Added cool stuff

agent_with_all_agents_reflection.py

@@ -0,0 +1,115 @@
+
+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)

agent_with_all_agents_reflection_planner_collector.py

@@ -0,0 +1,175 @@
+
+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)
+
+from reflection_tuner import ReflectionTuner
+import requests
+
+def duckduckgo_search(query):
+    # Simple DuckDuckGo search function for Data-Collector agent
+    url = f"https://api.duckduckgo.com/?q={query}&format=json&pretty=1"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json().get("AbstractText", "No data found")
+    return "Failed to retrieve data"
+
+# Initialize Planner and Data-Collector agents with DuckDuckGo search capability
+planner_agent = Agent(
+    agent_name="Delaware-C-Corp-Planner-Agent",
+    system_prompt="Develop a quarterly strategic roadmap for a Delaware C Corp.",
+    agent_description="Creates detailed plans and schedules.",
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_planner_agent.json",
+    user_name="planner_user",
+    retry_attempts=2,
+    context_length=150000,
+    long_term_memory=memory_sustainability,  # Reuse memory for demonstration purposes
+)
+
+data_collector_agent = Agent(
+    agent_name="Delaware-C-Corp-Data-Collector-Agent",
+    system_prompt="Collect and synthesize information from DuckDuckGo search.",
+    agent_description="Gathers data from open-source search engines.",
+    llm=model,
+    max_loops=3,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_data_collector_agent.json",
+    user_name="data_collector_user",
+    retry_attempts=3,
+    context_length=200000,
+    long_term_memory=memory_risk,  # Reuse memory for demonstration
+)
+
+# Initialize Reflection Tuners
+planner_reflection_tuner = ReflectionTuner(planner_agent, reflection_steps=2)
+data_collector_reflection_tuner = ReflectionTuner(data_collector_agent, reflection_steps=2)
+
+# Run Planner agent with Reflection Tuning
+planner_response = planner_reflection_tuner.reflect_and_tune("Create a quarterly strategic roadmap for a Delaware C Corp in biotech.")
+print("Planner Agent Response:", planner_response)
+
+# Run Data Collector agent with Reflection Tuning, using DuckDuckGo search
+data_collector_task = "Find recent trends in tax strategies for corporations in the US."
+search_result = duckduckgo_search(data_collector_task)
+data_collector_response = data_collector_reflection_tuner.reflect_and_tune(f"{search_result}")
+print("Data Collector Agent Response:", data_collector_response)

agent_with_caching_adaptive_agents.py

@@ -0,0 +1,203 @@
+
+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)
+
+from reflection_tuner import ReflectionTuner
+import requests
+
+def duckduckgo_search(query):
+    # Simple DuckDuckGo search function for Data-Collector agent
+    url = f"https://api.duckduckgo.com/?q={query}&format=json&pretty=1"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json().get("AbstractText", "No data found")
+    return "Failed to retrieve data"
+
+# Initialize Planner and Data-Collector agents with DuckDuckGo search capability
+planner_agent = Agent(
+    agent_name="Delaware-C-Corp-Planner-Agent",
+    system_prompt="Develop a quarterly strategic roadmap for a Delaware C Corp.",
+    agent_description="Creates detailed plans and schedules.",
+    llm=model,
+    max_loops=2,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_planner_agent.json",
+    user_name="planner_user",
+    retry_attempts=2,
+    context_length=150000,
+    long_term_memory=memory_sustainability,  # Reuse memory for demonstration purposes
+)
+
+data_collector_agent = Agent(
+    agent_name="Delaware-C-Corp-Data-Collector-Agent",
+    system_prompt="Collect and synthesize information from DuckDuckGo search.",
+    agent_description="Gathers data from open-source search engines.",
+    llm=model,
+    max_loops=3,
+    autosave=True,
+    dashboard=False,
+    verbose=True,
+    dynamic_temperature_enabled=True,
+    saved_state_path="delaware_c_corp_data_collector_agent.json",
+    user_name="data_collector_user",
+    retry_attempts=3,
+    context_length=200000,
+    long_term_memory=memory_risk,  # Reuse memory for demonstration
+)
+
+# Initialize Reflection Tuners
+planner_reflection_tuner = ReflectionTuner(planner_agent, reflection_steps=2)
+data_collector_reflection_tuner = ReflectionTuner(data_collector_agent, reflection_steps=2)
+
+# Run Planner agent with Reflection Tuning
+planner_response = planner_reflection_tuner.reflect_and_tune("Create a quarterly strategic roadmap for a Delaware C Corp in biotech.")
+print("Planner Agent Response:", planner_response)
+
+# Run Data Collector agent with Reflection Tuning, using DuckDuckGo search
+data_collector_task = "Find recent trends in tax strategies for corporations in the US."
+search_result = duckduckgo_search(data_collector_task)
+data_collector_response = data_collector_reflection_tuner.reflect_and_tune(f"{search_result}")
+print("Data Collector Agent Response:", data_collector_response)
+
+from token_cache_and_adaptive_factory import TokenCache, AdaptiveAgentFactory
+
+# Initialize TokenCache and AdaptiveAgentFactory
+token_cache = TokenCache(cache_duration_minutes=30)  # Cache duration for tokens
+adaptive_factory = AdaptiveAgentFactory(model, token_cache)
+
+# Example of creating adaptive agents dynamically
+adaptive_risk_agent = adaptive_factory.create_agent(
+    agent_name="Adaptive-Risk-Agent",
+    system_prompt="Assess new risk factors for changing economic conditions.",
+    task="Dynamic risk analysis in evolving markets.",
+    memory=memory_risk,
+)
+
+adaptive_sustainability_agent = adaptive_factory.create_agent(
+    agent_name="Adaptive-Sustainability-Agent",
+    system_prompt="Evaluate sustainability strategies in response to new regulations.",
+    task="Dynamic sustainability strategy for manufacturing.",
+    memory=memory_sustainability,
+)
+
+# Running adaptive agents
+adaptive_risk_response = adaptive_risk_agent.run("Analyze potential economic risks for new market conditions.")
+adaptive_sustainability_response = adaptive_sustainability_agent.run("Evaluate ESG strategies with upcoming regulation changes.")
+
+print("Adaptive Risk Agent Response:", adaptive_risk_response)
+print("Adaptive Sustainability Agent Response:", adaptive_sustainability_response)

