[FIX][Alternative swarm architectures]

docs/swarms/concept/swarm_architectures.md

@@ -156,7 +156,7 @@ graph TD
 
 
 
-# Mixture of Agents Architecture
+### Mixture of Agents Architecture
 
 
 ```mermaid
@@ -176,4 +176,415 @@ graph TD
     J --> K[Final Output]
 
 
+```
+
+
+## Alternative Experimental Architectures
+
+### **1. Circular Swarm**
+
+#### Input Arguments:
+- **name** (str): Name of the swarm.
+- **description** (str): Description of the swarm.
+- **goal** (str): Goal of the swarm.
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+- **return_full_history** (bool): Whether to return the full conversation history.
+
+#### Functionality:
+Agents pass tasks in a circular manner, where each agent works on the next task in the list.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Agent1 --> Agent2
+    Agent2 --> Agent3
+    Agent3 --> Task2
+    Task2 --> Agent1
+```
+
+---
+
+### **2. Linear Swarm**
+
+#### Input Arguments:
+- **name** (str): Name of the swarm.
+- **description** (str): Description of the swarm.
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+- **conversation** (Conversation): Conversation object.
+- **return_full_history** (bool): Whether to return the full conversation history.
+
+#### Functionality:
+Agents pass tasks in a linear fashion, each agent working on one task sequentially.
+
+```mermaid
+graph LR
+    Task1 --> Agent1
+    Agent1 --> Agent2
+    Agent2 --> Agent3
+    Agent3 --> Task2
+```
+
+---
+
+### **3. Star Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+A central agent (Agent 1) executes the tasks first, followed by the other agents working in parallel.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Agent1 --> Agent2
+    Agent1 --> Agent3
+    Agent1 --> Agent4
+```
+
+---
+
+### **4. Mesh Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+Each agent works on tasks randomly from a task queue, until the task queue is empty.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Task2 --> Agent2
+    Task3 --> Agent3
+    Task4 --> Agent4
+    Task5 --> Agent1
+    Task6 --> Agent2
+```
+
+---
+
+### **5. Grid Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+Agents are structured in a grid, and tasks are distributed accordingly.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Task2 --> Agent2
+    Task3 --> Agent3
+    Task4 --> Agent4
+```
+
+---
+
+### **6. Pyramid Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+Agents are arranged in a pyramid structure. Each level of agents works in sequence.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Agent1 --> Agent2
+    Agent2 --> Agent3
+    Agent3 --> Task2
+```
+
+---
+
+### **7. Fibonacci Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+Agents work according to the Fibonacci sequence, where the number of agents working on tasks follows this progression.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Agent1 --> Agent2
+    Agent2 --> Agent3
+    Task2 --> Agent5
+    Agent5 --> Agent8
+```
+
+---
+
+### **8. Prime Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+Agents are assigned tasks based on prime number indices in the list of agents.
+
+```mermaid
+graph TD
+    Task1 --> Agent2
+    Task2 --> Agent3
+    Task3 --> Agent5
+    Task4 --> Agent7
+```
+
+---
+
+### **9. Power Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+Agents work on tasks following powers of two.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Task2 --> Agent2
+    Task3 --> Agent4
+    Task4 --> Agent8
+```
+
+---
+
+### **10. Sigmoid Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **tasks** (List[str]): List of tasks for the agents.
+
+#### Functionality:
+Agents are selected based on the sigmoid function, with higher-indexed agents handling more complex tasks.
+
+```mermaid
+graph TD
+    Task1 --> Agent1
+    Task2 --> Agent2
+    Task3 --> Agent3
+    Task4 --> Agent4
+```
+
+---
+
+### **11. Sinusoidal Swarm**
+
+#### Input Arguments:
+- **agents** (AgentListType): List of agents involved.
+- **task** (str): Task for the agents to work on.
+
+#### Functionality:
+Agents are assigned tasks based on a sinusoidal pattern.
+
+```mermaid
+graph TD
+    Task --> Agent1
+    Agent1 --> Agent2
+    Agent2 --> Agent3
+    Agent3 --> Task2
+```
+
+---
+
+Each of these swarm architectures enables different task distribution and agent coordination strategies, making it possible to select the right architecture for specific types of agent-based problem-solving scenarios.
+
+
+
+## Examples
+
+```python
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from swarm_models import OpenAIChat
+from tickr_agent.main import TickrAgent
+
+from swarms.structs.swarming_architectures import (
+    circular_swarm,
+    linear_swarm,
+    mesh_swarm,
+    pyramid_swarm,
+    star_swarm,
+)
+
+# Load environment variables (API keys)
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+
+# Initialize the OpenAI model
+model = OpenAIChat(
+    openai_api_key=api_key, model_name="gpt-4", temperature=0.1
+)
+
+# Custom Financial Agent System Prompts
+STOCK_ANALYSIS_PROMPT = """
+You are an expert financial analyst. Your task is to analyze stock market data for a company 
