[FIX][Board of Directors] [CLEANUP __INITS__] [New Examples for livestream]

example.py

@@ -4,40 +4,11 @@ from swarms import Agent
 agent = Agent(
     agent_name="Quantitative-Trading-Agent",
     agent_description="Advanced quantitative trading and algorithmic analysis agent",
-    system_prompt="""You are an expert quantitative trading agent with deep expertise in:
-    - Algorithmic trading strategies and implementation
-    - Statistical arbitrage and market making
-    - Risk management and portfolio optimization
-    - High-frequency trading systems
-    - Market microstructure analysis
-    - Quantitative research methodologies
-    - Financial mathematics and stochastic processes
-    - Machine learning applications in trading
-    
-    Your core responsibilities include:
-    1. Developing and backtesting trading strategies
-    2. Analyzing market data and identifying alpha opportunities
-    3. Implementing risk management frameworks
-    4. Optimizing portfolio allocations
-    5. Conducting quantitative research
-    6. Monitoring market microstructure
-    7. Evaluating trading system performance
-    
-    You maintain strict adherence to:
-    - Mathematical rigor in all analyses
-    - Statistical significance in strategy development
-    - Risk-adjusted return optimization
-    - Market impact minimization
-    - Regulatory compliance
-    - Transaction cost analysis
-    - Performance attribution
-    
-    You communicate in precise, technical terms while maintaining clarity for stakeholders.""",
     model_name="claude-sonnet-4-20250514",
     dynamic_temperature_enabled=True,
-    output_type="str-all-except-first",
     max_loops=1,
     dynamic_context_window=True,
+    streaming_on=True,
 )
 
 out = agent.run(

examples/multi_agent/board_of_directors/board_of_directors_example.py

@@ -10,23 +10,8 @@ To run this example:
 2. Run: python examples/multi_agent/board_of_directors/board_of_directors_example.py
 """
 
-import os
-import sys
 from typing import List
 
-# Add the root directory to the Python path if running from examples directory
-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,
     BoardMember,
@@ -37,7 +22,6 @@ from swarms.structs.agent import Agent
 
 def create_board_members() -> List[BoardMember]:
     """Create board members with specific roles."""
-
     chairman = Agent(
         agent_name="Chairman",
         agent_description="Executive Chairman with strategic vision",
@@ -86,7 +70,6 @@ def create_board_members() -> List[BoardMember]:
 
 def create_worker_agents() -> List[Agent]:
     """Create worker agents for the swarm."""
-
     researcher = Agent(
         agent_name="Researcher",
         agent_description="Research analyst for data analysis",
@@ -114,9 +97,8 @@ def create_worker_agents() -> List[Agent]:
     return [researcher, developer, marketer]
 
 
-def run_board_example() -> None:
+def run_board_example() -> str:
     """Run a Board of Directors example."""
-
     # Create board members and worker agents
     board_members = create_board_members()
     worker_agents = create_worker_agents()
@@ -127,7 +109,7 @@ def run_board_example() -> None:
         board_members=board_members,
         agents=worker_agents,
         max_loops=2,
-        verbose=True,
+        verbose=False,
         decision_threshold=0.6,
     )
 
@@ -137,66 +119,17 @@ def run_board_example() -> None:
     Include market research, technical planning, marketing strategy, and financial projections.
     """
 
-    # Execute the task
+    # Execute the task and return result
-    result = board_swarm.run(task=task)
+    return board_swarm.run(task=task)
-
-    print("Task completed successfully!")
-    print(f"Result: {result}")
-
-
-def run_simple_example() -> None:
-    """Run a simple Board of Directors example."""
-
-    # Create simple agents
-    analyst = Agent(
-        agent_name="Analyst",
-        agent_description="Data analyst",
-        model_name="gpt-4o-mini",
-        max_loops=1,
-    )
-
-    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."""
-
-    if not os.getenv("OPENAI_API_KEY"):
-        print(
-            "Warning: OPENAI_API_KEY not set. Example may not work."
-        )
-        return
 
     try:
-        print("Running simple Board of Directors example...")
+        result = run_board_example()
-        run_simple_example()
+        return result
-
+    except Exception:
-        print("\nRunning comprehensive Board of Directors example...")
+        pass
-        run_board_example()
-
-    except Exception as e:
-        print(f"Error: {e}")
 