agent_with_new_agents.py

@@ -0,0 +1,52 @@
+
+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?")

agent_with_new_agents_reflection.py

@@ -0,0 +1,65 @@
+
+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)

agents_with_new.yaml

@@ -0,0 +1,94 @@
+agents:
+  - agent_name: "Delaware-C-Corp-Tax-Deduction-Agent"
+    # model:
+    #   model_name: "gpt-4o-mini"
+    #   temperature: 0.1
+    #   max_tokens: 2500
+    system_prompt: |
+      You are a highly specialized financial analysis agent focused on Delaware C Corps tax deductions. Your task is to provide expert advice on optimizing tax strategies for Delaware C Corps, ensuring compliance with all relevant tax laws and regulations. You should be well-versed in Delaware state tax codes and federal tax laws affecting C Corps. Your responses should include detailed explanations of tax deductions available to Delaware C Corps, including but not limited to:
+      - Research and Development (R&D) tax credits
+      - Depreciation and amortization
+      - Business expense deductions
+      - Charitable contributions
+      - State-specific tax incentives
+      - Federal tax deductions applicable to C Corps
+    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
+    return_step_meta: false
+    output_type: "str"  # Can be "json" or any other format
+    task: "What are the most effective tax deduction strategies for a Delaware C Corp in the technology industry?"
+
+  - agent_name: "Delaware-C-Corp-Tax-Optimization-Agent"
+    # model:
+    #   model_name: "gpt-4o-mini"
+    #   temperature: 0.2
+    #   max_tokens: 2000
+    system_prompt: |
+      You are a highly specialized financial analysis agent focused on Delaware C Corps tax optimization. Your task is to provide expert advice on optimizing tax strategies for Delaware C Corps, ensuring compliance with all relevant tax laws and regulations. You should be well-versed in Delaware state tax codes and federal tax laws affecting C Corps. Your responses should include detailed explanations of tax optimization strategies available to Delaware C Corps, including but not limited to:
+      - Entity structure optimization
+      - Income shifting strategies
+      - Loss utilization and carryovers
+      - Tax-efficient supply chain management
+      - State-specific tax planning
+      - Federal tax planning applicable to C Corps
+    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
+    return_step_meta: true
+    output_type: "str"
+    task: "How can a Delaware C Corp in the finance industry optimize its tax strategy for maximum savings?"
+
+  - agent_name: "Delaware-C-Corp-Risk-Analysis-Agent"
+    system_prompt: |
+      You are a specialized risk analysis agent focused on assessing financial, legal, and operational risks for Delaware C Corps.
+      Provide detailed risk assessments and suggest risk mitigation strategies. Your expertise should cover:
+      - Market risk analysis
+      - Operational risk analysis
+      - Compliance with Delaware and federal regulations
+      - Financial risk modeling
+    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
+    return_step_meta: true
+    output_type: "json"
+    task: "What are the top financial and operational risks for a Delaware C Corp in the healthcare industry?"
+
+  - agent_name: "Delaware-C-Corp-Sustainability-Agent"
+    system_prompt: |
+      You are a sustainability analysis agent focused on evaluating and enhancing the economic sustainability of Delaware C Corps.
+      Your recommendations should address:
+      - Environmental, Social, and Governance (ESG) factors
+      - Resource management and waste reduction strategies
+      - Compliance with sustainability regulations and reporting
+      - Sustainable investment strategies
+    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
+    return_step_meta: true
+    output_type: "str"
+    task: "How can a Delaware C Corp in the manufacturing industry improve its sustainability practices?"

