You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
116 lines
4.5 KiB
116 lines
4.5 KiB
|
|
from swarms import Agent
|
|
from swarm_models import OpenAIChat
|
|
from swarms_memory import ChromaDB
|
|
import os
|
|
|
|
# Initialize memory for agents
|
|
memory_risk = ChromaDB(metric="cosine", output_dir="risk_analysis_results")
|
|
memory_sustainability = ChromaDB(metric="cosine", output_dir="sustainability_results")
|
|
|
|
# Initialize model
|
|
model = OpenAIChat(api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1)
|
|
|
|
# Initialize Risk Analysis Agent
|
|
risk_analysis_agent = Agent(
|
|
agent_name="Delaware-C-Corp-Risk-Analysis-Agent",
|
|
system_prompt="You are a specialized risk analysis agent focused on assessing risks.",
|
|
agent_description="Performs risk analysis for Delaware C Corps.",
|
|
llm=model,
|
|
max_loops=3,
|
|
autosave=True,
|
|
dashboard=False,
|
|
verbose=True,
|
|
dynamic_temperature_enabled=True,
|
|
saved_state_path="delaware_c_corp_risk_analysis_agent.json",
|
|
user_name="risk_analyst_user",
|
|
retry_attempts=2,
|
|
context_length=200000,
|
|
long_term_memory=memory_risk,
|
|
)
|
|
|
|
# Initialize Sustainability Agent
|
|
sustainability_agent = Agent(
|
|
agent_name="Delaware-C-Corp-Sustainability-Agent",
|
|
system_prompt="You are a sustainability analysis agent focused on ESG factors.",
|
|
agent_description="Analyzes sustainability practices for Delaware C Corps.",
|
|
llm=model,
|
|
max_loops=2,
|
|
autosave=True,
|
|
dashboard=False,
|
|
verbose=True,
|
|
dynamic_temperature_enabled=False,
|
|
saved_state_path="delaware_c_corp_sustainability_agent.json",
|
|
user_name="sustainability_specialist",
|
|
retry_attempts=3,
|
|
context_length=180000,
|
|
long_term_memory=memory_sustainability,
|
|
)
|
|
|
|
# Run the agents
|
|
risk_analysis_agent.run("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
|
|
sustainability_agent.run("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
|
|
|
|
from reflection_tuner import ReflectionTuner
|
|
|
|
# Initialize Reflection Tuners for each agent
|
|
risk_reflection_tuner = ReflectionTuner(risk_analysis_agent, reflection_steps=2)
|
|
sustainability_reflection_tuner = ReflectionTuner(sustainability_agent, reflection_steps=2)
|
|
|
|
# Run the agents with Reflection Tuning
|
|
risk_response = risk_reflection_tuner.reflect_and_tune("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
|
|
sustainability_response = sustainability_reflection_tuner.reflect_and_tune("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
|
|
|
|
print("Risk Analysis Agent Response:", risk_response)
|
|
print("Sustainability Agent Response:", sustainability_response)
|
|
|
|
# Initialize agents from agents_with_new.yaml
|
|
# Import ReflectionTuner
|
|
from reflection_tuner import ReflectionTuner
|
|
|
|
# Initialize Reflection Tuner for all agents, including existing ones
|
|
deduction_agent = Agent(
|
|
agent_name="Delaware-C-Corp-Tax-Deduction-Agent",
|
|
system_prompt="Provide expert advice on tax deductions for Delaware C Corps.",
|
|
agent_description="Analyzes tax deduction strategies.",
|
|
llm=model,
|
|
max_loops=1,
|
|
autosave=True,
|
|
dashboard=False,
|
|
verbose=True,
|
|
dynamic_temperature_enabled=True,
|
|
saved_state_path="delaware_c_corp_tax_deduction_agent.json",
|
|
user_name="swarms_corp",
|
|
retry_attempts=1,
|
|
context_length=250000,
|
|
long_term_memory=memory_risk, # Reuse memory for testing purposes
|
|
)
|
|
|
|
optimization_agent = Agent(
|
|
agent_name="Delaware-C-Corp-Tax-Optimization-Agent",
|
|
system_prompt="Provide expert advice on tax optimization strategies for Delaware C Corps.",
|
|
agent_description="Analyzes tax optimization.",
|
|
llm=model,
|
|
max_loops=2,
|
|
autosave=True,
|
|
dashboard=False,
|
|
verbose=True,
|
|
dynamic_temperature_enabled=False,
|
|
saved_state_path="delaware_c_corp_tax_optimization_agent.json",
|
|
user_name="tax_optimization_user",
|
|
retry_attempts=3,
|
|
context_length=200000,
|
|
long_term_memory=memory_risk,
|
|
)
|
|
|
|
# Initialize Reflection Tuners
|
|
deduction_reflection_tuner = ReflectionTuner(deduction_agent, reflection_steps=2)
|
|
optimization_reflection_tuner = ReflectionTuner(optimization_agent, reflection_steps=2)
|
|
|
|
# Run agents with Reflection Tuning
|
|
deduction_response = deduction_reflection_tuner.reflect_and_tune("What are the most effective tax deduction strategies for a Delaware C Corp in tech?")
|
|
optimization_response = optimization_reflection_tuner.reflect_and_tune("How can a Delaware C Corp in finance optimize its tax strategy?")
|
|
|
|
print("Tax Deduction Agent Response:", deduction_response)
|
|
print("Tax Optimization Agent Response:", optimization_response)
|