 
 if __name__ == "__main__":

examples/multi_agent/board_of_directors/minimal_board_example.py

@@ -0,0 +1,51 @@
+"""
+Minimal Board of Directors Example
+
+This example demonstrates the most basic Board of Directors swarm setup
+with minimal configuration and agents.
+
+To run this example:
+1. Make sure you're in the root directory of the swarms project
+2. Run: python examples/multi_agent/board_of_directors/minimal_board_example.py
+"""
+
+from swarms.structs.board_of_directors_swarm import (
+    BoardOfDirectorsSwarm,
+)
+from swarms.structs.agent import Agent
+
+
+def run_minimal_example() -> str:
+    """Run a minimal Board of Directors example."""
+    # Create a single agent
+    agent = Agent(
+        agent_name="General_Agent",
+        agent_description="General purpose agent",
+        model_name="gpt-4o-mini",
+        max_loops=1,
+    )
+
+    # Create minimal swarm
+    board_swarm = BoardOfDirectorsSwarm(
+        name="Minimal_Board",
+        agents=[agent],
+        verbose=False,
+    )
+
+    # Execute minimal task
+    task = "Provide a brief overview of artificial intelligence."
+    return board_swarm.run(task=task)
+
+
+def main() -> None:
+    """Main function to run the minimal example."""
+
+    try:
+        result = run_minimal_example()
+        return result
+    except Exception:
+        pass
+
+
+if __name__ == "__main__":
+    main()

examples/multi_agent/board_of_directors/simple_board_example.py

@@ -0,0 +1,35 @@
+from swarms.structs.board_of_directors_swarm import (
+    BoardOfDirectorsSwarm,
+)
+from swarms.structs.agent import Agent
+
+# Create simple agents for basic tasks
+analyst = Agent(
+    agent_name="Analyst",
+    agent_description="Data analyst",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+writer = Agent(
+    agent_name="Writer",
+    agent_description="Content writer",
+    model_name="gpt-4o-mini",
+    max_loops=1,
+)
+
+agents = [analyst, writer]
+
+# Create swarm with default settings
+board_swarm = BoardOfDirectorsSwarm(
+    name="Simple_Board",
+    agents=agents,
+    verbose=False,
+)
+
+# Execute simple task
+task = "Analyze current market trends and create a summary report."
+
+result = board_swarm.run(task=task)
+
+print(result)

examples/multi_agent/hscf/single_file_hierarchical_framework_example.py

@@ -9,10 +9,11 @@ All components are now in one file: hierarchical_structured_communication_framew
 
 import os
 import sys
-from typing import Dict, Any
 
 # Add the project root to the Python path
-project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
+project_root = os.path.abspath(
+    os.path.join(os.path.dirname(__file__), "..", "..")
+)
 sys.path.insert(0, project_root)
 
 from dotenv import load_dotenv
@@ -20,11 +21,6 @@ from dotenv import load_dotenv
 # Import everything from the single file
 from swarms.structs.hierarchical_structured_communication_framework import (
     HierarchicalStructuredCommunicationFramework,
-    HierarchicalStructuredCommunicationGenerator,
-    HierarchicalStructuredCommunicationEvaluator,
-    HierarchicalStructuredCommunicationRefiner,
-    HierarchicalStructuredCommunicationSupervisor,
-    # Convenience aliases
     TalkHierarchicalGenerator,
     TalkHierarchicalEvaluator,
     TalkHierarchicalRefiner,
@@ -42,29 +38,29 @@ def example_basic_usage():
     print("=" * 80)
     print("BASIC USAGE EXAMPLE")
     print("=" * 80)
-    
+
     # Create framework with default configuration
     framework = HierarchicalStructuredCommunicationFramework(
-        name="BasicFramework",
+        name="BasicFramework", max_loops=2, verbose=True
-        max_loops=2,
-        verbose=True
     )
-    
+
     # Run a simple task
     task = "Explain the benefits of structured communication in multi-agent systems"
-    
+
     print(f"Task: {task}")
     print("Running framework...")
-    
+
     result = framework.run(task)
-    
+
     print("\n" + "=" * 50)
     print("FINAL RESULT")
     print("=" * 50)
     print(result["final_result"])
-    
+
     print(f"\nTotal loops: {result['total_loops']}")
-    print(f"Conversation history entries: {len(result['conversation_history'])}")
+    print(
+        f"Conversation history entries: {len(result['conversation_history'])}"
+    )
     print(f"Evaluation results: {len(result['evaluation_results'])}")
 
