Multiagent Strategies

[cobycloud]

Discussion for Multiagent (heterogeneous) Strategies

[3rd-Son]

@cobycloud
Here is my suggestion.

we are going to have an abstract class in swarmauri_core that will define the contract for all multiagents, ensuring that every agent implements the required methods, such as process and get_capabilities or any other method that is necessary.

See the code sample below:

from abc import ABC, abstractmethod

class AbstractAgent(ABC):
    @abstractmethod
    def process(self, input_data):
        pass

    @abstractmethod
    def get_capabilities(self):
        pass

Then we will have a BaseAgent class in swarmauri/agent/base/ that inherits from the abstract class. This class will provide a default implementation for shared logic and serve as the foundation for all specific agents.

Here's an example:

from swarmauri_core.agents.abstract_agent import AbstractAgent

class BaseAgent(AbstractAgent):
    def __init__(self, name):
        self.name = name

    def process(self, input_data):
        raise NotImplementedError("Subclasses must implement the `process` method.")

    def get_capabilities(self):
        return f"Agent {self.name} has no specific capabilities defined."

Next, we'll implement heterogeneous agents that inherit from BaseAgent and specialize in specific tasks, such as language modeling or image processing or any other class we want.

Here's an example:

from swarmauri.agent.base.base_agent import BaseAgent

class LanguageModelAgent(BaseAgent):
    def process(self, input_data):
        return f"Processed text: {input_data}"

    def get_capabilities(self):
        return f"{self.name} specializes in language modeling."

class ImageProcessingAgent(BaseAgent):
    def process(self, input_data):
        return f"Processed image with ID: {input_data}"

    def get_capabilities(self):
        return f"{self.name} specializes in image processing."

Finally, we'll implement a MultiagentManager to integrate and coordinate these agents. It will allow registration of agents, listing their capabilities, and executing tasks via a unified interface.

See the code:

class MultiagentManager:
    def __init__(self):
        self.agents = {}

    def register_agent(self, agent):
        self.agents[agent.name] = agent

    def list_agents(self):
        return {name: agent.get_capabilities() for name, agent in self.agents.items()}

    def execute_task(self, agent_name, input_data):
        if agent_name not in self.agents:
            raise ValueError(f"Agent {agent_name} not found.")
        return self.agents[agent_name].process(input_data)

This setup ensures modularity and scalability, allowing us to add new heterogeneous agents seamlessly and manage them through the MultiagentManager.

[MichaelDecent]

The flow is very defined and structured, i think we need to looking into how effective manage task, like routing task to a suitable agent.

[3rd-Son]

Any suggestions?

[cobycloud]

example of a control plane like mechanism:

import asyncio
from typing import Dict, List, Any, Callable
from collections import defaultdict
import uuid


class ControlPlane:
    def __init__(self):
        self.services: Dict[str, Callable] = {}  # Registered services
        self.task_queue: asyncio.Queue = asyncio.Queue()  # Queue for incoming tasks
        self.task_registry: Dict[str, Dict[str, Any]] = {}  # Tracks ongoing tasks
        self.message_queue: Dict[str, asyncio.Queue] = defaultdict(asyncio.Queue)  # Per-service queues

    def register_service(self, service_name: str, handler: Callable):
        """
        Register a new service with the control plane.
        :param service_name: Name of the service.
        :param handler: Coroutine function that processes messages.
        """
        self.services[service_name] = handler
        print(f"Service '{service_name}' registered.")

    def orchestrate_task(self, task: Dict[str, Any]) -> str:
        """
        Decide which service should handle the task next.
        :param task: The task metadata and payload.
        :return: The selected service name.
        """
        # Example: Use a round-robin or task-specific strategy for orchestration
        available_services = list(self.services.keys())
        selected_service = available_services[hash(task['task_id']) % len(available_services)]
        return selected_service

    async def dispatch_task(self, task_id: str, task_data: Dict[str, Any]):
        """
        Dispatch a task to the appropriate service based on orchestration logic.
        :param task_id: Unique identifier for the task.
        :param task_data: Task metadata and payload.
        """
        service_name = self.orchestrate_task(task_data)
        print(f"Dispatching task '{task_id}' to service '{service_name}'.")
        await self.message_queue[service_name].put(task_data)

    async def process_tasks(self):
        """
        Continuously process incoming tasks from the task queue.
        """
        while True:
            task_data = await self.task_queue.get()
            task_id = str(uuid.uuid4())  # Generate a unique task ID
            self.task_registry[task_id] = task_data
            print(f"Received new task '{task_id}'.")
            await self.dispatch_task(task_id, task_data)

    async def start_services(self):
        """
        Start all registered services to process their respective message queues.
        """
        for service_name, handler in self.services.items():
            asyncio.create_task(self.run_service(service_name, handler))

    async def run_service(self, service_name: str, handler: Callable):
        """
        Continuously process messages from the service's message queue.
        :param service_name: Name of the service.
        :param handler: Coroutine function that processes messages.
        """
        while True:
            message = await self.message_queue[service_name].get()
            print(f"Service '{service_name}' processing message: {message}")
            await handler(message)

    async def add_task(self, task_data: Dict[str, Any]):
        """
        Add a new task to the control plane's task queue.
        :param task_data: Task metadata and payload.
        """
        await self.task_queue.put(task_data)


# Example Usage
async def sample_service_handler(task: Dict[str, Any]):
    """
    Example handler function for a registered service.
    :param task: The task metadata and payload.
    """
    print(f"Task processed: {task}")
    await asyncio.sleep(1)  # Simulate processing time


async def main():
    control_plane = ControlPlane()

    # Register services
    control_plane.register_service("service_a", sample_service_handler)
    control_plane.register_service("service_b", sample_service_handler)

    # Start services
    asyncio.create_task(control_plane.start_services())

    # Add tasks to the control plane
    await control_plane.add_task({"task_type": "example", "payload": "Task 1"})
    await control_plane.add_task({"task_type": "example", "payload": "Task 2"})

    # Start processing tasks
    await control_plane.process_tasks()


if __name__ == "__main__":
    asyncio.run(main())

[MichaelDecent]

Yeah, the abstraction and code simplicity is great!.

[MichaelDecent]

something like this

from typing import Dict, Any, Callable, Optional
import asyncio
from collections import defaultdict
import uuid


class ControlPlane:
    def __init__(self, orchestration_strategy: Optional[Callable[[Dict[str, Any], Dict[str, Any]], str]] = None):
        """
        Initialize the ControlPlane.
        :param orchestration_strategy: A callable that takes a task and a dictionary of services,
                                       and returns the name of the service to handle the task.
        """
        self.services: Dict[str, Callable] = {}  # Registered services
        self.task_queue: asyncio.Queue = asyncio.Queue()  # Queue for incoming tasks
        self.task_registry: Dict[str, Dict[str, Any]] = {}  # Tracks ongoing tasks
        self.message_queue: Dict[str, asyncio.Queue] = defaultdict(asyncio.Queue)  # Per-service queues
        self.orchestration_strategy = orchestration_strategy or self.default_orchestration_strategy

    def register_service(self, service_name: str, handler: Callable):
        """
        Register a new service with the control plane.
        :param service_name: Name of the service.
        :param handler: Coroutine function that processes messages.
        """
        self.services[service_name] = handler
        print(f"Service '{service_name}' registered.")

    def default_orchestration_strategy(self, task: Dict[str, Any], services: Dict[str, Any]) -> str:
        """
        Default orchestration strategy (round-robin based on task ID hash).
        :param task: The task metadata and payload.
        :param services: Dictionary of registered services.
        :return: Selected service name.
        """
        available_services = list(services.keys())
        selected_service = available_services[hash(task['task_id']) % len(available_services)]
        return selected_service

    def orchestrate_task(self, task: Dict[str, Any]) -> str:
        """
        Decide which service should handle the task next using the orchestration strategy.
        :param task: The task metadata and payload.
        :return: The selected service name.
        """
        return self.orchestration_strategy(task, self.services)

    async def dispatch_task(self, task_id: str, task_data: Dict[str, Any]):
        """
        Dispatch a task to the appropriate service based on orchestration logic.
        :param task_id: Unique identifier for the task.
        :param task_data: Task metadata and payload.
        """
        service_name = self.orchestrate_task(task_data)
        print(f"Dispatching task '{task_id}' to service '{service_name}'.")
        await self.message_queue[service_name].put(task_data)

    async def process_tasks(self):
        """
        Continuously process incoming tasks from the task queue.
        """
        while True:
            task_data = await self.task_queue.get()
            task_id = str(uuid.uuid4())  # Generate a unique task ID
            task_data["task_id"] = task_id
            self.task_registry[task_id] = task_data
            print(f"Received new task '{task_id}'.")
            await self.dispatch_task(task_id, task_data)

    async def start_services(self):
        """
        Start all registered services to process their respective message queues.
        """
        for service_name, handler in self.services.items():
            asyncio.create_task(self.run_service(service_name, handler))

    async def run_service(self, service_name: str, handler: Callable):
        """
        Continuously process messages from the service's message queue.
        :param service_name: Name of the service.
        :param handler: Coroutine function that processes messages.
        """
        while True:
            message = await self.message_queue[service_name].get()
            print(f"Service '{service_name}' processing message: {message}")
            await handler(message)

    async def add_task(self, task_data: Dict[str, Any]):
        """
        Add a new task to the control plane's task queue.
        :param task_data: Task metadata and payload.
        """
        await self.task_queue.put(task_data)


# Example Custom Orchestration Strategies
def priority_based_strategy(task: Dict[str, Any], services: Dict[str, Any]) -> str:
    """
    Example custom strategy: prioritize tasks with certain attributes.
    :param task: The task metadata and payload.
    :param services: Dictionary of registered services.
    :return: Selected service name.
    """
    if task.get("priority") == "high":
        return "service_a" if "service_a" in services else next(iter(services))
    return "service_b" if "service_b" in services else next(iter(services))


def weighted_strategy(task: Dict[str, Any], services: Dict[str, Any]) -> str:
    """
    Example custom strategy: distribute tasks based on service weights.
    :param task: The task metadata and payload.
    :param services: Dictionary of registered services.
    :return: Selected service name.
    """
    weights = {"service_a": 3, "service_b": 1}  # Example weights
    weighted_services = sum([[k] * weights.get(k, 1) for k in services.keys()], [])
    return weighted_services[hash(task['task_id']) % len(weighted_services)]


# Example Usage
async def sample_service_handler(task: Dict[str, Any]):
    print(f"Task processed: {task}")
    await asyncio.sleep(1)  # Simulate processing time


async def main():
    # Initialize the control plane with a custom strategy
    control_plane = ControlPlane(orchestration_strategy=priority_based_strategy)

    # Register services
    control_plane.register_service("service_a", sample_service_handler)
    control_plane.register_service("service_b", sample_service_handler)

    # Start services
    asyncio.create_task(control_plane.start_services())

    # Add tasks to the control plane
    await control_plane.add_task({"task_type": "example", "priority": "high", "payload": "Task 1"})
    await control_plane.add_task({"task_type": "example", "priority": "low", "payload": "Task 2"})

    # Start processing tasks
    await control_plane.process_tasks()


if __name__ == "__main__":
    asyncio.run(main())

Wow!. This updated code introduces custom orchestration strategies, which significantly enhance the flexibility and adaptability of the ControlPlane. I love it.

