Kye Gomez
1 week ago
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79e7d20d4f
commit
e5b1a0a483
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## Unique Swarm Examples
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In this section, we present a diverse collection of unique swarms, each with its own distinct characteristics and applications. These examples are designed to illustrate the versatility and potential of swarm intelligence in various domains. By exploring these examples, you can gain a deeper understanding of how swarms can be leveraged to solve complex problems and improve decision-making processes.
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```python
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import asyncio
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from typing import List
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from swarms.structs.agent import Agent
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from swarms.structs.swarming_architectures import (
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broadcast,
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circular_swarm,
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exponential_swarm,
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fibonacci_swarm,
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grid_swarm,
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linear_swarm,
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mesh_swarm,
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one_to_three,
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prime_swarm,
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sigmoid_swarm,
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sinusoidal_swarm,
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staircase_swarm,
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star_swarm,
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)
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def create_finance_agents() -> List[Agent]:
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"""Create specialized finance agents"""
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return [
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Agent(
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agent_name="MarketAnalyst",
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system_prompt="You are a market analysis expert. Analyze market trends and provide insights.",
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model_name="gpt-4o-mini"
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),
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Agent(
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agent_name="RiskManager",
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system_prompt="You are a risk management specialist. Evaluate risks and provide mitigation strategies.",
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model_name="gpt-4o-mini"
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),
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Agent(
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agent_name="PortfolioManager",
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system_prompt="You are a portfolio management expert. Optimize investment portfolios and asset allocation.",
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model_name="gpt-4o-mini"
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),
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Agent(
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agent_name="ComplianceOfficer",
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system_prompt="You are a financial compliance expert. Ensure regulatory compliance and identify issues.",
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model_name="gpt-4o-mini"
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)
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]
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def create_healthcare_agents() -> List[Agent]:
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"""Create specialized healthcare agents"""
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return [
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Agent(
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agent_name="Diagnostician",
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system_prompt="You are a medical diagnostician. Analyze symptoms and suggest potential diagnoses.",
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model_name="gpt-4o-mini"
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),
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Agent(
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agent_name="Treatment_Planner",
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system_prompt="You are a treatment planning specialist. Develop comprehensive treatment plans.",
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model_name="gpt-4o-mini"
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),
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Agent(
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agent_name="MedicalResearcher",
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system_prompt="You are a medical researcher. Analyze latest research and provide evidence-based recommendations.",
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model_name="gpt-4o-mini"
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),
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Agent(
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agent_name="PatientCareCoordinator",
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system_prompt="You are a patient care coordinator. Manage patient care workflow and coordination.",
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model_name="gpt-4o-mini"
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)
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]
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def print_separator():
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print("\n" + "="*50 + "\n")
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def run_finance_circular_swarm():
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"""Investment analysis workflow using circular swarm"""
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print_separator()
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print("FINANCE - INVESTMENT ANALYSIS (Circular Swarm)")
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agents = create_finance_agents()
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tasks = [
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"Analyze Tesla stock performance for Q4 2024",
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"Assess market risks and potential hedging strategies",
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"Recommend portfolio adjustments based on analysis"
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]
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print("\nTasks:")
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for i, task in enumerate(tasks, 1):
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print(f"{i}. {task}")
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result = circular_swarm(agents, tasks)
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print("\nResults:")
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for log in result['history']:
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print(f"\n{log['agent_name']}:")
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print(f"Task: {log['task']}")
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print(f"Response: {log['response']}")
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def run_healthcare_grid_swarm():
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"""Patient diagnosis and treatment planning using grid swarm"""
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print_separator()
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print("HEALTHCARE - PATIENT DIAGNOSIS (Grid Swarm)")
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agents = create_healthcare_agents()
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tasks = [
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"Review patient symptoms: fever, fatigue, joint pain",
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"Research latest treatment protocols",
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"Develop preliminary treatment plan",
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"Coordinate with specialists"
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]
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print("\nTasks:")
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for i, task in enumerate(tasks, 1):