+and provide insights on whether to buy, hold, or sell. Analyze trends, financial ratios, and market conditions.
+"""
+
+NEWS_SUMMARIZATION_PROMPT = """
+You are a financial news expert. Summarize the latest news related to a company and provide insights on 
+how it could impact its stock price. Be concise and focus on the key takeaways.
+"""
+
+RATIO_CALCULATION_PROMPT = """
+You are a financial ratio analyst. Your task is to calculate key financial ratios for a company 
+based on the available data, such as P/E ratio, debt-to-equity ratio, and return on equity. 
+Explain what each ratio means for investors.
+"""
+
+# Example Usage
+# Define stock tickers
+stocks = ["AAPL", "TSLA"]
+
+
+# Initialize Financial Analysis Agents
+stock_analysis_agent = TickrAgent(
+    agent_name="Stock-Analysis-Agent",
+    system_prompt=STOCK_ANALYSIS_PROMPT,
+    stocks=stocks,
+)
+
+news_summarization_agent = TickrAgent(
+    agent_name="News-Summarization-Agent",
+    system_prompt=NEWS_SUMMARIZATION_PROMPT,
+    stocks=stocks,
+
+)
+
+ratio_calculation_agent = TickrAgent(
+    agent_name="Ratio-Calculation-Agent",
+    system_prompt=RATIO_CALCULATION_PROMPT,
+    stocks=stocks,
+
+)
+# Create a list of agents for swarming
+agents = [
+    stock_analysis_agent,
+    news_summarization_agent,
+    ratio_calculation_agent,
+]
+
+# Define financial analysis tasks
+tasks = [
+    "Analyze the stock performance of Apple (AAPL) in the last 6 months.",
+    "Summarize the latest financial news on Tesla (TSLA).",
+    "Calculate the P/E ratio and debt-to-equity ratio for Amazon (AMZN).",
+]
+
+# -------------------------------# Showcase Circular Swarm
+# -------------------------------
+logger.info("Starting Circular Swarm for financial analysis.")
+circular_result = circular_swarm(agents, tasks)
+logger.info(f"Circular Swarm Result:\n{circular_result}\n")
+
+
+# -------------------------------
+# Showcase Linear Swarm
+# -------------------------------
+logger.info("Starting Linear Swarm for financial analysis.")
+linear_result = linear_swarm(agents, tasks)
+logger.info(f"Linear Swarm Result:\n{linear_result}\n")
+
+
+# -------------------------------
+# Showcase Star Swarm
+# -------------------------------
+logger.info("Starting Star Swarm for financial analysis.")
+star_result = star_swarm(agents, tasks)
+logger.info(f"Star Swarm Result:\n{star_result}\n")
+
+
+# -------------------------------
+# Showcase Mesh Swarm
+# -------------------------------
+logger.info("Starting Mesh Swarm for financial analysis.")
+mesh_result = mesh_swarm(agents, tasks)
+logger.info(f"Mesh Swarm Result:\n{mesh_result}\n")
+
+
+# -------------------------------
+# Showcase Pyramid Swarm
+# -------------------------------
+logger.info("Starting Pyramid Swarm for financial analysis.")
+pyramid_result = pyramid_swarm(agents, tasks)
+logger.info(f"Pyramid Swarm Result:\n{pyramid_result}\n")
+
+
+# -------------------------------
+# Example: One-to-One Communication between Agents
+# -------------------------------
+logger.info(
+    "Starting One-to-One communication between Stock and News agents."
+)
+one_to_one_result = stock_analysis_agent.run(
+    "Analyze Apple stock performance, and then send the result to the News Summarization Agent"
+)
+news_summary_result = news_summarization_agent.run(one_to_one_result)
+logger.info(
+    f"One-to-One Communication Result:\n{news_summary_result}\n"
+)
+
+
+# -------------------------------
+# Example: Broadcasting to all agents
+# -------------------------------
+async def broadcast_task():
+    logger.info("Broadcasting task to all agents.")
+    task = "Summarize the overall stock market performance today."
+    await asyncio.gather(*[agent.run(task) for agent in agents])
+
+
+asyncio.run(broadcast_task())
+
+
+# -------------------------------
+# Deep Comments & Explanations
+# -------------------------------
+
+"""
+Explanation of Key Components:
+
+1. **Agents**:
+   - We created three specialized agents for financial analysis: Stock Analysis, News Summarization, and Ratio Calculation.
+   - Each agent is provided with a custom system prompt that defines their unique task in analyzing stock data.
+
+2. **Swarm Examples**:
+   - **Circular Swarm**: Agents take turns processing tasks in a circular manner.
+   - **Linear Swarm**: Tasks are processed sequentially by each agent.
+   - **Star Swarm**: The first agent (Stock Analysis) processes all tasks before distributing them to other agents.
+   - **Mesh Swarm**: Agents work on random tasks from the task queue.
+   - **Pyramid Swarm**: Agents are arranged in a pyramid structure, processing tasks layer by layer.
+
+3. **One-to-One Communication**:
+   - This showcases how one agent can pass its result to another agent for further processing, useful for complex workflows where agents depend on each other.
+
+4. **Broadcasting**:
+   - The broadcasting function demonstrates how a single task can be sent to all agents simultaneously. This can be useful for situations like summarizing daily stock market performance across multiple agents.
+
+5. **Logging with Loguru**:
+   - We use `loguru` for detailed logging throughout the swarms. This helps to track the flow of information and responses from each agent.
+"""
+
+
+
 ```