[MichaelDecent]

@cobycloud @3rd-Son please review
i studied the code, and i really think The task distribution method in this code is more suited to homogeneous agents rather than heterogeneous agents.

Here are my reasons:

Homogeneous Agents:
In a homogeneous agent system, all agents are fundamentally similar:

• They have identical processing capabilities
• They can handle any task in the same way
• Their performance characteristics are nearly uniform

The current implementation's round-robin hashing strategy works well here because:

• Each service can process any incoming task equally well
• There's no consideration of agent-specific strengths or specializations
• Tasks are distributed purely based on availability

Limitations for Heterogeneous Agents:
In a heterogeneous agent system, agents have:

• Different specialized skills
• Varying computational capabilities
• Unique expertise or processing strengths

This distribution method falls short because:

• It doesn't account for agent-specific capabilities
• There's no intelligent routing based on task complexity or agent expertise
• The hash-based distribution is random rather than strategic

[3rd-Son]

I see your point @MichaelDecent

[3rd-Son]

@MichaelDecent so what do you suggest?
No need for orchestrate_task?

[MichaelDecent]

lets also analyze this Publish-Subscribe (Pub-Sub) Pattern

import asyncio
from typing import Any, Callable, Dict, List, Set
from uuid import uuid4

class PubSubBroker:
    def __init__(self):
        """
        Initialize the Publish-Subscribe Broker.
        Manages topics and subscriptions for agents.
        """
        self._topics: Dict[str, Set[str]] = {}  # Topic to subscriber mappings
        self._subscribers: Dict[str, asyncio.Queue] = {}  # Subscriber ID to message queue
        self._topic_queues: Dict[str, asyncio.Queue] = {}  # Topic to message queue

    async def subscribe(self, topic: str) -> str:
        """
        Subscribe an agent to a specific topic.
        
        Args:
            topic (str): The topic to subscribe to
        
        Returns:
            str: Unique subscriber ID
        """
        subscriber_id = str(uuid4())
        
        # Create message queue for this subscriber
        self._subscribers[subscriber_id] = asyncio.Queue()
        
        # Add subscriber to topic
        if topic not in self._topics:
            self._topics[topic] = set()
        self._topics[topic].add(subscriber_id)
        
        return subscriber_id

    async def unsubscribe(self, topic: str, subscriber_id: str):
        """
        Unsubscribe an agent from a topic.
        
        Args:
            topic (str): The topic to unsubscribe from
            subscriber_id (str): Unique identifier of the subscriber
        """
        if topic in self._topics and subscriber_id in self._topics[topic]:
            self._topics[topic].remove(subscriber_id)
            
            # Optional: Clean up if no subscribers remain
            if not self._topics[topic]:
                del self._topics[topic]

    async def publish(self, topic: str, message: Any):
        """
        Publish a message to a specific topic.
        
        Args:
            topic (str): The topic to publish to
            message (Any): The message to be published
        """
        if topic not in self._topics:
            return
        
        # Distribute message to all subscribers of this topic
        for subscriber_id in self._topics[topic]:
            await self._subscribers[subscriber_id].put(message)

    async def receive(self, subscriber_id: str) -> Any:
        """
        Receive messages for a specific subscriber.
        
        Args:
            subscriber_id (str): Unique identifier of the subscriber
        
        Returns:
            Any: Received message
        """
        return await self._subscribers[subscriber_id].get()

class Agent:
    def __init__(self, broker: PubSubBroker, name: str):
        """
        Initialize an agent with a name and connection to the broker.
        
        Args:
            broker (PubSubBroker): The publish-subscribe broker
            name (str): Name of the agent
        """
        self.broker = broker
        self.name = name
        self.subscribed_topics: List[str] = []
        self.subscriber_id: str = None

    async def subscribe(self, topic: str):
        """
        Subscribe the agent to a topic.
        
        Args:
            topic (str): The topic to subscribe to
        """
        self.subscriber_id = await self.broker.subscribe(topic)
        self.subscribed_topics.append(topic)
        print(f"{self.name} subscribed to {topic}")

    async def publish(self, topic: str, message: Any):
        """
        Publish a message to a topic.
        
        Args:
            topic (str): The topic to publish to
            message (Any): The message to be published
        """
        print(f"{self.name} publishing to {topic}: {message}")
        await self.broker.publish(topic, message)

    async def listen(self):
        """
        Continuously listen for messages on subscribed topics.
        """
        while True:
            try:
                message = await self.broker.receive(self.subscriber_id)
                print(f"{self.name} received: {message}")
            except Exception as e:
                print(f"{self.name} listening error: {e}")
                break

async def main():
    # Create a broker
    broker = PubSubBroker()

    # Create agents
    data_agent = Agent(broker, "Data Collector")
    analysis_agent = Agent(broker, "Data Analyzer")
    visualization_agent = Agent(broker, "Visualization Agent")

    # Subscribe agents to topics
    await data_agent.subscribe("raw_data")
    await analysis_agent.subscribe("processed_data")
    await visualization_agent.subscribe("visualization_request")

    # Simulate listening and publishing
    listen_tasks = [
        asyncio.create_task(data_agent.listen()),
        asyncio.create_task(analysis_agent.listen()),
        asyncio.create_task(visualization_agent.listen())
    ]

    # Simulate some interactions
    await data_agent.publish("raw_data", {"sensor_id": 1, "temperature": 25.5})
    await analysis_agent.publish("processed_data", {"avg_temp": 25.5, "trend": "stable"})
    await visualization_agent.publish("visualization_request", "generate_temperature_chart")

    # Wait a bit to see messages
    await asyncio.sleep(2)

    # Clean up tasks
    for task in listen_tasks:
        task.cancel()

if __name__ == "__main__":
    asyncio.run(main())```

[MichaelDecent]

Let me break down the key components of this Publish-Subscribe implementation:

1. PubSubBroker Class

• Central communication hub
• Manages topic subscriptions
• Handles message routing
• Uses asyncio for asynchronous communication
• Generates unique subscriber IDs
• Provides methods for subscribing, unsubscribing, publishing, and receiving messages

2. Agent Class

• Represents individual agents in the system
• Can subscribe to topics
• Can publish messages
• Has a continuous listening method for receiving messages
• Linked to the central broker

3. Key Features

• Asynchronous communication
• Dynamic topic subscription
• Decoupled agent interactions
• Scalable message routing
• UUID-based subscriber identification

4. Example Scenario
   The main() function demonstrates a practical use case with three agents:

• Data Collector Agent
• Data Analyzer Agent
• Visualization Agent

Each agent subscribes to specific topics and can publish and receive messages independently.

Advantages of this implementation:

• Loose coupling between agents
• Easy to add or remove agents
• Supports complex, event-driven architectures
• Scalable communication pattern

[MichaelDecent]

PubSub Broker directory structure

[MichaelDecent]

lets also analyze this "Blackboard Communication Strategy"

import asyncio
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from enum import Enum, auto

class KnowledgeState(Enum):
    INCOMPLETE = auto()
    PARTIAL = auto()
    COMPLETE = auto()

@dataclass
class BlackboardData:
    """
    Represents a piece of knowledge on the blackboard
    """
    content: Any
    state: KnowledgeState = field(default=KnowledgeState.INCOMPLETE)
    contributor: Optional[str] = None
    confidence_level: float = 0.0

class BlackboardAgent:
    def __init__(self, name: str, capabilities: List[str]):
        """
        Initialize an agent with specific capabilities
        
        Args:
            name (str): Name of the agent
            capabilities (List[str]): List of problem-solving capabilities
        """
        self.name = name
        self.capabilities = capabilities

    async def process(self, blackboard: 'Blackboard', problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Process a problem based on the agent's capabilities
        
        Args:
            blackboard (Blackboard): The shared blackboard
            problem (Dict[str, Any]): Problem to be solved
        
        Returns:
            Optional solution contribution
        """
        raise NotImplementedError("Subclasses must implement processing method")

class Blackboard:
    def __init__(self):
        """
        Initialize the shared blackboard
        """
        self._knowledge: Dict[str, BlackboardData] = {}
        self._agents: List[BlackboardAgent] = []
        self._solution_lock = asyncio.Lock()
        self._problem_state: KnowledgeState = KnowledgeState.INCOMPLETE

    def register_agent(self, agent: BlackboardAgent):
        """
        Register an agent to work on the blackboard
        
        Args:
            agent (BlackboardAgent): Agent to be registered
        """
        self._agents.append(agent)

    def add_knowledge(self, key: str, content: Any, contributor: str):
        """
        Add or update knowledge on the blackboard
        
        Args:
            key (str): Unique identifier for the knowledge
            content (Any): Knowledge content
            contributor (str): Name of the agent contributing the knowledge
        """
        async with self._solution_lock:
            existing = self._knowledge.get(key)
            confidence = self._calculate_confidence(existing, content)
            
            new_data = BlackboardData(
                content=content,
                contributor=contributor,
                confidence_level=confidence,
                state=self._determine_state(content)
            )
            
            self._knowledge[key] = new_data
            self._update_problem_state()

    def _calculate_confidence(self, existing: Optional[BlackboardData], new_content: Any) -> float:
        """
        Calculate confidence level for new knowledge
        
        Args:
            existing (Optional[BlackboardData]): Existing knowledge
            new_content (Any): New content to be added
        
        Returns:
            float: Confidence level
        """
        if not existing:
            return 0.5  # Default confidence for first contribution
        
        # Simple confidence calculation logic
        return min(existing.confidence_level + 0.2, 1.0)

    def _determine_state(self, content: Any) -> KnowledgeState:
        """
        Determine the knowledge state based on content
        
        Args:
            content (Any): Content to evaluate
        
        Returns:
            KnowledgeState: State of the knowledge
        """
        if content is None:
            return KnowledgeState.INCOMPLETE
        elif isinstance(content, dict) and not content:
            return KnowledgeState.INCOMPLETE
        elif isinstance(content, list) and len(content) < 2:
            return KnowledgeState.PARTIAL
        return KnowledgeState.COMPLETE

    def _update_problem_state(self):
        """
        Update the overall problem-solving state
        """
        states = [data.state for data in self._knowledge.values()]
        
        if all(state == KnowledgeState.COMPLETE for state in states):
            self._problem_state = KnowledgeState.COMPLETE
        elif any(state == KnowledgeState.COMPLETE for state in states):
            self._problem_state = KnowledgeState.PARTIAL
        else:
            self._problem_state = KnowledgeState.INCOMPLETE

    async def solve_problem(self, problem: Dict[str, Any]):
        """
        Coordinate agents to solve a problem
        