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print(f"{i}. {task}")
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result = grid_swarm(agents, tasks)
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print("\nGrid swarm processing completed")
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print(result)
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def run_finance_linear_swarm():
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"""Loan approval process using linear swarm"""
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print_separator()
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print("FINANCE - LOAN APPROVAL PROCESS (Linear Swarm)")
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agents = create_finance_agents()[:3]
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tasks = [
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"Review loan application and credit history",
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"Assess risk factors and compliance requirements",
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"Generate final loan recommendation"
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]
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print("\nTasks:")
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for i, task in enumerate(tasks, 1):
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print(f"{i}. {task}")
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result = linear_swarm(agents, tasks)
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print("\nResults:")
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for log in result['history']:
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print(f"\n{log['agent_name']}:")
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print(f"Task: {log['task']}")
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print(f"Response: {log['response']}")
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def run_healthcare_star_swarm():
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"""Complex medical case management using star swarm"""
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print_separator()
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print("HEALTHCARE - COMPLEX CASE MANAGEMENT (Star Swarm)")
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agents = create_healthcare_agents()
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tasks = [
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"Complex case: Patient with multiple chronic conditions",
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"Develop integrated care plan"
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]
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print("\nTasks:")
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for i, task in enumerate(tasks, 1):
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print(f"{i}. {task}")
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result = star_swarm(agents, tasks)
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print("\nResults:")
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for log in result['history']:
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print(f"\n{log['agent_name']}:")
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print(f"Task: {log['task']}")
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print(f"Response: {log['response']}")
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def run_finance_mesh_swarm():
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"""Market risk assessment using mesh swarm"""
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print_separator()
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print("FINANCE - MARKET RISK ASSESSMENT (Mesh Swarm)")
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agents = create_finance_agents()
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tasks = [
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"Analyze global market conditions",
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"Assess currency exchange risks",
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"Evaluate sector-specific risks",
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"Review portfolio exposure"
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]
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print("\nTasks:")
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for i, task in enumerate(tasks, 1):
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print(f"{i}. {task}")
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result = mesh_swarm(agents, tasks)
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print("\nResults:")
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for log in result['history']:
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print(f"\n{log['agent_name']}:")
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print(f"Task: {log['task']}")
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print(f"Response: {log['response']}")
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def run_mathematical_finance_swarms():
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"""Complex financial analysis using mathematical swarms"""
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print_separator()
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print("FINANCE - MARKET PATTERN ANALYSIS")
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agents = create_finance_agents()
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tasks = [
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"Analyze historical market patterns",
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"Predict market trends using technical analysis",
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"Identify potential arbitrage opportunities"
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]
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print("\nTasks:")
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for i, task in enumerate(tasks, 1):
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print(f"{i}. {task}")
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print("\nFibonacci Swarm Results:")
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result = fibonacci_swarm(agents, tasks.copy())
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print(result)
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print("\nPrime Swarm Results:")
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result = prime_swarm(agents, tasks.copy())
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print(result)
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print("\nExponential Swarm Results:")
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result = exponential_swarm(agents, tasks.copy())
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print(result)
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def run_healthcare_pattern_swarms():
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"""Patient monitoring using pattern swarms"""
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print_separator()
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print("HEALTHCARE - PATIENT MONITORING PATTERNS")
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agents = create_healthcare_agents()
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task = "Monitor and analyze patient vital signs: BP, heart rate, temperature, O2 saturation"
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print(f"\nTask: {task}")
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print("\nStaircase Pattern Analysis:")
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result = staircase_swarm(agents, task)
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print(result)
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print("\nSigmoid Pattern Analysis:")
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result = sigmoid_swarm(agents, task)
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print(result)
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print("\nSinusoidal Pattern Analysis:")
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result = sinusoidal_swarm(agents, task)
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print(result)
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async def run_communication_examples():
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"""Communication patterns for emergency scenarios"""
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print_separator()
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print("EMERGENCY COMMUNICATION PATTERNS")
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# Finance market alert
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finance_sender = create_finance_agents()[0]
