GOAP agents.
+
+Goals are organized into categories:
+- Survival goals: Immediate needs (thirst, hunger, heat, energy)
+- Resource goals: Building reserves
+- Economic goals: Trading and wealth building
+"""
+
+from .goal import Goal, GoalType, create_survival_goal, create_resource_stock_goal, create_economic_goal
+from .world_state import WorldState
+
+
+# =============================================================================
+# SURVIVAL GOALS
+# =============================================================================
+
+def _create_satisfy_thirst_goal() -> Goal:
+    """Satisfy immediate thirst."""
+    return create_survival_goal(
+        goal_type=GoalType.SATISFY_THIRST,
+        name="Satisfy Thirst",
+        stat_name="thirst",
+        target_pct=0.5,  # Want to get to 50%
+        base_priority=15.0,  # Highest base priority - thirst is most dangerous
+    )
+
+
+def _create_satisfy_hunger_goal() -> Goal:
+    """Satisfy immediate hunger."""
+    return create_survival_goal(
+        goal_type=GoalType.SATISFY_HUNGER,
+        name="Satisfy Hunger",
+        stat_name="hunger",
+        target_pct=0.5,
+        base_priority=12.0,
+    )
+
+
+def _create_maintain_heat_goal() -> Goal:
+    """Maintain body heat."""
+    return create_survival_goal(
+        goal_type=GoalType.MAINTAIN_HEAT,
+        name="Maintain Heat",
+        stat_name="heat",
+        target_pct=0.5,
+        base_priority=10.0,
+    )
+
+
+def _create_restore_energy_goal() -> Goal:
+    """Restore energy when low."""
+    def is_satisfied(state: WorldState) -> bool:
+        return state.energy_pct >= 0.4
+
+    def get_priority(state: WorldState) -> float:
+        if state.energy_pct >= 0.4:
+            return 0.0
+
+        # Priority increases as energy decreases
+        urgency = (0.4 - state.energy_pct) / 0.4
+
+        # But not if we have more urgent survival needs
+        max_vital_urgency = max(state.thirst_urgency, state.hunger_urgency, state.heat_urgency)
+        if max_vital_urgency > 1.5:
+            # Critical survival need - don't rest
+            return 0.0
+
+        base_priority = 6.0 * urgency
+
+        # Evening boost - want energy for night
+        if state.is_evening:
+            base_priority *= 1.5
+
+        return base_priority
+
+    return Goal(
+        goal_type=GoalType.RESTORE_ENERGY,
+        name="Restore Energy",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        target_state={"energy_pct": 0.6},
+        max_plan_depth=1,  # Just rest
+    )
+
+
+SURVIVAL_GOALS = [
+    _create_satisfy_thirst_goal(),
+    _create_satisfy_hunger_goal(),
+    _create_maintain_heat_goal(),
+    _create_restore_energy_goal(),
+]
+
+
+# =============================================================================
+# RESOURCE GOALS
+# =============================================================================
+
+def _create_stock_water_goal() -> Goal:
+    """Maintain water reserves."""
+    def is_satisfied(state: WorldState) -> bool:
+        target = int(2 * (0.5 + state.hoarding_rate))
+        return state.water_count >= target
+
+    def get_priority(state: WorldState) -> float:
+        target = int(2 * (0.5 + state.hoarding_rate))
+
+        if state.water_count >= target:
+            return 0.0
+
+        deficit = target - state.water_count
+        base_priority = 4.0 * (deficit / max(1, target))
+
+        # Lower if urgent survival needs
+        if max(state.thirst_urgency, state.hunger_urgency) > 1.0:
+            base_priority *= 0.3
+
+        # Evening boost
+        if state.is_evening:
+            base_priority *= 2.0
+
+        return base_priority
+
+    return Goal(
+        goal_type=GoalType.STOCK_WATER,
+        name="Stock Water",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        max_plan_depth=2,
+    )
+
+
+def _create_stock_food_goal() -> Goal:
+    """Maintain food reserves (meat + berries)."""
+    def is_satisfied(state: WorldState) -> bool:
+        target = int(3 * (0.5 + state.hoarding_rate))
+        return state.food_count >= target
+
+    def get_priority(state: WorldState) -> float:
+        target = int(3 * (0.5 + state.hoarding_rate))
+
+        if state.food_count >= target:
+            return 0.0
+
+        deficit = target - state.food_count
+        base_priority = 4.0 * (deficit / max(1, target))
+
+        # Lower if urgent survival needs
+        if max(state.thirst_urgency, state.hunger_urgency) > 1.0:
+            base_priority *= 0.3
+
+        # Evening boost
+        if state.is_evening:
+            base_priority *= 2.0
+
+        # Risk-takers may prefer hunting (more food per action)
+        base_priority *= (0.8 + state.risk_tolerance * 0.4)
+
+        return base_priority
+
+    return Goal(
+        goal_type=GoalType.STOCK_FOOD,
+        name="Stock Food",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        max_plan_depth=2,
+    )
+
+
+def _create_stock_wood_goal() -> Goal:
+    """Maintain wood reserves for fires."""
+    def is_satisfied(state: WorldState) -> bool:
+        target = int(2 * (0.5 + state.hoarding_rate))
+        return state.wood_count >= target
+
+    def get_priority(state: WorldState) -> float:
+        target = int(2 * (0.5 + state.hoarding_rate))
+
+        if state.wood_count >= target:
+            return 0.0
+
+        deficit = target - state.wood_count
+        base_priority = 3.0 * (deficit / max(1, target))
+
+        # Higher priority if heat is becoming an issue
+        if state.heat_urgency > 0.5:
+            base_priority *= 1.5
+
+        # Lower if urgent survival needs
+        if max(state.thirst_urgency, state.hunger_urgency) > 1.0:
+            base_priority *= 0.3
+
+        return base_priority
+
+    return Goal(
+        goal_type=GoalType.STOCK_WOOD,
+        name="Stock Wood",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        max_plan_depth=2,
+    )
+
+
+def _create_get_clothes_goal() -> Goal:
+    """Get clothes for heat protection."""
+    def is_satisfied(state: WorldState) -> bool:
+        return state.has_clothes
+
+    def get_priority(state: WorldState) -> float:
+        if state.has_clothes:
+            return 0.0
+
+        # Only pursue if we have hide
+        if state.hide_count < 1:
+            return 0.0
+
+        base_priority = 2.0
+
+        # Higher if heat is an issue
+        if state.heat_urgency > 0.3:
+            base_priority *= 1.5
+
+        return base_priority
+
+    return Goal(
+        goal_type=GoalType.GET_CLOTHES,
+        name="Get Clothes",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        max_plan_depth=1,
+    )
+
+
+RESOURCE_GOALS = [
+    _create_stock_water_goal(),
+    _create_stock_food_goal(),
+    _create_stock_wood_goal(),
+    _create_get_clothes_goal(),
+]
+
+
+# =============================================================================
+# ECONOMIC GOALS
+# =============================================================================
+
+def _create_build_wealth_goal() -> Goal:
+    """Accumulate money through trading."""
+    def is_satisfied(state: WorldState) -> bool:
+        return state.is_wealthy
+
+    def get_priority(state: WorldState) -> float:
+        if state.is_wealthy:
+            return 0.0
+
+        # Base priority scaled by wealth desire
+        base_priority = 2.0 * state.wealth_desire
+
+        # Only when survival is stable
+        max_urgency = max(state.thirst_urgency, state.hunger_urgency, state.heat_urgency)
+        if max_urgency > 0.5:
+            return 0.0
+
+        # Traders prioritize wealth more
+        if state.is_trader:
+            base_priority *= 2.0
+
+        return base_priority
+
+    return create_economic_goal(
+        goal_type=GoalType.BUILD_WEALTH,
+        name="Build Wealth",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+    )
+
+
+def _create_sell_excess_goal() -> Goal:
+    """Sell excess resources on the market."""
+    def is_satisfied(state: WorldState) -> bool:
+        # Satisfied if inventory is not getting full
+        return state.inventory_space > 3
+
+    def get_priority(state: WorldState) -> float:
+        if state.inventory_space > 5:
+            return 0.0  # Plenty of space
+
+        # Priority increases as inventory fills
+        fullness = 1.0 - (state.inventory_space / 12.0)
+        base_priority = 3.0 * fullness
+
+        # Low hoarders sell more readily
+        base_priority *= (1.5 - state.hoarding_rate)
+
+        # Only when survival is stable
+        max_urgency = max(state.thirst_urgency, state.hunger_urgency)
+        if max_urgency > 0.5:
+            base_priority *= 0.5
+
+        return base_priority
+
+    return create_economic_goal(
+        goal_type=GoalType.SELL_EXCESS,
+        name="Sell Excess",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+    )
+
+
+def _create_find_deals_goal() -> Goal:
+    """Find good deals on the market."""
+    def is_satisfied(state: WorldState) -> bool:
+        # This goal is never fully "satisfied" - it's opportunistic
+        return False
+
+    def get_priority(state: WorldState) -> float:
+        # Only pursue if we have money and market access
+        if state.money < 10:
+            return 0.0
+
+        # Check if there are deals available
+        has_deals = state.can_buy_water or state.can_buy_food or state.can_buy_wood
+        if not has_deals:
+            return 0.0
+
+        # Base priority from market affinity
+        base_priority = 2.0 * state.market_affinity
+
+        # Only when survival is stable
+        max_urgency = max(state.thirst_urgency, state.hunger_urgency)
+        if max_urgency > 0.5:
+            return 0.0
+
+        # Need inventory space
+        if state.inventory_space < 2:
+            return 0.0
+
+        return base_priority
+
+    return create_economic_goal(
+        goal_type=GoalType.FIND_DEALS,
+        name="Find Deals",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+    )
+
+
+def _create_trader_arbitrage_goal() -> Goal:
+    """Trader-specific arbitrage goal (buy low, sell high)."""
+    def is_satisfied(state: WorldState) -> bool:
+        return False  # Always looking for opportunities
+
+    def get_priority(state: WorldState) -> float:
+        # Only for traders
+        if not state.is_trader:
+            return 0.0
+
+        # Need capital to trade
+        if state.money < 20:
+            return 1.0  # Low priority - need to sell something first
+
+        # Base priority for traders
+        base_priority = 5.0
+
+        # Only when survival is stable
+        max_urgency = max(state.thirst_urgency, state.hunger_urgency, state.heat_urgency)
+        if max_urgency > 0.3:
+            base_priority *= 0.5
+
+        return base_priority
+
+    return create_economic_goal(
+        goal_type=GoalType.TRADER_ARBITRAGE,
+        name="Trader Arbitrage",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+    )
+
+
+def _create_sleep_goal() -> Goal:
+    """Sleep at night."""
+    def is_satisfied(state: WorldState) -> bool:
+        return not state.is_night  # Satisfied when it's not night
+
+    def get_priority(state: WorldState) -> float:
+        if not state.is_night:
+            return 0.0
+
+        # Highest priority at night
+        return 100.0
+
+    return Goal(
+        goal_type=GoalType.SLEEP,
+        name="Sleep",
+        is_satisfied=is_satisfied,
+        get_priority=get_priority,
+        max_plan_depth=1,
+    )
+
+
+ECONOMIC_GOALS = [
+    _create_build_wealth_goal(),
+    _create_sell_excess_goal(),
+    _create_find_deals_goal(),
+    _create_trader_arbitrage_goal(),
+    _create_sleep_goal(),
+]
+
+
+def get_all_goals() -> list[Goal]:
+    """Get all available goals."""
+    return SURVIVAL_GOALS + RESOURCE_GOALS + ECONOMIC_GOALS
+
diff --git a/backend/core/goap/goap_ai.py b/backend/core/goap/goap_ai.py
new file mode 100644
index 0000000..281d0f6
--- /dev/null
+++ b/backend/core/goap/goap_ai.py
@@ -0,0 +1,411 @@
+"""GOAP-based AI decision system for agents.
+
+This module provides the main interface for GOAP-based decision making.
+It replaces the priority-based AgentAI with a goal-oriented planner.
+"""
+
+from dataclasses import dataclass, field
+from typing import Optional, TYPE_CHECKING
+
+from backend.domain.action import ActionType
+from backend.domain.resources import ResourceType
+from backend.domain.personality import get_trade_price_modifier
+
+from .world_state import WorldState, create_world_state
+from .goal import Goal
+from .action import GOAPAction
+from .planner import GOAPPlanner, ReactivePlanner, Plan
+from .goals import get_all_goals
+from .actions import get_all_actions
+
+if TYPE_CHECKING:
+    from backend.domain.agent import Agent
+    from backend.core.market import OrderBook
+    from backend.core.ai import AIDecision, TradeItem
+
+
+@dataclass
+class TradeItem:
+    """A single item to buy/sell in a trade."""
+    order_id: str
+    resource_type: ResourceType
+    quantity: int
+    price_per_unit: int
+
+
+@dataclass
+class AIDecision:
+    """A decision made by the AI for an agent."""
+    action: ActionType
+    target_resource: Optional[ResourceType] = None
+    order_id: Optional[str] = None
+    quantity: int = 1
+    price: int = 0
+    reason: str = ""
+    trade_items: list[TradeItem] = field(default_factory=list)
+    adjust_order_id: Optional[str] = None
+    new_price: Optional[int] = None
+
+    # GOAP-specific fields
+    goal_name: str = ""
+    plan_length: int = 0
+
+    def to_dict(self) -> dict:
+        return {
+            "action": self.action.value,
+            "target_resource": self.target_resource.value if self.target_resource else None,
+            "order_id": self.order_id,
+            "quantity": self.quantity,
+            "price": self.price,
+            "reason": self.reason,
+            "trade_items": [
+                {
+                    "order_id": t.order_id,
+                    "resource_type": t.resource_type.value,
+                    "quantity": t.quantity,
+                    "price_per_unit": t.price_per_unit,
+                }
+                for t in self.trade_items
+            ],
+            "adjust_order_id": self.adjust_order_id,
+            "new_price": self.new_price,
+            "goal_name": self.goal_name,
+            "plan_length": self.plan_length,
+        }
+
+
+class GOAPAgentAI:
+    """GOAP-based AI decision maker for agents.
+
+    This uses goal-oriented action planning to select actions:
+    1. Build world state from agent and market
+    2. Evaluate all goals and their priorities
+    3. Use planner to find action sequence for best goal
+    4. Return the first action as the decision
+
+    Falls back to reactive planning for simple decisions.
+    """
+
+    def __init__(
+        self,
+        agent: "Agent",
+        market: "OrderBook",
+        step_in_day: int = 1,
+        day_steps: int = 10,
+        current_turn: int = 0,
+        is_night: bool = False,
+    ):
+        self.agent = agent
+        self.market = market
+        self.step_in_day = step_in_day
+        self.day_steps = day_steps
+        self.current_turn = current_turn
+        self.is_night = is_night
+
+        # Build world state
+        self.state = create_world_state(
+            agent=agent,
+            market=market,
+            step_in_day=step_in_day,
+            day_steps=day_steps,
+            is_night=is_night,
+        )
+
+        # Initialize planners
+        self.planner = GOAPPlanner(max_iterations=50)
+        self.reactive_planner = ReactivePlanner()
+
+        # Get available goals and actions
+        self.goals = get_all_goals()
+        self.actions = get_all_actions()
+
+        # Personality shortcuts
+        self.p = agent.personality
+        self.skills = agent.skills
+
+    def decide(self) -> AIDecision:
+        """Make a decision using GOAP planning.