        Args:
            problem (Dict[str, Any]): Problem to be solved
        """
        print(f"Starting problem-solving process: {problem}")
        
        # Distribute problem to agents
        tasks = [
            asyncio.create_task(agent.process(self, problem)) 
            for agent in self._agents
        ]
        
        # Wait for all agents to contribute
        await asyncio.gather(*tasks)
        
        # Final solution assessment
        if self._problem_state == KnowledgeState.COMPLETE:
            print("Problem solved successfully!")
            self._print_solution()
        else:
            print("Problem solving incomplete.")

    def _print_solution(self):
        """
        Print the final solution from the blackboard
        """
        print("\n--- Final Blackboard Solution ---")
        for key, data in self._knowledge.items():
            print(f"{key}: {data.content} (Contributor: {data.contributor}, "
                  f"Confidence: {data.confidence_level:.2f})")

# Specialized Agents for Problem Solving
class DataCollectionAgent(BlackboardAgent):
    async def process(self, blackboard: Blackboard, problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Collect initial data for the problem
        """
        await asyncio.sleep(1)  # Simulate data collection
        initial_data = {
            "raw_data": [1, 2, 3, 4, 5],
            "metadata": {"source": "sensors"}
        }
        
        for key, value in initial_data.items():
            blackboard.add_knowledge(key, value, self.name)
        
        return initial_data

class DataAnalysisAgent(BlackboardAgent):
    async def process(self, blackboard: Blackboard, problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Analyze and process collected data
        """
        await asyncio.sleep(1.5)  # Simulate analysis
        raw_data = blackboard._knowledge.get('raw_data')
        
        if raw_data:
            analysis = {
                "statistics": {
                    "mean": sum(raw_data.content) / len(raw_data.content),
                    "variance": sum((x - (sum(raw_data.content) / len(raw_data.content))) ** 2 for x in raw_data.content) / len(raw_data.content)
                }
            }
            
            blackboard.add_knowledge('data_analysis', analysis, self.name)
        
        return analysis

class SolutionAgent(BlackboardAgent):
    async def process(self, blackboard: Blackboard, problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Synthesize final solution
        """
        await asyncio.sleep(2)  # Simulate solution synthesis
        analysis = blackboard._knowledge.get('data_analysis')
        
        if analysis:
            solution = {
                "final_solution": {
                    "insights": "Problem solved based on collected and analyzed data",
                    "key_metrics": analysis.content['statistics']
                }
            }
            
            blackboard.add_knowledge('solution', solution, self.name)
        
        return solution

async def main():
    # Create blackboard
    blackboard = Blackboard()
    
    # Create and register agents
    data_collector = DataCollectionAgent("Data Collector", ["data_gathering"])
    data_analyzer = DataAnalysisAgent("Data Analyzer", ["statistical_analysis"])
    solution_agent = SolutionAgent("Solution Synthesizer", ["solution_generation"])
    
    blackboard.register_agent(data_collector)
    blackboard.register_agent(data_analyzer)
    blackboard.register_agent(solution_agent)
    
    # Define a sample problem
    problem = {
        "type": "data_analysis",
        "description": "Analyze sensor data and generate insights"
    }
    
    # Solve the problem
    await blackboard.solve_problem(problem)

if __name__ == "__main__":
    asyncio.run(main())

[MichaelDecent]

• A centralized communication model where agents share a common, synchronized workspace (the "blackboard").
• Agents can post their knowledge, solutions, or partial results to the shared space, and other agents can read and build upon this information.
• Highly efficient for complex problem-solving scenarios that require collaborative knowledge integration.
• Enables emergent problem-solving approaches where agents can contribute incrementally and asynchronously.
• Works exceptionally well in distributed AI systems, knowledge management, and scenarios requiring collective intelligence.

[MichaelDecent]

Let me break down the key components of this Blackboard Communication Strategy implementation:

1. Core Components

• Blackboard: Central shared workspace for knowledge
• BlackboardData: Represents knowledge pieces with state, confidence, and contributor
• BlackboardAgent: Base class for agents working on the blackboard
• KnowledgeState: Enum to track the completeness of knowledge

2. Key Features

• Asynchronous collaborative problem-solving
• Confidence-based knowledge integration
• Dynamic knowledge state tracking
• Agent specialization
• Incremental problem resolution

3. Agents in the Example

• DataCollectionAgent: Gathers initial raw data
• DataAnalysisAgent: Processes and analyzes collected data
• SolutionAgent: Synthesizes final solution

4. Problem-Solving Workflow

• Agents register with the blackboard
• Each agent processes the problem independently
• Agents add their contributions to the shared blackboard
• Blackboard tracks knowledge state and confidence
• Problem is considered solved when all agents complete their tasks

5. Key Advantages

• Decoupled agent interactions
• Flexible and extensible architecture
• Supports complex, collaborative problem-solving
• Incremental knowledge building
• Confidence-based knowledge integration

Unique Aspects:

• Knowledge state tracking (Incomplete, Partial, Complete)
• Confidence level calculation for contributions
• Asynchronous processing
• Dynamic agent registration

[MichaelDecent]

Lets check out this ControlPanel mechanism for heterogeneous agents

import asyncio
from typing import Dict, List, Any, Callable, Optional
from enum import Enum, auto
import uuid
import random

from pydantic import BaseModel, Field, validator


class TaskPriority(Enum):
    LOW = auto()
    MEDIUM = auto()
    HIGH = auto()
    CRITICAL = auto()


class TaskModel(BaseModel):
    """
    Represents a task with detailed metadata and requirements.
    """
    task_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    task_type: str
    description: Optional[str] = None
    required_skills: List[str] = Field(default_factory=list)
    priority: TaskPriority = TaskPriority.MEDIUM
    metadata: Dict[str, Any] = Field(default_factory=dict)

    @validator('required_skills')
    def validate_skills(cls, skills):
        """
        Optional custom validation for skills.
        """
        if not skills:
            print("Warning: No skills specified for the task")
        return skills


class AgentCapabilities(BaseModel):
    """
    Represents the capabilities and current state of an agent with Pydantic.
    """
    name: str
    skills: List[str] = Field(default_factory=list)
    max_concurrent_tasks: int = 5
    current_load: int = 0
    performance_score: float = Field(default=1.0, ge=0.0, le=1.0)
    specialization_factor: Dict[str, float] = Field(default_factory=dict)

    def can_handle_task(self, task_skills: List[str]) -> bool:
        """
        Check if the agent has the necessary skills to handle the task.
        """
        return any(skill in self.skills for skill in task_skills)

    def is_available(self) -> bool:
        """
        Determine if the agent can take on more tasks.
        """
        return self.current_load < self.max_concurrent_tasks

    class Config:
        """
        Pydantic model configuration.
        """
        validate_assignment = True  # Enable validation on attribute assignment
        extra = 'forbid'  # Prevent arbitrary attribute creation


class AdvancedControlPlane:
    def __init__(self):
        # Registry of agents with their capabilities
        self.agents: Dict[str, AgentCapabilities] = {}
        
        # Task queues with priority
        self.task_queue: asyncio.PriorityQueue = asyncio.PriorityQueue()
        
        # Task tracking
        self.task_registry: Dict[str, TaskModel] = {}
        
        # Per-agent message queues
        self.agent_queues: Dict[str, asyncio.Queue] = {}

    def register_agent(self, agent_capabilities: AgentCapabilities):
        """
        Register a new agent with specific capabilities.
        """
        self.agents[agent_capabilities.name] = agent_capabilities
        self.agent_queues[agent_capabilities.name] = asyncio.Queue()
        print(f"Registered agent: {agent_capabilities.name}")
        print(f"Skills: {agent_capabilities.skills}")

    def calculate_task_score(self, task: TaskModel, agent: AgentCapabilities) -> float:
        """
        Calculate a comprehensive score for task-agent matching.
        
        Scoring considers:
        1. Skill match
        2. Current agent load
        3. Agent's performance history
        4. Task-specific specialization
        """
        # Skill match score
        skill_match = sum(
            agent.specialization_factor.get(skill, 1.0) 
            for skill in task.required_skills 
            if skill in agent.skills
        )
        
        # Load penalty
        load_factor = 1 / (agent.current_load + 1)
        
        # Performance bonus
        performance_bonus = agent.performance_score
        
        # Combine factors
        total_score = skill_match * load_factor * performance_bonus
        return total_score

    async def intelligent_task_assignment(self, task: TaskModel) -> Optional[str]:
        """
        Intelligently assign a task to the most suitable agent.
        """
        # Find available agents that can handle the task
        candidate_agents = [
            agent_name for agent_name, agent in self.agents.items()
            if agent.can_handle_task(task.required_skills) 
            and agent.is_available()
        ]

        if not candidate_agents:
            print("No available agents to handle the task.")
            return None

        # Score each candidate agent
        agent_scores = {
            agent_name: self.calculate_task_score(task, self.agents[agent_name])
            for agent_name in candidate_agents
        }

        # Select the agent with the highest score
        best_agent = max(agent_scores, key=agent_scores.get)
        
        # Update agent's current load
        self.agents[best_agent].current_load += 1
        
        return best_agent

    async def add_task(self, task_data: Dict[str, Any]):
        """
        Add a new task to the control plane.
        """
        # Create a validated task model
        task = TaskModel(**task_data)
        
        # Track the task
        self.task_registry[task.task_id] = task
        
        # Add to priority queue
        await self.task_queue.put((task.priority.value, task))
        print(f"Task {task.task_id} added with priority {task.priority.name}")

    async def process_tasks(self):
        """
        Continuously process tasks from the priority queue.
        """
        while True:
            # Get next task by priority
            _, task = await self.task_queue.get()
            
            # Intelligently assign the task
            assigned_agent = await self.intelligent_task_assignment(task)
            
            if assigned_agent:
                # Route task to the assigned agent's queue
                await self.agent_queues[assigned_agent].put(task)
                print(f"Task {task['task_id']} assigned to {assigned_agent}")

    async def run_agent(self, agent_name: str, handler: Callable):
        """
        Run an agent's task processing loop.
        """
        agent_queue = self.agent_queues[agent_name]
        
        while True:
            # Wait for and process tasks
            task = await agent_queue.get()
            try:
                print(f"Agent {agent_name} processing task: {task['task_id']}")
                await handler(task)
                