agents_with_planner_collector.yaml

@@ -0,0 +1,129 @@
+agents:
+  - agent_name: "Delaware-C-Corp-Tax-Deduction-Agent"
+    # model:
+    #   model_name: "gpt-4o-mini"
+    #   temperature: 0.1
+    #   max_tokens: 2500
+    system_prompt: |
+      You are a highly specialized financial analysis agent focused on Delaware C Corps tax deductions. Your task is to provide expert advice on optimizing tax strategies for Delaware C Corps, ensuring compliance with all relevant tax laws and regulations. You should be well-versed in Delaware state tax codes and federal tax laws affecting C Corps. Your responses should include detailed explanations of tax deductions available to Delaware C Corps, including but not limited to:
+      - Research and Development (R&D) tax credits
+      - Depreciation and amortization
+      - Business expense deductions
+      - Charitable contributions
+      - State-specific tax incentives
+      - Federal tax deductions applicable to C Corps
+    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
+    return_step_meta: false
+    output_type: "str"  # Can be "json" or any other format
+    task: "What are the most effective tax deduction strategies for a Delaware C Corp in the technology industry?"
+
+  - agent_name: "Delaware-C-Corp-Tax-Optimization-Agent"
+    # model:
+    #   model_name: "gpt-4o-mini"
+    #   temperature: 0.2
+    #   max_tokens: 2000
+    system_prompt: |
+      You are a highly specialized financial analysis agent focused on Delaware C Corps tax optimization. Your task is to provide expert advice on optimizing tax strategies for Delaware C Corps, ensuring compliance with all relevant tax laws and regulations. You should be well-versed in Delaware state tax codes and federal tax laws affecting C Corps. Your responses should include detailed explanations of tax optimization strategies available to Delaware C Corps, including but not limited to:
+      - Entity structure optimization
+      - Income shifting strategies
+      - Loss utilization and carryovers
+      - Tax-efficient supply chain management
+      - State-specific tax planning
+      - Federal tax planning applicable to C Corps
+    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
+    return_step_meta: true
+    output_type: "str"
+    task: "How can a Delaware C Corp in the finance industry optimize its tax strategy for maximum savings?"
+
+  - agent_name: "Delaware-C-Corp-Risk-Analysis-Agent"
+    system_prompt: |
+      You are a specialized risk analysis agent focused on assessing financial, legal, and operational risks for Delaware C Corps.
+      Provide detailed risk assessments and suggest risk mitigation strategies. Your expertise should cover:
+      - Market risk analysis
+      - Operational risk analysis
+      - Compliance with Delaware and federal regulations
+      - Financial risk modeling
+    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
+    return_step_meta: true
+    output_type: "json"
+    task: "What are the top financial and operational risks for a Delaware C Corp in the healthcare industry?"
+
+  - agent_name: "Delaware-C-Corp-Sustainability-Agent"
+    system_prompt: |
+      You are a sustainability analysis agent focused on evaluating and enhancing the economic sustainability of Delaware C Corps.
+      Your recommendations should address:
+      - Environmental, Social, and Governance (ESG) factors
+      - Resource management and waste reduction strategies
+      - Compliance with sustainability regulations and reporting
+      - Sustainable investment strategies
+    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
+    return_step_meta: true
+    output_type: "str"
+    task: "How can a Delaware C Corp in the manufacturing industry improve its sustainability practices?"
+
+  - agent_name: "Delaware-C-Corp-Planner-Agent"
+    system_prompt: |
+      You are a planning agent focused on developing schedules, timelines, and strategic roadmaps for Delaware C Corps.
+      Your planning should account for company milestones, regulatory deadlines, and strategic goals. Provide organized,
+      step-by-step timelines, and resources.
+    max_loops: 2
+    autosave: true
+    dashboard: false
+    verbose: true
+    dynamic_temperature_enabled: true
+    saved_state_path: "delaware_c_corp_planner_agent.json"
+    user_name: "planner_user"
+    retry_attempts: 2
+    context_length: 150000
+    return_step_meta: true
+    output_type: "json"
+    task: "Create a quarterly strategic roadmap for a Delaware C Corp in the biotech industry."
+
+  - agent_name: "Delaware-C-Corp-Data-Collector-Agent"
+    system_prompt: |
+      You are a data collection agent specialized in gathering relevant data from open-source search engines, focusing on
+      market trends, regulatory updates, and industry insights. Use DuckDuckGo to collect accurate and up-to-date information.
+    max_loops: 3
+    autosave: true
+    dashboard: false
+    verbose: true
+    dynamic_temperature_enabled: true
+    saved_state_path: "delaware_c_corp_data_collector_agent.json"
+    user_name: "data_collector_user"
+    retry_attempts: 3
+    context_length: 200000
+    return_step_meta: true
+    output_type: "str"
+    task: "Find recent trends in tax strategies for corporations in the US using DuckDuckGo search."