+
+        Decision flow:
+        1. Force sleep if night
+        2. Try to find a plan for the highest priority goal
+        3. If no plan found, use reactive selection
+        4. Convert GOAP action to AIDecision with proper parameters
+        """
+        # Night time - mandatory sleep
+        if self.is_night:
+            return AIDecision(
+                action=ActionType.SLEEP,
+                reason="Night time: sleeping",
+                goal_name="Sleep",
+            )
+
+        # Try GOAP planning
+        plan = self.planner.plan_for_goals(
+            initial_state=self.state,
+            goals=self.goals,
+            available_actions=self.actions,
+        )
+
+        if plan and not plan.is_empty:
+            # We have a plan - execute first action
+            goap_action = plan.first_action
+            return self._convert_to_decision(
+                goap_action=goap_action,
+                goal=plan.goal,
+                plan=plan,
+            )
+
+        # Fallback to reactive selection
+        best_action = self.reactive_planner.select_best_action(
+            state=self.state,
+            goals=self.goals,
+            available_actions=self.actions,
+        )
+
+        if best_action:
+            return self._convert_to_decision(
+                goap_action=best_action,
+                goal=None,
+                plan=None,
+            )
+
+        # Ultimate fallback - rest
+        return AIDecision(
+            action=ActionType.REST,
+            reason="No valid action found, resting",
+        )
+
+    def _convert_to_decision(
+        self,
+        goap_action: GOAPAction,
+        goal: Optional[Goal],
+        plan: Optional[Plan],
+    ) -> AIDecision:
+        """Convert a GOAP action to an AIDecision with proper parameters.
+
+        This handles the translation from abstract GOAP actions to
+        concrete decisions with order IDs, prices, etc.
+        """
+        action_type = goap_action.action_type
+        target_resource = goap_action.target_resource
+
+        # Build reason string
+        if goal:
+            reason = f"{goal.name}: {goap_action.name}"
+        else:
+            reason = f"Reactive: {goap_action.name}"
+
+        # Handle different action types
+        if action_type == ActionType.CONSUME:
+            return AIDecision(
+                action=action_type,
+                target_resource=target_resource,
+                reason=reason,
+                goal_name=goal.name if goal else "",
+                plan_length=len(plan.actions) if plan else 0,
+            )
+
+        elif action_type == ActionType.TRADE:
+            return self._create_trade_decision(goap_action, goal, plan, reason)
+
+        elif action_type in [ActionType.HUNT, ActionType.GATHER, ActionType.CHOP_WOOD,
+                            ActionType.GET_WATER, ActionType.WEAVE]:
+            return AIDecision(
+                action=action_type,
+                target_resource=target_resource,
+                reason=reason,
+                goal_name=goal.name if goal else "",
+                plan_length=len(plan.actions) if plan else 0,
+            )
+
+        elif action_type == ActionType.BUILD_FIRE:
+            return AIDecision(
+                action=action_type,
+                target_resource=ResourceType.WOOD,
+                reason=reason,
+                goal_name=goal.name if goal else "",
+                plan_length=len(plan.actions) if plan else 0,
+            )
+
+        elif action_type in [ActionType.REST, ActionType.SLEEP]:
+            return AIDecision(
+                action=action_type,
+                reason=reason,
+                goal_name=goal.name if goal else "",
+                plan_length=len(plan.actions) if plan else 0,
+            )
+
+        # Default case
+        return AIDecision(
+            action=action_type,
+            target_resource=target_resource,
+            reason=reason,
+            goal_name=goal.name if goal else "",
+            plan_length=len(plan.actions) if plan else 0,
+        )
+
+    def _create_trade_decision(
+        self,
+        goap_action: GOAPAction,
+        goal: Optional[Goal],
+        plan: Optional[Plan],
+        reason: str,
+    ) -> AIDecision:
+        """Create a trade decision with actual market parameters.
+
+        This translates abstract "Buy X" or "Sell X" actions into
+        concrete decisions with order IDs, prices, and quantities.
+        """
+        target_resource = goap_action.target_resource
+        action_name = goap_action.name.lower()
+
+        if "buy" in action_name:
+            # Find the best order to buy from
+            order = self.market.get_cheapest_order(target_resource)
+
+            if order and order.seller_id != self.agent.id:
+                # Calculate quantity to buy
+                can_afford = self.agent.money // max(1, order.price_per_unit)
+                space = self.agent.inventory_space()
+                quantity = min(2, can_afford, space, order.quantity)
+
+                if quantity > 0:
+                    return AIDecision(
+                        action=ActionType.TRADE,
+                        target_resource=target_resource,
+                        order_id=order.id,
+                        quantity=quantity,
+                        price=order.price_per_unit,
+                        reason=f"{reason} @ {order.price_per_unit}c",
+                        goal_name=goal.name if goal else "",
+                        plan_length=len(plan.actions) if plan else 0,
+                    )
+
+            # Can't buy - fallback to gathering
+            return self._create_gather_fallback(target_resource, reason, goal, plan)
+
+        elif "sell" in action_name:
+            # Create a sell order
+            quantity_available = self.agent.get_resource_count(target_resource)
+
+            # Calculate minimum to keep
+            min_keep = self._get_min_keep(target_resource)
+            quantity_to_sell = min(3, quantity_available - min_keep)
+
+            if quantity_to_sell > 0:
+                price = self._calculate_sell_price(target_resource)
+
+                return AIDecision(
+                    action=ActionType.TRADE,
+                    target_resource=target_resource,
+                    quantity=quantity_to_sell,
+                    price=price,
+                    reason=f"{reason} @ {price}c",
+                    goal_name=goal.name if goal else "",
+                    plan_length=len(plan.actions) if plan else 0,
+                )
+
+        # Invalid trade action - rest
+        return AIDecision(
+            action=ActionType.REST,
+            reason="Trade not possible",
+        )
+
+    def _create_gather_fallback(
+        self,
+        resource_type: ResourceType,
+        reason: str,
+        goal: Optional[Goal],
+        plan: Optional[Plan],
+    ) -> AIDecision:
+        """Create a gather action as fallback when buying isn't possible."""
+        # Map resource to gather action
+        action_map = {
+            ResourceType.WATER: ActionType.GET_WATER,
+            ResourceType.BERRIES: ActionType.GATHER,
+            ResourceType.MEAT: ActionType.HUNT,
+            ResourceType.WOOD: ActionType.CHOP_WOOD,
+        }
+
+        action = action_map.get(resource_type, ActionType.GATHER)
+
+        return AIDecision(
+            action=action,
+            target_resource=resource_type,
+            reason=f"{reason} (gathering instead)",
+            goal_name=goal.name if goal else "",
+            plan_length=len(plan.actions) if plan else 0,
+        )
+
+    def _get_min_keep(self, resource_type: ResourceType) -> int:
+        """Get minimum quantity to keep for survival."""
+        # Adjusted by hoarding rate
+        hoarding_mult = 0.5 + self.p.hoarding_rate
+
+        base_min = {
+            ResourceType.WATER: 2,
+            ResourceType.MEAT: 1,
+            ResourceType.BERRIES: 2,
+            ResourceType.WOOD: 1,
+            ResourceType.HIDE: 0,
+        }
+
+        return int(base_min.get(resource_type, 1) * hoarding_mult)
+
+    def _calculate_sell_price(self, resource_type: ResourceType) -> int:
+        """Calculate sell price based on fair value and market conditions."""
+        # Get energy cost to produce
+        from backend.core.ai import get_energy_cost
+        from backend.config import get_config
+
+        config = get_config()
+        economy = getattr(config, 'economy', None)
+        energy_to_money_ratio = getattr(economy, 'energy_to_money_ratio', 150) if economy else 150
+        min_price = getattr(economy, 'min_price', 100) if economy else 100
+
+        energy_cost = get_energy_cost(resource_type)
+        fair_value = max(min_price, int(energy_cost * energy_to_money_ratio))
+
+        # Apply trading skill
+        sell_modifier = get_trade_price_modifier(self.skills.trading, is_buying=False)
+
+        # Get market signal
+        signal = self.market.get_market_signal(resource_type)
+
+        if signal == "sell":  # Scarcity
+            price = int(fair_value * 1.3 * sell_modifier)
+        elif signal == "hold":
+            price = int(fair_value * sell_modifier)
+        else:  # Surplus
+            cheapest = self.market.get_cheapest_order(resource_type)
+            if cheapest and cheapest.seller_id != self.agent.id:
+                price = max(min_price, cheapest.price_per_unit - 1)
+            else:
+                price = int(fair_value * 0.8 * sell_modifier)
+
+        return max(min_price, price)
+
+
+def get_goap_decision(
+    agent: "Agent",
+    market: "OrderBook",
+    step_in_day: int = 1,
+    day_steps: int = 10,
+    current_turn: int = 0,
+    is_night: bool = False,
+) -> AIDecision:
+    """Convenience function to get a GOAP-based AI decision for an agent.
+
+    This is the main entry point for the GOAP AI system.
+    """
+    ai = GOAPAgentAI(
+        agent=agent,
+        market=market,
+        step_in_day=step_in_day,
+        day_steps=day_steps,
+        current_turn=current_turn,
+        is_night=is_night,
+    )
+    return ai.decide()
+
diff --git a/backend/core/goap/planner.py b/backend/core/goap/planner.py
new file mode 100644
index 0000000..bab4c36
--- /dev/null
+++ b/backend/core/goap/planner.py
@@ -0,0 +1,335 @@
+"""GOAP Planner using A* search.
+
+The planner finds optimal action sequences to achieve goals.
+It uses A* search with the goal condition as the target.
+"""
+
+import heapq
+from dataclasses import dataclass, field
+from typing import Optional
+
+from .world_state import WorldState
+from .goal import Goal
+from .action import GOAPAction
+
+
+@dataclass(order=True)
+class PlanNode:
+    """A node in the planning search tree."""
+    f_cost: float  # Total cost (g + h)
+    g_cost: float = field(compare=False)  # Cost so far
+    state: WorldState = field(compare=False)
+    action: Optional[GOAPAction] = field(compare=False, default=None)
+    parent: Optional["PlanNode"] = field(compare=False, default=None)
+    depth: int = field(compare=False, default=0)
+
+
+@dataclass
+class Plan:
+    """A plan is a sequence of actions to achieve a goal."""
+    goal: Goal
+    actions: list[GOAPAction]
+    total_cost: float
+    expected_final_state: WorldState
+
+    @property
+    def first_action(self) -> Optional[GOAPAction]:
+        """Get the first action to execute."""
+        return self.actions[0] if self.actions else None
+
+    @property
+    def is_empty(self) -> bool:
+        return len(self.actions) == 0
+
+    def __repr__(self) -> str:
+        action_names = " -> ".join(a.name for a in self.actions)
+        return f"Plan({self.goal.name}: {action_names} [cost={self.total_cost:.1f}])"
+
+
+class GOAPPlanner:
+    """A* planner for GOAP.
+
+    Finds the lowest-cost sequence of actions that transforms
+    the current world state into one where the goal is satisfied.
+    """
+
+    def __init__(self, max_iterations: int = 100):
+        self.max_iterations = max_iterations
+
+    def plan(
+        self,
+        initial_state: WorldState,
+        goal: Goal,
+        available_actions: list[GOAPAction],
+    ) -> Optional[Plan]:
+        """Find an action sequence to achieve the goal.
+
+        Uses A* search where:
+        - g(n) = accumulated action costs
+        - h(n) = heuristic estimate to goal (0 for now - effectively Dijkstra's)
+
+        Returns None if no plan found within iteration limit.
+        """
+        # Check if goal is already satisfied
+        if goal.satisfied(initial_state):
+            return Plan(
+                goal=goal,
+                actions=[],
+                total_cost=0.0,
+                expected_final_state=initial_state,
+            )
+
+        # Priority queue for A*
+        open_set: list[PlanNode] = []
+        start_node = PlanNode(
+            f_cost=0.0,
+            g_cost=0.0,
+            state=initial_state,
+            action=None,
+            parent=None,
+            depth=0,
+        )
+        heapq.heappush(open_set, start_node)
+
+        # Track visited states to avoid cycles
+        # We use a simplified state hash for efficiency
+        visited: set[tuple] = set()
+
+        iterations = 0
+
+        while open_set and iterations < self.max_iterations:
+            iterations += 1
+
+            # Get node with lowest f_cost
+            current = heapq.heappop(open_set)
+
+            # Check depth limit
+            if current.depth >= goal.max_plan_depth:
+                continue
+
+            # Create state hash for cycle detection
+            state_hash = self._hash_state(current.state)
+            if state_hash in visited:
+                continue
+            visited.add(state_hash)
+
+            # Try each action
+            for action in available_actions:
+                # Check if action is valid in current state
+                if not action.is_valid(current.state):
+                    continue
+
+                # Apply action to get new state
+                new_state = action.apply(current.state)
+
+                # Calculate costs
+                action_cost = action.get_cost(current.state)
+                g_cost = current.g_cost + action_cost
+                h_cost = self._heuristic(new_state, goal)
+                f_cost = g_cost + h_cost
+
+                # Create new node
+                new_node = PlanNode(
+                    f_cost=f_cost,
+                    g_cost=g_cost,
+                    state=new_state,
+                    action=action,
+                    parent=current,
+                    depth=current.depth + 1,
+                )
+
+                # Check if goal is satisfied
+                if goal.satisfied(new_state):
+                    # Reconstruct and return plan
+                    return self._reconstruct_plan(new_node, goal)
+
+                # Add to open set
+                heapq.heappush(open_set, new_node)
+
+        # No plan found
+        return None
+
+    def plan_for_goals(
+        self,
+        initial_state: WorldState,
+        goals: list[Goal],
+        available_actions: list[GOAPAction],
+    ) -> Optional[Plan]:
+        """Find the best plan among multiple goals.
+
+        Selects the highest-priority goal that has a valid plan,
+        considering both goal priority and plan cost.
+        """
+        # Sort goals by priority (highest first)
+        sorted_goals = sorted(goals, key=lambda g: g.priority(initial_state), reverse=True)
+
+        best_plan: Optional[Plan] = None
+        best_score = float('-inf')
+
+        for goal in sorted_goals:
+            priority = goal.priority(initial_state)
+
+            # Skip low-priority goals if we already have a good plan
+            if priority <= 0:
+                continue
+
+            if best_plan and priority < best_score * 0.5:
+                # This goal is much lower priority, skip
+                break
+
+            plan = self.plan(initial_state, goal, available_actions)
+
+            if plan:
+                # Score = priority / (cost + 1)
+                # Higher priority and lower cost = better
+                score = priority / (plan.total_cost + 1.0)
+
+                if score > best_score:
+                    best_score = score
+                    best_plan = plan
+
+        return best_plan
+
+    def _hash_state(self, state: WorldState) -> tuple:
+        """Create a hashable representation of key state values.
+
+        We don't hash everything - just the values that matter for planning.
+        """
+        return (
+            round(state.thirst_pct, 1),
+            round(state.hunger_pct, 1),
+            round(state.heat_pct, 1),
+            round(state.energy_pct, 1),
+            state.water_count,
+            state.food_count,
+            state.wood_count,
+            state.money // 10,  # Bucket money
+        )
+
+    def _heuristic(self, state: WorldState, goal: Goal) -> float:
+        """Estimate cost to reach goal from state.
+
+        For now, we use a simple heuristic based on the distance
+        from current state values to goal target values.
+        """
+        if not goal.target_state:
+            return 0.0
+
+        h = 0.0
+        for key, target in goal.target_state.items():
+            if hasattr(state, key):
+                current = getattr(state, key)
+                if isinstance(current, (int, float)) and isinstance(target, (int, float)):
+                    diff = abs(target - current)
+                    h += diff
+
+        return h
+
+    def _reconstruct_plan(self, final_node: PlanNode, goal: Goal) -> Plan:
+        """Reconstruct the action sequence from the final node."""
+        actions = []
+        node = final_node
+
+        while node.parent is not None:
+            if node.action:
+                actions.append(node.action)
+            node = node.parent
+
+        actions.reverse()
+
+        return Plan(
+            goal=goal,
+            actions=actions,
+            total_cost=final_node.g_cost,
+            expected_final_state=final_node.state,
+        )
+
+
+class ReactivePlanner:
+    """A simpler reactive planner for immediate needs.
+
+    Sometimes we don't need full planning - we just need to
+    pick the best immediate action. This planner evaluates
+    single actions against goals.
+    """
+
+    def select_best_action(
+        self,
+        state: WorldState,
+        goals: list[Goal],
+        available_actions: list[GOAPAction],
+    ) -> Optional[GOAPAction]:
+        """Select the single best action to take right now.
+
+        Evaluates each valid action and scores it based on how well
+        it progresses toward high-priority goals.