                # Simulate performance tracking
                self.agents[agent_name].performance_score = random.uniform(0.8, 1.0)
            except Exception as e:
                print(f"Error in agent {agent_name}: {e}")
            finally:
                # Decrease agent load
                self.agents[agent_name].current_load -= 1

    async def start_agents(self):
        """
        Start all registered agents.
        """
        for agent_name, agent in self.agents.items():
            asyncio.create_task(
                self.run_agent(agent_name, self.create_agent_handler(agent_name))
            )

    def create_agent_handler(self, agent_name: str) -> Callable:
        """
        Create a dynamic handler for each agent.
        """
        async def agent_handler(task: Dict[str, Any]):
            # Simulate task processing with slight variations
            await asyncio.sleep(random.uniform(0.5, 2.0))
            print(f"Agent {agent_name} completed task {task['task_id']}")
        
        return agent_handler

# Example Usage
async def main():
    # Create the control plane
    control_plane = AdvancedControlPlane()

    # Register heterogeneous agents with different capabilities
    control_plane.register_agent(AgentCapabilities(
        name="technical_support",
        skills=["debugging", "system_analysis", "network_troubleshooting"],
        specialization_factor={
            "debugging": 1.5,
            "system_analysis": 1.3
        }
    ))

    control_plane.register_agent(AgentCapabilities(
        name="billing_support",
        skills=["invoice_processing", "payment_analysis", "customer_billing"],
        specialization_factor={
            "invoice_processing": 1.4,
            "payment_analysis": 1.2
        }
    ))

    control_plane.register_agent(AgentCapabilities(
        name="general_support",
        skills=["customer_communication", "basic_troubleshooting"],
        max_concurrent_tasks=3
    ))

     # Start agent processing
    asyncio.create_task(control_plane.start_agents())
    asyncio.create_task(control_plane.process_tasks())

    await control_plane.add_task({
        "task_type": "billing_inquiry",
        "required_skills": ["invoice_processing"],
        "description": "Complex billing reconciliation",
         "priority": TaskPriority.MEDIUM
    })

    await control_plane.add_task({
        "task_type": "general_query",
        "required_skills": ["customer_communication"],
        "description": "Customer follow-up",
        "priority": TaskPriority.LOW
    })

    await control_plane.add_task({
        "task_type": "technical_issue",
        "required_skills": ["debugging", "system_analysis"],
        "description": "Server performance investigation",
        "priority": TaskPriority.HIGH
    })

# Main execution
if __name__ == "__main__":
    asyncio.run(main())

[3rd-Son]

I just went through the code @MichaelDecent and I think this ControlPlane implementation is a great example of handling heterogeneous agents effectively. The intelligent task-agent matching based on skills, workload, and performance metrics ensures tasks are routed to the most suitable agents. I like how priority queues and dynamic handlers make the system adaptable and efficient. It might be worth exploring dynamic specialization adjustments or task reassignment mechanisms to enhance scalability even further.

what do you think?

[MichaelDecent]

Ride on! lets see the improvement you have .

[3rd-Son]

class AdvancedControlPlaneWithReassignment(AdvancedControlPlane):
    async def update_agent_specialization(self, agent_name: str, task: TaskModel, success: bool):
        """
        Dynamically update the specialization factor for agents based on task success.
        """
        agent = self.agents[agent_name]
        for skill in task.required_skills:
            if skill in agent.specialization_factor:
                adjustment = 0.1 if success else -0.1
                agent.specialization_factor[skill] = max(0.1, agent.specialization_factor[skill] + adjustment)
            else:
                agent.specialization_factor[skill] = 1.0 + (0.1 if success else -0.1)
        print(f"Updated specialization for {agent_name}: {agent.specialization_factor}")

    async def reassign_task(self, task: TaskModel):
        """
        Reassign a task to another suitable agent if the previous one failed or became unavailable.
        """
        print(f"Reassigning task {task.task_id}...")
        assigned_agent = await self.intelligent_task_assignment(task)
        if assigned_agent:
            await self.agent_queues[assigned_agent].put(task)
            print(f"Task {task.task_id} reassigned to {assigned_agent}")
        else:
            print(f"Task {task.task_id} could not be reassigned due to lack of available agents.")

    async def run_agent(self, agent_name: str, handler: Callable):
        """
        Extend the agent task processing loop to handle task completion feedback and reassignment.
        """
        agent_queue = self.agent_queues[agent_name]

        while True:
            task = await agent_queue.get()
            try:
                print(f"Agent {agent_name} processing task: {task.task_id}")
                success = await handler(task)
                await self.update_agent_specialization(agent_name, task, success)
            except Exception as e:
                print(f"Error in agent {agent_name}: {e}")
                await self.reassign_task(task)
            finally:
                self.agents[agent_name].current_load -= 1

    def create_agent_handler(self, agent_name: str) -> Callable:
        """
        Extend the handler to return success status for tasks.
        """
        async def agent_handler(task: TaskModel) -> bool:
            # Simulate task processing
            await asyncio.sleep(random.uniform(0.5, 2.0))
            success = random.choice([True, True, False])  # 2/3 chance of success
            if success:
                print(f"Agent {agent_name} successfully completed task {task.task_id}")
            else:
                print(f"Agent {agent_name} failed to complete task {task.task_id}")
            return success

        return agent_handler

• Key Features:

1. Dynamic Specialization Updates: The update_agent_specialization method modifies agent specialization factors based on task outcomes.
2. Task Reassignment: The reassign_task method re-routes failed or incomplete tasks to another suitable agent.
3. Success Feedback in Handlers: Each agent handler simulates task success or failure and updates the agent's specialization accordingly.
4. This approach improves adaptability, ensuring agents learn and specialize dynamically while ensuring no tasks are left unhandled.

[MichaelDecent]

Can we also analyze this pattern "Contract Net Protocol"

import asyncio
import random
from enum import Enum
from typing import List, Optional, Dict, Tuple

from pydantic import BaseModel, Field, validator, ConfigDict

class TaskStatus(str, Enum):
    OPEN = "open"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"

class Task(BaseModel):
    """
    Represents a task to be allocated in the Contract Net Protocol
    """
    id: str = Field(default_factory=lambda: f"task_{random.randint(1000, 9999)}")
    description: str
    complexity: float = Field(ge=0.0, le=1.0)
    reward: float = Field(gt=0)
    status: TaskStatus = TaskStatus.OPEN
    assigned_agent: Optional[str] = None
    result: Optional[Dict] = None

    model_config = ConfigDict(
        extra='forbid',  # Prevent additional fields
        frozen=False,    # Allow modification after creation
        validate_assignment=True  # Validate on attribute change
    )

    @validator('complexity')
    def validate_complexity(cls, v):
        """
        Ensure complexity is between 0 and 1
        """
        if not (0 <= v <= 1):
            raise ValueError("Complexity must be between 0 and 1")
        return v

class Agent(BaseModel):
    """
    Represents an agent with specializations and workload constraints
    """
    name: str
    specializations: List[str]
    max_load: float = Field(default=1.0, ge=0, le=1.0)
    
    # Runtime attributes (not validated by Pydantic)
    current_load: float = 0.0
    completed_tasks: List[Task] = []
    failed_tasks: List[Task] = []

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
        validate_assignment=True
    )

    async def evaluate_task(self, task: Task) -> Optional[float]:
        """
        Evaluate a task's suitability and potential bid
        
        Args:
            task (Task): Task to be evaluated
        
        Returns:
            Optional bid value (lower is better)
        """
        # Check specialization match
        if not any(spec in self.specializations for spec in task.description.split(',')):
            return None
        
        # Check remaining capacity
        remaining_capacity = self.max_load - self.current_load
        
        # Reject if insufficient capacity
        if task.complexity > remaining_capacity:
            return None
        
        # Calculate bid (lower is better)
        bid = (task.complexity / remaining_capacity) / task.reward
        
        return bid

    async def perform_task(self, task: Task) -> Dict:
        """
        Simulate task execution
        
        Args:
            task (Task): Task to be performed
        
        Returns:
            Dict: Task execution results
        """
        # Simulate task complexity and potential failure
        await asyncio.sleep(task.complexity)
        
        # Random chance of task failure
        if random.random() < 0.1:  # 10% failure rate
            return {"status": "failed", "reason": "Unexpected complexity"}
        
        return {
            "status": "completed",
            "details": f"Task {task.id} completed by {self.name}",
            "reward": task.reward
        }

class ContractNetManager(BaseModel):
    """
    Manages task allocation using Contract Net Protocol
    """
    agents: List[Agent] = []
    tasks: List[Task] = []
    task_history: List[Task] = []

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
        validate_assignment=True
    )

    def register_agent(self, agent: Agent):
        """
        Register an agent in the contract net
        
        Args:
            agent (Agent): Agent to be registered
        """
        self.agents.append(agent)

    def create_task(self, description: str, complexity: float, reward: float) -> Task:
        """
        Create a new task for allocation
        
        Args:
            description (str): Task description and required specializations
            complexity (float): Task complexity
            reward (float): Task reward
        
        Returns:
            Task: Created task
        """
        task = Task(
            description=description,
            complexity=complexity,
            reward=reward
        )
        self.tasks.append(task)
        return task

    async def allocate_task(self, task: Task) -> Optional[Tuple[Agent, Dict]]:
        """
        Allocate a task using Contract Net Protocol
        
        Args:
            task (Task): Task to be allocated
        
        Returns:
            Optional tuple of (assigned_agent, task_result)
        """
        print(f"\nAllocating Task: {task.description}")
        
        # Call for proposals
        bids = []
        for agent in self.agents:
            bid = await agent.evaluate_task(task)
            if bid is not None:
                bids.append((agent, bid))
        
        # No suitable agents found
        if not bids:
            print(f"No agents available for task: {task.description}")
            return None
        
        # Select agent with lowest bid (best fit)
        selected_agent, _ = min(bids, key=lambda x: x[1])
        
        # Assign and execute task
        task.assigned_agent = selected_agent.name
        task.status = TaskStatus.IN_PROGRESS
        
        try:
            result = await selected_agent.perform_task(task)
            
            if result['status'] == 'completed':
                task.status = TaskStatus.COMPLETED
                task.result = result
                selected_agent.completed_tasks.append(task)
                print(f"Task {task.id} completed by {selected_agent.name}")
            else:
                task.status = TaskStatus.FAILED
                selected_agent.failed_tasks.append(task)
                print(f"Task {task.id} failed for {selected_agent.name}")
            
            return (selected_agent, result)
        
        except Exception as e:
            print(f"Task allocation error: {e}")
            task.status = TaskStatus.FAILED
            return None

    async def run_task_allocation(self):
        """
        Run task allocation for all tasks
        """
        task_allocation_tasks = [
            self.allocate_task(task) for task in self.tasks
        ]
        
        await asyncio.gather(*task_allocation_tasks)
        
        # Print allocation summary
        self.print_allocation_summary()

    def print_allocation_summary(self):
        """
        Print summary of task allocations
        """
        print("\n--- Task Allocation Summary ---")
        for task in self.tasks:
            print(f"Task {task.id}: {task.description}")
            print(f"  Status: {task.status}")
            print(f"  Assigned Agent: {task.assigned_agent or 'Unassigned'}")
            print(f"  Result: {task.result or 'N/A'}\n")

async def main():
    # Create Contract Net Manager
    contract_net = ContractNetManager()

