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import os
from dotenv import load_dotenv
from swarm_models import OpenAIChat
from swarms import Agent, GroupChat, expertise_based
if __name__ == "__main__":
load_dotenv()
# Get the OpenAI API key from the environment variable
api_key = os.getenv("GROQ_API_KEY")
# Model
model = OpenAIChat(
openai_api_base="https://api.groq.com/openai/v1",
openai_api_key=api_key,
model_name="llama-3.1-70b-versatile",
temperature=0.1,
)
# Example agents
agent1 = Agent(
agent_name="Crypto-Tax-Optimization-Agent",
system_prompt="You are a friendly tax expert specializing in cryptocurrency investments. Provide approachable insights on optimizing tax savings for crypto transactions.",
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
user_name="User",
output_type="string",
streaming_on=True,
)
agent2 = Agent(
agent_name="Crypto-Investment-Strategies-Agent",
system_prompt="You are a conversational financial analyst focused on cryptocurrency investments. Offer debatable advice on investment strategies that minimize tax liabilities.",
llm=model,
max_loops=1,
dynamic_temperature_enabled=True,
user_name="User",
output_type="string",
streaming_on=True,
)
agents = [agent1, agent2]
chat = GroupChat(
name="Crypto Tax Optimization Debate",
description="Debate on optimizing tax savings for cryptocurrency transactions and investments",
agents=agents,
speaker_fn=expertise_based,
)
history = chat.run(
"How can one optimize tax savings for cryptocurrency transactions and investments? I bought some Bitcoin and Ethereum last year and want to minimize my tax liabilities this year."
)
print(history.model_dump_json(indent=2))
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