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finance_receivers = create_finance_agents()[1:]
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market_alert = "URGENT: Major market volatility detected - immediate risk assessment required"
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print("\nFinance Market Alert:")
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print(f"Alert: {market_alert}")
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result = await broadcast(finance_sender, finance_receivers, market_alert)
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print("\nBroadcast Results:")
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for log in result['history']:
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print(f"\n{log['agent_name']}:")
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print(f"Response: {log['response']}")
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# Healthcare emergency
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health_sender = create_healthcare_agents()[0]
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health_receivers = create_healthcare_agents()[1:4]
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emergency_case = "EMERGENCY: Trauma patient with multiple injuries - immediate consultation required"
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print("\nHealthcare Emergency:")
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print(f"Case: {emergency_case}")
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result = await one_to_three(health_sender, health_receivers, emergency_case)
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print("\nConsultation Results:")
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for log in result['history']:
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print(f"\n{log['agent_name']}:")
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print(f"Response: {log['response']}")
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async def run_all_examples():
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"""Execute all swarm examples"""
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print("\n=== SWARM ARCHITECTURE EXAMPLES ===\n")
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# Finance examples
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run_finance_circular_swarm()
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run_finance_linear_swarm()
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run_finance_mesh_swarm()
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run_mathematical_finance_swarms()
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# Healthcare examples
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run_healthcare_grid_swarm()
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run_healthcare_star_swarm()
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run_healthcare_pattern_swarms()
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# Communication examples
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await run_communication_examples()
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print("\n=== ALL EXAMPLES COMPLETED ===")
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if __name__ == "__main__":
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asyncio.run(run_all_examples())
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```
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@ -1,328 +0,0 @@
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import os
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import traceback
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from datetime import datetime
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from typing import Callable, Dict, List, Optional
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from loguru import logger
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from swarm_models import OpenAIChat
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from swarms.structs.agent import Agent
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from swarms.structs.rearrange import AgentRearrange
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class TestResult:
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"""Class to store test results and metadata"""
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def __init__(self, test_name: str):
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self.test_name = test_name
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self.start_time = datetime.now()
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self.end_time = None
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self.success = False
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self.error = None
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self.traceback = None
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self.function_output = None
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def complete(
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self, success: bool, error: Optional[Exception] = None
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):
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"""Complete the test execution with results"""
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self.end_time = datetime.now()
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self.success = success
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if error:
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self.error = str(error)
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self.traceback = traceback.format_exc()
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def duration(self) -> float:
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"""Calculate test duration in seconds"""
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if self.end_time:
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return (self.end_time - self.start_time).total_seconds()
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return 0
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def run_test(test_func: Callable) -> TestResult:
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"""
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Decorator to run tests with error handling and logging
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Args:
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test_func (Callable): Test function to execute
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Returns:
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TestResult: Object containing test execution details
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"""
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def wrapper(*args, **kwargs) -> TestResult:
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result = TestResult(test_func.__name__)
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logger.info(
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f"\n{'='*20} Running test: {test_func.__name__} {'='*20}"
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)
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try:
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output = test_func(*args, **kwargs)
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result.function_output = output
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result.complete(success=True)
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logger.success(
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f"✅ Test {test_func.__name__} passed successfully"
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)
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except Exception as e:
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result.complete(success=False, error=e)
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logger.error(
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f"❌ Test {test_func.__name__} failed with error: {str(e)}"
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)
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logger.error(f"Traceback: {traceback.format_exc()}")
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logger.info(
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f"Test duration: {result.duration():.2f} seconds\n"
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)
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return result
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return wrapper
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def create_functional_agents() -> List[Agent]:
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"""
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Create a list of functional agents with real LLM integration for testing.