+        """
+        best_action: Optional[GOAPAction] = None
+        best_score = float('-inf')
+
+        for action in available_actions:
+            if not action.is_valid(state):
+                continue
+
+            score = self._score_action(state, action, goals)
+
+            if score > best_score:
+                best_score = score
+                best_action = action
+
+        return best_action
+
+    def _score_action(
+        self,
+        state: WorldState,
+        action: GOAPAction,
+        goals: list[Goal],
+    ) -> float:
+        """Score an action based on its contribution to goals."""
+        # Apply action to get expected new state
+        new_state = action.apply(state)
+        action_cost = action.get_cost(state)
+
+        total_score = 0.0
+
+        for goal in goals:
+            priority = goal.priority(state)
+            if priority <= 0:
+                continue
+
+            # Check if this action helps with the goal
+            was_satisfied = goal.satisfied(state)
+            now_satisfied = goal.satisfied(new_state)
+
+            if now_satisfied and not was_satisfied:
+                # Action satisfies the goal - big bonus!
+                total_score += priority * 10.0
+            elif not was_satisfied:
+                # Check if we made progress
+                # This is a simplified check based on urgencies
+                old_urgency = self._get_goal_urgency(goal, state)
+                new_urgency = self._get_goal_urgency(goal, new_state)
+
+                if new_urgency < old_urgency:
+                    improvement = old_urgency - new_urgency
+                    total_score += priority * improvement * 5.0
+
+        # Subtract cost
+        total_score -= action_cost
+
+        return total_score
+
+    def _get_goal_urgency(self, goal: Goal, state: WorldState) -> float:
+        """Get the urgency related to a goal."""
+        # Map goal types to state urgencies
+        from .goal import GoalType
+
+        urgency_map = {
+            GoalType.SATISFY_THIRST: state.thirst_urgency,
+            GoalType.SATISFY_HUNGER: state.hunger_urgency,
+            GoalType.MAINTAIN_HEAT: state.heat_urgency,
+            GoalType.RESTORE_ENERGY: state.energy_urgency,
+        }
+
+        return urgency_map.get(goal.goal_type, 0.0)
+
diff --git a/backend/core/goap/world_state.py b/backend/core/goap/world_state.py
new file mode 100644
index 0000000..991f5cb
--- /dev/null
+++ b/backend/core/goap/world_state.py
@@ -0,0 +1,303 @@
+"""World State representation for GOAP planning.
+
+The WorldState is a symbolic representation of the agent's current situation,
+used by the planner to reason about actions and goals.
+"""
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:
+    from backend.domain.agent import Agent
+    from backend.core.market import OrderBook
+
+
+@dataclass
+class WorldState:
+    """Symbolic representation of the world from an agent's perspective.
+
+    This captures all relevant state needed for GOAP planning:
+    - Agent vital stats (as percentages 0-1)
+    - Resource counts in inventory
+    - Market availability
+    - Economic state
+    - Time of day
+
+    The state uses normalized values (0-1) for stats to make
+    threshold comparisons easy and consistent.
+    """
+
+    # Vital stats as percentages (0.0 to 1.0)
+    thirst_pct: float = 1.0
+    hunger_pct: float = 1.0
+    heat_pct: float = 1.0
+    energy_pct: float = 1.0
+
+    # Resource counts in inventory
+    water_count: int = 0
+    food_count: int = 0  # meat + berries
+    meat_count: int = 0
+    berries_count: int = 0
+    wood_count: int = 0
+    hide_count: int = 0
+
+    # Inventory state
+    has_clothes: bool = False
+    inventory_space: int = 0
+    inventory_full: bool = False
+
+    # Economic state
+    money: int = 0
+    is_wealthy: bool = False  # Has comfortable money reserves
+
+    # Market availability (can we buy these?)
+    can_buy_water: bool = False
+    can_buy_food: bool = False
+    can_buy_meat: bool = False
+    can_buy_berries: bool = False
+    can_buy_wood: bool = False
+    water_market_price: int = 0
+    food_market_price: int = 0  # Cheapest of meat/berries
+    wood_market_price: int = 0
+
+    # Time state
+    is_night: bool = False
+    is_evening: bool = False  # Near end of day
+    step_in_day: int = 0
+    day_steps: int = 10
+
+    # Agent personality shortcuts (affect goal priorities)
+    wealth_desire: float = 0.5
+    hoarding_rate: float = 0.5
+    risk_tolerance: float = 0.5
+    market_affinity: float = 0.5
+    is_trader: bool = False
+
+    # Critical thresholds (from config)
+    critical_threshold: float = 0.25
+    low_threshold: float = 0.45
+
+    # Calculated urgencies (how urgent is each need?)
+    thirst_urgency: float = 0.0
+    hunger_urgency: float = 0.0
+    heat_urgency: float = 0.0
+    energy_urgency: float = 0.0
+
+    def __post_init__(self):
+        """Calculate urgencies after initialization."""
+        self._calculate_urgencies()
+
+    def _calculate_urgencies(self):
+        """Calculate urgency values for each vital stat.
+
+        Urgency is 0 when stat is full, and increases as stat decreases.
+        Urgency > 1.0 when in critical range.
+        """
+        # Urgency increases as stat decreases
+        # 0.0 = no urgency, 1.0 = needs attention, 2.0+ = critical
+
+        def calc_urgency(pct: float, critical: float, low: float) -> float:
+            if pct >= low:
+                return 0.0
+            elif pct >= critical:
+                # Linear increase from 0 to 1 as we go from low to critical
+                return 1.0 - (pct - critical) / (low - critical)
+            else:
+                # Exponential increase below critical
+                return 1.0 + (critical - pct) / critical * 2.0
+
+        self.thirst_urgency = calc_urgency(self.thirst_pct, self.critical_threshold, self.low_threshold)
+        self.hunger_urgency = calc_urgency(self.hunger_pct, self.critical_threshold, self.low_threshold)
+        self.heat_urgency = calc_urgency(self.heat_pct, self.critical_threshold, self.low_threshold)
+
+        # Energy urgency is different - we care about absolute level for work
+        if self.energy_pct < 0.25:
+            self.energy_urgency = 2.0
+        elif self.energy_pct < 0.40:
+            self.energy_urgency = 1.0
+        else:
+            self.energy_urgency = 0.0
+
+    def copy(self) -> "WorldState":
+        """Create a copy of this world state."""
+        return WorldState(
+            thirst_pct=self.thirst_pct,
+            hunger_pct=self.hunger_pct,
+            heat_pct=self.heat_pct,
+            energy_pct=self.energy_pct,
+            water_count=self.water_count,
+            food_count=self.food_count,
+            meat_count=self.meat_count,
+            berries_count=self.berries_count,
+            wood_count=self.wood_count,
+            hide_count=self.hide_count,
+            has_clothes=self.has_clothes,
+            inventory_space=self.inventory_space,
+            inventory_full=self.inventory_full,
+            money=self.money,
+            is_wealthy=self.is_wealthy,
+            can_buy_water=self.can_buy_water,
+            can_buy_food=self.can_buy_food,
+            can_buy_meat=self.can_buy_meat,
+            can_buy_berries=self.can_buy_berries,
+            can_buy_wood=self.can_buy_wood,
+            water_market_price=self.water_market_price,
+            food_market_price=self.food_market_price,
+            wood_market_price=self.wood_market_price,
+            is_night=self.is_night,
+            is_evening=self.is_evening,
+            step_in_day=self.step_in_day,
+            day_steps=self.day_steps,
+            wealth_desire=self.wealth_desire,
+            hoarding_rate=self.hoarding_rate,
+            risk_tolerance=self.risk_tolerance,
+            market_affinity=self.market_affinity,
+            is_trader=self.is_trader,
+            critical_threshold=self.critical_threshold,
+            low_threshold=self.low_threshold,
+        )
+
+    def to_dict(self) -> dict:
+        """Convert to dictionary for debugging/logging."""
+        return {
+            "vitals": {
+                "thirst": round(self.thirst_pct, 2),
+                "hunger": round(self.hunger_pct, 2),
+                "heat": round(self.heat_pct, 2),
+                "energy": round(self.energy_pct, 2),
+            },
+            "urgencies": {
+                "thirst": round(self.thirst_urgency, 2),
+                "hunger": round(self.hunger_urgency, 2),
+                "heat": round(self.heat_urgency, 2),
+                "energy": round(self.energy_urgency, 2),
+            },
+            "inventory": {
+                "water": self.water_count,
+                "meat": self.meat_count,
+                "berries": self.berries_count,
+                "wood": self.wood_count,
+                "hide": self.hide_count,
+                "space": self.inventory_space,
+            },
+            "economy": {
+                "money": self.money,
+                "is_wealthy": self.is_wealthy,
+            },
+            "market": {
+                "can_buy_water": self.can_buy_water,
+                "can_buy_food": self.can_buy_food,
+                "can_buy_wood": self.can_buy_wood,
+            },
+            "time": {
+                "is_night": self.is_night,
+                "is_evening": self.is_evening,
+                "step": self.step_in_day,
+            },
+        }
+
+
+def create_world_state(
+    agent: "Agent",
+    market: "OrderBook",
+    step_in_day: int = 1,
+    day_steps: int = 10,
+    is_night: bool = False,
+) -> WorldState:
+    """Create a WorldState from an agent and market.
+
+    This is the main factory function for creating world states.
+    It extracts all relevant information from the agent and market.
+    """
+    from backend.domain.resources import ResourceType
+    from backend.config import get_config
+
+    config = get_config()
+    agent_config = config.agent_stats
+    economy_config = getattr(config, 'economy', None)
+
+    stats = agent.stats
+
+    # Calculate stat percentages
+    thirst_pct = stats.thirst / stats.MAX_THIRST
+    hunger_pct = stats.hunger / stats.MAX_HUNGER
+    heat_pct = stats.heat / stats.MAX_HEAT
+    energy_pct = stats.energy / stats.MAX_ENERGY
+
+    # Get resource counts
+    water_count = agent.get_resource_count(ResourceType.WATER)
+    meat_count = agent.get_resource_count(ResourceType.MEAT)
+    berries_count = agent.get_resource_count(ResourceType.BERRIES)
+    wood_count = agent.get_resource_count(ResourceType.WOOD)
+    hide_count = agent.get_resource_count(ResourceType.HIDE)
+    food_count = meat_count + berries_count
+
+    # Check market availability
+    def get_market_info(resource_type: ResourceType) -> tuple[bool, int]:
+        """Get market availability and price for a resource."""
+        order = market.get_cheapest_order(resource_type)
+        if order and order.seller_id != agent.id and agent.money >= order.price_per_unit:
+            return True, order.price_per_unit
+        return False, 0
+
+    can_buy_water, water_price = get_market_info(ResourceType.WATER)
+    can_buy_meat, meat_price = get_market_info(ResourceType.MEAT)
+    can_buy_berries, berries_price = get_market_info(ResourceType.BERRIES)
+    can_buy_wood, wood_price = get_market_info(ResourceType.WOOD)
+
+    # Can buy food if we can buy either meat or berries
+    can_buy_food = can_buy_meat or can_buy_berries
+    food_price = min(
+        meat_price if can_buy_meat else float('inf'),
+        berries_price if can_buy_berries else float('inf')
+    )
+    food_price = food_price if food_price != float('inf') else 0
+
+    # Wealth calculation
+    min_wealth_target = getattr(economy_config, 'min_wealth_target', 50) if economy_config else 50
+    wealth_target = int(min_wealth_target * (0.5 + agent.personality.wealth_desire))
+    is_wealthy = agent.money >= wealth_target
+
+    # Trader check
+    is_trader = agent.personality.trade_preference > 1.3 and agent.personality.market_affinity > 0.5
+
+    # Evening check (last 2 steps before night)
+    is_evening = step_in_day >= day_steps - 2
+
+    return WorldState(
+        thirst_pct=thirst_pct,
+        hunger_pct=hunger_pct,
+        heat_pct=heat_pct,
+        energy_pct=energy_pct,
+        water_count=water_count,
+        food_count=food_count,
+        meat_count=meat_count,
+        berries_count=berries_count,
+        wood_count=wood_count,
+        hide_count=hide_count,
+        has_clothes=agent.has_clothes(),
+        inventory_space=agent.inventory_space(),
+        inventory_full=agent.inventory_full(),
+        money=agent.money,
+        is_wealthy=is_wealthy,
+        can_buy_water=can_buy_water,
+        can_buy_food=can_buy_food,
+        can_buy_meat=can_buy_meat,
+        can_buy_berries=can_buy_berries,
+        can_buy_wood=can_buy_wood,
+        water_market_price=water_price,
+        food_market_price=int(food_price),
+        wood_market_price=wood_price,
+        is_night=is_night,
+        is_evening=is_evening,
+        step_in_day=step_in_day,
+        day_steps=day_steps,
+        wealth_desire=agent.personality.wealth_desire,
+        hoarding_rate=agent.personality.hoarding_rate,
+        risk_tolerance=agent.personality.risk_tolerance,
+        market_affinity=agent.personality.market_affinity,
+        is_trader=is_trader,
+        critical_threshold=agent_config.critical_threshold,
+        low_threshold=0.45,  # Could also be in config
+    )
+
diff --git a/backend/core/market.py b/backend/core/market.py
index a95d25a..a447780 100644
--- a/backend/core/market.py
+++ b/backend/core/market.py
@@ -40,31 +40,32 @@ class Order:
     seller_id: str = ""
     resource_type: ResourceType = ResourceType.BERRIES
     quantity: int = 1
-    price_per_unit: int = 1
+    price_per_unit: int = 100  # Default to min_price from config
     created_turn: int = 0
     status: OrderStatus = OrderStatus.ACTIVE
-    
+
     # Price adjustment tracking
     turns_without_sale: int = 0
     original_price: int = 0
     last_adjusted_turn: int = 0  # Track when price was last changed
-    
+
     def __post_init__(self):
         if self.original_price == 0:
             self.original_price = self.price_per_unit
         if self.last_adjusted_turn == 0:
             self.last_adjusted_turn = self.created_turn
-    
+
     @property
     def total_price(self) -> int:
         """Get total price for all units."""
         return self.quantity * self.price_per_unit
-    
+
     def apply_discount(self, percentage: float = 0.1) -> None:
         """Apply a discount to the price."""
+        min_price = _get_min_price()
         reduction = max(1, int(self.price_per_unit * percentage))
-        self.price_per_unit = max(1, self.price_per_unit - reduction)
-    
+        self.price_per_unit = max(min_price, self.price_per_unit - reduction)
+
     def adjust_price(self, new_price: int, current_turn: int) -> bool:
         """Adjust the order's price. Returns True if successful."""
         if new_price < 1:
@@ -72,11 +73,11 @@ class Order:
         self.price_per_unit = new_price
         self.last_adjusted_turn = current_turn
         return True
-    
+
     def can_raise_price(self, current_turn: int, min_turns: int = 2) -> bool:
         """Check if enough time has passed to raise the price again."""
         return current_turn - self.last_adjusted_turn >= min_turns
-    
+
     def to_dict(self) -> dict:
         """Convert to dictionary for API serialization."""
         return {
@@ -104,7 +105,7 @@ class TradeResult:
     quantity: int = 0
     total_paid: int = 0
     message: str = ""
-    
+
     def to_dict(self) -> dict:
         return {
             "success": self.success,
@@ -124,31 +125,39 @@ def _get_market_config():
     return get_config().market
 
 
+def _get_min_price() -> int:
+    """Get minimum price floor from economy config."""
+    from backend.config import get_config
+    config = get_config()
+    economy = getattr(config, 'economy', None)
+    return getattr(economy, 'min_price', 100) if economy else 100
+
+
 @dataclass
 class OrderBook:
     """Central market order book with supply/demand tracking.
-    
+
     Features:
     - Track price history per resource type
     - Calculate supply/demand scores
     - Suggest prices based on market conditions
     - Allow sellers to adjust prices dynamically
-    
+
     Configuration is loaded from config.json.
     """
     orders: list[Order] = field(default_factory=list)
     trade_history: list[TradeResult] = field(default_factory=list)
     price_history: dict[ResourceType, PriceHistory] = field(default_factory=dict)
-    
+
     # Configuration - defaults loaded from config.json in __post_init__
     TURNS_BEFORE_DISCOUNT: int = 15
     DISCOUNT_RATE: float = 0.12
-    
+
     # Supply/demand thresholds
     LOW_SUPPLY_THRESHOLD: int = 3  # Less than this = scarcity
     HIGH_SUPPLY_THRESHOLD: int = 10  # More than this = surplus
     DEMAND_DECAY: float = 0.95  # How fast demand score decays per turn
-    
+
     def __post_init__(self):
         """Initialize price history and load config values."""
