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66 lines
2.5 KiB
66 lines
2.5 KiB
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from swarms import Agent
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from swarm_models import OpenAIChat
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from swarms_memory import ChromaDB
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import os
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# Initialize memory for agents
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memory_risk = ChromaDB(metric="cosine", output_dir="risk_analysis_results")
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memory_sustainability = ChromaDB(metric="cosine", output_dir="sustainability_results")
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# Initialize model
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model = OpenAIChat(api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1)
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# Initialize Risk Analysis Agent
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risk_analysis_agent = Agent(
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agent_name="Delaware-C-Corp-Risk-Analysis-Agent",
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system_prompt="You are a specialized risk analysis agent focused on assessing risks.",
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agent_description="Performs risk analysis for Delaware C Corps.",
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llm=model,
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max_loops=3,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=True,
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saved_state_path="delaware_c_corp_risk_analysis_agent.json",
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user_name="risk_analyst_user",
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retry_attempts=2,
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context_length=200000,
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long_term_memory=memory_risk,
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)
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# Initialize Sustainability Agent
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sustainability_agent = Agent(
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agent_name="Delaware-C-Corp-Sustainability-Agent",
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system_prompt="You are a sustainability analysis agent focused on ESG factors.",
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agent_description="Analyzes sustainability practices for Delaware C Corps.",
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llm=model,
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max_loops=2,
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autosave=True,
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dashboard=False,
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verbose=True,
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dynamic_temperature_enabled=False,
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saved_state_path="delaware_c_corp_sustainability_agent.json",
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user_name="sustainability_specialist",
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retry_attempts=3,
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context_length=180000,
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long_term_memory=memory_sustainability,
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)
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# Run the agents
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risk_analysis_agent.run("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
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sustainability_agent.run("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
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from reflection_tuner import ReflectionTuner
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# Initialize Reflection Tuners for each agent
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risk_reflection_tuner = ReflectionTuner(risk_analysis_agent, reflection_steps=2)
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sustainability_reflection_tuner = ReflectionTuner(sustainability_agent, reflection_steps=2)
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# Run the agents with Reflection Tuning
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risk_response = risk_reflection_tuner.reflect_and_tune("What are the top financial and operational risks for a Delaware C Corp in healthcare?")
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sustainability_response = sustainability_reflection_tuner.reflect_and_tune("How can a Delaware C Corp in manufacturing improve its sustainability practices?")
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print("Risk Analysis Agent Response:", risk_response)
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print("Sustainability Agent Response:", sustainability_response)
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