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Using OpenAI's GPT model for realistic agent behavior testing.
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"""
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# Initialize OpenAI Chat model
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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logger.warning(
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"No OpenAI API key found. Using mock agents instead."
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)
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return [
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create_mock_agent("TestAgent1"),
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create_mock_agent("TestAgent2"),
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]
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try:
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model = OpenAIChat(
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api_key=api_key, model_name="gpt-4o", temperature=0.1
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)
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# Create boss agent
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boss_agent = Agent(
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agent_name="BossAgent",
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system_prompt="""
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You are the BossAgent responsible for managing and overseeing test scenarios.
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Your role is to coordinate tasks between agents and ensure efficient collaboration.
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Analyze inputs, break down tasks, and provide clear directives to other agents.
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Maintain a structured approach to task management and result compilation.
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""",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="test_boss_agent.json",
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)
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# Create analysis agent
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analysis_agent = Agent(
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agent_name="AnalysisAgent",
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system_prompt="""
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You are the AnalysisAgent responsible for detailed data processing and analysis.
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Your role is to examine input data, identify patterns, and provide analytical insights.
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Focus on breaking down complex information into clear, actionable components.
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""",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="test_analysis_agent.json",
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)
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# Create summary agent
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summary_agent = Agent(
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agent_name="SummaryAgent",
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system_prompt="""
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You are the SummaryAgent responsible for consolidating and summarizing information.
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Your role is to take detailed analysis and create concise, actionable summaries.
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Focus on highlighting key points and ensuring clarity in communication.
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""",
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llm=model,
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max_loops=1,
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dashboard=False,
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streaming_on=True,
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verbose=True,
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stopping_token="<DONE>",
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state_save_file_type="json",
