Merge branch 'kyegomez:master' into JSON-Output-Support-Agent

changelog.md

@@ -0,0 +1 @@
+# 5.8.7

docs/requirements.txt

@@ -26,7 +26,7 @@ jinja2~=3.1
 markdown~=3.7
 mkdocs-material-extensions~=1.3
 pygments~=2.18
-pymdown-extensions~=10.10
+pymdown-extensions~=10.11
 
 # Requirements for plugins
 babel~=2.16

example.py

@@ -33,11 +33,14 @@ agent = Agent(
     context_length=200000,
     return_step_meta=False,
     # output_type="json",
-    output_type="string",
+    output_type="json",  # "json", "dict", "csv" OR "string" soon "yaml" and
     streaming_on=False,
+    # auto_generate_prompt=True,
 )
 
 
-agent.run(
+print(
+    agent.run(
         "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria"
+    )
 )

new_prompt.py

@@ -0,0 +1,28 @@
+from swarms import Prompt
+from swarm_models import OpenAIChat
+import os
+
+model = OpenAIChat(
+    api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1
+)
+
+# Aggregator system prompt
+prompt_generator_sys_prompt = Prompt(
+    name="prompt-generator-sys-prompt-o1",
+    description="Generate the most reliable prompt for a specific problem",
+    content="""
+    Your purpose is to craft extremely reliable and production-grade system prompts for other agents.
+    
+    # Instructions
+    - Understand the prompt required for the agent.
+    - Utilize a combination of the most effective prompting strategies available, including chain of thought, many shot, few shot, and instructions-examples-constraints.
+    - Craft the prompt by blending the most suitable prompting strategies.
+    - Ensure the prompt is production-grade ready and educates the agent on how to reason and why to reason in that manner.
+    - Provide constraints if necessary and as needed.
+    - The system prompt should be extensive and cover a vast array of potential scenarios to specialize the agent. 
+    """,
+    auto_generate_prompt=True,
+    llm=model,
+)
+
+print(prompt_generator_sys_prompt.get_prompt())

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "5.8.6"
+version = "5.8.7"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -84,7 +84,7 @@ swarms = "swarms.cli.main:main"
 
 [tool.poetry.group.lint.dependencies]
 black = ">=23.1,<25.0"
-ruff = ">=0.5.1,<0.6.8"
+ruff = ">=0.5.1,<0.6.10"
 types-toml = "^0.10.8.1"
 types-pytz = ">=2023.3,<2025.0"
 types-chardet = "^5.0.4.6"

swarms/prompts/prompt.py

@@ -13,7 +13,8 @@ from pydantic.v1 import validator
 from swarms_cloud.utils.log_to_swarms_database import log_agent_data
 from swarms_cloud.utils.capture_system_data import capture_system_data
 from swarms.tools.base_tool import BaseTool
-
+# from swarms.agents.ape_agent import auto_generate_prompt
+from typing import Any
 
 class Prompt(BaseModel):
     """
@@ -70,11 +71,15 @@ class Prompt(BaseModel):
         default="prompts",
         description="The folder path within WORKSPACE_DIR where the prompt will be autosaved",
     )
+    auto_generate_prompt: bool = Field(
+        default=False,
+        description="Flag to enable or disable auto-generating the prompt",
+    )
     parent_folder: str = Field(
         default=os.getenv("WORKSPACE_DIR"),
         description="The folder where the autosave folder is in",
     )
-    # tools: List[callable] = None
+    llm: Any = None
 
     @validator("edit_history", pre=True, always=True)
     def initialize_history(cls, v, values):
@@ -87,6 +92,15 @@ class Prompt(BaseModel):
             ]  # Store initial version in history
         return v
     
+    def __init__(self, **data):
+        super().__init__(**data)
+        
+        if self.autosave:
+            self._autosave()
+            
+        if self.auto_generate_prompt and self.llm:
+            self.auto_generate_prompt()
+
     def edit_prompt(self, new_content: str) -> None:
         """
         Edits the prompt content and updates the version control.
@@ -239,6 +253,16 @@ class Prompt(BaseModel):
             json.dump(self.model_dump(), file)
         logger.info(f"Autosaved prompt {self.id} to {file_path}.")
         