swarm_architecture_examples.py

@@ -0,0 +1,175 @@
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+from swarm_models import OpenAIChat
+from tickr_agent.main import TickrAgent
+
+from swarms.structs.swarming_architectures import (
+    circular_swarm,
+    linear_swarm,
+    mesh_swarm,
+    pyramid_swarm,
+    star_swarm,
+)
+
+# Load environment variables (API keys)
+load_dotenv()
+api_key = os.getenv("OPENAI_API_KEY")
+
+# Initialize the OpenAI model
+model = OpenAIChat(
+    openai_api_key=api_key, model_name="gpt-4", temperature=0.1
+)
+
+# Custom Financial Agent System Prompts
+STOCK_ANALYSIS_PROMPT = """
+You are an expert financial analyst. Your task is to analyze stock market data for a company 
+and provide insights on whether to buy, hold, or sell. Analyze trends, financial ratios, and market conditions.
+"""
+
+NEWS_SUMMARIZATION_PROMPT = """
+You are a financial news expert. Summarize the latest news related to a company and provide insights on 
+how it could impact its stock price. Be concise and focus on the key takeaways.
+"""
+
+RATIO_CALCULATION_PROMPT = """
+You are a financial ratio analyst. Your task is to calculate key financial ratios for a company 
+based on the available data, such as P/E ratio, debt-to-equity ratio, and return on equity. 
+Explain what each ratio means for investors.
+"""
+
+# Example Usage
+# Define stock tickers
+stocks = ["AAPL", "TSLA"]
+
+
+# Initialize Financial Analysis Agents
+stock_analysis_agent = TickrAgent(
+    agent_name="Stock-Analysis-Agent",
+    system_prompt=STOCK_ANALYSIS_PROMPT,
+    stocks=stocks,
+)
+
+news_summarization_agent = TickrAgent(
+    agent_name="News-Summarization-Agent",
+    system_prompt=NEWS_SUMMARIZATION_PROMPT,
+    stocks=stocks,
+
+)
+
+ratio_calculation_agent = TickrAgent(
+    agent_name="Ratio-Calculation-Agent",
+    system_prompt=RATIO_CALCULATION_PROMPT,
+    stocks=stocks,
+
+)
+# Create a list of agents for swarming
+agents = [
+    stock_analysis_agent,
+    news_summarization_agent,
+    ratio_calculation_agent,
+]
+
+# Define financial analysis tasks
+tasks = [
+    "Analyze the stock performance of Apple (AAPL) in the last 6 months.",
+    "Summarize the latest financial news on Tesla (TSLA).",
+    "Calculate the P/E ratio and debt-to-equity ratio for Amazon (AMZN).",
+]
+
+# -------------------------------# Showcase Circular Swarm
+# -------------------------------
+logger.info("Starting Circular Swarm for financial analysis.")
+circular_result = circular_swarm(agents, tasks)
+logger.info(f"Circular Swarm Result:\n{circular_result}\n")
+
+
+# -------------------------------
+# Showcase Linear Swarm
+# -------------------------------
+logger.info("Starting Linear Swarm for financial analysis.")
+linear_result = linear_swarm(agents, tasks)
+logger.info(f"Linear Swarm Result:\n{linear_result}\n")
+
+
+# -------------------------------
+# Showcase Star Swarm
+# -------------------------------
+logger.info("Starting Star Swarm for financial analysis.")
+star_result = star_swarm(agents, tasks)
+logger.info(f"Star Swarm Result:\n{star_result}\n")
+
+
+# -------------------------------
+# Showcase Mesh Swarm
+# -------------------------------
+logger.info("Starting Mesh Swarm for financial analysis.")
+mesh_result = mesh_swarm(agents, tasks)
+logger.info(f"Mesh Swarm Result:\n{mesh_result}\n")
+
+
+# -------------------------------
+# Showcase Pyramid Swarm
+# -------------------------------
+logger.info("Starting Pyramid Swarm for financial analysis.")