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saved_state_path="test_summary_agent.json",
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)
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logger.info(
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"Successfully created functional agents with LLM integration"
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)
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return [boss_agent, analysis_agent, summary_agent]
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except Exception as e:
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logger.error(f"Failed to create functional agents: {str(e)}")
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logger.warning("Falling back to mock agents")
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return [
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create_mock_agent("TestAgent1"),
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create_mock_agent("TestAgent2"),
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]
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def create_mock_agent(name: str) -> Agent:
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"""Create a mock agent for testing when LLM integration is not available"""
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return Agent(
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agent_name=name,
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system_prompt=f"You are a test agent named {name}",
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llm=None,
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)
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@run_test
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def test_init():
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"""Test AgentRearrange initialization with functional agents"""
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logger.info("Creating agents for initialization test")
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agents = create_functional_agents()
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rearrange = AgentRearrange(
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name="TestRearrange",
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agents=agents,
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flow=f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}",
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)
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assert rearrange.name == "TestRearrange"
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assert len(rearrange.agents) == 3
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assert (
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rearrange.flow
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== f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}"
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)
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logger.info(
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f"Initialized AgentRearrange with {len(agents)} agents"
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)
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return True
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@run_test
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def test_validate_flow():
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"""Test flow validation logic"""
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agents = create_functional_agents()
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rearrange = AgentRearrange(
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agents=agents,
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flow=f"{agents[0].agent_name} -> {agents[1].agent_name}",
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)
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logger.info("Testing valid flow pattern")
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valid = rearrange.validate_flow()
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assert valid is True
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|
||||
logger.info("Testing invalid flow pattern")
|
||||
rearrange.flow = f"{agents[0].agent_name} {agents[1].agent_name}" # Missing arrow
|
||||
try:
|
||||
rearrange.validate_flow()