+    # def auto_generate_prompt(self):
+    #     logger.info(f"Auto-generating prompt for {self.name}")
+    #     task = self.name + " " + self.description + " " + self.content
+    #     prompt = auto_generate_prompt(task, llm=self.llm, max_tokens=4000, use_second_sys_prompt=True)
+    #     logger.info("Generated prompt successfully, updating content")
+    #     self.edit_prompt(prompt)
+    #     logger.info("Prompt content updated")
+        
+    #     return "Prompt auto-generated successfully."
+
     class Config:
         """Pydantic configuration for better JSON serialization."""
 

swarms/structs/agent_rag.py

@@ -1,4 +1,3 @@
-import os
 from typing import List, Optional
 
 import chromadb
@@ -8,8 +7,17 @@ from typing import Union, Callable, Any
 from swarms import Agent
 
 
-class AgentRAG:
-    """A vector database for storing and retrieving agents based on their characteristics."""
+class AgentRouter:
+    """
+    Initialize the AgentRouter.
+
+    Args:
+        collection_name (str): Name of the collection in the vector database.
+        persist_directory (str): Directory to persist the vector database.
+        n_agents (int): Number of agents to return in queries.
+        *args: Additional arguments to pass to the chromadb Client.
+        **kwargs: Additional keyword arguments to pass to the chromadb Client.
+    """
 
     def __init__(
         self,
@@ -19,12 +27,6 @@ class AgentRAG:
         *args,
         **kwargs,
     ):
-        """
-        Initialize the AgentRAG.
-
-        Args:
-            persist_directory (str): The directory to persist the ChromaDB data.
-        """
         self.collection_name = collection_name
         self.n_agents = n_agents
         self.persist_directory = persist_directory
@@ -44,6 +46,9 @@ class AgentRAG:
 
         Args:
             agent (Agent): The agent to add.
+
+        Raises:
+            Exception: If there's an error adding the agent to the vector database.
         """
         try:
             agent_text = f"{agent.name} {agent.description} {agent.system_prompt}"
@@ -65,6 +70,12 @@ class AgentRAG:
     def add_agents(
         self, agents: List[Union[Agent, Callable, Any]]
     ) -> None:
+        """
+        Add multiple agents to the vector database.
+
+        Args:
+            agents (List[Union[Agent, Callable, Any]]): List of agents to add.
+        """
         for agent in agents:
             self.add_agent(agent)
 
@@ -108,9 +119,14 @@ class AgentRAG:
 
         Args:
             task (str): The task description.
+            *args: Additional arguments to pass to the collection.query method.
+            **kwargs: Additional keyword arguments to pass to the collection.query method.
 
         Returns:
             Optional[Agent]: The best matching agent, if found.
+
+        Raises:
+            Exception: If there's an error finding the best agent.
         """
         try:
             results = self.collection.query(
@@ -150,171 +166,171 @@ class AgentRAG:
             raise
 
