Update board_of_directors_example.py

examples/multi_agent/board_of_directors/board_of_directors_example.py

@@ -5,19 +5,25 @@ This example demonstrates how to use the Board of Directors swarm feature
 in the Swarms Framework. It shows how to create a board, configure it,
 and use it to orchestrate tasks across multiple agents.
 
-The example includes:
+To run this example:
-1. Basic Board of Directors setup and usage
+1. Make sure you're in the root directory of the swarms project
-2. Custom board member configuration
+2. Run: python examples/multi_agent/board_of_directors/board_of_directors_example.py
-3. Task execution and feedback
-4. Configuration management
 """
 
 import os
 import sys
-from typing import List, Optional
+from typing import List
-
+
-# Add the parent directory to the path to import swarms
+# Add the root directory to the Python path if running from examples directory
-sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))))
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if 'examples' in current_dir:
+    root_dir = current_dir
+    while os.path.basename(root_dir) != 'examples' and root_dir != os.path.dirname(root_dir):
+        root_dir = os.path.dirname(root_dir)
+    if os.path.basename(root_dir) == 'examples':
+        root_dir = os.path.dirname(root_dir)
+        if root_dir not in sys.path:
+            sys.path.insert(0, root_dir)
 
 from swarms.structs.board_of_directors_swarm import (
     BoardOfDirectorsSwarm,
@@ -25,413 +31,167 @@ from swarms.structs.board_of_directors_swarm import (
     BoardMemberRole,
 )
 from swarms.structs.agent import Agent
-from swarms.config.board_config import (
-    enable_board_feature,
-    disable_board_feature,
-    is_board_feature_enabled,
-    create_default_config_file,
-    set_board_size,
-    set_decision_threshold,
-    set_board_model,
-    enable_verbose_logging,
-    disable_verbose_logging,
-)
-
-
-def enable_board_directors_feature() -> None:
-    """
-    Enable the Board of Directors feature.
-    
-    This function demonstrates how to enable the Board of Directors feature
-    globally and create a default configuration file.
-    """
-    print("🔧 Enabling Board of Directors feature...")
-    
-    try:
-        # Create a default configuration file
-        create_default_config_file("swarms_board_config.yaml")
-        
-        # Enable the feature
-        enable_board_feature("swarms_board_config.yaml")
-        
-        # Configure some default settings
-        set_board_size(3)
-        set_decision_threshold(0.6)
-        set_board_model("gpt-4o-mini")
-        enable_verbose_logging("swarms_board_config.yaml")
-        
-        print("✅ Board of Directors feature enabled successfully!")
-        print("📁 Configuration file created: swarms_board_config.yaml")
-        
-    except Exception as e:
-        print(f"❌ Failed to enable Board of Directors feature: {e}")
-        raise
 
 
-def create_custom_board_members() -> List[BoardMember]:
+def create_board_members() -> List[BoardMember]:
-    """
+    """Create board members with specific roles."""
-    Create custom board members with specific roles and expertise.
-    
-    This function demonstrates how to create a custom board with
-    specialized roles and expertise areas.
     
-    Returns:
-        List[BoardMember]: List of custom board members
-    """
-    print("👥 Creating custom board members...")
-    
-    # Create specialized board members
     chairman = Agent(
-        agent_name="Executive_Chairman",
+        agent_name="Chairman",
-        agent_description="Executive Chairman with strategic vision and leadership expertise",
+        agent_description="Executive Chairman with strategic vision",
         model_name="gpt-4o-mini",
         max_loops=1,
-        system_prompt="""You are the Executive Chairman of the Board. Your role is to:
+        system_prompt="You are the Executive Chairman. Provide strategic leadership and facilitate decision-making.",
-1. Provide strategic leadership and vision
-2. Facilitate high-level decision-making
-3. Ensure board effectiveness and governance
-4. Represent the organization's interests
-5. Guide long-term strategic planning
-
-You should be visionary, strategic, and focused on organizational success.""",
     )
     
     cto = Agent(
         agent_name="CTO",
-        agent_description="Chief Technology Officer with deep technical expertise",
+        agent_description="Chief Technology Officer with technical expertise",
         model_name="gpt-4o-mini",
         max_loops=1,
-        system_prompt="""You are the Chief Technology Officer. Your role is to:
+        system_prompt="You are the CTO. Provide technical leadership and evaluate technology solutions.",
-1. Provide technical leadership and strategy
-2. Evaluate technology solutions and architectures
-3. Ensure technical feasibility of proposed solutions
-4. Guide technology-related decisions
-5. Maintain technical standards and best practices
-
-You should be technically proficient, innovative, and focused on technical excellence.""",
     )
     
     cfo = Agent(
         agent_name="CFO",
-        agent_description="Chief Financial Officer with financial and risk management expertise",
+        agent_description="Chief Financial Officer with financial expertise",
         model_name="gpt-4o-mini",
         max_loops=1,
-        system_prompt="""You are the Chief Financial Officer. Your role is to:
+        system_prompt="You are the CFO. Provide financial analysis and ensure fiscal responsibility.",
-1. Provide financial analysis and insights
-2. Evaluate financial implications of decisions
-3. Ensure financial sustainability and risk management
-4. Guide resource allocation and budgeting
-5. Maintain financial controls and compliance
-
-You should be financially astute, risk-aware, and focused on financial health.""",
     )
     