         # Load market config from config.json
@@ -158,11 +167,11 @@ class OrderBook:
             self.DISCOUNT_RATE = cfg.discount_rate
         except Exception:
             pass  # Use defaults if config not available
-        
+
         if not self.price_history:
             for resource_type in ResourceType:
                 self.price_history[resource_type] = PriceHistory()
-    
+
     def place_order(
         self,
         seller_id: str,
@@ -181,7 +190,7 @@ class OrderBook:
         )
         self.orders.append(order)
         return order
-    
+
     def cancel_order(self, order_id: str, seller_id: str) -> bool:
         """Cancel an order. Returns True if successful."""
         for order in self.orders:
@@ -190,11 +199,11 @@ class OrderBook:
                     order.status = OrderStatus.CANCELLED
                     return True
         return False
-    
+
     def get_active_orders(self) -> list[Order]:
         """Get all active orders."""
         return [o for o in self.orders if o.status == OrderStatus.ACTIVE]
-    
+
     def get_orders_by_type(self, resource_type: ResourceType) -> list[Order]:
         """Get all active orders for a specific resource type, sorted by price."""
         orders = [
@@ -202,19 +211,19 @@ class OrderBook:
             if o.status == OrderStatus.ACTIVE and o.resource_type == resource_type
         ]
         return sorted(orders, key=lambda o: o.price_per_unit)
-    
+
     def get_cheapest_order(self, resource_type: ResourceType) -> Optional[Order]:
         """Get the cheapest active order for a resource type."""
         orders = self.get_orders_by_type(resource_type)
         return orders[0] if orders else None
-    
+
     def get_orders_by_seller(self, seller_id: str) -> list[Order]:
         """Get all active orders from a specific seller."""
         return [
             o for o in self.orders
             if o.status == OrderStatus.ACTIVE and o.seller_id == seller_id
         ]
-    
+
     def cancel_seller_orders(self, seller_id: str) -> list[Order]:
         """Cancel all orders from a seller (e.g., when they die). Returns cancelled orders."""
         cancelled = []
@@ -223,7 +232,7 @@ class OrderBook:
                 order.status = OrderStatus.CANCELLED
                 cancelled.append(order)
         return cancelled
-    
+
     def execute_buy(
         self,
         buyer_id: str,
@@ -238,13 +247,13 @@ class OrderBook:
             if o.id == order_id and o.status == OrderStatus.ACTIVE:
                 order = o
                 break
-        
+
         if order is None:
             return TradeResult(
                 success=False,
                 message="Order not found or no longer active",
             )
-        
+
         # Check quantity
         actual_quantity = min(quantity, order.quantity)
         if actual_quantity <= 0:
@@ -252,7 +261,7 @@ class OrderBook:
                 success=False,
                 message="Invalid quantity",
             )
-        
+
         # Check buyer has enough money
         total_cost = actual_quantity * order.price_per_unit
         if buyer_money < total_cost:
@@ -265,12 +274,12 @@ class OrderBook:
                     message="Insufficient funds",
                 )
             total_cost = actual_quantity * order.price_per_unit
-        
+
         # Execute the trade
         order.quantity -= actual_quantity
         if order.quantity <= 0:
             order.status = OrderStatus.FILLED
-        
+
         result = TradeResult(
             success=True,
             order_id=order.id,
@@ -281,12 +290,12 @@ class OrderBook:
             total_paid=total_cost,
             message=f"Bought {actual_quantity} {order.resource_type.value} for {total_cost} coins",
         )
-        
+
         # Record sale for price history (we need current_turn but don't have it here)
         # The turn will be passed via the _record_sale call from engine
         self.trade_history.append(result)
         return result
-    
+
     def execute_multi_buy(
         self,
         buyer_id: str,
@@ -296,30 +305,30 @@ class OrderBook:
         """Execute multiple buy orders in one action. Returns list of trade results."""
         results = []
         remaining_money = buyer_money
-        
+
         for order_id, quantity in purchases:
             result = self.execute_buy(buyer_id, order_id, quantity, remaining_money)
             results.append(result)
             if result.success:
                 remaining_money -= result.total_paid
-        
+
         return results
-    
+
     def update_prices(self, current_turn: int) -> None:
         """Update order prices and supply/demand scores."""
         # Update supply/demand scores
         self._update_supply_demand_scores(current_turn)
-        
+
         # Apply automatic discounts to stale orders (keeping original behavior)
         for order in self.orders:
             if order.status != OrderStatus.ACTIVE:
                 continue
-            
+
             turns_waiting = current_turn - order.created_turn
             if turns_waiting > 0 and turns_waiting % self.TURNS_BEFORE_DISCOUNT == 0:
                 order.turns_without_sale = turns_waiting
                 order.apply_discount(self.DISCOUNT_RATE)
-    
+
     def _update_supply_demand_scores(self, current_turn: int) -> None:
         """Calculate current supply and demand scores for each resource."""
         for resource_type in ResourceType:
@@ -327,7 +336,7 @@ class OrderBook:
             if not history:
                 history = PriceHistory()
                 self.price_history[resource_type] = history
-            
+
             # Calculate supply score based on available quantity
             total_supply = self.get_total_supply(resource_type)
             if total_supply <= self.LOW_SUPPLY_THRESHOLD:
@@ -336,36 +345,36 @@ class OrderBook:
                 history.supply_score = min(1.0, 0.5 + (total_supply / self.HIGH_SUPPLY_THRESHOLD) * 0.5)
             else:
                 history.supply_score = 0.5
-            
+
             # Decay demand score over time
             history.demand_score *= self.DEMAND_DECAY
-    
+
     def get_total_supply(self, resource_type: ResourceType) -> int:
         """Get total quantity available for a resource type."""
         return sum(o.quantity for o in self.get_orders_by_type(resource_type))
-    
+
     def get_supply_demand_ratio(self, resource_type: ResourceType) -> float:
         """Get supply/demand ratio. <1 means scarcity, >1 means surplus."""
         history = self.price_history.get(resource_type, PriceHistory())
         demand = max(0.1, history.demand_score)
         supply = max(0.1, history.supply_score)
         return supply / demand
-    
+
     def get_suggested_price(self, resource_type: ResourceType, base_price: int) -> int:
         """Suggest a price based on supply/demand conditions.
-        
+
         Returns an adjusted price that accounts for market conditions:
         - Scarcity (low supply, high demand) -> higher price
         - Surplus (high supply, low demand) -> lower price
         """
         ratio = self.get_supply_demand_ratio(resource_type)
         history = self.price_history.get(resource_type, PriceHistory())
-        
+
         # Use average sale price as reference if available
         reference_price = base_price
         if history.avg_sale_price > 0:
             reference_price = int((base_price + history.avg_sale_price) / 2)
-        
+
         # Adjust based on supply/demand
         if ratio < 0.7:  # Scarcity - raise price
             price_multiplier = 1.0 + (0.7 - ratio) * 0.5  # Up to 35% increase
@@ -374,10 +383,10 @@ class OrderBook:
             price_multiplier = max(0.5, price_multiplier)  # Floor at 50%
         else:
             price_multiplier = 1.0
-        
+
         suggested = int(reference_price * price_multiplier)
-        return max(1, suggested)
-    
+        return max(_get_min_price(), suggested)
+
     def adjust_order_price(self, order_id: str, seller_id: str, new_price: int, current_turn: int) -> bool:
         """Adjust the price of an existing order. Returns True if successful."""
         for order in self.orders:
@@ -385,37 +394,37 @@ class OrderBook:
                 if order.status == OrderStatus.ACTIVE:
                     return order.adjust_price(new_price, current_turn)
         return False
-    
+
     def _record_sale(self, resource_type: ResourceType, price: int, quantity: int, current_turn: int) -> None:
         """Record a sale for price history tracking."""
         history = self.price_history.get(resource_type)
         if not history:
             history = PriceHistory()
             self.price_history[resource_type] = history
-        
+
         history.last_sale_price = price
         history.last_sale_turn = current_turn
-        
+
         # Update average sale price
         old_total = history.avg_sale_price * history.total_sold
         history.total_sold += quantity
         history.avg_sale_price = (old_total + price * quantity) / history.total_sold
-        
+
         # Increase demand score when sales happen
         history.demand_score = min(1.0, history.demand_score + 0.1 * quantity)
-    
+
     def cleanup_old_orders(self, max_age: int = 50) -> list[Order]:
         """Remove very old orders. Returns removed orders."""
         # For now, we don't auto-expire orders, but this could be enabled
         return []
-    
+
     def get_market_prices(self) -> dict[str, dict]:
         """Get current market price summary for each resource."""
         prices = {}
         for resource_type in ResourceType:
             orders = self.get_orders_by_type(resource_type)
             history = self.price_history.get(resource_type, PriceHistory())
-            
+
             if orders:
                 prices[resource_type.value] = {
                     "lowest_price": orders[0].price_per_unit,
@@ -437,7 +446,7 @@ class OrderBook:
                     "demand_score": round(history.demand_score, 2),
                 }
         return prices
-    
+
     def get_market_signal(self, resource_type: ResourceType) -> str:
         """Get a simple market signal for a resource: 'sell', 'hold', or 'buy'."""
         ratio = self.get_supply_demand_ratio(resource_type)
@@ -446,7 +455,7 @@ class OrderBook:
         elif ratio > 1.3:
             return "buy"   # Good time to buy - surplus
         return "hold"
-    
+
     def get_state_snapshot(self) -> dict:
         """Get market state for API."""
         return {
diff --git a/config.json b/config.json
index 7cbff9c..e2d124b 100644
--- a/config.json
+++ b/config.json
@@ -1,4 +1,10 @@
 {
+  "ai": {
+    "use_goap": true,
+    "goap_max_iterations": 50,
+    "goap_max_plan_depth": 3,
+    "reactive_fallback": true
+  },
   "agent_stats": {
     "max_energy": 50,
     "max_hunger": 100,
@@ -19,27 +25,27 @@
     "meat_decay": 10,
     "berries_decay": 6,
     "clothes_decay": 20,
-    "meat_hunger": 35,
-    "meat_energy": 12,
-    "berries_hunger": 10,
-    "berries_thirst": 4,
+    "meat_hunger": 45,
+    "meat_energy": 15,
+    "berries_hunger": 8,
+    "berries_thirst": 2,
     "water_thirst": 50,
     "fire_heat": 20
   },
   "actions": {
     "sleep_energy": 55,
     "rest_energy": 12,
-    "hunt_energy": -7,
-    "gather_energy": -3,
+    "hunt_energy": -5,
+    "gather_energy": -4,
     "chop_wood_energy": -6,
     "get_water_energy": -2,
     "weave_energy": -6,
     "build_fire_energy": -4,
     "trade_energy": -1,
-    "hunt_success": 0.70,
-    "chop_wood_success": 0.90,
+    "hunt_success": 0.85,
+    "chop_wood_success": 0.9,
     "hunt_meat_min": 2,
-    "hunt_meat_max": 5,
+    "hunt_meat_max": 4,
     "hunt_hide_min": 0,
     "hunt_hide_max": 2,
     "gather_min": 2,
@@ -54,7 +60,7 @@
     "day_steps": 10,
     "night_steps": 1,
     "inventory_slots": 12,
-    "starting_money": 80
+    "starting_money": 8000
   },
   "market": {
     "turns_before_discount": 15,
@@ -62,10 +68,11 @@
     "base_price_multiplier": 1.3
   },
   "economy": {
-    "energy_to_money_ratio": 1.5,
+    "energy_to_money_ratio": 150,
+    "min_price": 100,
     "wealth_desire": 0.35,
     "buy_efficiency_threshold": 0.75,
-    "min_wealth_target": 50,
+    "min_wealth_target": 5000,
     "max_price_markup": 2.5,
     "min_price_discount": 0.4
   },
diff --git a/config_goap_optimized.json b/config_goap_optimized.json
new file mode 100644
index 0000000..1a00e63
--- /dev/null
+++ b/config_goap_optimized.json
@@ -0,0 +1,79 @@
+{
+  "ai": {
+    "use_goap": true,
+    "goap_max_iterations": 50,
+    "goap_max_plan_depth": 3,
+    "reactive_fallback": true
+  },
+  "agent_stats": {
+    "max_energy": 50,
+    "max_hunger": 100,
+    "max_thirst": 100,
+    "max_heat": 100,
+    "start_energy": 50,
+    "start_hunger": 70,
+    "start_thirst": 75,
+    "start_heat": 100,
+    "energy_decay": 1,
+    "hunger_decay": 2,
+    "thirst_decay": 3,
+    "heat_decay": 3,
+    "critical_threshold": 0.25,
+    "low_energy_threshold": 12
+  },
+  "resources": {
+    "meat_decay": 10,
+    "berries_decay": 6,
+    "clothes_decay": 20,
+    "meat_hunger": 45,
+    "meat_energy": 15,
+    "berries_hunger": 8,
+    "berries_thirst": 2,
+    "water_thirst": 50,
+    "fire_heat": 20
+  },
+  "actions": {
+    "sleep_energy": 55,
+    "rest_energy": 12,
+    "hunt_energy": -5,
+    "gather_energy": -4,
+    "chop_wood_energy": -6,
+    "get_water_energy": -2,
+    "weave_energy": -6,
+    "build_fire_energy": -4,
+    "trade_energy": -1,
+    "hunt_success": 0.85,
+    "chop_wood_success": 0.9,
+    "hunt_meat_min": 2,
+    "hunt_meat_max": 4,
+    "hunt_hide_min": 0,
+    "hunt_hide_max": 2,
+    "gather_min": 2,
+    "gather_max": 4,
+    "chop_wood_min": 1,
+    "chop_wood_max": 3
+  },
+  "world": {
+    "width": 25,
+    "height": 25,
+    "initial_agents": 25,
+    "day_steps": 10,
+    "night_steps": 1,
+    "inventory_slots": 12,
+    "starting_money": 80
+  },
+  "market": {
+    "turns_before_discount": 15,
+    "discount_rate": 0.12,
+    "base_price_multiplier": 1.3
+  },
+  "economy": {
+    "energy_to_money_ratio": 1.5,
+    "wealth_desire": 0.35,
+    "buy_efficiency_threshold": 0.75,
+    "min_wealth_target": 50,
+    "max_price_markup": 2.5,
+    "min_price_discount": 0.4
+  },
+  "auto_step_interval": 0.15
+}
\ No newline at end of file
diff --git a/tools/optimize_goap.py b/tools/optimize_goap.py
new file mode 100644
index 0000000..c9ba27d
--- /dev/null
+++ b/tools/optimize_goap.py
@@ -0,0 +1,496 @@
+#!/usr/bin/env python3
+"""
+GOAP Economy Optimizer for Village Simulation
+
+This script optimizes the simulation parameters specifically for the GOAP AI system.
+The goal is to achieve:
+- Balanced action diversity (hunting, gathering, trading)
+- Active economy with trading
+- Good survival rates
+- Meat production through hunting
+
+Key insight: GOAP uses action COSTS to choose actions. Lower cost = preferred.
+We need to tune:
+1. Action energy costs (config.json)
+2. GOAP action cost functions (goap/actions.py)
+3. Goal priorities (goap/goals.py)
+
+Usage:
+    python tools/optimize_goap.py [--iterations 15] [--steps 300]
+    python tools/optimize_goap.py --analyze  # Analyze current GOAP behavior
+"""
+
+import argparse
+import json
+import random
+import sys
+from collections import defaultdict
+from datetime import datetime
+from pathlib import Path
+
+# Add parent directory for imports
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from backend.config import get_config, reload_config
+from backend.core.engine import GameEngine
+from backend.domain.action import reset_action_config_cache
+from backend.domain.resources import reset_resource_cache
+
+
+def analyze_goap_behavior(num_steps: int = 100, num_agents: int = 10):
+    """Analyze current GOAP behavior in detail."""