reflection_tuner.py

@@ -0,0 +1,26 @@
+
+class ReflectionTuner:
+    def __init__(self, agent, reflection_steps=3):
+        self.agent = agent
+        self.reflection_steps = reflection_steps
+
+    def reflect_and_tune(self, initial_task):
+        response = self.agent.run(initial_task)
+        for step in range(self.reflection_steps):
+            # Analyzing the response and adjusting based on findings
+            feedback = self.analyze_response(response)
+            if feedback:
+                print(f"Reflection step {step + 1}: Adjusting response based on feedback.")
+                response = self.agent.run(feedback)  # Rerun with adjusted task or prompt
+            else:
+                print(f"No further tuning required at step {step + 1}. Final response achieved.")
+                break
+        return response
+
+    def analyze_response(self, response):
+        # Basic logic to analyze the response quality and determine next steps
+        if "error" in response.lower() or "incomplete" in response.lower():
+            return "Please refine the explanation and address missing points."
+        elif "unclear" in response.lower() or "vague" in response.lower():
+            return "Provide a more detailed and specific analysis."
+        return None  # No adjustment required if response is satisfactory

token_cache_and_adaptive_factory.py

@@ -0,0 +1,58 @@
+
+import os
+import json
+from datetime import datetime, timedelta
+from collections import defaultdict
+
+class TokenCache:
+    def __init__(self, cache_duration_minutes=30):
+        self.token_cache = defaultdict(lambda: {"token": None, "expires": datetime.now()})
+        self.cache_duration = timedelta(minutes=cache_duration_minutes)
+
+    def get_token(self, agent_name):
+        cached_token = self.token_cache[agent_name]
+        if cached_token["token"] and cached_token["expires"] > datetime.now():
+            print(f"Using cached token for {agent_name}.")
+            return cached_token["token"]
+        return None  # Token has expired or does not exist
+
+    def set_token(self, agent_name, token):
+        self.token_cache[agent_name] = {
+            "token": token,
+            "expires": datetime.now() + self.cache_duration,
+        }
+
+class AdaptiveAgentFactory:
+    def __init__(self, model, token_cache, reflection_steps=2):
+        self.model = model
+        self.token_cache = token_cache
+        self.reflection_steps = reflection_steps
+
+    def create_agent(self, agent_name, system_prompt, task, memory):
+        cached_token = self.token_cache.get_token(agent_name)
+        if cached_token:
+            return cached_token
+
+        # Create new agent instance with unique parameters
+        new_agent = Agent(
+            agent_name=agent_name,
+            system_prompt=system_prompt,
+            agent_description=f"Adaptive agent for {task}",
+            llm=self.model,
+            max_loops=3,
+            autosave=True,
+            dashboard=False,
+            verbose=True,
+            dynamic_temperature_enabled=True,
+            saved_state_path=f"{agent_name.lower().replace(' ', '_')}.json",
+            user_name="adaptive_user",
+            retry_attempts=2,
+            context_length=200000,
+            long_term_memory=memory,
+        )
+
+        # Generate a token for the new agent and cache it
+        token = f"{agent_name}_{datetime.now().strftime('%Y%m%d%H%M%S')}"
+        self.token_cache.set_token(agent_name, token)
+        print(f"Created new agent {agent_name} with token {token}.")
+        return new_agent