+pyramid_result = pyramid_swarm(agents, tasks)
+logger.info(f"Pyramid Swarm Result:\n{pyramid_result}\n")
+
+
+# -------------------------------
+# Example: One-to-One Communication between Agents
+# -------------------------------
+logger.info(
+    "Starting One-to-One communication between Stock and News agents."
+)
+one_to_one_result = stock_analysis_agent.run(
+    "Analyze Apple stock performance, and then send the result to the News Summarization Agent"
+)
+news_summary_result = news_summarization_agent.run(one_to_one_result)
+logger.info(
+    f"One-to-One Communication Result:\n{news_summary_result}\n"
+)
+
+
+# -------------------------------
+# Example: Broadcasting to all agents
+# -------------------------------
+async def broadcast_task():
+    logger.info("Broadcasting task to all agents.")
+    task = "Summarize the overall stock market performance today."
+    await asyncio.gather(*[agent.run(task) for agent in agents])
+
+
+asyncio.run(broadcast_task())
+
+
+# -------------------------------
+# Deep Comments & Explanations
+# -------------------------------
+
+"""
+Explanation of Key Components:
+
+1. **Agents**:
+   - We created three specialized agents for financial analysis: Stock Analysis, News Summarization, and Ratio Calculation.
+   - Each agent is provided with a custom system prompt that defines their unique task in analyzing stock data.
+
+2. **Swarm Examples**:
+   - **Circular Swarm**: Agents take turns processing tasks in a circular manner.
+   - **Linear Swarm**: Tasks are processed sequentially by each agent.
+   - **Star Swarm**: The first agent (Stock Analysis) processes all tasks before distributing them to other agents.
+   - **Mesh Swarm**: Agents work on random tasks from the task queue.
+   - **Pyramid Swarm**: Agents are arranged in a pyramid structure, processing tasks layer by layer.
+
+3. **One-to-One Communication**:
+   - This showcases how one agent can pass its result to another agent for further processing, useful for complex workflows where agents depend on each other.
+
+4. **Broadcasting**:
+   - The broadcasting function demonstrates how a single task can be sent to all agents simultaneously. This can be useful for situations like summarizing daily stock market performance across multiple agents.
+
+5. **Logging with Loguru**:
+   - We use `loguru` for detailed logging throughout the swarms. This helps to track the flow of information and responses from each agent.
+"""

swarms/structs/swarming_architectures.py

@@ -1,176 +1,224 @@
 import asyncio
 import math
-from typing import List
+from typing import List, Union
+
+from loguru import logger
+from pydantic import BaseModel
 
 from swarms.structs.agent import Agent
-from swarms.utils.loguru_logger import logger
-from swarms.structs.conversation import Conversation
-from swarms.structs.concat import concat_strings
 from swarms.structs.omni_agent_types import AgentListType
 
-# from swarms.structs.swarm_registry import swarm_registry, SwarmRegistry
 
+# Define Pydantic schema for logging agent responses
+class AgentLog(BaseModel):
+    agent_name: str
+    task: str
+    response: str
 
-# @swarm_registry
-def circular_swarm(
-    name: str = "Circular Swarm",
-    description: str = "A circular swarm is a type of swarm where agents pass tasks in a circular manner.",
-    goal: str = None,
-    agents: AgentListType = None,
-    tasks: List[str] = None,
-    return_full_history: bool = True,
-):
-    if not agents:
-        raise ValueError("Agents list cannot be empty.")
 