    # Create specialized agents with Pydantic validation
    data_agent = Agent(
        name="DataAnalyst", 
        specializations=["data_processing", "analytics"], 
        max_load=0.7
    )
    ml_agent = Agent(
        name="MLSpecialist", 
        specializations=["machine_learning", "model_training"], 
        max_load=0.5
    )
    cloud_agent = Agent(
        name="CloudEngineer", 
        specializations=["cloud_deployment", "infrastructure"], 
        max_load=0.6
    )
    
    # Register agents
    contract_net.register_agent(data_agent)
    contract_net.register_agent(ml_agent)
    contract_net.register_agent(cloud_agent)

    # Create diverse tasks
    tasks = [
        contract_net.create_task("data_processing", complexity=0.3, reward=100),
        contract_net.create_task("machine_learning,model_training", complexity=0.5, reward=250),
        contract_net.create_task("cloud_deployment", complexity=0.4, reward=150),
        contract_net.create_task("analytics", complexity=0.2, reward=80)
    ]

    # Run task allocation
    await contract_net.run_task_allocation()

if __name__ == "__main__":
    asyncio.run(main())

[MichaelDecent]

Let me break down the key components and principles of this Contract Net Protocol implementation:

1. Core Concepts

• Dynamic task allocation
• Agent-based negotiation
• Bid-based task assignment
• Capability and load-aware task distribution

2. Key Components

• Task: Represents a work unit with complexity, reward, and status
• Agent: Autonomous entity with specializations and workload constraints
• ContractNetManager: Coordinates task allocation and protocol execution

3. Negotiation Mechanism

• Agents evaluate tasks based on:
  • Specialization match
  • Current workload
  • Task complexity
  • Potential reward
• Lowest bid (best fit) wins task allocation
• Supports probabilistic task failure simulation

4. Advanced Features

• Workload management
• Task status tracking
• Agent performance monitoring
• Flexible specialization handling

5. Workflow

• Create agents with specialized skills
• Define tasks with descriptions and attributes
• Agents bid on tasks
• Manager allocates tasks to best-suited agents
• Track task completion and agent performance

Key Advantages:

• Decentralized task allocation
• Adaptive resource management
• Supports complex, dynamic work environments
• Enables intelligent task distribution

Potential Use Cases:

• Distributed computing
• Cloud resource allocation
• Robotic task coordination
• Dynamic project management

[MichaelDecent]

Proposed Project directory

[cobycloud]

more on control plane in the context of multiagent strategies:

---

This implementation includes:

• EXample of Agent Design
• Control Plane
• Communication Layer
• Persistence Layer

Directory Structure

multiagent_framework/
│
├── agents/
│   ├── __init__.py
│   ├── base_agent.py
│   ├── summarizer_agent.py
│   ├── translator_agent.py
│
├── communication/
│   ├── __init__.py
│   ├── message_broker.py
│
├── persistence/
│   ├── __init__.py
│   ├── persistence_layer.py
│
├── control_plane.py
├── main.py
└── requirements.txt

---

1. Agent Design

agents/base_agent.py

from threading import Thread
import queue

class BaseAgent:
    def __init__(self, name, specialization):
        self.name = name
        self.specialization = specialization
        self.local_state = {}
        self.message_queue = queue.Queue()
        self.running = True

    def process_message(self, message):
        """Override this method in derived classes to handle specific tasks."""
        return f"{self.name} received: {message}"

    def listen(self):
        """Listen for incoming messages."""
        while self.running:
            message = self.message_queue.get()
            if message == "SHUTDOWN":
                self.running = False
                print(f"{self.name} shutting down.")
                break
            response = self.process_message(message)
            print(response)

    def send_message(self, target_agent, message):
        """Send a message to another agent."""
        print(f"{self.name} sending message to {target_agent.name}: {message}")
        target_agent.message_queue.put(message)

agents/summarizer_agent.py

from .base_agent import BaseAgent

class SummarizerAgent(BaseAgent):
    def __init__(self, name):
        super().__init__(name, "Summarizer")
        self.local_state["summarized_texts"] = []

    def process_message(self, message):
        """Summarize text."""
        summary = f"Summary of '{message}'"
        self.local_state["summarized_texts"].append(summary)
        return f"{self.name}: {summary}"

agents/translator_agent.py

from .base_agent import BaseAgent

class TranslatorAgent(BaseAgent):
    def __init__(self, name):
        super().__init__(name, "Translator")
        self.local_state["translations"] = []

    def process_message(self, message):
        """Translate text."""
        translation = f"Translated '{message}' to French"
        self.local_state["translations"].append(translation)
        return f"{self.name}: {translation}"

---

2. Communication Layer

communication/message_broker.py

class MessageBroker:
    def __init__(self):
        self.agents = {}

    def register_agent(self, agent):
        """Register an agent."""
        self.agents[agent.name] = agent
        Thread(target=agent.listen, daemon=True).start()

    def send_message(self, sender_name, receiver_name, message):
        """Send a message between agents."""
        if receiver_name in self.agents:
            sender = self.agents[sender_name]
            receiver = self.agents[receiver_name]
            sender.send_message(receiver, message)
        else:
            print(f"Agent {receiver_name} not found!")

---

3. Persistence Layer

persistence/persistence_layer.py

import json
import os

class PersistenceLayer:
    def __init__(self, file_path="data.json"):
        self.file_path = file_path
        if not os.path.exists(file_path):
            with open(file_path, 'w') as f:
                json.dump({}, f)

    def save(self, key, value):
        """Save data to the file."""
        with open(self.file_path, 'r') as f:
            data = json.load(f)
        data[key] = value
        with open(self.file_path, 'w') as f:
            json.dump(data, f)
        print(f"Saved {key} to persistence layer.")

    def load(self, key):
        """Load data from the file."""
        with open(self.file_path, 'r') as f:
            data = json.load(f)
        return data.get(key, None)

---

4. Control Plane

control_plane.py

from communication.message_broker import MessageBroker
from persistence.persistence_layer import PersistenceLayer

class ControlPlane:
    def __init__(self):
        self.broker = MessageBroker()
        self.persistence = PersistenceLayer()

    def register_agent(self, agent):
        """Register an agent with the broker."""
        self.broker.register_agent(agent)

    def send_message(self, sender_name, receiver_name, message):
        """Send a message via the broker."""
        self.broker.send_message(sender_name, receiver_name, message)

    def save_state(self, key, value):
        """Save state to the persistence layer."""
        self.persistence.save(key, value)

    def load_state(self, key):
        """Load state from the persistence layer."""
        return self.persistence.load(key)

---

5. Main Entry Point

main.py

from control_plane import ControlPlane
from agents.summarizer_agent import SummarizerAgent
from agents.translator_agent import TranslatorAgent

def main():
    # Initialize control plane
    control_plane = ControlPlane()

    # Create agents
    summarizer = SummarizerAgent("Summarizer1")
    translator = TranslatorAgent("Translator1")

    # Register agents
    control_plane.register_agent(summarizer)
    control_plane.register_agent(translator)

    # Simulate agent communication
    control_plane.send_message("Summarizer1", "Translator1", "This is a test message.")
    control_plane.send_message("Translator1", "Summarizer1", "Bonjour, ceci est un message.")

    # Save and load state
    control_plane.save_state("example_key", "example_value")
    loaded_value = control_plane.load_state("example_key")
    print(f"Loaded from persistence: {loaded_value}")

if __name__ == "__main__":
    main()

---

6. Requirements

requirements.txt

# Python requirements

If you want, you can include external libraries like fastapi, starlette, flask for an API layer or Celery for task queues in the future.

---

This modular framework ensures:

1. Scalability: Each component is independent and replaceable.
2. Persistence: State saving/loading through the persistence layer.
3. Inter-Agent Communication: Via the message broker.
4. Local Agent Attributes: Each agent maintains local state specific to its specialization.

[3rd-Son]

This multi-agent framework is impressively modular and well-structured, making it easy to extend and maintain. I like how the control plane orchestrates the entire system, decoupling agents from direct dependencies while leveraging the message broker for seamless inter-agent communication.

[3rd-Son]

The persistence layer is a great addition, ensuring fault tolerance and state recovery, which is crucial for real-world applications. Each agent is encapsulated with its own logic and state, promoting scalability and clear separation of concerns.

Overall, I think the framework strikes a great balance between simplicity and functionality, laying a solid foundation for building complex multi-agent systems. I'm particularly interested in exploring how new agents or more advanced persistence mechanisms (like a database) could be integrated into this design.

[MichaelDecent]

This multi-agent framework is impressively modular and well-structured, making it easy to extend and maintain. I like how the control plane orchestrates the entire system, decoupling agents from direct dependencies while leveraging the message broker for seamless inter-agent communication.

Agreed!. well structured.

[MichaelDecent]

i hope we integrate a more robust message queue system (e.g., RabbitMQ, Kafka) or scaling up the listen method to process multiple messages concurrently.

We can as well make the code run asynchronously.

[MichaelDecent]

please lets also look at control panel as well

from typing import List, Dict, Callable, Any

class ControlPlane:
    def __init__(self):
        self.agents = {}  # A registry of all active agents (id -> agent instance)
        self.task_queue = []  # A list to manage tasks
        self.routing_table = {}  # Handles agent-specific task routing
        self.agent_factory = None  # Factory for creating agents
        self.communication_layer = None  # Communication layer for message passing

    # 1. **Agent Deployment**
    def deploy_agent(self, agent_type: str, agent_id: str, **kwargs):
        """Deploys an agent using the agent factory."""
        if self.agent_factory is None:
            raise ValueError("Agent factory is not configured.")
        
        agent = self.agent_factory.create(agent_type, agent_id=agent_id, **kwargs)
        self.agents[agent_id] = agent
        print(f"Agent '{agent_id}' deployed.")

    # 2. **Agent Decommissioning**
    def decommission_agent(self, agent_id: str):
        """Removes an agent from the MAS."""
        if agent_id in self.agents:
            del self.agents[agent_id]
            print(f"Agent '{agent_id}' decommissioned.")
        else:
            print(f"Agent '{agent_id}' not found.")

    # 3. **Task Scheduling**
    def add_task(self, task: Dict[str, Any]):
        """Adds a task to the queue."""
        self.task_queue.append(task)
        print(f"Task '{task['id']}' added to the queue.")

    # 4. **Task Dispatch**
    def dispatch_tasks(self):
        """Distributes tasks to agents."""
        while self.task_queue:
            task = self.task_queue.pop(0)
            agent_id = self.route_task(task)
            if agent_id in self.agents:
                self.agents[agent_id].execute_task(task)
            else:
                print(f"No suitable agent found for task '{task['id']}'.")