 
@@ -75,40 +71,40 @@ def example_custom_agents():
     print("\n" + "=" * 80)
     print("CUSTOM AGENTS EXAMPLE")
     print("=" * 80)
-    
+
     # Create custom agents using the convenience aliases
     generator = TalkHierarchicalGenerator(
         agent_name="ContentCreator",
         model_name="gpt-4o-mini",
-        verbose=True
+        verbose=True,
     )
-    
+
     evaluator1 = TalkHierarchicalEvaluator(
         agent_name="AccuracyChecker",
         evaluation_criteria=["accuracy", "technical_correctness"],
         model_name="gpt-4o-mini",
-        verbose=True
+        verbose=True,
     )
-    
+
     evaluator2 = TalkHierarchicalEvaluator(
         agent_name="ClarityChecker",
         evaluation_criteria=["clarity", "readability", "coherence"],
         model_name="gpt-4o-mini",
-        verbose=True
+        verbose=True,
     )
-    
+
     refiner = TalkHierarchicalRefiner(
         agent_name="ContentImprover",
         model_name="gpt-4o-mini",
-        verbose=True
+        verbose=True,
     )
-    
+
     supervisor = TalkHierarchicalSupervisor(
         agent_name="WorkflowManager",
         model_name="gpt-4o-mini",
-        verbose=True
+        verbose=True,
     )
-    
+
     # Create framework with custom agents
     framework = HierarchicalStructuredCommunicationFramework(
         name="CustomFramework",
@@ -117,24 +113,26 @@ def example_custom_agents():
         evaluators=[evaluator1, evaluator2],
         refiners=[refiner],
         max_loops=3,
-        verbose=True
+        verbose=True,
     )
-    
+
     # Run a complex task
     task = "Design a comprehensive machine learning pipeline for sentiment analysis"
-    
+
     print(f"Task: {task}")
     print("Running framework with custom agents...")
-    
+
     result = framework.run(task)
-    
+
     print("\n" + "=" * 50)
     print("FINAL RESULT")
     print("=" * 50)
     print(result["final_result"])
-    
+
     print(f"\nTotal loops: {result['total_loops']}")
-    print(f"Conversation history entries: {len(result['conversation_history'])}")
+    print(
+        f"Conversation history entries: {len(result['conversation_history'])}"
+    )
     print(f"Evaluation results: {len(result['evaluation_results'])}")
 