-    if not tasks:
+class Conversation(BaseModel):
-        raise ValueError("Tasks list cannot be empty.")
+    logs: List[AgentLog] = []
 
-    conversation = Conversation(
+    def add_log(
-        time_enabled=True,
+        self, agent_name: str, task: str, response: str
-    )
+    ) -> None:
+        log_entry = AgentLog(
+            agent_name=agent_name, task=task, response=response
+        )
+        self.logs.append(log_entry)
+        logger.info(
+            f"Agent: {agent_name} | Task: {task} | Response: {response}"
+        )
 
+    def return_history(self) -> dict:
+        return {
+            "history": [
+                {
+                    "agent_name": log.agent_name,
+                    "task": log.task,
+                    "response": log.response,
+                }
+                for log in self.logs
+            ]
+        }
+
+
+# Circular Swarm: Agents pass tasks in a circular manner
+def circular_swarm(
+    agents: AgentListType,
+    tasks: List[str],
+    return_full_history: bool = True,
+) -> Union[str, List[str]]:
+    if not agents or not tasks:
+        raise ValueError("Agents and tasks lists cannot be empty.")
+
+    conversation = Conversation()
     responses = []
 
     for task in tasks:
         for agent in agents:
-            # Log the task
+            response = agent.run(task)
-            out = agent.run(task)
+            conversation.add_log(
-            # print(f"Task: {task}, Response {out}")
+                agent_name=agent.agent_name,
-            # prompt = f"Task: {task}, Response {out}"
+                task=task,
-            logger.info(f"Agent: {agent.agent_name} Response {out}")
+                response=response,
-
-            conversation.add(
-                role=agent.agent_name,
-                content=out,
             )
+            responses.append(response)
 
-            # Response list
+    return (
-            responses.append(out)
+        conversation.return_history()
-
+        if return_full_history
-    if return_full_history:
+        else responses
-        return conversation.return_history_as_string()
+    )
-    else:
-        return responses
 
 
-# @swarm_registry()
+def grid_swarm(agents: AgentListType, tasks: List[str]):
+    grid_size = int(
+        len(agents) ** 0.5
+    )  # Assuming agents can form a perfect square grid
+    for i in range(grid_size):
+        for j in range(grid_size):
+            if tasks:
+                task = tasks.pop(0)
+                agents[i * grid_size + j].run(task)
+                
+                
+# Linear Swarm: Agents process tasks in a sequential linear manner
 def linear_swarm(
-    name: str = "Linear Swarm",
+    agents: AgentListType,
-    description: str = "A linear swarm is a type of swarm where agents pass tasks in a linear manner.",
+    tasks: List[str],
-    agents: AgentListType = None,
-    tasks: List[str] = None,
-    conversation: Conversation = None,
     return_full_history: bool = True,
-):
+) -> Union[str, List[str]]:
-    if not agents:
+    if not agents or not tasks:
-        raise ValueError("Agents list cannot be empty.")
+        raise ValueError("Agents and tasks lists cannot be empty.")
-
-    if not tasks:
-        raise ValueError("Tasks list cannot be empty.")
-
-    if not conversation:
-        conversation = Conversation(
-            time_enabled=True,
-        )
 
+    conversation = Conversation()
     responses = []
 
-    for i in range(len(agents)):
+    for agent in agents:
         if tasks:
             task = tasks.pop(0)
-            out = agents[i].run(task)
+            response = agent.run(task)
-
+            conversation.add_log(
-            conversation.add(
+                agent_name=agent.agent_name,
-                role=agents[i].agent_name,
+                task=task,
-                content=f"Task: {task}, Response {out}",
+                response=response,
             )
+            responses.append(response)
 
-            responses.append(out)
+    return (
-
+        conversation.return_history()
-    if return_full_history:
+        if return_full_history
-        return conversation.return_history_as_string()
+        else responses
-    else:
-        return responses
-
-
-# print(SwarmRegistry().list_swarms())
-
-# def linear_swarm(agents: AgentListType, tasks: List[str]):
-#     logger.info(f"Running linear swarm with {len(agents)} agents")
-#     for i in range(len(agents)):
-#         if tasks:
-#             task = tasks.pop(0)
-#             agents[i].run(task)
-
-
-def star_swarm(agents: AgentListType, tasks: List[str]) -> str:
-    logger.info(
-        f"Running star swarm with {len(agents)} agents and {len(tasks)} tasks"
     )
 
-    if not agents:
-        raise ValueError("Agents list cannot be empty.")
-
-    if not tasks:
-        raise ValueError("Tasks list cannot be empty.")
 