-    # Create BoardMember objects with roles and expertise
+    return [
-    board_members = [
         BoardMember(
             agent=chairman,
             role=BoardMemberRole.CHAIRMAN,
             voting_weight=2.0,
-            expertise_areas=["strategic_planning", "leadership", "governance", "business_strategy"]
+            expertise_areas=["leadership", "strategy"]
         ),
         BoardMember(
             agent=cto,
             role=BoardMemberRole.EXECUTIVE_DIRECTOR,
             voting_weight=1.5,
-            expertise_areas=["technology", "architecture", "innovation", "technical_strategy"]
+            expertise_areas=["technology", "innovation"]
         ),
         BoardMember(
             agent=cfo,
             role=BoardMemberRole.EXECUTIVE_DIRECTOR,
             voting_weight=1.5,
-            expertise_areas=["finance", "risk_management", "budgeting", "financial_analysis"]
+            expertise_areas=["finance", "risk_management"]
         ),
     ]
-    
-    print(f"✅ Created {len(board_members)} custom board members")
-    for member in board_members:
-        print(f"   - {member.agent.agent_name} ({member.role.value})")
-    
-    return board_members
 
 
 def create_worker_agents() -> List[Agent]:
-    """
+    """Create worker agents for the swarm."""
-    Create worker agents for the swarm.
-    
-    This function creates specialized worker agents that will be
-    managed by the Board of Directors.
     
-    Returns:
-        List[Agent]: List of worker agents
-    """
-    print("🛠️ Creating worker agents...")
-    
-    # Create specialized worker agents
     researcher = Agent(
-        agent_name="Research_Analyst",
+        agent_name="Researcher",
-        agent_description="Research analyst specializing in market research and data analysis",
+        agent_description="Research analyst for data analysis",
         model_name="gpt-4o-mini",
         max_loops=1,
-        system_prompt="""You are a Research Analyst. Your responsibilities include:
+        system_prompt="You are a Research Analyst. Conduct thorough research and provide data-driven insights.",
-1. Conducting thorough research on assigned topics
-2. Analyzing data and market trends
-3. Preparing comprehensive research reports
-4. Providing data-driven insights and recommendations
-5. Maintaining high standards of research quality
-
-You should be analytical, thorough, and evidence-based in your work.""",
     )
     
     developer = Agent(
-        agent_name="Software_Developer",
+        agent_name="Developer",
-        agent_description="Software developer with expertise in system design and implementation",
+        agent_description="Software developer for implementation",
         model_name="gpt-4o-mini",
         max_loops=1,
-        system_prompt="""You are a Software Developer. Your responsibilities include:
+        system_prompt="You are a Software Developer. Design and implement software solutions.",
-1. Designing and implementing software solutions
-2. Writing clean, maintainable code
-3. Conducting code reviews and testing
-4. Collaborating with team members
-5. Following best practices and coding standards
-
-You should be technically skilled, detail-oriented, and focused on quality.""",
     )
     
     marketer = Agent(
-        agent_name="Marketing_Specialist",
+        agent_name="Marketer",
-        agent_description="Marketing specialist with expertise in digital marketing and brand strategy",
+        agent_description="Marketing specialist for strategy",
         model_name="gpt-4o-mini",
         max_loops=1,
-        system_prompt="""You are a Marketing Specialist. Your responsibilities include:
+        system_prompt="You are a Marketing Specialist. Develop marketing strategies and campaigns.",
-1. Developing marketing strategies and campaigns
-2. Creating compelling content and messaging
-3. Analyzing market trends and customer behavior
-4. Managing brand presence and reputation
-5. Measuring and optimizing marketing performance
-
-You should be creative, strategic, and customer-focused in your approach.""",
     )
     
-    agents = [researcher, developer, marketer]
+    return [researcher, developer, marketer]
-    
-    print(f"✅ Created {len(agents)} worker agents")
-    for agent in agents:
-        print(f"   - {agent.agent_name}: {agent.agent_description}")
-    
-    return agents
 
 
-def run_board_of_directors_example() -> None:
+def run_board_example() -> None:
-    """
+    """Run a Board of Directors example."""
-    Run a comprehensive Board of Directors example.
     