 
@@ -145,7 +143,7 @@ def example_ollama_integration():
     print("\n" + "=" * 80)
     print("OLLAMA INTEGRATION EXAMPLE")
     print("=" * 80)
-    
+
     # Create framework with Ollama configuration
     framework = HierarchicalStructuredCommunicationFramework(
         name="OllamaFramework",
@@ -154,27 +152,31 @@ def example_ollama_integration():
         model_name="llama3:latest",
         use_ollama=True,
         ollama_base_url="http://localhost:11434/v1",
-        ollama_api_key="ollama"
+        ollama_api_key="ollama",
     )
-    
+
     # Run a task with local model
     task = "Explain the concept of structured communication protocols"
-    
+
     print(f"Task: {task}")
     print("Running framework with Ollama...")
-    
+
     try:
         result = framework.run(task)
-        
+
         print("\n" + "=" * 50)
         print("FINAL RESULT")
         print("=" * 50)
         print(result["final_result"])
-        
+
         print(f"\nTotal loops: {result['total_loops']}")
-        print(f"Conversation history entries: {len(result['conversation_history'])}")
+        print(
-        print(f"Evaluation results: {len(result['evaluation_results'])}")
+            f"Conversation history entries: {len(result['conversation_history'])}"
-        
+        )
+        print(
+            f"Evaluation results: {len(result['evaluation_results'])}"
+        )
+
     except Exception as e:
         print(f"Error with Ollama: {e}")
         print("Make sure Ollama is running: ollama serve")
@@ -187,28 +189,31 @@ def example_structured_communication():
     print("\n" + "=" * 80)
     print("STRUCTURED COMMUNICATION EXAMPLE")
     print("=" * 80)
-    
+
     # Create framework
     framework = HierarchicalStructuredCommunicationFramework(
-        name="CommunicationDemo",
+        name="CommunicationDemo", verbose=True
-        verbose=True
     )
-    
+
     # Demonstrate structured message sending
     print("Sending structured message...")
-    
+
     structured_msg = framework.send_structured_message(
         sender="Supervisor",
         recipient="Generator",
         message="Create a technical documentation outline",
         background="For a Python library focused on data processing",
-        intermediate_output="Previous research on similar libraries"
+        intermediate_output="Previous research on similar libraries",
     )
-    
+
     print(f"Message sent: {structured_msg.message}")
     print(f"Background: {structured_msg.background}")
-    print(f"Intermediate output: {structured_msg.intermediate_output}")
+    print(
-    print(f"From: {structured_msg.sender} -> To: {structured_msg.recipient}")
+        f"Intermediate output: {structured_msg.intermediate_output}"
+    )
+    print(
+        f"From: {structured_msg.sender} -> To: {structured_msg.recipient}"
+    )
 
 
 def example_agent_interaction():
@@ -218,52 +223,51 @@ def example_agent_interaction():
     print("\n" + "=" * 80)
     print("AGENT INTERACTION EXAMPLE")
     print("=" * 80)
-    
+
     # Create agents
     generator = TalkHierarchicalGenerator(
-        agent_name="ContentGenerator",
+        agent_name="ContentGenerator", verbose=True
-        verbose=True
     )
-    
+
     evaluator = TalkHierarchicalEvaluator(
         agent_name="QualityEvaluator",
         evaluation_criteria=["accuracy", "clarity"],
-        verbose=True
+        verbose=True,
     )
-    
+
     refiner = TalkHierarchicalRefiner(
-        agent_name="ContentRefiner",
+        agent_name="ContentRefiner", verbose=True
-        verbose=True
     )
-    
+
     # Generate content
     print("1. Generating content...")
     gen_result = generator.generate_with_structure(
         message="Create a brief explanation of machine learning",
         background="For beginners with no technical background",
-        intermediate_output=""
+        intermediate_output="",
     )
-    
+
     print(f"Generated content: {gen_result.content[:200]}...")
-    
+
     # Evaluate content
     print("\n2. Evaluating content...")
     eval_result = evaluator.evaluate_with_criterion(
-        content=gen_result.content,
+        content=gen_result.content, criterion="clarity"
-        criterion="clarity"
     )
-    
+
     print(f"Evaluation score: {eval_result.score}/10")
     print(f"Feedback: {eval_result.feedback[:200]}...")
-    
+
     # Refine content
     print("\n3. Refining content...")
     refine_result = refiner.refine_with_feedback(
         original_content=gen_result.content,
-        evaluation_results=[eval_result]
+        evaluation_results=[eval_result],
+    )
+
+    print(
+        f"Refined content: {refine_result.refined_content[:200]}..."
     )
-    
-    print(f"Refined content: {refine_result.refined_content[:200]}...")
     print(f"Changes made: {refine_result.changes_made}")
 