+    print("\n" + "=" * 70)
+    print("🔍 GOAP BEHAVIOR ANALYSIS")
+    print("=" * 70)
+
+    # Reset engine
+    GameEngine._instance = None
+    engine = GameEngine()
+    engine.initialize(num_agents=num_agents)
+
+    # Track statistics
+    action_counts = defaultdict(int)
+    goal_counts = defaultdict(int)
+    reactive_count = 0
+    planned_count = 0
+
+    # Resource tracking
+    resources_produced = defaultdict(int)
+    resources_consumed = defaultdict(int)
+
+    # Run simulation
+    for step in range(num_steps):
+        if not engine.is_running:
+            print(f"  Simulation ended at step {step}")
+            break
+
+        log = engine.next_step()
+
+        for action_data in log.agent_actions:
+            decision = action_data.get("decision", {})
+            result = action_data.get("result", {})
+
+            action_type = decision.get("action", "unknown")
+            action_counts[action_type] += 1
+
+            # Track goal/reactive
+            goal_name = decision.get("goal_name", "")
+            reason = decision.get("reason", "")
+
+            if goal_name:
+                goal_counts[goal_name] += 1
+                planned_count += 1
+            elif "Reactive" in reason:
+                goal_counts["(reactive)"] += 1
+                reactive_count += 1
+
+            # Track resources
+            if result and result.get("success"):
+                for res in result.get("resources_gained", []):
+                    resources_produced[res.get("type", "unknown")] += res.get("quantity", 0)
+                for res in result.get("resources_consumed", []):
+                    resources_consumed[res.get("type", "unknown")] += res.get("quantity", 0)
+
+    # Print results
+    total_actions = sum(action_counts.values())
+
+    print(f"\n📊 Action Distribution ({num_steps} turns, {num_agents} agents)")
+    print("-" * 50)
+    for action, count in sorted(action_counts.items(), key=lambda x: -x[1]):
+        pct = count * 100 / total_actions if total_actions > 0 else 0
+        bar = "█" * int(pct / 2)
+        print(f"  {action:12} {count:4} ({pct:5.1f}%) {bar}")
+
+    print(f"\n🎯 Goal Distribution")
+    print("-" * 50)
+    total_goals = sum(goal_counts.values())
+    for goal, count in sorted(goal_counts.items(), key=lambda x: -x[1])[:15]:
+        pct = count * 100 / total_goals if total_goals > 0 else 0
+        print(f"  {goal:20} {count:4} ({pct:5.1f}%)")
+
+    print(f"\n  Planned actions: {planned_count} ({planned_count*100/total_actions:.1f}%)")
+    print(f"  Reactive actions: {reactive_count} ({reactive_count*100/total_actions:.1f}%)")
+
+    print(f"\n📦 Resources Produced")
+    print("-" * 50)
+    for res, qty in sorted(resources_produced.items(), key=lambda x: -x[1]):
+        print(f"  {res:12} {qty:4}")
+
+    print(f"\n🔥 Resources Consumed")
+    print("-" * 50)
+    for res, qty in sorted(resources_consumed.items(), key=lambda x: -x[1]):
+        print(f"  {res:12} {qty:4}")
+
+    # Diagnose issues
+    print(f"\n⚠️  ISSUES DETECTED:")
+    print("-" * 50)
+
+    hunt_pct = action_counts.get("hunt", 0) * 100 / total_actions if total_actions > 0 else 0
+    gather_pct = action_counts.get("gather", 0) * 100 / total_actions if total_actions > 0 else 0
+
+    if hunt_pct < 5:
+        print("  ❌ Almost no hunting! Hunt action cost too high or meat not valued enough.")
+        print("     → Reduce hunt energy cost or increase meat benefits")
+
+    if resources_produced.get("meat", 0) == 0:
+        print("  ❌ No meat produced! Agents never hunt successfully.")
+
+    trade_pct = action_counts.get("trade", 0) * 100 / total_actions if total_actions > 0 else 0
+    if trade_pct < 5:
+        print("  ❌ Low trading activity. Market goals not prioritized.")
+
+    if reactive_count > planned_count:
+        print("  ⚠️  More reactive than planned actions. Goals may be too easily satisfied.")
+
+    return {
+        "action_counts": dict(action_counts),
+        "goal_counts": dict(goal_counts),
+        "resources_produced": dict(resources_produced),
+        "resources_consumed": dict(resources_consumed),
+    }
+
+
+def test_config(config_overrides: dict, num_steps: int = 200, num_agents: int = 10, verbose: bool = True):
+    """Test a configuration and return metrics."""
+    # Save original config
+    config_path = Path("config.json")
+    with open(config_path) as f:
+        original_config = json.load(f)
+
+    # Apply overrides
+    test_config = json.loads(json.dumps(original_config))
+    for section, values in config_overrides.items():
+        if section in test_config:
+            test_config[section].update(values)
+        else:
+            test_config[section] = values
+
+    # Save temp config
+    temp_path = Path("config_temp.json")
+    with open(temp_path, 'w') as f:
+        json.dump(test_config, f, indent=2)
+
+    # Reload config
+    reload_config(str(temp_path))
+    reset_action_config_cache()
+    reset_resource_cache()
+
+    # Run simulation
+    GameEngine._instance = None
+    engine = GameEngine()
+    engine.initialize(num_agents=num_agents)
+
+    action_counts = defaultdict(int)
+    resources_produced = defaultdict(int)
+    deaths = 0
+    trades_completed = 0
+
+    for step in range(num_steps):
+        if not engine.is_running:
+            break
+
+        log = engine.next_step()
+        deaths += len(log.deaths)
+
+        for action_data in log.agent_actions:
+            decision = action_data.get("decision", {})
+            result = action_data.get("result", {})
+
+            action_type = decision.get("action", "unknown")
+            action_counts[action_type] += 1
+
+            if result and result.get("success"):
+                for res in result.get("resources_gained", []):
+                    resources_produced[res.get("type", "unknown")] += res.get("quantity", 0)
+
+                if action_type == "trade" and "Bought" in result.get("message", ""):
+                    trades_completed += 1
+
+    final_pop = len(engine.world.get_living_agents())
+
+    # Cleanup
+    engine.logger.close()
+    temp_path.unlink(missing_ok=True)
+
+    # Restore original config
+    reload_config(str(config_path))
+    reset_action_config_cache()
+    reset_resource_cache()
+
+    # Calculate score
+    total_actions = sum(action_counts.values())
+    hunt_ratio = action_counts.get("hunt", 0) / total_actions if total_actions > 0 else 0
+    gather_ratio = action_counts.get("gather", 0) / total_actions if total_actions > 0 else 0
+    trade_ratio = action_counts.get("trade", 0) / total_actions if total_actions > 0 else 0
+
+    survival_rate = final_pop / num_agents
+
+    # Score components
+    # 1. Hunt ratio: want 10-25%
+    hunt_score = min(25, hunt_ratio * 100) if hunt_ratio > 0.05 else 0
+
+    # 2. Trade activity: want 5-15%
+    trade_score = min(20, trade_ratio * 100 * 2)
+
+    # 3. Resource diversity
+    has_meat = resources_produced.get("meat", 0) > 0
+    has_berries = resources_produced.get("berries", 0) > 0
+    has_wood = resources_produced.get("wood", 0) > 0
+    has_water = resources_produced.get("water", 0) > 0
+    diversity_score = (int(has_meat) + int(has_berries) + int(has_wood) + int(has_water)) * 5
+
+    # 4. Survival
+    survival_score = survival_rate * 30
+
+    # 5. Meat production bonus
+    meat_score = min(15, resources_produced.get("meat", 0) / 5)
+
+    total_score = hunt_score + trade_score + diversity_score + survival_score + meat_score
+
+    if verbose:
+        print(f"\n  Score: {total_score:.1f}/100")
+        print(f"  ├─ Hunt: {hunt_ratio*100:.1f}% ({hunt_score:.1f} pts)")
+        print(f"  ├─ Trade: {trade_ratio*100:.1f}% ({trade_score:.1f} pts)")
+        print(f"  ├─ Diversity: {diversity_score:.1f} pts")
+        print(f"  ├─ Survival: {survival_rate*100:.0f}% ({survival_score:.1f} pts)")
+        print(f"  └─ Meat produced: {resources_produced.get('meat', 0)} ({meat_score:.1f} pts)")
+        print(f"  Actions: hunt={action_counts.get('hunt',0)}, gather={action_counts.get('gather',0)}, trade={action_counts.get('trade',0)}")
+
+    return {
+        "score": total_score,
+        "action_counts": dict(action_counts),
+        "resources": dict(resources_produced),
+        "survival_rate": survival_rate,
+        "deaths": deaths,
+    }
+
+
+def optimize_for_goap(iterations: int = 15, steps: int = 300):
+    """Run optimization focused on GOAP-specific parameters."""
+    print("\n" + "=" * 70)
+    print("🧬 GOAP ECONOMY OPTIMIZER")
+    print("=" * 70)
+    print(f"  Iterations: {iterations}")
+    print(f"  Steps per test: {steps}")
+    print("=" * 70)
+
+    # Key parameters to optimize for GOAP
+    # Focus on making hunting more attractive
+
+    configs_to_test = [
+        # Baseline
+        {
+            "name": "Baseline (current)",
+            "config": {}
+        },
+        # Cheaper hunting
+        {
+            "name": "Cheaper Hunt (-5 energy)",
+            "config": {
+                "actions": {
+                    "hunt_energy": -5,
+                    "hunt_success": 0.8,
+                }
+            }
+        },
+        # More valuable meat
+        {
+            "name": "Valuable Meat (+45 hunger)",
+            "config": {
+                "resources": {
+                    "meat_hunger": 45,
+                    "meat_energy": 15,
+                },
+                "actions": {
+                    "hunt_energy": -6,
+                    "hunt_success": 0.8,
+                }
+            }
+        },
+        # Make berries less attractive
+        {
+            "name": "Nerfed Berries",
+            "config": {
+                "resources": {
+                    "meat_hunger": 45,
+                    "meat_energy": 15,
+                    "berries_hunger": 8,
+                    "berries_thirst": 2,
+                },
+                "actions": {
+                    "hunt_energy": -5,
+                    "gather_energy": -4,
+                    "hunt_success": 0.85,
+                    "hunt_meat_min": 2,
+                    "hunt_meat_max": 4,
+                }
+            }
+        },
+        # Higher hunt output
+        {
+            "name": "High Hunt Output",
+            "config": {
+                "resources": {
+                    "meat_hunger": 40,
+                    "meat_energy": 12,
+                },
+                "actions": {
+                    "hunt_energy": -6,
+                    "hunt_success": 0.85,
+                    "hunt_meat_min": 3,
+                    "hunt_meat_max": 6,
+                    "hunt_hide_min": 1,
+                    "hunt_hide_max": 2,
+                }
+            }
+        },
+        # Balanced economy
+        {
+            "name": "Balanced Economy",
+            "config": {
+                "resources": {
+                    "meat_hunger": 40,
+                    "meat_energy": 15,
+                    "berries_hunger": 8,
+                },
+                "actions": {
+                    "hunt_energy": -5,
+                    "gather_energy": -4,
+                    "hunt_success": 0.8,
+                    "hunt_meat_min": 2,
+                    "hunt_meat_max": 5,
+                },
+                "economy": {
+                    "buy_efficiency_threshold": 0.9,
+                    "min_wealth_target": 40,
+                }
+            }
+        },
+        # Pro-hunting config
+        {
+            "name": "Pro-Hunting",
+            "config": {
+                "agent_stats": {
+                    "hunger_decay": 3,  # Higher hunger decay = need more food
+                },
+                "resources": {
+                    "meat_hunger": 50,  # Meat is very filling
+                    "meat_energy": 15,
+                    "berries_hunger": 6,  # Berries less filling
+                },
+                "actions": {
+                    "hunt_energy": -4,  # Very cheap to hunt
+                    "gather_energy": -4,
+                    "hunt_success": 0.85,
+                    "hunt_meat_min": 3,
+                    "hunt_meat_max": 5,
+                }
+            }
+        },
+        # Full rebalance
+        {
+            "name": "Full Rebalance",
+            "config": {
+                "agent_stats": {
+                    "start_hunger": 70,
+                    "hunger_decay": 3,
+                    "thirst_decay": 3,
+                },
+                "resources": {
+                    "meat_hunger": 50,
+                    "meat_energy": 15,
+                    "berries_hunger": 8,
+                    "berries_thirst": 3,
+                    "water_thirst": 45,
+                },
+                "actions": {
+                    "hunt_energy": -5,
+                    "gather_energy": -4,
+                    "chop_wood_energy": -5,
+                    "get_water_energy": -3,
+                    "hunt_success": 0.8,
+                    "hunt_meat_min": 2,
+                    "hunt_meat_max": 5,
+                    "hunt_hide_min": 0,
+                    "hunt_hide_max": 1,
+                    "gather_min": 2,
+                    "gather_max": 3,
+                }
+            }
+        },
+    ]
+
+    best_config = None
+    best_score = 0
+    best_name = ""
+
+    for cfg in configs_to_test:
+        print(f"\n🧪 Testing: {cfg['name']}")
+        print("-" * 50)
+
+        result = test_config(cfg["config"], steps, verbose=True)
+
+        if result["score"] > best_score:
+            best_score = result["score"]
+            best_config = cfg["config"]
+            best_name = cfg["name"]
+            print(f"  ⭐ New best!")
+
+    print("\n" + "=" * 70)
+    print("🏆 OPTIMIZATION COMPLETE")
+    print("=" * 70)
+    print(f"\n  Best Config: {best_name}")
+    print(f"  Best Score: {best_score:.1f}/100")
+
+    if best_config:
+        print("\n  📝 Configuration to apply:")
+        print("-" * 50)
+        print(json.dumps(best_config, indent=2))
+
+        # Ask to apply
+        print("\n  Would you like to apply this configuration? (y/n)")
+
+        # Save as optimized config
+        output_path = Path("config_goap_optimized.json")
+        with open("config.json") as f:
+            full_config = json.load(f)
+
+        for section, values in best_config.items():
+            if section in full_config:
+                full_config[section].update(values)
+            else:
+                full_config[section] = values
+
+        with open(output_path, 'w') as f:
+            json.dump(full_config, f, indent=2)
+
+        print(f"\n  ✅ Saved to: {output_path}")
+        print("  To apply: cp config_goap_optimized.json config.json")
+
+    return best_config
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Optimize GOAP economy parameters")
+    parser.add_argument("--analyze", "-a", action="store_true", help="Analyze current behavior")
+    parser.add_argument("--iterations", "-i", type=int, default=15, help="Optimization iterations")
+    parser.add_argument("--steps", "-s", type=int, default=200, help="Steps per simulation")
+    parser.add_argument("--apply", action="store_true", help="Auto-apply best config")
+
+    args = parser.parse_args()
+
+    if args.analyze:
+        analyze_goap_behavior(args.steps)
+    else:
+        best = optimize_for_goap(args.iterations, args.steps)
+
+        if args.apply and best:
+            # Apply the config
+            import shutil
+            shutil.copy("config_goap_optimized.json", "config.json")
+            print("\n  ✅ Configuration applied!")