|
||||
assert False, "Should have raised ValueError"
|
||||
except ValueError as e:
|
||||
logger.info(
|
||||
f"Successfully caught invalid flow error: {str(e)}"
|
||||
)
|
||||
assert True
|
||||
|
||||
return True
|
||||
|
||||
|
||||
@run_test
|
||||
def test_add_remove_agent():
|
||||
"""Test adding and removing agents from the swarm"""
|
||||
agents = create_functional_agents()
|
||||
rearrange = AgentRearrange(
|
||||
agents=agents[:2]
|
||||
) # Start with first two agents
|
||||
|
||||
logger.info("Testing agent addition")
|
||||
new_agent = agents[2] # Use the third agent as new agent
|
||||
rearrange.add_agent(new_agent)
|
||||
assert new_agent.agent_name in rearrange.agents
|
||||
|
||||
logger.info("Testing agent removal")
|
||||
rearrange.remove_agent(new_agent.agent_name)
|
||||
assert new_agent.agent_name not in rearrange.agents
|
||||
|
||||
return True
|
||||
|
||||
|
||||
@run_test
|
||||
def test_basic_run():
|
||||
"""Test basic task execution with the swarm"""
|
||||
agents = create_functional_agents()
|
||||
rearrange = AgentRearrange(
|
||||
name="TestSwarm",
|
||||
agents=agents,
|
||||
flow=f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}",
|
||||
max_loops=1,
|
||||
)
|
||||
|
||||
test_task = (
|
||||
"Analyze this test message and provide a brief summary."
|
||||
)
|
||||
logger.info(f"Running test task: {test_task}")
|
||||
|
||||
try:
|
||||
result = rearrange.run(test_task)
|
||||
assert result is not None
|
||||
logger.info(
|
||||
f"Successfully executed task with result length: {len(str(result))}"
|
||||
)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"Task execution failed: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
def run_all_tests() -> Dict[str, TestResult]:
|
||||
"""
|
||||
Run all test cases and collect results
|
||||
|
||||
Returns:
|
||||
Dict[str, TestResult]: Dictionary mapping test names to their results
|
||||
"""
|
||||
logger.info("\n🚀 Starting AgentRearrange test suite execution")
|
||||
test_functions = [
|
||||
test_init,
|
||||
test_validate_flow,
|
||||
test_add_remove_agent,
|
||||
test_basic_run,
|
||||
]
|
||||
|
||||
results = {}
|
||||
for test in test_functions:
|
||||
result = test()
|
||||
results[test.__name__] = result
|
||||
|
||||
# Log summary
|
||||
total_tests = len(results)
|
||||
passed_tests = sum(1 for r in results.values() if r.success)
|
||||
failed_tests = total_tests - passed_tests
|
||||
|
||||
logger.info("\n📊 Test Suite Summary:")
|
||||
logger.info(f"Total Tests: {total_tests}")
|
||||
print(f"✅ Passed: {passed_tests}")
|
||||
|
||||
if failed_tests > 0:
|
||||
logger.error(f"❌ Failed: {failed_tests}")
|
||||
|
||||
# Detailed failure information
|
||||
if failed_tests > 0:
|
||||
logger.error("\n❌ Failed Tests Details:")
|
||||
for name, result in results.items():
|
||||
if not result.success:
|
||||
logger.error(f"\n{name}:")
|
||||
logger.error(f"Error: {result.error}")
|
||||
logger.error(f"Traceback: {result.traceback}")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("🌟 Starting AgentRearrange Test Suite")
|
||||
results = run_all_tests()
|
||||
print("🏁 Test Suite Execution Completed")
|
||||
@ -0,0 +1,290 @@
|
||||
import asyncio
|
||||
from typing import List
|
||||
|
||||
from swarms.structs.agent import Agent
|
||||
from swarms.structs.swarming_architectures import (
|
||||
broadcast,
|
||||
circular_swarm,
|
||||
exponential_swarm,
|
||||
fibonacci_swarm,
|
||||
grid_swarm,
|
||||
linear_swarm,
|
||||
mesh_swarm,
|
||||
one_to_three,
|
||||
prime_swarm,
|
||||
sigmoid_swarm,
|
||||
sinusoidal_swarm,
|
||||
staircase_swarm,
|
||||
star_swarm,
|
||||
)
|
||||
|
||||
|
||||
def create_finance_agents() -> List[Agent]:
|
||||
"""Create specialized finance agents"""
|
||||
return [
|
||||
Agent(
|
||||
agent_name="MarketAnalyst",
|
||||
system_prompt="You are a market analysis expert. Analyze market trends and provide insights.",
|
||||
model_name="gpt-4o-mini"
|
||||
),
|
||||
Agent(
|
||||
agent_name="RiskManager",
|
||||
system_prompt="You are a risk management specialist. Evaluate risks and provide mitigation strategies.",
|
||||
model_name="gpt-4o-mini"
|
||||
),
|
||||
Agent(
|
||||
agent_name="PortfolioManager",
|
||||
system_prompt="You are a portfolio management expert. Optimize investment portfolios and asset allocation.",
|
||||