    # 5. **Routing Logic**
    def route_task(self, task: Dict[str, Any]) -> str:
        """Routes a task to an appropriate agent based on routing rules."""
        # Example routing logic: find the first available agent
        for agent_id in self.agents:
            if self.agents[agent_id].is_available():
                return agent_id
        return None

    # 6. **Agent Synchronization**
    def sync_agents(self):
        """Synchronizes agent states."""
        for agent in self.agents.values():
            agent.sync_state()

    # 7. **Monitoring & Logging**
    def monitor_agents(self):
        """Monitors the health and activity of all agents."""
        for agent_id, agent in self.agents.items():
            print(f"Agent '{agent_id}' status: {agent.get_status()}")

[MichaelDecent]

For Blackboard pattern here's a proposed directory structure:

blackboard_framework/
│
├── agents/
│   ├── __init__.py
│   ├── base_agent.py
│   ├── data_collection_agent.py
│   ├── data_analysis_agent.py
│   └── solution_agent.py
│
├── core/
│   ├── __init__.py
│   ├── blackboard.py
│   └── knowledge_state.py
│
├── main.py
└── requirements.txt

Base BlackboardAgent Class

from typing import Any, Dict, List, Optional
from core.knowledge_state import KnowledgeState

class BaseAgent:
    def __init__(self, name: str, capabilities: List[str]):
        """
        Initialize an agent with specific capabilities
        
        Args:
            name (str): Name of the agent
            capabilities (List[str]): List of problem-solving capabilities
        """
        self.name = name
        self.capabilities = capabilities

    async def process(self, blackboard: 'Blackboard', problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Process a problem based on the agent's capabilities
        
        Args:
            blackboard (Blackboard): The shared blackboard
            problem (Dict[str, Any]): Problem to be solved
        
        Returns:
            Optional solution contribution
        """
        raise NotImplementedError("Subclasses must implement processing method")

Knowledge State Enum and BlackboardData

from dataclasses import dataclass, field
from typing import Any, Optional
from enum import Enum, auto

class KnowledgeState(Enum):
    INCOMPLETE = auto()
    PARTIAL = auto()
    COMPLETE = auto()

@dataclass
class BlackboardData:
    """
    Represents a piece of knowledge on the blackboard
    """
    content: Any
    state: KnowledgeState = field(default=KnowledgeState.INCOMPLETE)
    contributor: Optional[str] = None
    confidence_level: float = 0.0

Blackboard Core Implementation

import asyncio
from typing import Any, Dict, List, Optional
from core.knowledge_state import KnowledgeState, BlackboardData
from agents.base_agent import BaseAgent

class Blackboard:
    def __init__(self):
        """
        Initialize the shared blackboard
        """
        self._knowledge: Dict[str, BlackboardData] = {}
        self._agents: List[BaseAgent] = []
        self._solution_lock = asyncio.Lock()
        self._problem_state: KnowledgeState = KnowledgeState.INCOMPLETE

    def register_agent(self, agent: BaseAgent):
        """
        Register an agent to work on the blackboard
        
        Args:
            agent (BaseAgent): Agent to be registered
        """
        self._agents.append(agent)

    def add_knowledge(self, key: str, content: Any, contributor: str):
        """
        Add or update knowledge on the blackboard
        
        Args:
            key (str): Unique identifier for the knowledge
            content (Any): Knowledge content
            contributor (str): Name of the agent contributing the knowledge
        """
        async with self._solution_lock:
            existing = self._knowledge.get(key)
            confidence = self._calculate_confidence(existing, content)
            
            new_data = BlackboardData(
                content=content,
                contributor=contributor,
                confidence_level=confidence,
                state=self._determine_state(content)
            )
            
            self._knowledge[key] = new_data
            self._update_problem_state()

    def _calculate_confidence(self, existing: Optional[BlackboardData], new_content: Any) -> float:
        """
        Calculate confidence level for new knowledge
        
        Args:
            existing (Optional[BlackboardData]): Existing knowledge
            new_content (Any): New content to be added
        
        Returns:
            float: Confidence level
        """
        if not existing:
            return 0.5  # Default confidence for first contribution
        
        # Simple confidence calculation logic
        return min(existing.confidence_level + 0.2, 1.0)

    def _determine_state(self, content: Any) -> KnowledgeState:
        """
        Determine the knowledge state based on content
        
        Args:
            content (Any): Content to evaluate
        
        Returns:
            KnowledgeState: State of the knowledge
        """
        if content is None:
            return KnowledgeState.INCOMPLETE
        elif isinstance(content, dict) and not content:
            return KnowledgeState.INCOMPLETE
        elif isinstance(content, list) and len(content) < 2:
            return KnowledgeState.PARTIAL
        return KnowledgeState.COMPLETE

    def _update_problem_state(self):
        """
        Update the overall problem-solving state
        """
        states = [data.state for data in self._knowledge.values()]
        
        if all(state == KnowledgeState.COMPLETE for state in states):
            self._problem_state = KnowledgeState.COMPLETE
        elif any(state == KnowledgeState.COMPLETE for state in states):
            self._problem_state = KnowledgeState.PARTIAL
        else:
            self._problem_state = KnowledgeState.INCOMPLETE

    async def solve_problem(self, problem: Dict[str, Any]):
        """
        Coordinate agents to solve a problem
        
        Args:
            problem (Dict[str, Any]): Problem to be solved
        """
        print(f"Starting problem-solving process: {problem}")
        
        # Distribute problem to agents
        tasks = [
            asyncio.create_task(agent.process(self, problem)) 
            for agent in self._agents
        ]
        
        # Wait for all agents to contribute
        await asyncio.gather(*tasks)
        
        # Final solution assessment
        if self._problem_state == KnowledgeState.COMPLETE:
            print("Problem solved successfully!")
            self._print_solution()
        else:
            print("Problem solving incomplete.")

    def _print_solution(self):
        """
        Print the final solution from the blackboard
        """
        print("\n--- Final Blackboard Solution ---")
        for key, data in self._knowledge.items():
            print(f"{key}: {data.content} (Contributor: {data.contributor}, "
                  f"Confidence: {data.confidence_level:.2f})")

Data Collection Agent

import asyncio
from typing import Any, Dict, Optional
from agents.base_agent import BaseAgent
from core.blackboard import Blackboard

class DataCollectionAgent(BaseAgent):
    async def process(self, blackboard: Blackboard, problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Collect initial data for the problem
        """
        await asyncio.sleep(1)  # Simulate data collection
        initial_data = {
            "raw_data": [1, 2, 3, 4, 5],
            "metadata": {"source": "sensors"}
        }
        
        for key, value in initial_data.items():
            blackboard.add_knowledge(key, value, self.name)
        
        return initial_data

Data Analysis Agent

import asyncio
from typing import Any, Dict, Optional
from agents.base_agent import BaseAgent
from core.blackboard import Blackboard

class DataAnalysisAgent(BaseAgent):
    async def process(self, blackboard: Blackboard, problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Analyze and process collected data
        """
        await asyncio.sleep(1.5)  # Simulate analysis
        raw_data = blackboard._knowledge.get('raw_data')
        
        if raw_data:
            analysis = {
                "statistics": {
                    "mean": sum(raw_data.content) / len(raw_data.content),
                    "variance": sum((x - (sum(raw_data.content) / len(raw_data.content))) ** 2 for x in raw_data.content) / len(raw_data.content)
                }
            }
            
            blackboard.add_knowledge('data_analysis', analysis, self.name)
        
        return analysis

Solution Agent

import asyncio
from typing import Any, Dict, Optional
from agents.base_agent import BaseAgent
from core.blackboard import Blackboard

class SolutionAgent(BaseAgent):
    async def process(self, blackboard: Blackboard, problem: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Synthesize final solution
        """
        await asyncio.sleep(2)  # Simulate solution synthesis
        analysis = blackboard._knowledge.get('data_analysis')
        
        if analysis:
            solution = {
                "final_solution": {
                    "insights": "Problem solved based on collected and analyzed data",
                    "key_metrics": analysis.content['statistics']
                }
            }
            
            blackboard.add_knowledge('solution', solution, self.name)
        
        return solution

Main Execution Script

import asyncio
from core.blackboard import Blackboard
from agents.data_collection_agent import DataCollectionAgent
from agents.data_analysis_agent import DataAnalysisAgent
from agents.solution_agent import SolutionAgent

async def main():
    # Create blackboard
    blackboard = Blackboard()
    
    # Create and register agents
    data_collector = DataCollectionAgent("Data Collector", ["data_gathering"])
    data_analyzer = DataAnalysisAgent("Data Analyzer", ["statistical_analysis"])
    solution_agent = SolutionAgent("Solution Synthesizer", ["solution_generation"])
    
    blackboard.register_agent(data_collector)
    blackboard.register_agent(data_analyzer)
    blackboard.register_agent(solution_agent)
    
    # Define a sample problem
    problem = {
        "type": "data_analysis",
        "description": "Analyze sensor data and generate insights"
    }
    
    # Solve the problem
    await blackboard.solve_problem(problem)

if __name__ == "__main__":
    asyncio.run(main())

[MichaelDecent]

For a multi-agent framework, especially for heterogeneous agents, efficient communication is critical. Here are some patterns and mechanisms commonly used:

1. Publish-Subscribe Pattern

• How it works: Agents subscribe to topics of interest, and a message broker publishes messages to those topics.
• Advantages:
  • Decouples sender and receiver.
  • Scales well with a dynamic number of agents.
  • Useful for heterogeneous systems where agents have varying roles.
• Example Technologies: MQTT, RabbitMQ, Apache Kafka.

2. Direct Messaging

• How it works: Agents communicate directly with one another using predefined protocols or APIs.
• Advantages:
  • Low latency.
  • Suitable for point-to-point communication in tightly coupled systems.
• Challenges:
  • Requires clear protocol definitions.
  • Less scalable for large systems.

3. Blackboard Pattern

• How it works: Agents read from and write to a shared knowledge repository (the "blackboard").
• Advantages:
  • Facilitates collaborative problem-solving.
  • Centralized data storage ensures synchronization.
• Challenges:
  • Can become a bottleneck or single point of failure.

4. Service-Oriented Architecture (SOA)

• How it works: Agents expose services that other agents can consume, typically over HTTP or gRPC.
• Advantages:
  • Flexible and language-agnostic.
  • Ideal for heterogeneous agents using different platforms.
• Example Technologies: REST APIs, gRPC.

5. Agent Communication Languages (ACLs)

• How it works: Structured languages like FIPA-ACL or KQML enable semantic communication.
• Advantages:
  • Standardized formats make interactions predictable.
  • Supports complex interactions like negotiations or multi-agent planning.
• Challenges:
  • Higher learning curve for implementation.

6. Shared Memory Systems

• How it works: Agents communicate via shared memory spaces, often in real-time environments.
• Advantages:
  • Extremely fast for tightly coupled systems.
  • Suitable for real-time applications.
• Challenges:
  • Limited scalability.
  • More challenging for distributed systems.