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/web_frontend/goap_debug.html b/web_frontend/goap_debug.html
new file mode 100644
index 0000000..6a6ab9f
--- /dev/null
+++ b/web_frontend/goap_debug.html
@@ -0,0 +1,820 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>GOAP Debug Visualizer - VillSim</title>
+    <link rel="preconnect" href="https://fonts.googleapis.com">
+    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+    <link href="https://fonts.googleapis.com/css2?family=IBM+Plex+Sans:wght@400;500;600&family=IBM+Plex+Mono:wght@400;500&display=swap" rel="stylesheet">
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.1/dist/chart.umd.min.js"></script>
+    <style>
+        :root {
+            --bg-primary: #0d1117;
+            --bg-secondary: #161b22;
+            --bg-tertiary: #21262d;
+            --border-color: #30363d;
+            --text-primary: #e6edf3;
+            --text-secondary: #8b949e;
+            --text-muted: #6e7681;
+            --accent-blue: #58a6ff;
+            --accent-green: #3fb950;
+            --accent-orange: #d29922;
+            --accent-red: #f85149;
+            --accent-purple: #a371f7;
+            --accent-cyan: #39c5cf;
+        }
+
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: 'IBM Plex Sans', -apple-system, BlinkMacSystemFont, sans-serif;
+            background: var(--bg-primary);
+            color: var(--text-primary);
+            min-height: 100vh;
+        }
+
+        .header {
+            background: var(--bg-secondary);
+            border-bottom: 1px solid var(--border-color);
+            padding: 16px 24px;
+            display: flex;
+            align-items: center;
+            justify-content: space-between;
+        }
+
+        .header h1 {
+            font-size: 20px;
+            font-weight: 600;
+            color: var(--accent-cyan);
+        }
+
+        .header-controls {
+            display: flex;
+            gap: 12px;
+            align-items: center;
+        }
+
+        .btn {
+            padding: 8px 16px;
+            border: 1px solid var(--border-color);
+            border-radius: 6px;
+            background: var(--bg-tertiary);
+            color: var(--text-primary);
+            font-family: inherit;
+            font-size: 14px;
+            cursor: pointer;
+            transition: all 0.15s ease;
+        }
+
+        .btn:hover {
+            background: var(--border-color);
+            border-color: var(--text-muted);
+        }
+
+        .btn-primary {
+            background: var(--accent-blue);
+            border-color: var(--accent-blue);
+            color: #fff;
+        }
+
+        .btn-primary:hover {
+            background: #4c9aff;
+        }
+
+        .status-badge {
+            padding: 4px 12px;
+            border-radius: 20px;
+            font-size: 12px;
+            font-weight: 500;
+        }
+
+        .status-connected {
+            background: rgba(63, 185, 80, 0.2);
+            color: var(--accent-green);
+        }
+
+        .status-disconnected {
+            background: rgba(248, 81, 73, 0.2);
+            color: var(--accent-red);
+        }
+
+        .main-content {
+            display: grid;
+            grid-template-columns: 280px 1fr 400px;
+            height: calc(100vh - 65px);
+        }
+
+        .panel {
+            background: var(--bg-secondary);
+            border-right: 1px solid var(--border-color);
+            overflow-y: auto;
+        }
+
+        .panel:last-child {
+            border-right: none;
+            border-left: 1px solid var(--border-color);
+        }
+
+        .panel-header {
+            padding: 16px;
+            border-bottom: 1px solid var(--border-color);
+            background: var(--bg-tertiary);
+            position: sticky;
+            top: 0;
+            z-index: 10;
+        }
+
+        .panel-header h2 {
+            font-size: 14px;
+            font-weight: 600;
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+            color: var(--text-secondary);
+        }
+
+        .agent-list {
+            padding: 8px;
+        }
+
+        .agent-item {
+            padding: 12px;
+            border-radius: 8px;
+            cursor: pointer;
+            margin-bottom: 4px;
+            transition: background 0.15s ease;
+        }
+
+        .agent-item:hover {
+            background: var(--bg-tertiary);
+        }
+
+        .agent-item.selected {
+            background: rgba(88, 166, 255, 0.15);
+            border: 1px solid var(--accent-blue);
+        }
+
+        .agent-item .agent-name {
+            font-weight: 500;
+            margin-bottom: 4px;
+        }
+
+        .agent-item .agent-action {
+            font-size: 12px;
+            color: var(--text-secondary);
+            font-family: 'IBM Plex Mono', monospace;
+        }
+
+        .agent-item .agent-goal {
+            font-size: 11px;
+            color: var(--accent-cyan);
+            margin-top: 4px;
+        }
+
+        .center-panel {
+            display: flex;
+            flex-direction: column;
+            background: var(--bg-primary);
+        }
+
+        .plan-view {
+            padding: 24px;
+            flex: 1;
+            overflow-y: auto;
+        }
+
+        .plan-header {
+            display: flex;
+            align-items: center;
+            gap: 16px;
+            margin-bottom: 24px;
+        }
+
+        .plan-header h2 {
+            font-size: 24px;
+            font-weight: 600;
+        }
+
+        .plan-goal-badge {
+            padding: 6px 16px;
+            background: rgba(163, 113, 247, 0.2);
+            color: var(--accent-purple);
+            border-radius: 20px;
+            font-size: 14px;
+            font-weight: 500;
+        }
+
+        .world-state-grid {
+            display: grid;
+            grid-template-columns: repeat(4, 1fr);
+            gap: 12px;
+            margin-bottom: 32px;
+        }
+
+        .state-card {
+            background: var(--bg-secondary);
+            border: 1px solid var(--border-color);
+            border-radius: 8px;
+            padding: 16px;
+        }
+
+        .state-card .label {
+            font-size: 11px;
+            color: var(--text-muted);
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+            margin-bottom: 8px;
+        }
+
+        .state-card .value {
+            font-size: 24px;
+            font-weight: 600;
+            font-family: 'IBM Plex Mono', monospace;
+        }
+
+        .state-card .bar {
+            height: 4px;
+            background: var(--bg-tertiary);
+            border-radius: 2px;
+            margin-top: 8px;
+            overflow: hidden;
+        }
+
+        .state-card .bar-fill {
+            height: 100%;
+            border-radius: 2px;
+            transition: width 0.3s ease;
+        }
+
+        .bar-thirst .bar-fill { background: var(--accent-blue); }
+        .bar-hunger .bar-fill { background: var(--accent-orange); }
+        .bar-heat .bar-fill { background: var(--accent-red); }
+        .bar-energy .bar-fill { background: var(--accent-green); }
+
+        .plan-visualization {
+            background: var(--bg-secondary);
+            border: 1px solid var(--border-color);
+            border-radius: 12px;
+            padding: 24px;
+            margin-bottom: 24px;
+        }
+
+        .plan-visualization h3 {
+            font-size: 14px;
+            color: var(--text-secondary);
+            margin-bottom: 16px;
+        }
+
+        .plan-steps {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            flex-wrap: wrap;
+        }
+
+        .plan-step {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .step-node {
+            padding: 12px 20px;
+            background: var(--bg-tertiary);
+            border: 2px solid var(--border-color);
+            border-radius: 8px;
+            font-family: 'IBM Plex Mono', monospace;
+            font-size: 14px;
+            font-weight: 500;
+        }
+
+        .step-node.current {
+            border-color: var(--accent-green);
+            background: rgba(63, 185, 80, 0.15);
+            color: var(--accent-green);
+        }
+
+        .step-arrow {
+            color: var(--text-muted);
+            font-size: 20px;
+        }
+
+        .goal-result {
+            padding: 12px 20px;
+            background: rgba(163, 113, 247, 0.15);
+            border: 2px solid var(--accent-purple);
+            border-radius: 8px;
+            color: var(--accent-purple);
+            font-weight: 500;
+        }
+
+        .no-plan {
+            text-align: center;
+            padding: 40px;
+            color: var(--text-muted);
+        }
+
+        .goals-chart-container {
+            background: var(--bg-secondary);
+            border: 1px solid var(--border-color);
+            border-radius: 12px;
+            padding: 24px;
+        }
+
+        .goals-chart-container h3 {
+            font-size: 14px;
+            color: var(--text-secondary);
+            margin-bottom: 16px;
+        }
+
+        .chart-wrapper {
+            height: 300px;
+        }
+
+        .detail-section {
+            padding: 16px;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        .detail-section h3 {
+            font-size: 12px;
+            color: var(--text-muted);
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+            margin-bottom: 12px;
+        }
+
+        .detail-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 8px;
+        }
+
+        .detail-item {
+            display: flex;
+            justify-content: space-between;
+            padding: 6px 0;
+            font-size: 13px;
+        }
+
+        .detail-item .label {
+            color: var(--text-secondary);
+        }
+
+        .detail-item .value {
+            font-family: 'IBM Plex Mono', monospace;
+            color: var(--text-primary);
+        }
+
+        .action-list {
+            max-height: 300px;
+            overflow-y: auto;
+        }
+
+        .action-item {
+            display: flex;
+            align-items: center;
+            padding: 8px 12px;
+            border-radius: 6px;
+            margin-bottom: 4px;
+            font-size: 13px;
+        }
+
+        .action-item.valid {
+            background: var(--bg-tertiary);
+        }
+
+        .action-item.invalid {
+            background: transparent;
+            opacity: 0.5;
+        }
+
+        .action-item.in-plan {
+            background: rgba(63, 185, 80, 0.15);
+            border: 1px solid var(--accent-green);
+        }
+
+        .action-item .action-name {
+            flex: 1;
+            font-family: 'IBM Plex Mono', monospace;
+        }
+
+        .action-item .action-cost {
+            font-size: 11px;
+            color: var(--text-muted);
+            margin-left: 8px;
+        }
+
+        .action-item .action-order {
+            width: 20px;
+            height: 20px;
+            background: var(--accent-green);
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 11px;
+            font-weight: 600;
+            color: #000;
+            margin-right: 8px;
+        }
+
+        .inventory-grid {
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 8px;
+        }
+
+        .inv-item {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            padding: 8px;
+            background: var(--bg-tertiary);
+            border-radius: 6px;
+            font-size: 13px;
+        }
+
+        .inv-item .icon {
+            font-size: 16px;
+        }
+
+        .inv-item .count {
+            margin-left: auto;
+            font-family: 'IBM Plex Mono', monospace;
+            font-weight: 500;
+        }
+
+        .loading {
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            height: 100%;
+            color: var(--text-muted);
+        }
+
+        .urgency-indicator {
+            display: inline-block;
+            width: 8px;
+            height: 8px;
+            border-radius: 50%;
+            margin-left: 8px;
+        }
+
+        .urgency-none { background: var(--accent-green); }
+        .urgency-low { background: var(--accent-orange); }
+        .urgency-high { background: var(--accent-red); }
+
+        @keyframes pulse {
+            0%, 100% { opacity: 1; }
+            50% { opacity: 0.5; }
+        }
+
+        .updating {
+            animation: pulse 1s infinite;
+        }
+    </style>
+</head>
+<body>
+    <header class="header">
+        <h1>🧠 GOAP Debug Visualizer</h1>
+        <div class="header-controls">
+            <span id="turn-display">Turn 0</span>
+            <span id="status-badge" class="status-badge status-disconnected">Disconnected</span>
+            <button class="btn" onclick="refreshData()">↻ Refresh</button>
+            <button class="btn btn-primary" id="auto-refresh-btn" onclick="toggleAutoRefresh()">▶ Auto</button>
+        </div>
+    </header>
+
+    <main class="main-content">
+        <!-- Left Panel: Agent List -->
+        <div class="panel">
+            <div class="panel-header">
+                <h2>Agents</h2>
+            </div>
+            <div id="agent-list" class="agent-list">
+                <div class="loading">Loading...</div>
+            </div>
+        </div>
+
+        <!-- Center Panel: Plan Visualization -->
+        <div class="center-panel">
+            <div class="plan-view" id="plan-view">
+                <div class="loading">Select an agent to view GOAP details</div>
+            </div>
+        </div>
+
+        <!-- Right Panel: Details -->
+        <div class="panel">
+            <div class="panel-header">
+                <h2>Details</h2>
+            </div>
+            <div id="details-panel">
+                <div class="loading">Select an agent</div>
+            </div>
+        </div>
+    </main>
+
+    <script>
+        const API_BASE = 'http://localhost:8000/api';
+        let selectedAgentId = null;
+        let allAgentsData = [];
+        let autoRefreshInterval = null;
+        let goalsChart = null;
+
+        // Initialize
+        document.addEventListener('DOMContentLoaded', () => {
+            refreshData();
+        });
+
+        async function refreshData() {
+            try {
+                const response = await fetch(`${API_BASE}/goap/debug`);
+                if (!response.ok) throw new Error('API error');
+
+                const data = await response.json();
+                allAgentsData = data.agents;
+
+                document.getElementById('turn-display').textContent = `Turn ${data.current_turn}`;
+                document.getElementById('status-badge').className = 'status-badge status-connected';
+                document.getElementById('status-badge').textContent = data.is_night ? '🌙 Night' : '☀️ Connected';
+
+                renderAgentList();
+
+                if (selectedAgentId) {
+                    const agent = allAgentsData.find(a => a.agent_id === selectedAgentId);
+                    if (agent) {
+                        renderAgentDetails(agent);
+                    }
+                }
+            } catch (error) {
+                console.error('Failed to fetch data:', error);
+                document.getElementById('status-badge').className = 'status-badge status-disconnected';
+                document.getElementById('status-badge').textContent = 'Disconnected';
+            }
+        }
+
+        function renderAgentList() {
+            const container = document.getElementById('agent-list');
+
+            if (allAgentsData.length === 0) {
+                container.innerHTML = '<div class="loading">No agents found</div>';
+                return;
+            }
+
+            container.innerHTML = allAgentsData.map(agent => `
+                <div class="agent-item ${agent.agent_id === selectedAgentId ? 'selected' : ''}"
+                     onclick="selectAgent('${agent.agent_id}')">
+                    <div class="agent-name">${agent.agent_name}</div>
+                    <div class="agent-action">${agent.selected_action || 'No action'}</div>
+                    <div class="agent-goal">${agent.current_plan ? '🎯 ' + agent.current_plan.goal_name : '(reactive)'}</div>
+                </div>
+            `).join('');
+        }
+
+        function selectAgent(agentId) {
+            selectedAgentId = agentId;
+            renderAgentList();
+
+            const agent = allAgentsData.find(a => a.agent_id === agentId);
+            if (agent) {
+                renderAgentDetails(agent);
+            }
+        }
+
+        function renderAgentDetails(agent) {
+            renderPlanView(agent);
+            renderDetailsPanel(agent);
+        }
+
+        function getUrgencyClass(urgency) {
+            if (urgency <= 0) return 'urgency-none';
+            if (urgency <= 1) return 'urgency-low';
+            return 'urgency-high';
+        }
+
+        function renderPlanView(agent) {
+            const container = document.getElementById('plan-view');
+            const ws = agent.world_state;
+            const plan = agent.current_plan;
+
+            container.innerHTML = `
+                <div class="plan-header">
+                    <h2>${agent.agent_name}</h2>
+                    ${plan ? `<span class="plan-goal-badge">🎯 ${plan.goal_name}</span>` : ''}
+                </div>
+
+                <div class="world-state-grid">
+                    <div class="state-card bar-thirst">
+                        <div class="label">Thirst</div>
+                        <div class="value">${Math.round(ws.vitals.thirst * 100)}%
+                            <span class="urgency-indicator ${getUrgencyClass(ws.urgencies.thirst)}"></span>
+                        </div>
+                        <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.thirst * 100}%"></div></div>
+                    </div>
+                    <div class="state-card bar-hunger">
+                        <div class="label">Hunger</div>
+                        <div class="value">${Math.round(ws.vitals.hunger * 100)}%