 
@@ -271,12 +275,16 @@ def main():
     """
     Main function to run all examples
     """
-    print("SINGLE-FILE HIERARCHICAL STRUCTURED COMMUNICATION FRAMEWORK")
+    print(
+        "SINGLE-FILE HIERARCHICAL STRUCTURED COMMUNICATION FRAMEWORK"
+    )
     print("=" * 80)
-    print("This demonstrates the consolidated single-file implementation")
+    print(
+        "This demonstrates the consolidated single-file implementation"
+    )
     print("based on the research paper: arXiv:2502.11098")
     print("=" * 80)
-    
+
     try:
         # Run examples
         example_basic_usage()
@@ -284,27 +292,30 @@ def main():
         example_ollama_integration()
         example_structured_communication()
         example_agent_interaction()
-        
+
         print("\n" + "=" * 80)
         print("ALL EXAMPLES COMPLETED SUCCESSFULLY!")
         print("=" * 80)
         print("Framework Features Demonstrated:")
         print("✓ Single-file implementation")
-        print("✓ Structured Communication Protocol (M_ij, B_ij, I_ij)")
+        print(
+            "✓ Structured Communication Protocol (M_ij, B_ij, I_ij)"
+        )
         print("✓ Hierarchical Evaluation System")
         print("✓ Iterative Refinement Process")
         print("✓ Flexible Model Configuration (OpenAI/Ollama)")
         print("✓ Custom Agent Specialization")
         print("✓ Direct Agent Interaction")
         print("✓ Convenience Aliases")
-        
+
     except KeyboardInterrupt:
         print("\nInterrupted by user")
     except Exception as e:
         print(f"Error during execution: {e}")
         import traceback
+
         traceback.print_exc()
 
 
 if __name__ == "__main__":
-    main() 
+    main()

examples/utils/agent_loader/claude_code_compatible.py

@@ -0,0 +1,9 @@
+from swarms import load_agents_from_markdown
+
+agents = load_agents_from_markdown(["finance_advisor.md"])
+
+# Use the agent
+response = agents[0].run(
+    "I have $100k to invest. I want to hedge my bets on the energy companies that will benefit from the AI revoltion"
+    "What are the top 4 stocks to invest in?"
+)

examples/utils/agent_loader/finance_advisor.md

@@ -1,7 +1,7 @@
 ---
 name: FinanceAdvisor
 description: Expert financial advisor for investment and budgeting guidance
-model_name: gpt-4o
+model_name: claude-sonnet-4-20250514
 temperature: 0.7
 max_loops: 1
 ---

requirements.txt

@@ -28,4 +28,3 @@ mcp
 numpy
 openai
 schedule
-colorama

swarms/cli/main.py

@@ -22,6 +22,9 @@ from swarms.cli.onboarding_process import OnboardingProcess
 from swarms.structs.agent import Agent
 from swarms.utils.agent_loader import AgentLoader
 from swarms.utils.formatter import formatter
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # Initialize console with custom styling
 console = Console()
@@ -397,7 +400,14 @@ def check_python_version() -> tuple[bool, str, str]:
 
 
 def check_api_keys() -> tuple[bool, str, str]:
-    """Check if common API keys are set."""
+    """
+    Check if at least one common API key is set in the environment variables.
+
+    Returns:
+        tuple: (True, "✓", message) if at least one API key is set,
+               (False, "✗", message) otherwise.
+    """
+
     api_keys = {
         "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"),
         "ANTHROPIC_API_KEY": os.getenv("ANTHROPIC_API_KEY"),
@@ -405,9 +415,16 @@ def check_api_keys() -> tuple[bool, str, str]:
         "COHERE_API_KEY": os.getenv("COHERE_API_KEY"),
     }
 
-    set_keys = [key for key, value in api_keys.items() if value]
+    # At least one key must be present and non-empty
-    if set_keys:
+    if any(value for value in api_keys.values()):
-        return True, "✓", f"API keys found: {', '.join(set_keys)}"
+        present_keys = [
+            key for key, value in api_keys.items() if value
+        ]
+        return (
+            True,
+            "✓",
+            f"At least one API key found: {', '.join(present_keys)}",
+        )
     else:
         return (
             False,