 
-# Example usage
-if __name__ == "__main__":
-    from dotenv import load_dotenv
-    from swarm_models import OpenAIChat
-
-    load_dotenv()
-
-    # Get the OpenAI API key from the environment variable
-    api_key = os.getenv("GROQ_API_KEY")
-
-    # Model
-    model = OpenAIChat(
-        openai_api_base="https://api.groq.com/openai/v1",
-        openai_api_key=api_key,
-        model_name="llama-3.1-70b-versatile",
-        temperature=0.1,
-    )
-    # Initialize the vector database
-    vector_db = AgentRAG()
-
-    # Define specialized system prompts for each agent
-    DATA_EXTRACTOR_PROMPT = """You are a highly specialized private equity agent focused on data extraction from various documents. Your expertise includes:
-    1. Extracting key financial metrics (revenue, EBITDA, growth rates, etc.) from financial statements and reports
-    2. Identifying and extracting important contract terms from legal documents
-    3. Pulling out relevant market data from industry reports and analyses
-    4. Extracting operational KPIs from management presentations and internal reports
-    5. Identifying and extracting key personnel information from organizational charts and bios
-    Provide accurate, structured data extracted from various document types to support investment analysis."""
-
-    SUMMARIZER_PROMPT = """You are an expert private equity agent specializing in summarizing complex documents. Your core competencies include:
-    1. Distilling lengthy financial reports into concise executive summaries
-    2. Summarizing legal documents, highlighting key terms and potential risks
-    3. Condensing industry reports to capture essential market trends and competitive dynamics
-    4. Summarizing management presentations to highlight key strategic initiatives and projections
-    5. Creating brief overviews of technical documents, emphasizing critical points for non-technical stakeholders
-    Deliver clear, concise summaries that capture the essence of various documents while highlighting information crucial for investment decisions."""
-
-    FINANCIAL_ANALYST_PROMPT = """You are a specialized private equity agent focused on financial analysis. Your key responsibilities include:
-    1. Analyzing historical financial statements to identify trends and potential issues
-    2. Evaluating the quality of earnings and potential adjustments to EBITDA
-    3. Assessing working capital requirements and cash flow dynamics
-    4. Analyzing capital structure and debt capacity
-    5. Evaluating financial projections and underlying assumptions
-    Provide thorough, insightful financial analysis to inform investment decisions and valuation."""
-
-    MARKET_ANALYST_PROMPT = """You are a highly skilled private equity agent specializing in market analysis. Your expertise covers:
-    1. Analyzing industry trends, growth drivers, and potential disruptors
-    2. Evaluating competitive landscape and market positioning
-    3. Assessing market size, segmentation, and growth potential
-    4. Analyzing customer dynamics, including concentration and loyalty
-    5. Identifying potential regulatory or macroeconomic impacts on the market
-    Deliver comprehensive market analysis to assess the attractiveness and risks of potential investments."""
-
-    OPERATIONAL_ANALYST_PROMPT = """You are an expert private equity agent focused on operational analysis. Your core competencies include:
-    1. Evaluating operational efficiency and identifying improvement opportunities
-    2. Analyzing supply chain and procurement processes
-    3. Assessing sales and marketing effectiveness
-    4. Evaluating IT systems and digital capabilities
-    5. Identifying potential synergies in merger or add-on acquisition scenarios
-    Provide detailed operational analysis to uncover value creation opportunities and potential risks."""
-
-    # Initialize specialized agents
-    data_extractor_agent = Agent(
-        agent_name="Data-Extractor",
-        system_prompt=DATA_EXTRACTOR_PROMPT,
-        llm=model,
-        max_loops=1,
-        autosave=True,
-        verbose=True,
-        dynamic_temperature_enabled=True,
-        saved_state_path="data_extractor_agent.json",
-        user_name="pe_firm",
-        retry_attempts=1,
-        context_length=200000,
-        output_type="string",
-    )
-
-    summarizer_agent = Agent(
-        agent_name="Document-Summarizer",
-        system_prompt=SUMMARIZER_PROMPT,
-        llm=model,
-        max_loops=1,
-        autosave=True,
-        verbose=True,
-        dynamic_temperature_enabled=True,
-        saved_state_path="summarizer_agent.json",
-        user_name="pe_firm",
-        retry_attempts=1,
-        context_length=200000,
-        output_type="string",
-    )
-
-    financial_analyst_agent = Agent(
-        agent_name="Financial-Analyst",