+                            <span class="urgency-indicator ${getUrgencyClass(ws.urgencies.hunger)}"></span>
+                        </div>
+                        <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.hunger * 100}%"></div></div>
+                    </div>
+                    <div class="state-card bar-heat">
+                        <div class="label">Heat</div>
+                        <div class="value">${Math.round(ws.vitals.heat * 100)}%
+                            <span class="urgency-indicator ${getUrgencyClass(ws.urgencies.heat)}"></span>
+                        </div>
+                        <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.heat * 100}%"></div></div>
+                    </div>
+                    <div class="state-card bar-energy">
+                        <div class="label">Energy</div>
+                        <div class="value">${Math.round(ws.vitals.energy * 100)}%
+                            <span class="urgency-indicator ${getUrgencyClass(ws.urgencies.energy)}"></span>
+                        </div>
+                        <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.energy * 100}%"></div></div>
+                    </div>
+                </div>
+
+                <div class="plan-visualization">
+                    <h3>Current Plan</h3>
+                    ${plan && plan.actions.length > 0 ? `
+                        <div class="plan-steps">
+                            ${plan.actions.map((action, i) => `
+                                <div class="plan-step">
+                                    <div class="step-node ${i === 0 ? 'current' : ''}">${action}</div>
+                                    ${i < plan.actions.length - 1 ? '<span class="step-arrow">→</span>' : ''}
+                                </div>
+                            `).join('')}
+                            <span class="step-arrow">→</span>
+                            <div class="goal-result">✓ ${plan.goal_name}</div>
+                        </div>
+                        <div style="margin-top: 12px; font-size: 13px; color: var(--text-muted);">
+                            Total Cost: ${plan.total_cost.toFixed(1)} | Steps: ${plan.plan_length}
+                        </div>
+                    ` : `
+                        <div class="no-plan">
+                            <p style="font-size: 16px; margin-bottom: 8px;">No plan - using reactive selection</p>
+                            <p>Selected: <strong>${agent.selected_action || 'None'}</strong></p>
+                        </div>
+                    `}
+                </div>
+
+                <div class="goals-chart-container">
+                    <h3>Goal Priorities</h3>
+                    <div class="chart-wrapper">
+                        <canvas id="goals-chart"></canvas>
+                    </div>
+                </div>
+            `;
+
+            renderGoalsChart(agent);
+        }
+
+        function renderGoalsChart(agent) {
+            const ctx = document.getElementById('goals-chart');
+            if (!ctx) return;
+
+            // Sort goals by priority
+            const sortedGoals = [...agent.goals].sort((a, b) => b.priority - a.priority);
+            const topGoals = sortedGoals.slice(0, 10);
+
+            if (goalsChart) {
+                goalsChart.destroy();
+            }
+
+            goalsChart = new Chart(ctx, {
+                type: 'bar',
+                data: {
+                    labels: topGoals.map(g => g.name),
+                    datasets: [{
+                        label: 'Priority',
+                        data: topGoals.map(g => g.priority),
+                        backgroundColor: topGoals.map(g => {
+                            if (g.is_selected) return 'rgba(163, 113, 247, 0.8)';
+                            if (g.is_satisfied) return 'rgba(63, 185, 80, 0.5)';
+                            if (g.priority > 0) return 'rgba(88, 166, 255, 0.7)';
+                            return 'rgba(110, 118, 129, 0.3)';
+                        }),
+                        borderColor: topGoals.map(g => {
+                            if (g.is_selected) return '#a371f7';
+                            if (g.is_satisfied) return '#3fb950';
+                            if (g.priority > 0) return '#58a6ff';
+                            return '#6e7681';
+                        }),
+                        borderWidth: 2,
+                    }]
+                },
+                options: {
+                    indexAxis: 'y',
+                    responsive: true,
+                    maintainAspectRatio: false,
+                    plugins: {
+                        legend: { display: false },
+                    },
+                    scales: {
+                        x: {
+                            beginAtZero: true,
+                            grid: { color: '#30363d' },
+                            ticks: { color: '#8b949e' },
+                        },
+                        y: {
+                            grid: { display: false },
+                            ticks: {
+                                color: '#e6edf3',
+                                font: { family: 'IBM Plex Mono', size: 11 }
+                            },
+                        }
+                    }
+                }
+            });
+        }
+
+        function renderDetailsPanel(agent) {
+            const container = document.getElementById('details-panel');
+            const ws = agent.world_state;
+
+            const validActions = agent.actions.filter(a => a.is_valid);
+            const inPlanActions = agent.actions.filter(a => a.is_in_plan).sort((a, b) => a.plan_order - b.plan_order);
+
+            container.innerHTML = `
+                <div class="detail-section">
+                    <h3>Inventory</h3>
+                    <div class="inventory-grid">
+                        <div class="inv-item"><span class="icon">💧</span> Water <span class="count">${ws.inventory.water}</span></div>
+                        <div class="inv-item"><span class="icon">🍖</span> Meat <span class="count">${ws.inventory.meat}</span></div>
+                        <div class="inv-item"><span class="icon">🫐</span> Berries <span class="count">${ws.inventory.berries}</span></div>
+                        <div class="inv-item"><span class="icon">🪵</span> Wood <span class="count">${ws.inventory.wood}</span></div>
+                        <div class="inv-item"><span class="icon">🥩</span> Hide <span class="count">${ws.inventory.hide}</span></div>
+                        <div class="inv-item"><span class="icon">📦</span> Space <span class="count">${ws.inventory.space}</span></div>
+                    </div>
+                </div>
+
+                <div class="detail-section">
+                    <h3>Economy</h3>
+                    <div class="detail-grid">
+                        <div class="detail-item">
+                            <span class="label">Money</span>
+                            <span class="value" style="color: var(--accent-orange)">${ws.economy.money}c</span>
+                        </div>
+                        <div class="detail-item">
+                            <span class="label">Wealthy</span>
+                            <span class="value">${ws.economy.is_wealthy ? '✓' : '✗'}</span>
+                        </div>
+                    </div>
+                </div>
+
+                <div class="detail-section">
+                    <h3>Market Access</h3>
+                    <div class="detail-grid">
+                        <div class="detail-item">
+                            <span class="label">Buy Water</span>
+                            <span class="value">${ws.market.can_buy_water ? '✓' : '✗'}</span>
+                        </div>
+                        <div class="detail-item">
+                            <span class="label">Buy Food</span>
+                            <span class="value">${ws.market.can_buy_food ? '✓' : '✗'}</span>
+                        </div>
+                        <div class="detail-item">
+                            <span class="label">Buy Wood</span>
+                            <span class="value">${ws.market.can_buy_wood ? '✓' : '✗'}</span>
+                        </div>
+                    </div>
+                </div>
+
+                <div class="detail-section">
+                    <h3>Actions (${validActions.length} valid)</h3>
+                    <div class="action-list">
+                        ${agent.actions.map(action => `
+                            <div class="action-item ${action.is_valid ? 'valid' : 'invalid'} ${action.is_in_plan ? 'in-plan' : ''}">
+                                ${action.is_in_plan ? `<span class="action-order">${action.plan_order + 1}</span>` : ''}
+                                <span class="action-name">${action.name}</span>
+                                <span class="action-cost">${action.cost >= 0 ? action.cost.toFixed(1) : '∞'}</span>
+                            </div>
+                        `).join('')}
+                    </div>
+                </div>
+            `;
+        }
+
+        function toggleAutoRefresh() {
+            const btn = document.getElementById('auto-refresh-btn');
+
+            if (autoRefreshInterval) {
+                clearInterval(autoRefreshInterval);
+                autoRefreshInterval = null;
+                btn.textContent = '▶ Auto';
+                btn.classList.remove('btn-primary');
+            } else {
+                autoRefreshInterval = setInterval(refreshData, 500);
+                btn.textContent = '⏸ Stop';
+                btn.classList.add('btn-primary');
+            }
+        }
+    </script>
+</body>
+</html>
+
diff --git a/web_frontend/index.html b/web_frontend/index.html
index 664d493..c6a99ab 100644
--- a/web_frontend/index.html
+++ b/web_frontend/index.html
@@ -135,6 +135,7 @@
                         <button class="tab-btn" data-tab="resources">Resources</button>
                         <button class="tab-btn" data-tab="market">Market</button>
                         <button class="tab-btn" data-tab="agents">Agents</button>
+                        <button class="tab-btn" data-tab="goap">🧠 GOAP</button>
                     </div>
                 </div>
                 <div class="stats-header-right">
@@ -240,6 +241,41 @@
                         </div>
                     </div>
                 </div>
+                <!-- GOAP Tab -->
+                <div id="tab-goap" class="tab-panel">
+                    <div class="goap-container">
+                        <div class="goap-header">
+                            <h3>Goal-Oriented Action Planning</h3>
+                            <p class="goap-subtitle">Real-time visualization of agent decision-making</p>
+                        </div>
+                        <div class="goap-grid">
+                            <div class="goap-panel goap-agents-panel">
+                                <h4>Agents</h4>
+                                <div id="goap-agent-list" class="goap-agent-list">
+                                    <p class="loading-text">Loading agents...</p>
+                                </div>
+                            </div>
+                            <div class="goap-panel goap-plan-panel">
+                                <h4>Current Plan</h4>
+                                <div id="goap-plan-view" class="goap-plan-view">
+                                    <p class="no-selection-text">Select an agent to view their GOAP plan</p>
+                                </div>
+                            </div>
+                            <div class="goap-panel goap-goals-panel">
+                                <h4>Goal Priorities</h4>
+                                <div class="chart-wrapper">
+                                    <canvas id="chart-goap-goals"></canvas>
+                                </div>
+                            </div>
+                            <div class="goap-panel goap-actions-panel">
+                                <h4>Available Actions</h4>
+                                <div id="goap-actions-list" class="goap-actions-list">
+                                    <p class="no-selection-text">Select an agent</p>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
             </div>
             <div class="stats-footer">
                 <div class="stats-summary-bar">
diff --git a/web_frontend/src/api.js b/web_frontend/src/api.js
index 704cbaf..0eb5cf1 100644
--- a/web_frontend/src/api.js
+++ b/web_frontend/src/api.js
@@ -124,6 +124,21 @@ class SimulationAPI {
     async getLogs(limit = 10) {
         return await this.request(`/api/logs?limit=${limit}`);
     }
+
+    // GOAP: Get debug info for all agents
+    async getGOAPDebug() {
+        return await this.request('/api/goap/debug');
+    }
+
+    // GOAP: Get debug info for specific agent
+    async getAgentGOAPDebug(agentId) {
+        return await this.request(`/api/goap/debug/${agentId}`);
+    }
+
+    // Generic GET helper (for compatibility)
+    async get(endpoint) {
+        return await this.request(`/api${endpoint}`);
+    }
 }
 
 // Export singleton instance
diff --git a/web_frontend/src/scenes/GameScene.js b/web_frontend/src/scenes/GameScene.js
index 88dd5f3..d9ac547 100644
--- a/web_frontend/src/scenes/GameScene.js
+++ b/web_frontend/src/scenes/GameScene.js
@@ -127,7 +127,16 @@ export default class GameScene extends Phaser.Scene {
             statsGold: document.getElementById('stats-gold'),
             statsAvgWealth: document.getElementById('stats-avg-wealth'),
             statsGini: document.getElementById('stats-gini'),
+            // GOAP elements
+            goapAgentList: document.getElementById('goap-agent-list'),
+            goapPlanView: document.getElementById('goap-plan-view'),
+            goapActionsList: document.getElementById('goap-actions-list'),
+            chartGoapGoals: document.getElementById('chart-goap-goals'),
         };
+
+        // GOAP state
+        this.goapData = null;
+        this.selectedGoapAgentId = null;
     }
 
     cleanup() {
@@ -741,6 +750,15 @@ export default class GameScene extends Phaser.Scene {
                 <span class="action-label">Current Action</span>
                 <div>${actionData.icon} ${action.message || actionData.verb}</div>
             </div>
+            <div class="agent-goap-info" id="agent-goap-section" data-agent-id="${agentData.id}">
+                <h5 class="subsection-title" style="display: flex; align-items: center; gap: 6px;">
+                    🧠 GOAP Plan
+                    <button class="btn-mini" onclick="window.villsimGame.scene.scenes[1].loadAgentGOAP('${agentData.id}')" style="font-size: 0.6rem; padding: 2px 6px;">↻</button>
+                </h5>
+                <div id="agent-goap-content" style="font-size: 0.75rem; color: var(--text-muted);">
+                    Click ↻ to load GOAP info
+                </div>
+            </div>
             <h5 class="subsection-title">Personal Log</h5>
             <div class="agent-log">
                 ${renderActionLog()}
@@ -1023,6 +1041,7 @@ export default class GameScene extends Phaser.Scene {
             case 'resources': this.renderResourceCharts(); break;
             case 'market': this.renderMarketCharts(); break;
             case 'agents': this.renderAgentStatsCharts(); break;
+            case 'goap': this.fetchAndRenderGOAP(); break;
         }
     }
 
@@ -1627,6 +1646,291 @@ export default class GameScene extends Phaser.Scene {
         };
     }
 
+    // =================================
+    // GOAP Visualization Methods
+    // =================================
+
+    async loadAgentGOAP(agentId) {
+        const contentEl = document.getElementById('agent-goap-content');
+        if (!contentEl) return;
+
+        contentEl.innerHTML = '<span style="color: var(--text-muted);">Loading...</span>';
+
+        try {
+            const data = await api.getAgentGOAPDebug(agentId);
+            const plan = data.current_plan;
+
+            if (plan && plan.actions.length > 0) {
+                contentEl.innerHTML = `
+                    <div style="margin-bottom: 4px;">
+                        <strong style="color: var(--accent-sapphire);">Goal:</strong> ${plan.goal_name}
+                    </div>
+                    <div style="font-family: var(--font-mono); font-size: 0.7rem;">
+                        ${plan.actions.map((a, i) =>
+                            `<span style="${i === 0 ? 'color: var(--accent-emerald);' : ''}">${a}</span>`
+                        ).join(' → ')}
+                    </div>
+                    <div style="margin-top: 4px; color: var(--text-muted); font-size: 0.65rem;">
+                        Cost: ${plan.total_cost.toFixed(1)} | Steps: ${plan.plan_length}
+                    </div>
+                `;
+            } else {
+                contentEl.innerHTML = `
+                    <div style="color: var(--text-muted);">
+                        No plan (reactive mode)<br>
+                        <span style="color: var(--text-primary);">${data.selected_action || 'No action'}</span>
+                    </div>
+                `;
+            }
+        } catch (error) {
+            console.error('Failed to load GOAP info:', error);
+            contentEl.innerHTML = '<span style="color: var(--accent-ruby);">Failed to load</span>';
+        }
+    }
+
+    async fetchAndRenderGOAP() {
+        try {
+            const response = await api.get('/goap/debug');
+            this.goapData = response;
+            this.renderGOAPAgentList();
+
+            // If we have a selected agent, render their details
+            if (this.selectedGoapAgentId) {
+                const agent = this.goapData.agents.find(a => a.agent_id === this.selectedGoapAgentId);
+                if (agent) {
+                    this.renderGOAPAgentDetails(agent);
+                }
