Provides access to OpenAI's powerful NLP models, such as GPT-4, enabling the agent to understand and generate human-like responses.
Example Usage: Generating interactive responses based on user input.
langchain.chains:
LangChain is a framework for developing applications powered by language models. MemoryChain allows agents to retain stateful information and make decisions based on historical data.
Example Usage: Creating agents that recall prior conversations or user preferences.
stable_baselines3.PPO:
Proximal Policy Optimization (PPO) is a reinforcement learning algorithm used to train agents in dynamic environments.
Example Usage: Optimizing trading or decision-making strategies in decentralized finance (DeFi).
chainlink_sdk.OracleClient:
A Chainlink SDK utility for fetching real-world data, such as asset prices or weather data, into the blockchain space.
Example Usage: Accessing and integrating on-chain price feeds for market analysis.
web3:
A Python library for interacting with Ethereum nodes and smart contracts, enabling the agent to perform blockchain operations.
Example Usage: Deploying smart contracts, executing transactions, or querying blockchain data.
//
```python
import openai
from langchain.chains import MemoryChain
from stable_baselines3 import PPO
from chainlink_sdk import OracleClient
from web3 import Web3
class SybilAgent:
def __init__(self, name, objectives):
self.name = name
self.objectives = objectives
self.memory = MemoryChain() # Long-term memory
self.oracle = OracleClient()
self.web3 = Web3(Web3.HTTPProvider('https://arbitrum.network'))
self.trading_model = PPO("MlpPolicy", env=None, verbose=1) # Reinforcement learning model
def interact(self, user_input):
"""Handles user interaction using NLP models."""
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "system", "content": "You are an AI agent."},
{"role": "user", "content": user_input}]
)
return response["choices"][0]["message"]["content"]
def analyze_market(self):
"""Leverages on-chain data to identify opportunities."""
price_data = self.oracle.get_price("ETH/USD")
# Simple logic: Buy if price drops significantly
if price_data['percent_change'] < -5:
return f"Opportunity detected: ETH price dropped by {price_data['percent_change']}%."
return "No significant market opportunities currently."
def execute_strategy(self):
"""Reinforcement learning to optimize financial strategies."""
action = self.trading_model.predict(observation=None) # Placeholder for environment data
return f"Executing action: {action}"
def audit_protocol(self, smart_contract_code):
"""Uses auditing tools to check for vulnerabilities."""
issues = MythX.analyze(smart_contract_code)
if issues:
return f"Security risks detected: {issues}"
return "Smart contract is secure."
def generate_content(self):
"""Creates visual or textual content for social influence."""
content = openai.Image.create(
prompt="Generate a futuristic depiction of blockchain technology.",
n=1,
size="1024x1024"
)
return content["data"][0]["url"]
agent = SybilAgent(name="Alpha", objectives=["Yield Optimization", "Social Influence"])
user_interaction = agent.interact("What is the current market trend?")
market_analysis = agent.analyze_market()
action_execution = agent.execute_strategy()
content_generation = agent.generate_content()
print(f"User Interaction: {user_interaction}")
print(f"Market Analysis: {market_analysis}")
print(f"Action Execution: {action_execution}")
print(f"Generated Content URL: {content_generation}")
```
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