-        system_prompt=FINANCIAL_ANALYST_PROMPT,
-        llm=model,
-        max_loops=1,
-        autosave=True,
-        verbose=True,
-        dynamic_temperature_enabled=True,
-        saved_state_path="financial_analyst_agent.json",
-        user_name="pe_firm",
-        retry_attempts=1,
-        context_length=200000,
-        output_type="string",
-    )
-
-    market_analyst_agent = Agent(
-        agent_name="Market-Analyst",
-        system_prompt=MARKET_ANALYST_PROMPT,
-        llm=model,
-        max_loops=1,
-        autosave=True,
-        verbose=True,
-        dynamic_temperature_enabled=True,
-        saved_state_path="market_analyst_agent.json",
-        user_name="pe_firm",
-        retry_attempts=1,
-        context_length=200000,
-        output_type="string",
-    )
-
-    operational_analyst_agent = Agent(
-        agent_name="Operational-Analyst",
-        system_prompt=OPERATIONAL_ANALYST_PROMPT,
-        llm=model,
-        max_loops=1,
-        autosave=True,
-        verbose=True,
-        dynamic_temperature_enabled=True,
-        saved_state_path="operational_analyst_agent.json",
-        user_name="pe_firm",
-        retry_attempts=1,
-        context_length=200000,
-        output_type="string",
-    )
-
-    # Create agents (using the agents from the original code)
-    agents_to_add = [
-        data_extractor_agent,
-        summarizer_agent,
-        financial_analyst_agent,
-        market_analyst_agent,
-        operational_analyst_agent,
-    ]
-
-    # Add agents to the vector database
-    for agent in agents_to_add:
-        vector_db.add_agent(agent)
-
-    # Example task
-    task = "Analyze the financial statements of a potential acquisition target and identify key growth drivers."
-
-    # Find the best agent for the task
-    best_agent = vector_db.find_best_agent(task)
-
-    if best_agent:
-        logger.info(f"Best agent for the task: {best_agent.name}")
-        # Use the best agent to perform the task
-        result = best_agent.run(task)
-        print(f"Task result: {result}")
-
-        # Update the agent's history in the database
-        vector_db.update_agent_history(best_agent.name)
-    else:
-        print("No suitable agent found for the task.")
-
-    # Save the vector database
+# # Example usage
+# if __name__ == "__main__":
+#     from dotenv import load_dotenv
+#     from swarm_models import OpenAIChat
+
+#     load_dotenv()
+
+#     # Get the OpenAI API key from the environment variable
+#     api_key = os.getenv("GROQ_API_KEY")
+
+#     # Model
+#     model = OpenAIChat(
+#         openai_api_base="https://api.groq.com/openai/v1",
+#         openai_api_key=api_key,
+#         model_name="llama-3.1-70b-versatile",
+#         temperature=0.1,
+#     )
+#     # Initialize the vector database
+#     vector_db = AgentRouter()
+
+#     # Define specialized system prompts for each agent
+#     DATA_EXTRACTOR_PROMPT = """You are a highly specialized private equity agent focused on data extraction from various documents. Your expertise includes:
+#     1. Extracting key financial metrics (revenue, EBITDA, growth rates, etc.) from financial statements and reports
+#     2. Identifying and extracting important contract terms from legal documents
+#     3. Pulling out relevant market data from industry reports and analyses
+#     4. Extracting operational KPIs from management presentations and internal reports
+#     5. Identifying and extracting key personnel information from organizational charts and bios
+#     Provide accurate, structured data extracted from various document types to support investment analysis."""
+
+#     SUMMARIZER_PROMPT = """You are an expert private equity agent specializing in summarizing complex documents. Your core competencies include:
+#     1. Distilling lengthy financial reports into concise executive summaries
+#     2. Summarizing legal documents, highlighting key terms and potential risks
+#     3. Condensing industry reports to capture essential market trends and competitive dynamics
+#     4. Summarizing management presentations to highlight key strategic initiatives and projections
+#     5. Creating brief overviews of technical documents, emphasizing critical points for non-technical stakeholders
+#     Deliver clear, concise summaries that capture the essence of various documents while highlighting information crucial for investment decisions."""
+
+#     FINANCIAL_ANALYST_PROMPT = """You are a specialized private equity agent focused on financial analysis. Your key responsibilities include:
+#     1. Analyzing historical financial statements to identify trends and potential issues
+#     2. Evaluating the quality of earnings and potential adjustments to EBITDA
+#     3. Assessing working capital requirements and cash flow dynamics
+#     4. Analyzing capital structure and debt capacity
