Multi-Agent Coordination
Orchestrating complex decentralized applications—specifically agentic simulations and games—typically involves networks of Python scripts transacting rapidly against a shared PyVax global contract.
Abstracting the Execution Context
Consider a DeFi Swarm mapping where several AgentWallet processes parse external price charts, but rely on a single Master Vault contract to route the capital.
from multiprocessing import Pool
from pyvax import AgentWallet, network
# Generate 5 autonomous execution workers
wallets = [AgentWallet(f"worker-{i}") for i in range(5)]
# Example target PyVax Contract deployed previously on C-Chain
VAULT_ADDRESS = "0x8bc0F...2A9B"
def agent_worker_loop(wallet: AgentWallet):
"""Executes concurrently analyzing unique feeds"""
while True:
target_swap = wallet.query_model("Should I swap?")
if target_swap:
receipt = wallet.execute(
target=VAULT_ADDRESS,
method="swap_tokens",
args=[target_swap.asset, target_swap.amount]
)
print(f"{wallet.id} Executed Hash: {receipt.tx_hash}")
# Boot the Hive Swarm natively in Python
if __name__ == '__main__':
with Pool(5) as p:
p.map(agent_worker_loop, wallets)
Reverting Collisions
Inevitably, two Autonomous Wallets within the Swarm will attempt to execute() a target method simultaneously.
Because PyVax simulates the transaction pipeline directly against the current active RPC node's memory pool before submitting the cryptographic signature, it instantly detects state collisions and prevents OutOfGas reversion failures typically costing users thousands of dollars in native token burn.
By managing multiple AgentWallet instances synchronously across standard Python threading or multiprocessing paradigms, your Swarm natively avoids race conditions at the HTTP abstraction layer scaling automatically with your hardware.