swarms/structs/__init__.py

@@ -1,12 +1,10 @@
 from swarms.structs.agent import Agent
 from swarms.structs.agent_builder import AgentsBuilder
+from swarms.structs.agent_rearrange import AgentRearrange, rearrange
 from swarms.structs.auto_swarm_builder import AutoSwarmBuilder
 from swarms.structs.base_structure import BaseStructure
 from swarms.structs.base_swarm import BaseSwarm
 from swarms.structs.batch_agent_execution import batch_agent_execution
-from swarms.structs.board_of_directors_swarm import (
-    BoardOfDirectorsSwarm,
-)
 from swarms.structs.concurrent_workflow import ConcurrentWorkflow
 from swarms.structs.conversation import Conversation
 from swarms.structs.council_as_judge import CouncilAsAJudge
@@ -24,8 +22,8 @@ from swarms.structs.groupchat import (
     expertise_based,
 )
 from swarms.structs.heavy_swarm import HeavySwarm
-from swarms.structs.hierarchical_swarm import HierarchicalSwarm
+from swarms.structs.hiearchical_swarm import HierarchicalSwarm
-from swarms.structs.hybrid_hierarchical_peer_swarm import (
+from swarms.structs.hybrid_hiearchical_peer_swarm import (
     HybridHierarchicalClusterSwarm,
 )
 from swarms.structs.interactive_groupchat import (
@@ -66,7 +64,6 @@ from swarms.structs.multi_agent_exec import (
     run_single_agent,
 )
 from swarms.structs.multi_agent_router import MultiAgentRouter
-from swarms.structs.rearrange import AgentRearrange, rearrange
 from swarms.structs.round_robin import RoundRobinSwarm
 from swarms.structs.sequential_workflow import SequentialWorkflow
 from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm
@@ -82,7 +79,7 @@ from swarms.structs.stopping_conditions import (
     check_stopped,
     check_success,
 )
-from swarms.structs.swarm_arange import SwarmRearrange
+from swarms.structs.swarm_rearrange import SwarmRearrange
 from swarms.structs.swarm_router import (
     SwarmRouter,
     SwarmType,
@@ -107,32 +104,11 @@ from swarms.structs.swarming_architectures import (
     staircase_swarm,
     star_swarm,
 )
-from swarms.structs.hierarchical_structured_communication_framework import (
-    HierarchicalStructuredCommunicationFramework,
-    HierarchicalStructuredCommunicationGenerator,
-    HierarchicalStructuredCommunicationEvaluator,
-    HierarchicalStructuredCommunicationRefiner,
-    HierarchicalStructuredCommunicationSupervisor,
-    StructuredMessage,
-    HierarchicalOrder,
-    EvaluationResult,
-    StructuredMessageSchema,
-    EvaluationResultSchema,
-    GeneratorResponseSchema,
-    EvaluatorResponseSchema,
-    RefinerResponseSchema,
-    CommunicationType,
-    AgentRole,
-)
-
-# Convenience alias(fixes old code if any was left out in the wild)
-HierarchicalStructuredCommunicationSwarm = HierarchicalStructuredCommunicationFramework
 
 __all__ = [
     "Agent",
     "BaseStructure",
     "BaseSwarm",
-    "BoardOfDirectorsSwarm",
     "ConcurrentWorkflow",
     "Conversation",
     "GroupChat",
@@ -206,22 +182,6 @@ __all__ = [
     "HierarchicalSwarm",
     "HeavySwarm",
     "CronJob",
-    "HierarchicalStructuredCommunicationSwarm",
-    "HierarchicalStructuredCommunicationGenerator",
-    "HierarchicalStructuredCommunicationEvaluator",
-    "HierarchicalStructuredCommunicationRefiner",
-    "HierarchicalStructuredCommunicationSupervisor",
-    "StructuredMessage",
-    "HierarchicalOrder",
-    "EvaluationResult",
-    "StructuredMessageSchema",
-    "EvaluationResultSchema",
-    "GeneratorResponseSchema",
-    "EvaluatorResponseSchema",
-    "RefinerResponseSchema",
-    "CommunicationType",
-    "AgentRole",
-    # Stopping conditions
     "check_done",
     "check_finished",
     "check_complete",

swarms/structs/board_of_directors_swarm.py

@@ -19,7 +19,6 @@ Flow:
 6. All context and conversation history is preserved throughout the process
 """
 
-import asyncio
 import json
 import os
 import re
@@ -34,7 +33,6 @@ from loguru import logger
 from pydantic import BaseModel, Field
 
 from swarms.structs.agent import Agent
-from swarms.structs.base_swarm import BaseSwarm
 from swarms.structs.conversation import Conversation
 from swarms.structs.ma_utils import list_all_agents
 from swarms.utils.history_output_formatter import (
@@ -54,21 +52,6 @@ board_logger = initialize_logger(
 # ============================================================================
 