model_name="gpt-4o-mini"
|
||||
),
|
||||
Agent(
|
||||
agent_name="ComplianceOfficer",
|
||||
system_prompt="You are a financial compliance expert. Ensure regulatory compliance and identify issues.",
|
||||
model_name="gpt-4o-mini"
|
||||
)
|
||||
]
|
||||
|
||||
def create_healthcare_agents() -> List[Agent]:
|
||||
"""Create specialized healthcare agents"""
|
||||
return [
|
||||
Agent(
|
||||
agent_name="Diagnostician",
|
||||
system_prompt="You are a medical diagnostician. Analyze symptoms and suggest potential diagnoses.",
|
||||
model_name="gpt-4o-mini"
|
||||
),
|
||||
Agent(
|
||||
agent_name="Treatment_Planner",
|
||||
system_prompt="You are a treatment planning specialist. Develop comprehensive treatment plans.",
|
||||
model_name="gpt-4o-mini"
|
||||
),
|
||||
Agent(
|
||||
agent_name="MedicalResearcher",
|
||||
system_prompt="You are a medical researcher. Analyze latest research and provide evidence-based recommendations.",
|
||||
model_name="gpt-4o-mini"
|
||||
),
|
||||
Agent(
|
||||
agent_name="PatientCareCoordinator",
|
||||
system_prompt="You are a patient care coordinator. Manage patient care workflow and coordination.",
|
||||
model_name="gpt-4o-mini"
|
||||
)
|
||||
]
|
||||
|
||||
def print_separator():
|
||||
print("\n" + "="*50 + "\n")
|
||||
|
||||
def run_finance_circular_swarm():
|
||||
"""Investment analysis workflow using circular swarm"""
|
||||
print_separator()
|
||||
print("FINANCE - INVESTMENT ANALYSIS (Circular Swarm)")
|
||||
|
||||
agents = create_finance_agents()
|
||||
tasks = [
|
||||
"Analyze Tesla stock performance for Q4 2024",
|
||||
"Assess market risks and potential hedging strategies",
|
||||
"Recommend portfolio adjustments based on analysis"
|
||||
]
|
||||
|
||||
print("\nTasks:")
|
||||
for i, task in enumerate(tasks, 1):
|
||||
print(f"{i}. {task}")
|
||||
|
||||
result = circular_swarm(agents, tasks)
|
||||
print("\nResults:")
|
||||
for log in result['history']:
|
||||
print(f"\n{log['agent_name']}:")
|
||||
print(f"Task: {log['task']}")
|
||||
print(f"Response: {log['response']}")
|
||||
|
||||
def run_healthcare_grid_swarm():
|
||||
"""Patient diagnosis and treatment planning using grid swarm"""
|
||||
print_separator()
|
||||
print("HEALTHCARE - PATIENT DIAGNOSIS (Grid Swarm)")
|
||||
|
||||
agents = create_healthcare_agents()
|
||||
tasks = [
|
||||
"Review patient symptoms: fever, fatigue, joint pain",
|
||||
"Research latest treatment protocols",
|
||||
"Develop preliminary treatment plan",
|
||||
"Coordinate with specialists"
|
||||
]
|
||||
|
||||
print("\nTasks:")
|
||||
for i, task in enumerate(tasks, 1):
|
||||
print(f"{i}. {task}")
|
||||
|
||||
result = grid_swarm(agents, tasks)
|
||||
print("\nGrid swarm processing completed")
|
||||
print(result)
|
||||
|
||||
def run_finance_linear_swarm():
|
||||
"""Loan approval process using linear swarm"""
|
||||
print_separator()
|
||||
print("FINANCE - LOAN APPROVAL PROCESS (Linear Swarm)")
|
||||
|
||||
agents = create_finance_agents()[:3]
|
||||
tasks = [
|
||||
"Review loan application and credit history",
|
||||
"Assess risk factors and compliance requirements",
|
||||
"Generate final loan recommendation"
|
||||
]
|
||||
|
||||
print("\nTasks:")
|
||||
for i, task in enumerate(tasks, 1):
|
||||
print(f"{i}. {task}")
|
||||
|
||||
result = linear_swarm(agents, tasks)
|
||||
print("\nResults:")
|
||||
for log in result['history']:
|
||||
print(f"\n{log['agent_name']}:")
|
||||
print(f"Task: {log['task']}")
|
||||
print(f"Response: {log['response']}")
|
||||
|
||||
def run_healthcare_star_swarm():
|
||||
"""Complex medical case management using star swarm"""
|
||||
print_separator()
|
||||
print("HEALTHCARE - COMPLEX CASE MANAGEMENT (Star Swarm)")
|
||||
|
||||
agents = create_healthcare_agents()
|
||||
tasks = [
|
||||
"Complex case: Patient with multiple chronic conditions",
|
||||
"Develop integrated care plan"
|
||||
]
|
||||
|
||||
print("\nTasks:")
|
||||
for i, task in enumerate(tasks, 1):
|
||||
print(f"{i}. {task}")
|
||||
|
||||
result = star_swarm(agents, tasks)
|
||||
print("\nResults:")
|
||||
for log in result['history']:
|
||||
print(f"\n{log['agent_name']}:")
|
||||
print(f"Task: {log['task']}")
|
||||
print(f"Response: {log['response']}")
|
||||
|
||||
def run_finance_mesh_swarm():
|
||||
"""Market risk assessment using mesh swarm"""
|