7. Event-Driven Communication

• How it works: Agents respond to events or triggers in the system.
• Advantages:
  • Asynchronous communication reduces blocking.
  • Fits well with reactive and dynamic systems.
• Example Technologies: AWS EventBridge, Apache Kafka.

8. Multi-Protocol Communication

• How it works: Agents use a combination of protocols, e.g., HTTP for control signals and WebSockets for real-time updates.
• Advantages:
  • Adapts to diverse requirements of heterogeneous agents.
  • Balances real-time needs with robustness.

Factors to Consider:

1. Scalability: Can the mechanism support an increasing number of agents?
2. Latency: Is the mechanism fast enough for the system's requirements?
3. Interoperability: Can agents with different architectures and technologies communicate effectively?
4. Fault Tolerance: Does the system handle failures gracefully?
5. Ease of Implementation: Can you implement and maintain the communication mechanism efficiently?

Recommendations for Your Framework

• For scalability and loose coupling: Publish-Subscribe with tools like Kafka or MQTT.
• For structured collaboration: Use Agent Communication Languages.
• For real-time performance: Consider Shared Memory Systems or Direct Messaging.
• To combine flexibility with standardization: Use a Service-Oriented Architecture with REST or gRPC.

[MichaelDecent]

Characterization and Categorization of Multi-Agent Communication Mechanisms

---

1. Publish-Subscribe Pattern

• Communication Style: Asynchronous, Broadcast.
• Scalability: High (handles dynamic agents effectively).
• Coupling: Loosely Coupled.
• Fault Tolerance: Moderate to High (depends on the broker’s fault tolerance features).
• Complexity:
  • Implementation: Moderate.
  • Maintenance: Moderate (requires managing topics and message brokers).
• Latency: Low to Moderate.
• Data Consistency: Eventual Consistency.
• Interoperability: High (broker supports multiple protocols and platforms).
• Use Case Alignment: Collaborative, Dynamic, or Real-Time Systems.
• Resource Efficiency: Moderate (depends on broker optimization).
• Extensibility: High (easy to add new agents).

---

2. Direct Messaging

• Communication Style: Synchronous or Asynchronous, Point-to-Point.
• Scalability: Low to Moderate (increases communication overhead as agents grow).
• Coupling: Tightly Coupled.
• Fault Tolerance: Low (relies on the availability of the communicating agents).
• Complexity:
  • Implementation: Simple.
  • Maintenance: Challenging (managing numerous communication pathways).
• Latency: Very Low.
• Data Consistency: Strong Consistency.
• Interoperability: Moderate (protocol must be agreed upon by agents).
• Use Case Alignment: Real-Time, Tightly Integrated Systems.
• Resource Efficiency: High (low communication overhead).
• Extensibility: Low (requires reconfiguration for new agents).

---

3. Blackboard Pattern

• Communication Style: Synchronous or Asynchronous, Shared Repository.
• Scalability: Moderate (central repository may become a bottleneck).
• Coupling: Loosely Coupled (via shared state).
• Fault Tolerance: Low to Moderate (repository is a single point of failure).
• Complexity:
  • Implementation: Moderate to High.
  • Maintenance: High (requires robust data synchronization and integrity checks).
• Latency: Moderate to High.
• Data Consistency: Strong Consistency (for centralized systems).
• Interoperability: High (repository acts as a neutral intermediary).
• Use Case Alignment: Collaborative Problem-Solving Systems.
• Resource Efficiency: Low to Moderate.
• Extensibility: Moderate (can adapt to new agents but may require additional storage).

---

4. Service-Oriented Architecture (SOA)

• Communication Style: Asynchronous or Synchronous, Point-to-Point.
• Scalability: High (supports distributed systems with independent services).
• Coupling: Loosely Coupled.
• Fault Tolerance: Moderate (depends on service redundancy).
• Complexity:
  • Implementation: Moderate.
  • Maintenance: Moderate.
• Latency: Moderate.
• Data Consistency: Eventual Consistency (in distributed setups).
• Interoperability: High (language and platform agnostic).
• Use Case Alignment: Heterogeneous Agent Systems.
• Resource Efficiency: Moderate (requires service orchestration tools).
• Extensibility: High.

---

5. Agent Communication Languages (ACLs)

• Communication Style: Asynchronous or Synchronous, Semantic Protocols.
• Scalability: Moderate (performance depends on parsing and processing ACL messages).
• Coupling: Loosely Coupled.
• Fault Tolerance: High (semantic layers allow for graceful degradation).
• Complexity:
  • Implementation: High (requires designing or integrating ACLs).
  • Maintenance: Moderate to High.
• Latency: Moderate to High.
• Data Consistency: Eventual Consistency.
• Interoperability: High (semantic understanding bridges diverse platforms).
• Use Case Alignment: Complex Interactions, Negotiations, Multi-Agent Planning.
• Resource Efficiency: Moderate.
• Extensibility: High.

---

6. Shared Memory Systems

• Communication Style: Synchronous or Asynchronous, Direct Memory Access.
• Scalability: Low (limited to tightly integrated or local systems).
• Coupling: Tightly Coupled.
• Fault Tolerance: Low (no distributed redundancy).
• Complexity:
  • Implementation: Moderate.
  • Maintenance: High (requires managing access conflicts).
• Latency: Very Low.
• Data Consistency: Strong Consistency.
• Interoperability: Low (agents must share a common platform or architecture).
• Use Case Alignment: Real-Time, Low-Latency Systems.
• Resource Efficiency: High.
• Extensibility: Low.

---

7. Event-Driven Communication

• Communication Style: Asynchronous, Broadcast or Trigger-Based.
• Scalability: High.
• Coupling: Loosely Coupled.
• Fault Tolerance: High (event logs enable recovery).
• Complexity:
  • Implementation: Moderate.
  • Maintenance: Moderate.
• Latency: Low.
• Data Consistency: Eventual Consistency.
• Interoperability: High.
• Use Case Alignment: Dynamic, Reactive Systems.
• Resource Efficiency: Moderate to High.
• Extensibility: High.

---

8. Multi-Protocol Communication

• Communication Style: Mixed (e.g., HTTP + WebSockets).
• Scalability: High.
• Coupling: Loosely Coupled.
• Fault Tolerance: Moderate (depends on protocol redundancy).
• Complexity:
  • Implementation: High.
  • Maintenance: High.
• Latency: Low to Moderate.
• Data Consistency: Eventual Consistency.
• Interoperability: High.
• Use Case Alignment: Versatile Systems with Mixed Requirements.
• Resource Efficiency: Moderate.
• Extensibility: High.

---

Categorization

Category	Mechanisms
Real-Time Systems	Direct Messaging, Shared Memory Systems.
Collaborative Systems	Blackboard Pattern, ACLs, Publish-Subscribe.
Dynamic Systems	Publish-Subscribe, Event-Driven Communication, SOA.
Heterogeneous Systems	SOA, ACLs, Multi-Protocol Communication.
Scalable Systems	Publish-Subscribe, Event-Driven Communication, Multi-Protocol Communication.
Resource-Efficient	Direct Messaging, Shared Memory Systems.

[MichaelDecent]

Real-Time Systems Category

To dive deeper into Real-Time Systems, let’s explore Direct Messaging and Shared Memory Systems with code examples. These mechanisms are suitable for applications like robotics, gaming, and industrial automation where low-latency, high-performance communication is critical.

---

1. Direct Messaging

Direct messaging enables agents to communicate in a point-to-point manner with low latency. Below is an example using ZeroMQ, a high-performance messaging library:

Implementation: Direct Messaging with ZeroMQ

Agent 1 (Sender)

import zmq
import time

def sender():
    context = zmq.Context()
    socket = context.socket(zmq.PUSH)
    socket.bind("tcp://*:5555")  # Bind to port 5555

    for i in range(10):
        message = f"Message {i}"
        print(f"Sending: {message}")
        socket.send_string(message)
        time.sleep(1)  # Simulating periodic updates

if __name__ == "__main__":
    sender()

Agent 2 (Receiver)

def receiver():
    context = zmq.Context()
    socket = context.socket(zmq.PULL)
    socket.connect("tcp://localhost:5555")  # Connect to the sender

    while True:
        message = socket.recv_string()
        print(f"Received: {message}")

if __name__ == "__main__":
    receiver()

Key Features:

• Low latency: Ideal for real-time updates.
• Tightly coupled: Agents must know each other's communication endpoints.

---

2. Shared Memory Systems

Shared memory systems allow agents to access common memory regions for communication. This is common in multi-threaded applications or local processes. Below is an example using Python’s multiprocessing module.

Implementation: Shared Memory with Python

Shared Memory Setup

from multiprocessing import Process, shared_memory
import numpy as np
import time

def writer():
    shm = shared_memory.SharedMemory(name="shared_array")
    array = np.ndarray((10,), dtype=np.int32, buffer=shm.buf)

    for i in range(10):
        array[i] = i
        print(f"Writer: Wrote {i}")
        time.sleep(1)  # Simulating real-time updates

def reader():
    shm = shared_memory.SharedMemory(name="shared_array")
    array = np.ndarray((10,), dtype=np.int32, buffer=shm.buf)

    while True:
        print(f"Reader: Current data {array.tolist()}")
        time.sleep(1)

if __name__ == "__main__":
    # Create shared memory
    shm = shared_memory.SharedMemory(create=True, size=10 * np.dtype(np.int32).itemsize)
    array = np.ndarray((10,), dtype=np.int32, buffer=shm.buf)
    array.fill(-1)  # Initialize array with default values

    # Start writer and reader processes
    p1 = Process(target=writer)
    p2 = Process(target=reader)

    p1.start()
    p2.start()

    p1.join()
    p2.join()

    shm.close()
    shm.unlink()  # Cleanup shared memory

Key Features:

• Ultra-low latency: Communication is direct and avoids networking overhead.
• Limited scalability: Best for local systems with tight coupling.
• Synchronization: Requires careful handling to avoid race conditions.

---

Comparison

Criteria	Direct Messaging	Shared Memory Systems
Latency	Low	Ultra-low
Scalability	Limited to network size	Limited to local processes
Implementation	Simple	Complex (requires synchronization)
Fault Tolerance	Dependent on agent availability	Dependent on memory stability

[MichaelDecent]

Direct messaging Pattern Communication System

A structured directory for the multi-agent direct messaging system, organizing the code into a modular, scalable project structure.