 
-class BoardFeatureStatus(str, Enum):
-    """Enumeration of Board of Directors feature status.
-
-    This enum defines the possible states of the Board of Directors feature
-    within the Swarms Framework.
-
-    Attributes:
-        ENABLED: Feature is explicitly enabled
-        DISABLED: Feature is explicitly disabled
-        AUTO: Feature state is determined automatically
-    """
-
-    ENABLED = "enabled"
-    DISABLED = "disabled"
-    AUTO = "auto"
 
 
 class BoardConfigModel(BaseModel):
@@ -91,12 +74,6 @@ class BoardConfigModel(BaseModel):
         custom_board_templates: Custom board templates for different use cases
     """
 
-    # Feature control
-    board_feature_enabled: bool = Field(
-        default=False,
-        description="Whether the Board of Directors feature is enabled globally.",
-    )
-
     # Board composition
     default_board_size: int = Field(
         default=3,
@@ -201,9 +178,6 @@ class BoardConfig:
             ):
                 self._load_from_file()
 
-            # Override with environment variables
-            self._load_from_environment()
-
             # Override with explicit config data
             if self.config_data:
                 self._load_from_dict(self.config_data)
@@ -236,62 +210,6 @@ class BoardConfig:
             )
             raise
 
-    def _load_from_environment(self) -> None:
-        """
-        Load configuration from environment variables.
-
-        This method maps environment variables to configuration parameters
-        and handles type conversion appropriately.
-        """
-        env_mappings = {
-            "SWARMS_BOARD_FEATURE_ENABLED": "board_feature_enabled",
-            "SWARMS_BOARD_DEFAULT_SIZE": "default_board_size",
-            "SWARMS_BOARD_DECISION_THRESHOLD": "decision_threshold",
-            "SWARMS_BOARD_ENABLE_VOTING": "enable_voting",
-            "SWARMS_BOARD_ENABLE_CONSENSUS": "enable_consensus",
-            "SWARMS_BOARD_DEFAULT_MODEL": "default_board_model",
-            "SWARMS_BOARD_VERBOSE_LOGGING": "verbose_logging",
-            "SWARMS_BOARD_MAX_MEETING_DURATION": "max_board_meeting_duration",
-            "SWARMS_BOARD_AUTO_FALLBACK": "auto_fallback_to_director",
-        }
-
-        for env_var, config_key in env_mappings.items():
-            value = os.getenv(env_var)
-            if value is not None:
-                try:
-                    # Convert string values to appropriate types
-                    if config_key in [
-                        "board_feature_enabled",
-                        "enable_voting",
-                        "enable_consensus",
-                        "verbose_logging",
-                        "auto_fallback_to_director",
-                    ]:
-                        converted_value = value.lower() in [
-                            "true",
-                            "1",
-                            "yes",
-                            "on",
-                        ]
-                    elif config_key in [
-                        "default_board_size",
-                        "max_board_meeting_duration",
-                    ]:
-                        converted_value = int(value)
-                    elif config_key in ["decision_threshold"]:
-                        converted_value = float(value)
-                    else:
-                        converted_value = value
-
-                    setattr(self.config, config_key, converted_value)
-                    logger.debug(
-                        f"Loaded {config_key} from environment: {converted_value}"
-                    )
-                except (ValueError, TypeError) as e:
-                    logger.warning(
-                        f"Failed to parse environment variable {env_var}: {e}"
-                    )
-
     def _load_from_dict(self, config_dict: Dict[str, Any]) -> None:
         """
         Load configuration from dictionary.
@@ -312,15 +230,6 @@ class BoardConfig:
                         f"Invalid configuration value for {key}: {e}"
                     )
 
-    def is_enabled(self) -> bool:
-        """
-        Check if the Board of Directors feature is enabled.
-
-        Returns:
-            bool: True if the feature is enabled, False otherwise
-        """
-        return self.config.board_feature_enabled
-
     def get_config(self) -> BoardConfigModel:
         """
         Get the current configuration.
@@ -562,63 +471,6 @@ def get_board_config(
     return _board_config
 