-    This function demonstrates the complete workflow of using
+    # Create board members and worker agents
-    the Board of Directors swarm to orchestrate tasks.
+    board_members = create_board_members()
-    """
+    worker_agents = create_worker_agents()
-    print("\n" + "="*60)
-    print("🏛️ BOARD OF DIRECTORS SWARM EXAMPLE")
-    print("="*60)
     
-    try:
+    # Create the Board of Directors swarm
-        # Check if Board of Directors feature is enabled
+    board_swarm = BoardOfDirectorsSwarm(
-        if not is_board_feature_enabled():
+        name="Executive_Board",
-            print("⚠️ Board of Directors feature is not enabled. Enabling now...")
+        board_members=board_members,
-            enable_board_directors_feature()
+        agents=worker_agents,
-        
+        max_loops=2,
-        # Create custom board members
+        verbose=True,
-        board_members = create_custom_board_members()
+        decision_threshold=0.6,
-        
+    )
-        # Create worker agents
-        worker_agents = create_worker_agents()
-        
-        # Create the Board of Directors swarm
-        print("\n🏛️ Creating Board of Directors swarm...")
-        board_swarm = BoardOfDirectorsSwarm(
-            name="Executive_Board_Swarm",
-            description="Executive board with specialized roles for strategic decision-making",
-            board_members=board_members,
-            agents=worker_agents,
-            max_loops=2,
-            verbose=True,
-            decision_threshold=0.6,
-            enable_voting=True,
-            enable_consensus=True,
-        )
-        
-        print("✅ Board of Directors swarm created successfully!")
-        
-        # Display board summary
-        summary = board_swarm.get_board_summary()
-        print(f"\n📊 Board Summary:")
-        print(f"   Board Name: {summary['board_name']}")
-        print(f"   Total Members: {summary['total_members']}")
-        print(f"   Total Agents: {summary['total_agents']}")
-        print(f"   Max Loops: {summary['max_loops']}")
-        print(f"   Decision Threshold: {summary['decision_threshold']}")
-        
-        print(f"\n👥 Board Members:")
-        for member in summary['members']:
-            print(f"   - {member['name']} ({member['role']}) - Weight: {member['voting_weight']}")
-            print(f"     Expertise: {', '.join(member['expertise_areas'])}")
-        
-        # Define a complex task for the board to handle
-        task = """
-        Develop a comprehensive strategy for launching a new AI-powered product in the market.
-        
-        The task involves:
-        1. Market research and competitive analysis
-        2. Technical architecture and development planning
-        3. Marketing strategy and go-to-market plan
-        4. Financial projections and risk assessment
-        
-        Please coordinate the efforts of all team members to create a cohesive strategy.
-        """
-        
-        print(f"\n📋 Executing task: {task.strip()[:100]}...")
-        
-        # Execute the task using the Board of Directors swarm
-        result = board_swarm.run(task=task)
-        
-        print("\n✅ Task completed successfully!")
-        print(f"📄 Result type: {type(result)}")
-        
-        # Display conversation history
-        if hasattr(result, 'get') and callable(result.get):
-            conversation_history = result.get('conversation_history', [])
-            print(f"\n💬 Conversation History ({len(conversation_history)} messages):")
-            for i, message in enumerate(conversation_history[-5:], 1):  # Show last 5 messages
-                role = message.get('role', 'Unknown')
-                content = message.get('content', '')[:100] + "..." if len(message.get('content', '')) > 100 else message.get('content', '')
-                print(f"   {i}. {role}: {content}")
-        else:
-            print(f"\n📝 Result: {str(result)[:200]}...")
-        
-        print("\n🎉 Board of Directors example completed successfully!")
-        
-    except Exception as e:
-        print(f"❌ Error in Board of Directors example: {e}")
-        import traceback
-        traceback.print_exc()
-
-
-def run_simple_board_example() -> None:
-    """
-    Run a simple Board of Directors example with default settings.
     