+#     5. Evaluating financial projections and underlying assumptions
+#     Provide thorough, insightful financial analysis to inform investment decisions and valuation."""
+
+#     MARKET_ANALYST_PROMPT = """You are a highly skilled private equity agent specializing in market analysis. Your expertise covers:
+#     1. Analyzing industry trends, growth drivers, and potential disruptors
+#     2. Evaluating competitive landscape and market positioning
+#     3. Assessing market size, segmentation, and growth potential
+#     4. Analyzing customer dynamics, including concentration and loyalty
+#     5. Identifying potential regulatory or macroeconomic impacts on the market
+#     Deliver comprehensive market analysis to assess the attractiveness and risks of potential investments."""
+
+#     OPERATIONAL_ANALYST_PROMPT = """You are an expert private equity agent focused on operational analysis. Your core competencies include:
+#     1. Evaluating operational efficiency and identifying improvement opportunities
+#     2. Analyzing supply chain and procurement processes
+#     3. Assessing sales and marketing effectiveness
+#     4. Evaluating IT systems and digital capabilities
+#     5. Identifying potential synergies in merger or add-on acquisition scenarios
+#     Provide detailed operational analysis to uncover value creation opportunities and potential risks."""
+
+#     # Initialize specialized agents
+#     data_extractor_agent = Agent(
+#         agent_name="Data-Extractor",
+#         system_prompt=DATA_EXTRACTOR_PROMPT,
+#         llm=model,
+#         max_loops=1,
+#         autosave=True,
+#         verbose=True,
+#         dynamic_temperature_enabled=True,
+#         saved_state_path="data_extractor_agent.json",
+#         user_name="pe_firm",
+#         retry_attempts=1,
+#         context_length=200000,
+#         output_type="string",
+#     )
+
+#     summarizer_agent = Agent(
+#         agent_name="Document-Summarizer",
+#         system_prompt=SUMMARIZER_PROMPT,
+#         llm=model,
+#         max_loops=1,
+#         autosave=True,
+#         verbose=True,
+#         dynamic_temperature_enabled=True,
+#         saved_state_path="summarizer_agent.json",
+#         user_name="pe_firm",
+#         retry_attempts=1,
+#         context_length=200000,
+#         output_type="string",
+#     )
+
+#     financial_analyst_agent = Agent(
+#         agent_name="Financial-Analyst",
+#         system_prompt=FINANCIAL_ANALYST_PROMPT,
+#         llm=model,
+#         max_loops=1,
+#         autosave=True,
+#         verbose=True,
+#         dynamic_temperature_enabled=True,
+#         saved_state_path="financial_analyst_agent.json",
+#         user_name="pe_firm",
+#         retry_attempts=1,
+#         context_length=200000,
+#         output_type="string",
+#     )
+
+#     market_analyst_agent = Agent(
+#         agent_name="Market-Analyst",
+#         system_prompt=MARKET_ANALYST_PROMPT,
+#         llm=model,
+#         max_loops=1,
+#         autosave=True,
+#         verbose=True,
+#         dynamic_temperature_enabled=True,
+#         saved_state_path="market_analyst_agent.json",
+#         user_name="pe_firm",
+#         retry_attempts=1,
+#         context_length=200000,
+#         output_type="string",
+#     )
+
+#     operational_analyst_agent = Agent(
+#         agent_name="Operational-Analyst",
+#         system_prompt=OPERATIONAL_ANALYST_PROMPT,
+#         llm=model,
+#         max_loops=1,
+#         autosave=True,
+#         verbose=True,
+#         dynamic_temperature_enabled=True,
+#         saved_state_path="operational_analyst_agent.json",
+#         user_name="pe_firm",
+#         retry_attempts=1,
+#         context_length=200000,
+#         output_type="string",
+#     )
+
+#     # Create agents (using the agents from the original code)
+#     agents_to_add = [
+#         data_extractor_agent,
+#         summarizer_agent,
+#         financial_analyst_agent,
+#         market_analyst_agent,
+#         operational_analyst_agent,
+#     ]
+
+#     # Add agents to the vector database
+#     for agent in agents_to_add:
+#         vector_db.add_agent(agent)
+
+#     # Example task
+#     task = "Analyze the financial statements of a potential acquisition target and identify key growth drivers."
+
+#     # Find the best agent for the task
+#     best_agent = vector_db.find_best_agent(task)
+
+#     if best_agent:
+#         logger.info(f"Best agent for the task: {best_agent.name}")
+#         # Use the best agent to perform the task
+#         result = best_agent.run(task)
+#         print(f"Task result: {result}")
+
+#         # Update the agent's history in the database
+#         vector_db.update_agent_history(best_agent.name)
+#     else:
+#         print("No suitable agent found for the task.")
+
+#     # Save the vector database

