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Gas Optimization Techniques

While deploying to Avalanche intrinsically offers extremely cheap, sub-second transactional finality, running highly complex multithreaded AI Agents 24/7 evaluating billions of states onchain can still accumulate high operational costs.

PyVax transpiles Python down into the Yul / EVM Assembly layer, offering unique memory packaging optimizations automatically.

Storage Packing

Unlike dynamically sized Python objects, Solidity natively groups 256-bit slots. PyVax will automatically attempt to pack sequential memory variables to save deployment and modification gas limits.

python
from pyvax import Contract

class Unoptimized(Contract):
    # This consumes THREE 256-bit EVM slots (60,000 Gas initialization)
    health: int = 100
    is_alive: bool = True
    mana: int = 50

class Optimized(Contract):
    # PyVax maps Uint8 + Bool + Uint8 identically into exactly ONE 256-bit slot
    # (20,000 Gas initialization)
    health: pyvax.Uint8 = 100
    is_alive: bool = True
    mana: pyvax.Uint8 = 50

Abstracting Loops to Agents

Never execute unbounded iteration loops (such as mapping over unpredictable array lengths) natively within a @action or @agent_action. Instead, offload the computational processing loop entirely to the Python environment before submitting the signature.

python
class LootVault(Contract):
    
    # ❌ AVOID: Processing logic onchain burns exponential Gas
    @action
    def give_gold_to_everyone(self, players: list):
        for player in players:
            self._mint(player, "gold", 100)
            
    # ✅ PREFER: Using the CLI / Wallet SDK to batch interactions uniquely
    @agent_action
    def batch_mint(self, target_player: address):
        self._mint(target_player, "gold", 100)

And in your Agent script executing locally on your AWS droplet:

python
wallet = AgentWallet("dispatcher-bot")

# Process the O(N) loop Offchain at ZERO Cost
for player in fetch_active_players_from_db():
    wallet.execute("0xLootVault...", "batch_mint", [player])
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