||||
print_separator()
|
||||
print("FINANCE - MARKET RISK ASSESSMENT (Mesh Swarm)")
|
||||
|
||||
agents = create_finance_agents()
|
||||
tasks = [
|
||||
"Analyze global market conditions",
|
||||
"Assess currency exchange risks",
|
||||
"Evaluate sector-specific risks",
|
||||
"Review portfolio exposure"
|
||||
]
|
||||
|
||||
print("\nTasks:")
|
||||
for i, task in enumerate(tasks, 1):
|
||||
print(f"{i}. {task}")
|
||||
|
||||
result = mesh_swarm(agents, tasks)
|
||||
print("\nResults:")
|
||||
for log in result['history']:
|
||||
print(f"\n{log['agent_name']}:")
|
||||
print(f"Task: {log['task']}")
|
||||
print(f"Response: {log['response']}")
|
||||
|
||||
def run_mathematical_finance_swarms():
|
||||
"""Complex financial analysis using mathematical swarms"""
|
||||
print_separator()
|
||||
print("FINANCE - MARKET PATTERN ANALYSIS")
|
||||
|
||||
agents = create_finance_agents()
|
||||
tasks = [
|
||||
"Analyze historical market patterns",
|
||||
"Predict market trends using technical analysis",
|
||||
"Identify potential arbitrage opportunities"
|
||||
]
|
||||
|
||||
print("\nTasks:")
|
||||
for i, task in enumerate(tasks, 1):
|
||||
print(f"{i}. {task}")
|
||||
|
||||
print("\nFibonacci Swarm Results:")
|
||||
result = fibonacci_swarm(agents, tasks.copy())
|
||||
print(result)
|
||||
|
||||
print("\nPrime Swarm Results:")
|
||||
result = prime_swarm(agents, tasks.copy())
|
||||
print(result)
|
||||
|
||||
print("\nExponential Swarm Results:")
|
||||
result = exponential_swarm(agents, tasks.copy())
|
||||
print(result)
|
||||
|
||||
def run_healthcare_pattern_swarms():
|
||||
"""Patient monitoring using pattern swarms"""
|
||||
print_separator()
|
||||
print("HEALTHCARE - PATIENT MONITORING PATTERNS")
|
||||
|
||||
agents = create_healthcare_agents()
|
||||
task = "Monitor and analyze patient vital signs: BP, heart rate, temperature, O2 saturation"
|
||||
|
||||
print(f"\nTask: {task}")
|
||||
|
||||
print("\nStaircase Pattern Analysis:")
|
||||
result = staircase_swarm(agents, task)
|
||||
print(result)
|
||||
|
||||
print("\nSigmoid Pattern Analysis:")
|
||||
result = sigmoid_swarm(agents, task)
|
||||
print(result)
|
||||
|
||||
print("\nSinusoidal Pattern Analysis:")
|
||||
result = sinusoidal_swarm(agents, task)
|
||||
print(result)
|
||||
|
||||
async def run_communication_examples():
|
||||
"""Communication patterns for emergency scenarios"""
|
||||
print_separator()
|
||||
print("EMERGENCY COMMUNICATION PATTERNS")
|
||||
|
||||
# Finance market alert
|
||||
finance_sender = create_finance_agents()[0]
|
||||
finance_receivers = create_finance_agents()[1:]
|
||||
market_alert = "URGENT: Major market volatility detected - immediate risk assessment required"
|
||||
|
||||
print("\nFinance Market Alert:")
|
||||
print(f"Alert: {market_alert}")
|
||||
result = await broadcast(finance_sender, finance_receivers, market_alert)
|
||||
print("\nBroadcast Results:")
|
||||
for log in result['history']:
|
||||
print(f"\n{log['agent_name']}:")
|
||||
print(f"Response: {log['response']}")
|
||||
|
||||
# Healthcare emergency
|
||||
health_sender = create_healthcare_agents()[0]
|
||||
health_receivers = create_healthcare_agents()[1:4]
|
||||
emergency_case = "EMERGENCY: Trauma patient with multiple injuries - immediate consultation required"
|
||||
|
||||
print("\nHealthcare Emergency:")
|
||||
print(f"Case: {emergency_case}")
|
||||
result = await one_to_three(health_sender, health_receivers, emergency_case)
|
||||
print("\nConsultation Results:")
|
||||
for log in result['history']:
|
||||
print(f"\n{log['agent_name']}:")
|
||||
print(f"Response: {log['response']}")
|
||||
|
||||
async def run_all_examples():
|
||||
"""Execute all swarm examples"""
|
||||
print("\n=== SWARM ARCHITECTURE EXAMPLES ===\n")
|
||||
|
||||
# Finance examples
|
||||
run_finance_circular_swarm()
|
||||
run_finance_linear_swarm()
|
||||
run_finance_mesh_swarm()
|
||||
run_mathematical_finance_swarms()
|
||||
|
||||
# Healthcare examples
|
||||
run_healthcare_grid_swarm()
|
||||
run_healthcare_star_swarm()
|
||||
run_healthcare_pattern_swarms()
|
||||
|
||||
# Communication examples
|
||||
await run_communication_examples()
|
||||
|
||||
print("\n=== ALL EXAMPLES COMPLETED ===")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(run_all_examples())
|
||||
Loading…
Reference in new issue