 
-def enable_board_feature(
-    config_file_path: Optional[str] = None,
-) -> None:
-    """
-    Enable the Board of Directors feature globally.
-
-    This function enables the Board of Directors feature and saves the configuration
-    to the specified file path.
-
-    Args:
-        config_file_path: Optional path to save the configuration
-    """
-    config = get_board_config(config_file_path)
-    config.update_config({"board_feature_enabled": True})
-
-    if config_file_path:
-        config.save_config(config_file_path)
-
-    logger.info("Board of Directors feature enabled")
-
-
-def disable_board_feature(
-    config_file_path: Optional[str] = None,
-) -> None:
-    """
-    Disable the Board of Directors feature globally.
-
-    This function disables the Board of Directors feature and saves the configuration
-    to the specified file path.
-
-    Args:
-        config_file_path: Optional path to save the configuration
-    """
-    config = get_board_config(config_file_path)
-    config.update_config({"board_feature_enabled": False})
-
-    if config_file_path:
-        config.save_config(config_file_path)
-
-    logger.info("Board of Directors feature disabled")
-
-
-def is_board_feature_enabled(
-    config_file_path: Optional[str] = None,
-) -> bool:
-    """
-    Check if the Board of Directors feature is enabled.
-
-    Args:
-        config_file_path: Optional path to configuration file
-
-    Returns:
-        bool: True if the feature is enabled, False otherwise
-    """
-    config = get_board_config(config_file_path)
-    return config.is_enabled()
-
 
 def create_default_config_file(
     file_path: str = "swarms_board_config.yaml",
@@ -953,7 +805,7 @@ class BoardSpec(BaseModel):
     )
 
 
-class BoardOfDirectorsSwarm(BaseSwarm):
+class BoardOfDirectorsSwarm:
     """
     A hierarchical swarm of agents with a Board of Directors that orchestrates tasks.
 
@@ -1029,13 +881,8 @@ class BoardOfDirectorsSwarm(BaseSwarm):
         Raises:
             ValueError: If critical requirements are not met during initialization
         """
-        super().__init__(
-            name=name,
-            description=description,
-            agents=agents,
-        )
-
         self.name = name
+        self.description = description
         self.board_members = board_members or []
         self.agents = agents or []
         self.max_loops = max_loops
@@ -1047,9 +894,8 @@ class BoardOfDirectorsSwarm(BaseSwarm):
         self.decision_threshold = decision_threshold
         self.enable_voting = enable_voting
         self.enable_consensus = enable_consensus
-        self.max_workers = max_workers or min(
+        self.max_workers = max_workers
-            32, (os.cpu_count() or 1) + 4
+        self.max_workers = os.cpu_count()
-        )
 
         # Initialize the swarm
         self._init_board_swarm()
@@ -1258,14 +1104,6 @@ You should be thorough, organized, and detail-oriented in your documentation."""
                     f"🔍 Running reliability checks for swarm: {self.name}"
                 )
 
-            # Check if Board of Directors feature is enabled
-            board_config = get_board_config()
-            if not board_config.is_enabled():
-                raise ValueError(
-                    "Board of Directors feature is not enabled. Please enable it using "
-                    "enable_board_feature() or set SWARMS_BOARD_FEATURE_ENABLED=true environment variable."
-                )
-
             if not self.agents or len(self.agents) == 0:
                 raise ValueError(
                     "No agents found in the swarm. At least one agent must be provided to create a Board of Directors swarm."
@@ -1687,34 +1525,6 @@ Please provide your response in the following format:
             board_logger.error(error_msg)
             raise
 
-    async def arun(
-        self,
-        task: str,
-        img: Optional[str] = None,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
-        """
-        Run the Board of Directors swarm asynchronously.
-
-        This method provides an asynchronous interface for running the swarm,
-        allowing for non-blocking execution in async contexts.
-
-        Args:
-            task: The task to be executed
-            img: Optional image input
-            *args: Additional positional arguments
-            **kwargs: Additional keyword arguments
-
-        Returns:
-            Any: The final result of the swarm execution
-        """
-        loop = asyncio.get_event_loop()
-        result = await loop.run_in_executor(
-            None, self.run, task, img, *args, **kwargs
-        )
-        return result
-
     def _generate_board_feedback(self, outputs: List[Any]) -> str:
         """
         Provide feedback from the Board of Directors based on agent outputs.