-    conversation = Conversation(time_enabled=True)
+# Star Swarm: A central agent first processes all tasks, followed by others
-    center_agent = agents[0]
+def star_swarm(
+    agents: AgentListType,
+    tasks: List[str],
+    return_full_history: bool = True,
+) -> Union[str, List[str]]:
+    if not agents or not tasks:
+        raise ValueError("Agents and tasks lists cannot be empty.")
 
+    conversation = Conversation()
+    center_agent = agents[0]  # The central agent
     responses = []
 
     for task in tasks:
+        # Central agent processes the task
+        center_response = center_agent.run(task)
+        conversation.add_log(
+            agent_name=center_agent.agent_name,
+            task=task,
+            response=center_response,
+        )
+        responses.append(center_response)
 
-        out = center_agent.run(task)
+        # Other agents process the same task
-        log = f"Agent: {center_agent.agent_name} Response {out}"
-        logger.info(log)
-        conversation.add(center_agent.agent_name, out)
-        responses.append(out)
-
         for agent in agents[1:]:
+            response = agent.run(task)
+            conversation.add_log(
+                agent_name=agent.agent_name,
+                task=task,
+                response=response,
+            )
+            responses.append(response)
 
-            output = agent.run(task)
+    return (
-            log_two = f"Agent: {agent.agent_name} Response {output}"
+        conversation.return_history()
-            logger.info(log_two)
+        if return_full_history
-            conversation.add(agent.agent_name, output)
+        else responses
-            responses.append(out)
+    )
-
-    out = concat_strings(responses)
-    print(out)
 
-    return out
 
+# Mesh Swarm: Agents work on tasks randomly from a task queue until all tasks are processed
+def mesh_swarm(
+    agents: AgentListType,
+    tasks: List[str],
+    return_full_history: bool = True,
+) -> Union[str, List[str]]:
+    if not agents or not tasks:
+        raise ValueError("Agents and tasks lists cannot be empty.")
 
-def mesh_swarm(agents: AgentListType, tasks: List[str]):
+    conversation = Conversation()
     task_queue = tasks.copy()
+    responses = []
+
     while task_queue:
         for agent in agents:
             if task_queue:
                 task = task_queue.pop(0)
-                agent.run(task)
+                response = agent.run(task)
+                conversation.add_log(
+                    agent_name=agent.agent_name,
+                    task=task,
+                    response=response,
+                )
+                responses.append(response)
+
+    return (
+        conversation.return_history()
+        if return_full_history
+        else responses
+    )
 
 
-def grid_swarm(agents: AgentListType, tasks: List[str]):
+# Pyramid Swarm: Agents are arranged in a pyramid structure
-    grid_size = int(
+def pyramid_swarm(
-        len(agents) ** 0.5
+    agents: AgentListType,
-    )  # Assuming agents can form a perfect square grid
+    tasks: List[str],
-    for i in range(grid_size):
+    return_full_history: bool = True,
-        for j in range(grid_size):
+) -> Union[str, List[str]]:
-            if tasks:
+    if not agents or not tasks:
-                task = tasks.pop(0)
+        raise ValueError("Agents and tasks lists cannot be empty.")
-                agents[i * grid_size + j].run(task)
 
+    conversation = Conversation()
+    responses = []
 
-def pyramid_swarm(agents: AgentListType, tasks: List[str]):
     levels = int(
         (-1 + (1 + 8 * len(agents)) ** 0.5) / 2
-    )  # Assuming agents can form a perfect pyramid
+    )  # Number of levels in the pyramid
+
     for i in range(levels):
         for j in range(i + 1):
             if tasks:
                 task = tasks.pop(0)
-                agents[int(i * (i + 1) / 2 + j)].run(task)
+                agent_index = int(i * (i + 1) / 2 + j)
+                response = agents[agent_index].run(task)
+                conversation.add_log(
+                    agent_name=agents[agent_index].agent_name,
+                    task=task,
+                    response=response,
+                )
+                responses.append(response)
+
+    return (
+        conversation.return_history()
+        if return_full_history
+        else responses
+    )
 
 
 def fibonacci_swarm(agents: AgentListType, tasks: List[str]):
@@ -318,86 +366,92 @@ async def one_to_three(
         raise error
 