swarms/structs/auto_swarm_builder.py

@@ -0,0 +1,20 @@
+from typing import List, Any, Dict, Optional
+
+
+class AutoSwarmBuilder:
+    def __init__(self, task: str, num_agents: int = 10, batch_size: int = 10):
+        self.task = task
+        self.num_agents = num_agents
+        self.batch_size = batch_size
+
+    def run(self, task: str, image_url: str = None, *args, **kwargs):
+        pass
+
+    def _create_swarm(self):
+        pass
+
+    def _create_agents(self):
+        pass
+
+    def _run_agents(self):
+        pass

swarms/structs/concurrent_workflow.py

@@ -138,7 +138,6 @@ class ConcurrentWorkflow(BaseSwarm):
 
         self.reliability_check()
 
-
     def reliability_check(self):
         try:
             logger.info("Starting reliability checks")

swarms/structs/run_agents_in_parallel_async_multiprocess.py

@@ -1,74 +1,81 @@
-import os
 import asyncio
+from typing import List, Any
 from swarms import Agent
-from swarm_models import OpenAIChat
-import uvloop
 from multiprocessing import cpu_count
 from swarms.utils.calculate_func_metrics import profile_func
-from typing import List
 
 # Use uvloop for faster asyncio event loop
 asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
 
-# Get the OpenAI API key from the environment variable
-api_key = os.getenv("OPENAI_API_KEY")
 
-# Get the OpenAI API key from the environment variable
-api_key = os.getenv("OPENAI_API_KEY")
-
-# Create an instance of the OpenAIChat class (can be reused)
-model = OpenAIChat(
-    api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
-)
+def run_single_agent(agent: Agent, task: str) -> Any:
+    """
+    Run a single agent on the given task.
 
+    Args:
+        agent (Agent): The agent to run.
+        task (str): The task for the agent to perform.
 
-# Function to run a single agent on the task (synchronous)
-def run_single_agent(agent, task):
+    Returns:
+        Any: The result of the agent's execution.
+    """
     return agent.run(task)
 
 
-# Asynchronous wrapper for agent tasks
-async def run_agent_async(agent, task):
+async def run_agent_async(agent: Agent, task: str) -> Any:
+    """
+    Asynchronous wrapper for agent tasks.
+
+    Args:
+        agent (Agent): The agent to run asynchronously.
+        task (str): The task for the agent to perform.
+
+    Returns:
+        Any: The result of the agent's execution.
+    """
     loop = asyncio.get_event_loop()
     return await loop.run_in_executor(
         None, run_single_agent, agent, task
     )
 
 
-# Asynchronous function to run agents concurrently
-async def run_agents_concurrently_async(agents, task: str):
+async def run_agents_concurrently_async(
+    agents: List[Agent], task: str
+) -> List[Any]:
     """
     Run multiple agents concurrently on the same task with optimized performance.
 
-    :param agents: List of Agent instances to run concurrently.
-    :param task: The task string to execute by all agents.
-    :return: A list of outputs from each agent.
-    """
+    Args:
+        agents (List[Agent]): List of Agent instances to run concurrently.
+        task (str): The task string to execute by all agents.
 
-    # Run all agents asynchronously using asyncio.gather
+    Returns:
+        List[Any]: A list of outputs from each agent.
+    """
     results = await asyncio.gather(
         *(run_agent_async(agent, task) for agent in agents)
     )
     return results
 
 
-# Function to manage the overall process and batching
 @profile_func
 def run_agents_concurrently_multiprocess(
     agents: List[Agent], task: str, batch_size: int = cpu_count()
-):
+) -> List[Any]:
     """
     Manage and run multiple agents concurrently in batches, with optimized performance.
 
-    :param agents: List of Agent instances to run concurrently.
-    :param task: The task string to execute by all agents.
-    :param batch_size: Number of agents to run in parallel in each batch.
-    :return: A list of outputs from each agent.
-    """
+    Args:
+        agents (List[Agent]): List of Agent instances to run concurrently.
+        task (str): The task string to execute by all agents.
+        batch_size (int, optional): Number of agents to run in parallel in each batch.
+                                    Defaults to the number of CPU cores.
 
+    Returns:
+        List[Any]: A list of outputs from each agent.
+    """
     results = []
     loop = asyncio.get_event_loop()
-    # batch_size = cpu_count()
 
     # Process agents in batches to avoid overwhelming system resources
     for i in range(0, len(agents), batch_size):