+            }
+        } catch (error) {
+            console.error('Failed to fetch GOAP data:', error);
+            const { goapAgentList } = this.domCache;
+            if (goapAgentList) {
+                goapAgentList.innerHTML = '<p class="loading-text">Failed to load GOAP data. Make sure the server is running.</p>';
+            }
+        }
+    }
+
+    renderGOAPAgentList() {
+        const { goapAgentList } = this.domCache;
+        if (!goapAgentList || !this.goapData) return;
+
+        if (this.goapData.agents.length === 0) {
+            goapAgentList.innerHTML = '<p class="loading-text">No agents found</p>';
+            return;
+        }
+
+        goapAgentList.innerHTML = this.goapData.agents.map(agent => `
+            <div class="goap-agent-item ${agent.agent_id === this.selectedGoapAgentId ? 'selected' : ''}"
+                 data-agent-id="${agent.agent_id}">
+                <div class="agent-name">${agent.agent_name}</div>
+                <div class="agent-action">${agent.selected_action || 'No action'}</div>
+                <div class="agent-goal">${agent.current_plan ? '🎯 ' + agent.current_plan.goal_name : '(reactive)'}</div>
+            </div>
+        `).join('');
+
+        // Add click handlers
+        goapAgentList.querySelectorAll('.goap-agent-item').forEach(item => {
+            item.addEventListener('click', () => {
+                this.selectGoapAgent(item.dataset.agentId);
+            });
+        });
+    }
+
+    selectGoapAgent(agentId) {
+        this.selectedGoapAgentId = agentId;
+
+        // Update selection styling
+        const { goapAgentList } = this.domCache;
+        if (goapAgentList) {
+            goapAgentList.querySelectorAll('.goap-agent-item').forEach(item => {
+                item.classList.toggle('selected', item.dataset.agentId === agentId);
+            });
+        }
+
+        // Render details
+        if (this.goapData) {
+            const agent = this.goapData.agents.find(a => a.agent_id === agentId);
+            if (agent) {
+                this.renderGOAPAgentDetails(agent);
+            }
+        }
+    }
+
+    renderGOAPAgentDetails(agent) {
+        this.renderGOAPPlanView(agent);
+        this.renderGOAPActionsList(agent);
+        this.renderGOAPGoalsChart(agent);
+    }
+
+    getUrgencyClass(urgency) {
+        if (urgency <= 0) return 'none';
+        if (urgency <= 1) return 'low';
+        return 'high';
+    }
+
+    renderGOAPPlanView(agent) {
+        const { goapPlanView } = this.domCache;
+        if (!goapPlanView) return;
+
+        const ws = agent.world_state;
+        const plan = agent.current_plan;
+
+        goapPlanView.innerHTML = `
+            <div class="goap-world-state">
+                <div class="goap-stat-card thirst">
+                    <div class="label">Thirst</div>
+                    <div class="value">${Math.round(ws.vitals.thirst * 100)}%<span class="goap-urgency ${this.getUrgencyClass(ws.urgencies.thirst)}"></span></div>
+                    <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.thirst * 100}%"></div></div>
+                </div>
+                <div class="goap-stat-card hunger">
+                    <div class="label">Hunger</div>
+                    <div class="value">${Math.round(ws.vitals.hunger * 100)}%<span class="goap-urgency ${this.getUrgencyClass(ws.urgencies.hunger)}"></span></div>
+                    <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.hunger * 100}%"></div></div>
+                </div>
+                <div class="goap-stat-card heat">
+                    <div class="label">Heat</div>
+                    <div class="value">${Math.round(ws.vitals.heat * 100)}%<span class="goap-urgency ${this.getUrgencyClass(ws.urgencies.heat)}"></span></div>
+                    <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.heat * 100}%"></div></div>
+                </div>
+                <div class="goap-stat-card energy">
+                    <div class="label">Energy</div>
+                    <div class="value">${Math.round(ws.vitals.energy * 100)}%<span class="goap-urgency ${this.getUrgencyClass(ws.urgencies.energy)}"></span></div>
+                    <div class="bar"><div class="bar-fill" style="width: ${ws.vitals.energy * 100}%"></div></div>
+                </div>
+            </div>
+
+            <div class="goap-plan-steps">
+                <h5>Current Plan</h5>
+                ${plan && plan.actions.length > 0 ? `
+                    <div class="goap-plan-flow">
+                        ${plan.actions.map((action, i) => `
+                            <span class="goap-step-node ${i === 0 ? 'current' : ''}">${action}</span>
+                            ${i < plan.actions.length - 1 ? '<span class="goap-step-arrow">→</span>' : ''}
+                        `).join('')}
+                        <span class="goap-step-arrow">→</span>
+                        <span class="goap-goal-result">✓ ${plan.goal_name}</span>
+                    </div>
+                    <div style="margin-top: 8px; font-size: 0.75rem; color: var(--text-muted);">
+                        Total Cost: ${plan.total_cost.toFixed(1)} | Steps: ${plan.plan_length}
+                    </div>
+                ` : `
+                    <div style="color: var(--text-muted); font-size: 0.85rem;">
+                        No plan - using reactive selection<br>
+                        Selected: <strong>${agent.selected_action || 'None'}</strong>
+                    </div>
+                `}
+            </div>
+
+            <div style="margin-top: 12px;">
+                <h5 style="font-size: 0.7rem; color: var(--text-muted); margin-bottom: 8px;">INVENTORY</h5>
+                <div class="goap-inventory">
+                    <div class="goap-inv-item">💧<span class="count">${ws.inventory.water}</span></div>
+                    <div class="goap-inv-item">🍖<span class="count">${ws.inventory.meat}</span></div>
+                    <div class="goap-inv-item">🫐<span class="count">${ws.inventory.berries}</span></div>
+                    <div class="goap-inv-item">🪵<span class="count">${ws.inventory.wood}</span></div>
+                    <div class="goap-inv-item">🥩<span class="count">${ws.inventory.hide}</span></div>
+                    <div class="goap-inv-item">📦<span class="count">${ws.inventory.space}</span></div>
+                </div>
+            </div>
+
+            <div style="margin-top: 12px; display: flex; gap: 16px; font-size: 0.8rem;">
+                <span style="color: var(--accent-gold);">💰 ${ws.economy.money}c</span>
+                <span style="color: var(--text-muted);">Wealthy: ${ws.economy.is_wealthy ? '✓' : '✗'}</span>
+            </div>
+        `;
+    }
+
+    renderGOAPActionsList(agent) {
+        const { goapActionsList } = this.domCache;
+        if (!goapActionsList) return;
+
+        // Sort: plan actions first, then valid, then invalid
+        const sortedActions = [...agent.actions].sort((a, b) => {
+            if (a.is_in_plan && !b.is_in_plan) return -1;
+            if (!a.is_in_plan && b.is_in_plan) return 1;
+            if (a.is_in_plan && b.is_in_plan) return a.plan_order - b.plan_order;
+            if (a.is_valid && !b.is_valid) return -1;
+            if (!a.is_valid && b.is_valid) return 1;
+            return (a.cost || 999) - (b.cost || 999);
+        });
+
+        goapActionsList.innerHTML = sortedActions.map(action => `
+            <div class="goap-action-item ${action.is_valid ? 'valid' : 'invalid'} ${action.is_in_plan ? 'in-plan' : ''}">
+                ${action.is_in_plan ? `<span class="action-order">${action.plan_order + 1}</span>` : ''}
+                <span class="action-name">${action.name}</span>
+                <span class="action-cost">${action.cost >= 0 ? action.cost.toFixed(1) : '∞'}</span>
+            </div>
+        `).join('');
+    }
+
+    renderGOAPGoalsChart(agent) {
+        const { chartGoapGoals } = this.domCache;
+        if (!chartGoapGoals) return;
+
+        // Sort goals by priority and take top 10
+        const sortedGoals = [...agent.goals]
+            .sort((a, b) => b.priority - a.priority)
+            .slice(0, 10);
+
+        // Destroy existing chart
+        if (this.charts.goapGoals) {
+            this.charts.goapGoals.destroy();
+        }
+
+        this.charts.goapGoals = new Chart(chartGoapGoals, {
+            type: 'bar',
+            data: {
+                labels: sortedGoals.map(g => g.name),
+                datasets: [{
+                    label: 'Priority',
+                    data: sortedGoals.map(g => g.priority),
+                    backgroundColor: sortedGoals.map(g => {
+                        if (g.is_selected) return 'rgba(139, 111, 192, 0.8)';
+                        if (g.is_satisfied) return 'rgba(74, 156, 109, 0.5)';
+                        if (g.priority > 0) return 'rgba(90, 140, 200, 0.7)';
+                        return 'rgba(107, 101, 96, 0.3)';
+                    }),
+                    borderColor: sortedGoals.map(g => {
+                        if (g.is_selected) return '#8b6fc0';
+                        if (g.is_satisfied) return '#4a9c6d';
+                        if (g.priority > 0) return '#5a8cc8';
+                        return '#6b6560';
+                    }),
+                    borderWidth: 2,
+                }]
+            },
+            options: {
+                indexAxis: 'y',
+                responsive: true,
+                maintainAspectRatio: false,
+                animation: false,
+                plugins: {
+                    legend: { display: false },
+                    title: {
+                        display: true,
+                        text: 'Goal Priorities',
+                        color: '#e8e4dc',
+                        font: { family: "'Crimson Pro', serif", size: 14 },
+                    },
+                },
+                scales: {
+                    x: {
+                        beginAtZero: true,
+                        grid: { color: 'rgba(58, 67, 89, 0.3)' },
+                        ticks: { color: '#6b6560' },
+                    },
+                    y: {
+                        grid: { display: false },
+                        ticks: {
+                            color: '#e8e4dc',
+                            font: { family: "'JetBrains Mono', monospace", size: 10 }
+                        },
+                    }
+                }
+            }
+        });
+    }
+
     update(time, delta) {
         // Minimal update loop - no heavy operations here
     }
diff --git a/web_frontend/styles.css b/web_frontend/styles.css
index f808bf3..ee881c9 100644
--- a/web_frontend/styles.css
+++ b/web_frontend/styles.css
@@ -1051,3 +1051,351 @@ body {
     }
 }
 
+/* =================================
+   GOAP Visualization Styles
+   ================================= */
+
+.goap-container {
+    padding: var(--space-lg);
+    height: 100%;
+    display: flex;
+    flex-direction: column;
+}
+
+.goap-header {
+    margin-bottom: var(--space-lg);
+}
+
+.goap-header h3 {
+    font-size: 1.5rem;
+    color: var(--accent-sapphire);
+    margin-bottom: var(--space-xs);
+}
+
+.goap-subtitle {
+    font-size: 0.85rem;
+    color: var(--text-muted);
+}
+
+.goap-grid {
+    display: grid;
+    grid-template-columns: 250px 1fr 300px;
+    grid-template-rows: 1fr 1fr;
+    gap: var(--space-md);
+    flex: 1;
+    min-height: 0;
+}
+
+.goap-panel {
+    background: var(--bg-secondary);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-md);
+    padding: var(--space-md);
+    display: flex;
+    flex-direction: column;
+    overflow: hidden;
+}
+
+.goap-panel h4 {
+    font-size: 0.8rem;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    color: var(--text-muted);
+    margin-bottom: var(--space-md);
+    padding-bottom: var(--space-sm);
+    border-bottom: 1px solid var(--border-color);
+}
+
+.goap-agents-panel {
+    grid-row: span 2;
+}
+
+.goap-plan-panel {
+    grid-column: 2;
+}
+
+.goap-goals-panel {
+    grid-column: 3;
+    grid-row: span 2;
+}
+
+.goap-actions-panel {
+    grid-column: 2;
+}
+
+.goap-agent-list {
+    flex: 1;
+    overflow-y: auto;
+}
+
+.goap-agent-item {
+    padding: var(--space-sm) var(--space-md);
+    border-radius: var(--radius-sm);
+    margin-bottom: var(--space-xs);
+    cursor: pointer;
+    transition: background 0.15s ease;
+}
+
+.goap-agent-item:hover {
+    background: var(--bg-hover);
+}
+
+.goap-agent-item.selected {
+    background: rgba(90, 140, 200, 0.2);
+    border-left: 3px solid var(--accent-sapphire);
+}
+
+.goap-agent-item .agent-name {
+    font-weight: 600;
+    margin-bottom: 2px;
+}
+
+.goap-agent-item .agent-action {
+    font-size: 0.75rem;
+    font-family: var(--font-mono);
+    color: var(--text-secondary);
+}
+
+.goap-agent-item .agent-goal {
+    font-size: 0.7rem;
+    color: var(--accent-sapphire);
+    margin-top: 2px;
+}
+
+.goap-plan-view {
+    flex: 1;
+    overflow-y: auto;
+}
+
+.goap-world-state {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    gap: var(--space-sm);
+    margin-bottom: var(--space-md);
+}
+
+.goap-stat-card {
+    background: var(--bg-elevated);
+    border-radius: var(--radius-sm);
+    padding: var(--space-sm);
+    text-align: center;
+}
+
+.goap-stat-card .label {
+    font-size: 0.65rem;
+    color: var(--text-muted);
+    text-transform: uppercase;
+}
+
+.goap-stat-card .value {
+    font-size: 1.1rem;
+    font-family: var(--font-mono);
+    font-weight: 600;
+}
+
+.goap-stat-card .bar {
+    height: 3px;
+    background: var(--bg-deep);
+    border-radius: 2px;
+    margin-top: 4px;
+    overflow: hidden;
+}
+
+.goap-stat-card .bar-fill {
+    height: 100%;
+    border-radius: 2px;
+    transition: width 0.3s ease;
+}
+
+.goap-stat-card.thirst .bar-fill { background: var(--stat-thirst); }
+.goap-stat-card.hunger .bar-fill { background: var(--stat-hunger); }
+.goap-stat-card.heat .bar-fill { background: var(--stat-heat); }
+.goap-stat-card.energy .bar-fill { background: var(--stat-energy); }
+
+.goap-plan-steps {
+    background: var(--bg-elevated);
+    border-radius: var(--radius-sm);
+    padding: var(--space-md);
+    margin-bottom: var(--space-md);
+}
+
+.goap-plan-steps h5 {
+    font-size: 0.75rem;
+    color: var(--text-muted);
+    margin-bottom: var(--space-sm);
+}
+
+.goap-plan-flow {
+    display: flex;
+    align-items: center;
+    flex-wrap: wrap;
+    gap: var(--space-sm);
+}
+
+.goap-step-node {
+    padding: var(--space-sm) var(--space-md);
+    background: var(--bg-secondary);
+    border: 2px solid var(--border-color);
+    border-radius: var(--radius-sm);
+    font-family: var(--font-mono);
+    font-size: 0.85rem;
+}
+
+.goap-step-node.current {
+    border-color: var(--accent-emerald);
+    background: rgba(74, 156, 109, 0.15);
+    color: var(--accent-emerald);
+}
+
+.goap-step-arrow {
+    color: var(--text-muted);
+    font-size: 1.2rem;
+}
+
+.goap-goal-result {
+    padding: var(--space-sm) var(--space-md);
+    background: rgba(139, 111, 192, 0.15);
+    border: 2px solid #8b6fc0;
+    border-radius: var(--radius-sm);
+    color: #8b6fc0;
+    font-weight: 600;
+    font-size: 0.85rem;
+}
+
+.goap-inventory {
+    display: grid;
+    grid-template-columns: repeat(3, 1fr);
+    gap: var(--space-xs);
+}
+
+.goap-inv-item {
+    display: flex;
+    align-items: center;
+    gap: var(--space-xs);
+    padding: var(--space-xs) var(--space-sm);
+    background: var(--bg-elevated);
+    border-radius: var(--radius-sm);
+    font-size: 0.75rem;
+}
+
+.goap-inv-item .count {
+    margin-left: auto;
+    font-family: var(--font-mono);
+    font-weight: 500;
+}
+
+.goap-actions-list {
+    flex: 1;
+    overflow-y: auto;
+}
+
+.goap-action-item {
+    display: flex;
+    align-items: center;
+    padding: var(--space-xs) var(--space-sm);
+    border-radius: var(--radius-sm);
+    margin-bottom: 2px;
+    font-size: 0.8rem;
+}
+
+.goap-action-item.valid {
+    background: var(--bg-elevated);
+}
+
+.goap-action-item.invalid {
+    opacity: 0.4;
+}
+
+.goap-action-item.in-plan {
+    background: rgba(74, 156, 109, 0.15);
+    border-left: 3px solid var(--accent-emerald);
+}
+
+.goap-action-item .action-name {
+    flex: 1;
+    font-family: var(--font-mono);
+}
+
+.goap-action-item .action-cost {
+    font-size: 0.7rem;
+    color: var(--text-muted);
+}
+
+.goap-action-item .action-order {
+    width: 18px;
+    height: 18px;
+    background: var(--accent-emerald);
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 0.65rem;
+    font-weight: 700;
+    color: var(--bg-deep);
+    margin-right: var(--space-sm);
+}
+
+.no-selection-text, .loading-text {
+    color: var(--text-muted);
+    font-size: 0.85rem;
+    text-align: center;
+    padding: var(--space-lg);
+}
+
+.btn-mini {
+    background: var(--bg-elevated);
+    border: 1px solid var(--border-color);
+    border-radius: var(--radius-sm);
+    color: var(--text-secondary);
+    cursor: pointer;
+    font-family: inherit;
+    transition: all 0.15s ease;
+}
+
+.btn-mini:hover {
+    background: var(--bg-hover);
+    color: var(--text-primary);
+}
+
+.agent-goap-info {
+    margin-top: var(--space-sm);
+    padding: var(--space-sm);
+    background: var(--bg-deep);
+    border-radius: var(--radius-sm);
+    border: 1px solid var(--border-color);
+}
+
+.goap-urgency {
+    display: inline-block;
+    width: 6px;
+    height: 6px;
+    border-radius: 50%;
+    margin-left: 4px;
+}
+
+.goap-urgency.none { background: var(--accent-emerald); }
+.goap-urgency.low { background: var(--accent-gold); }
+.goap-urgency.high { background: var(--accent-ruby); }
+
+@media (max-width: 1400px) {
+    .goap-grid {
+        grid-template-columns: 200px 1fr 250px;
+    }
+}
+
+@media (max-width: 1000px) {
+    .goap-grid {
+        grid-template-columns: 1fr;
+        grid-template-rows: auto;
+    }
+
+    .goap-agents-panel,
+    .goap-goals-panel {
+        grid-row: auto;
+    }
+
+    .goap-panel {
+        max-height: 300px;
+    }
+}
+