-    This function demonstrates a basic usage of the Board of Directors
+    # Define task
-    swarm with minimal configuration.
+    task = """
+    Develop a strategy for launching a new AI-powered product in the market.
+    Include market research, technical planning, marketing strategy, and financial projections.
     """
-    print("\n" + "="*60)
-    print("🏛️ SIMPLE BOARD OF DIRECTORS EXAMPLE")
-    print("="*60)
     
-    try:
+    # Execute the task
-        # Create simple worker agents
+    result = board_swarm.run(task=task)
-        print("🛠️ Creating simple worker agents...")
+    
-        
+    print("Task completed successfully!")
-        analyst = Agent(
+    print(f"Result: {result}")
-            agent_name="Data_Analyst",
-            agent_description="Data analyst for processing and analyzing information",
-            model_name="gpt-4o-mini",
-            max_loops=1,
-        )
-        
-        writer = Agent(
-            agent_name="Content_Writer",
-            agent_description="Content writer for creating reports and documentation",
-            model_name="gpt-4o-mini",
-            max_loops=1,
-        )
-        
-        agents = [analyst, writer]
-        
-        # Create Board of Directors swarm with default settings
-        print("🏛️ Creating Board of Directors swarm with default settings...")
-        board_swarm = BoardOfDirectorsSwarm(
-            name="Simple_Board_Swarm",
-            agents=agents,
-            verbose=True,
-        )
-        
-        print("✅ Simple Board of Directors swarm created!")
-        
-        # Simple task
-        task = "Analyze the current market trends and create a summary report with recommendations."
-        
-        print(f"\n📋 Executing simple task: {task}")
-        
-        # Execute the task
-        result = board_swarm.run(task=task)
-        
-        print("\n✅ Simple task completed successfully!")
-        print(f"📄 Result type: {type(result)}")
-        
-        if hasattr(result, 'get') and callable(result.get):
-            conversation_history = result.get('conversation_history', [])
-            print(f"\n💬 Conversation History ({len(conversation_history)} messages):")
-            for i, message in enumerate(conversation_history[-3:], 1):  # Show last 3 messages
-                role = message.get('role', 'Unknown')
-                content = message.get('content', '')[:80] + "..." if len(message.get('content', '')) > 80 else message.get('content', '')
-                print(f"   {i}. {role}: {content}")
-        else:
-            print(f"\n📝 Result: {str(result)[:150]}...")
-        
-        print("\n🎉 Simple Board of Directors example completed!")
-        
-    except Exception as e:
-        print(f"❌ Error in simple Board of Directors example: {e}")
-        import traceback
-        traceback.print_exc()
 
 
-def check_environment() -> bool:
+def run_simple_example() -> None:
-    """
+    """Run a simple Board of Directors example."""
-    Check if the environment is properly set up for the example.
     
-    Returns:
+    # Create simple agents
-        bool: True if environment is ready, False otherwise
+    analyst = Agent(
-    """
+        agent_name="Analyst",
-    # Check for OpenAI API key
+        agent_description="Data analyst",
-    if not os.getenv("OPENAI_API_KEY"):
+        model_name="gpt-4o-mini",
-        print("⚠️ Warning: OPENAI_API_KEY environment variable not set.")
+        max_loops=1,
-        print("   The example may not work without a valid API key.")
+    )
-        print("   Please set your OpenAI API key: export OPENAI_API_KEY='your-key-here'")
-        return False
     
-    return True
+    writer = Agent(
+        agent_name="Writer",
+        agent_description="Content writer",
+        model_name="gpt-4o-mini",
+        max_loops=1,
+    )
+    
+    # Create swarm with default settings
+    board_swarm = BoardOfDirectorsSwarm(
+        name="Simple_Board",
+        agents=[analyst, writer],
+        verbose=True,
+    )
+    
+    # Execute simple task
+    task = "Analyze current market trends and create a summary report."
+    result = board_swarm.run(task=task)
+    
+    print("Simple example completed!")
+    print(f"Result: {result}")
 
 
 def main() -> None:
-    """
+    """Main function to run the examples."""
-    Main function to run the Board of Directors examples.
-    """
-    print("🚀 Board of Directors Swarm Examples")
-    print("="*50)
     
-    # Check environment
+    if not os.getenv("OPENAI_API_KEY"):
-    if not check_environment():
+        print("Warning: OPENAI_API_KEY not set. Example may not work.")
-        print("\n⚠️ Environment check failed. Please set up your environment properly.")
         return
     
     try:
-        # Run simple example first
+        print("Running simple Board of Directors example...")
-        run_simple_board_example()
+        run_simple_example()
-        
-        # Run comprehensive example
-        run_board_of_directors_example()
         
-        print("\n" + "="*60)
+        print("\nRunning comprehensive Board of Directors example...")
-        print("🎉 All Board of Directors examples completed successfully!")
+        run_board_example()
-        print("="*60)
         
-    except KeyboardInterrupt:
-        print("\n⚠️ Examples interrupted by user.")
     except Exception as e:
-        print(f"\n❌ Unexpected error: {e}")
+        print(f"Error: {e}")
-        import traceback
-        traceback.print_exc()
 
 
 if __name__ == "__main__":
-    main()
+    main()