 
-async def broadcast(
+"""
-    sender: Agent,
+This module contains functions for facilitating communication between agents in a swarm. It includes methods for one-to-one communication, broadcasting, and other swarm architectures.
-    agents: AgentListType,
+"""
-    task: str,
+
-):
+
+# One-to-One Communication between two agents
+def one_to_one(
+    sender: Agent, receiver: Agent, task: str, max_loops: int = 1
+) -> str:
     """
-    Broadcasts a message from the sender agent to a list of agents.
+    Facilitates one-to-one communication between two agents. The sender and receiver agents exchange messages for a specified number of loops.
 
     Args:
         sender (Agent): The agent sending the message.
-        agents (AgentListType): The list of agents to receive the message.
+        receiver (Agent): The agent receiving the message.
-        task (str): The message to be broadcasted.
+        task (str): The message to be sent.
-
+        max_loops (int, optional): The number of times the sender and receiver exchange messages. Defaults to 1.
-    Raises:
-        Exception: If an error occurs during the broadcast.
 
     Returns:
-        None
+        str: The conversation history between the sender and receiver.
-    """
-    if not sender:
-        raise ValueError("The sender cannot be empty.")
 
-    if not agents:
+    Raises:
-        raise ValueError("The agents list cannot be empty.")
+        Exception: If there is an error during the communication process.
-
+    """
-    if not task:
+    conversation = Conversation()
-        raise ValueError("The task cannot be empty.")
+    responses = []
 
     try:
-        receive_tasks = []
+        for _ in range(max_loops):
-        for agent in agents:
+            # Sender processes the task
-            receive_tasks.append(
+            sender_response = sender.run(task)
-                agent.receive_message(sender.agent_name, task)
+            conversation.add_log(
+                agent_name=sender.agent_name,
+                task=task,
+                response=sender_response,
             )
+            responses.append(sender_response)
+
+            # Receiver processes the result of the sender
+            receiver_response = receiver.run(sender_response)
+            conversation.add_log(
+                agent_name=receiver.agent_name,
+                task=task,
+                response=receiver_response,
+            )
+            responses.append(receiver_response)
 
-        await asyncio.gather(*receive_tasks)
     except Exception as error:
         logger.error(
-            f"[ERROR][CLASS: Agent][METHOD: broadcast] {error}"
+            f"Error during one_to_one communication: {error}"
         )
         raise error
 
+    return conversation.return_history()
 
-def one_to_one(
+
-    sender: Agent,
+# Broadcasting: A message from one agent to many
-    receiver: Agent,
+async def broadcast(
-    task: str,
+    sender: Agent, agents: AgentListType, task: str
-    max_loops: int = 1,
+) -> None:
-):
     """
-    Sends a message from the sender agent to the receiver agent.
+    Facilitates broadcasting of a message from one agent to multiple agents.
 
     Args:
         sender (Agent): The agent sending the message.
-        receiver (Agent): The agent to receive the message.
+        agents (AgentListType): The list of agents to receive the message.
         task (str): The message to be sent.
 
     Raises:
-        Exception: If an error occurs during the message sending.
+        ValueError: If the sender, agents, or task is empty.
-
+        Exception: If there is an error during the broadcasting process.
-    Returns:
-        None
     """
-    try:
+    conversation = Conversation()
-        responses = []
-        responses.append(task)
-        for i in range(max_loops):
 
-            # Run the agent on the task then pass the response to the receiver
+    if not sender or not agents or not task:
-            response = sender.run(task)
+        raise ValueError("Sender, agents, and task cannot be empty.")
-            log = f"Agent {sender.agent_name} Response: {response}"
-            responses.append(log)
 
-            # Send the response to the receiver
+    try:
-            out = receiver.run(concat_strings(responses))
+        receive_tasks = []
-            responses.append(out)
+        for agent in agents:
+            receive_tasks.append(agent.run(task))
+            conversation.add_log(
+                agent_name=agent.agent_name, task=task, response=task
+            )
 
-        return concat_strings(responses)
+        await asyncio.gather(*receive_tasks)
     except Exception as error:
-        logger.error(
+        logger.error(f"Error during broadcast: {error}")
-            f"[ERROR][CLASS: Agent][METHOD: one_to_one] {error}"
-        )
         raise error