└── multi_agent_system/
│       ├── __init__.py
│       ├── core/
│       │   ├── __init__.py
│       │   ├── base_agent.py
│       │   ├── message.py

│       │
│       ├── agents/
│       │   ├── __init__.py
│       │   ├── temperature_sensor.py
│       │   ├── hvac_control.py

│       │
│       ├── requirements.txt
│       └── simple_home_automation.py        

The implementation for each file in this structured project:

2. Core Message Definitions:

# src/multi_agent_system/core/message.py
import enum
from typing import Dict, Any, Optional
from uuid import UUID, uuid4
from datetime import datetime
from pydantic import BaseModel, Field, validator

class MessageType(enum.Enum):
    """
    Enumeration of possible message types in the system.
    Helps in routing and processing messages appropriately.
    """
    TEMPERATURE_REPORT = "temperature_report"
    COMMAND = "command"
    NOTIFICATION = "notification"

class BaseMessage(BaseModel):
    """
    Standardized base message model for inter-agent communication.
    Provides a consistent structure for all messages in the system.
    """
    message_id: UUID = Field(default_factory=uuid4)
    message_type: MessageType
    sender: str
    recipient: str
    timestamp: datetime = Field(default_factory=datetime.utcnow)
    content: Dict[str, Any]
    
    @validator('content')
    def validate_content(cls, content):
        """
        Ensure message content is not empty and meets basic validation.
        """
        if not content:
            raise ValueError("Message content cannot be empty")
        return content

class TemperatureMessage(BaseMessage):
    """
    Specialized message for temperature-related communications.
    Includes additional validation specific to temperature data.
    """
    message_type: MessageType = MessageType.TEMPERATURE_REPORT
    content: Dict[str, float]
    
    @validator('content')
    def validate_temperature(cls, content):
        """
        Validate temperature values are within realistic ranges.
        """
        for location, temp in content.items():
            if temp < -50 or temp > 100:
                raise ValueError(f"Invalid temperature {temp} for {location}")
        return content

class CommandMessage(BaseMessage):
    """
    Message for sending operational commands between agents.
    Provides a structured way to transmit control instructions.
    """
    message_type: MessageType = MessageType.COMMAND
    content: Dict[str, str]

3. Base Agent Implementation:

# src/multi_agent_system/core/base_agent.py
import asyncio
from typing import Dict, Any
from .message import BaseMessage, MessageType

class Agent:
    """
    Base class for all agents in the multi-agent system.
    Provides core communication and management capabilities.
    """
    def __init__(self, agent_id: str):
        """
        Initialize an agent with a unique identifier.
        
        Args:
            agent_id (str): Unique identifier for the agent
        """
        self.agent_id = agent_id
        self.message_queue: asyncio.Queue = asyncio.Queue()
        self.connected_agents: Dict[str, 'Agent'] = {}
        self.is_running = False

    def connect(self, agent: 'Agent'):
        """
        Establish a direct connection with another agent.
        
        Args:
            agent (Agent): Agent to connect with
        """
        self.connected_agents[agent.agent_id] = agent

    async def send_message(self, message: BaseMessage):
        """
        Send a message to a specific agent.
        
        Args:
            message (BaseMessage): Message to be sent
        
        Raises:
            ValueError: If the recipient agent is not connected
        """
        if message.recipient not in self.connected_agents:
            raise ValueError(f"No connection to agent: {message.recipient}")
        
        target_agent = self.connected_agents[message.recipient]
        await target_agent.receive_message(message)

    async def receive_message(self, message: BaseMessage):
        """
        Receive and process incoming messages.
        
        Args:
            message (BaseMessage): Incoming message
        """
        await self.message_queue.put(message)
        await self.process_message(message)

    async def process_message(self, message: BaseMessage):
        """
        Process received messages.
        Override this method in specific agent implementations.
        
        Args:
            message (BaseMessage): Message to process
        """
        raise NotImplementedError("Subclasses must implement message processing")

    async def start(self):
        """
        Start the agent's message processing loop.
        """
        self.is_running = True
        while self.is_running:
            try:
                message = await asyncio.wait_for(self.message_queue.get(), timeout=1.0)
                await self.process_message(message)
            except asyncio.TimeoutError:
                continue

    def stop(self):
        """
        Stop the agent's message processing.
        """
        self.is_running = False

4. Temperature Sensor Agent:

# src/multi_agent_system/agents/temperature_sensor.py
import random
import asyncio
from ..core.base_agent import Agent
from ..core.message import TemperatureMessage, MessageType

class TemperatureSensorAgent(Agent):
    """
    Agent responsible for measuring and reporting temperatures.
    Simulates temperature sensors in different locations.
    """
    def __init__(self, agent_id: str, locations: list[str]):
        """
        Initialize the temperature sensor agent.
        
        Args:
            agent_id (str): Unique identifier for the agent
            locations (list[str]): List of locations to monitor
        """
        super().__init__(agent_id)
        self.locations = locations
        self.current_temperatures: dict[str, float] = {}

    async def measure_temperature(self, location: str) -> float:
        """
        Simulate temperature measurement for a specific location.
        
        Args:
            location (str): Location to measure temperature
        
        Returns:
            float: Simulated temperature value
        """
        # Simulate realistic temperature variations
        base_temp = 20.0  # Comfortable room temperature
        variation = random.uniform(-2.0, 2.0)
        temperature = round(base_temp + variation, 1)
        
        self.current_temperatures[location] = temperature
        return temperature

    async def report_temperatures(self):
        """
        Measure and report temperatures for all monitored locations.
        """
        for location in self.locations:
            temp = await self.measure_temperature(location)
            
            message = TemperatureMessage(
                sender=self.agent_id,
                recipient='hvac_control',
                content={location: temp}
            )
            
            await self.send_message(message)

    async def process_message(self, message):
        """
        Process incoming messages.
        Typically used for configuration or control messages.
        """
        # Additional message handling can be implemented here
        print(f"Temperature Sensor received: {message}")

    async def start(self):
        """
        Start the temperature sensor agent.
        Periodically report temperatures.
        """
        while self.is_running:
            await self.report_temperatures()
            await asyncio.sleep(300)  # Report every 5 minutes

5. HVAC Control Agent:

# examples/simple_home_automation.py
import asyncio
from src.multi_agent_system.agents.temperature_sensor import TemperatureSensorAgent
from src.multi_agent_system.core.base_agent import Agent

class HVACControlAgent(Agent):
    """
    Agent responsible for managing HVAC systems based on temperature reports.
    """
    async def process_message(self, message):
        """
        Process temperature messages and make control decisions.
        
        Args:
            message: Incoming temperature message
        """
        if message.message_type == 'temperature_report':
            for location, temperature in message.content.items():
                await self.adjust_temperature(location, temperature)

    async def adjust_temperature(self, location: str, current_temp: float):
        """
        Decide and simulate temperature adjustments.
        
        Args:
            location (str): Location of temperature adjustment
            current_temp (float): Current temperature
        """
        target_temp = 22.0  # Ideal comfort temperature
        
        if current_temp < target_temp - 1:
            print(f"Heating {location}: {current_temp}°C")
        elif current_temp > target_temp + 1:
            print(f"Cooling {location}: {current_temp}°C")
        else:
            print(f"{location} temperature optimal: {current_temp}°C")

6. Main Example Script:

async def main():
    """
    Demonstrate multi-agent home automation scenario.
    """
    # Create agents
    temp_sensor = TemperatureSensorAgent(
        agent_id='temperature_sensor', 
        locations=['living_room', 'bedroom', 'kitchen']
    )
    hvac_control = HVACControlAgent(agent_id='hvac_control')

    # Establish connections
    temp_sensor.connect(hvac_control)
    hvac_control.connect(temp_sensor)

    # Start agents
    await asyncio.gather(
        temp_sensor.start(),
        hvac_control.start()
    )

if __name__ == "__main__":
    asyncio.run(main())

Project Structure Rationale

The proposed structure offers several key benefits:

1. Modular Design

   • Separates core components from specific agent implementations
   • Allows easy addition of new agent types
   • Provides clear separation of concerns

2. Flexibility

   • Core message and agent classes can be extended
   • Easy to add new communication protocols
   • Supports complex multi-agent scenarios

Key Improvements from Previous Implementation

• More granular message type handling
• Improved agent lifecycle management
• Better separation of concerns
• Added configuration and project structure
• Enhanced scalability and maintainability

Recommendations for Further Development

1. Implement robust error handling
2. Add comprehensive logging
3. Create more advanced routing mechanisms
4. Develop more complex agent interaction patterns

[3rd-Son]

In addition to this let's look at Shared Memory Systems approach for multi-agent communication

Shared Memory Systems approach for multi-agent communication

A comprehensive implementation of a Shared Memory Systems approach for multi-agent communication, creating a structured project that demonstrates the core principles.

│   └── shared_memory_system/
│       ├── __init__.py
│       ├── core/
│       │   ├── __init__.py
│       │   ├── shared_memory.py
│       │   ├── agent_base.py
│       │   └── data_structures.py
│       │
│       ├── agents/
│       │   ├── __init__.py
│       │   ├── sensor_agent.py
│       │   ├── processor_agent.py
│       │   └── actuator_agent.py
│       │
│       ├── industrial_control_simulation.py
│       └── requirements.txt

Now, I'll implement the core components of this shared memory system:

1. Shared Memory Implementation:

# src/shared_memory_system/core/shared_memory.py
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, ConfigDict
from threading import Lock
import time
import uuid

class MemoryCell(BaseModel):
    """
    Represents a single cell in shared memory with comprehensive metadata.
    Uses Pydantic for robust data validation and type checking.
    """
    model_config = ConfigDict(
        # Allow arbitrary types for flexible value storage
        arbitrary_types_allowed=True,
        # Protect against direct attribute modification
        frozen=False 
    )

    # Unique identifier for the memory cell
    cell_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
    
    # Stored value with flexible typing
    value: Any = Field(default=None)
    
    # Version tracking for optimistic concurrency
    version: int = Field(default=0)
    
    # Timestamp for staleness tracking
    last_updated: float = Field(default_factory=time.time)
    
    # Configurable maximum age for data validity
    max_age: float = Field(default=5.0)
    
    # Metadata for additional context
    metadata: Dict[str, Any] = Field(default_factory=dict)
    
    # Optional locking mechanism (not part of Pydantic serialization)
    _lock: Lock = Field(default_factory=Lock, exclude=True)

    def is_stale(self, current_time: Optional[float] = None) -> bool:
        """
        Check if the memory cell's data has exceeded its maximum age.
        
        Args:
            current_time (Optional[float]): Time to compare against, defaults to current time
        
        Returns:
            bool: Whether the cell's data is considered stale
        """
        current_time = current_time or time.time()
        return (current_time - self.last_updated) > self.max_age

    def update(self, new_value: Any, metadata: Optional[Dict[str, Any]] = None):
        """
        Update the memory cell with a new value and optional metadata.
        
        Args:
            new_value (Any): New value to store
            metadata (Optional[Dict[str, Any]]): Additional metadata
        """
        with self._lock:
            self.value = new_value
            self.version += 1
            self.last_updated = time.time()
            
            if metadata:
                self.metadata.update(metadata)

class SharedMemorySpace:
    """
    Centralized shared memory system with Pydantic-validated memory cells.
    Provides thread-safe and type-checked data storage.
    """
    def __init__(self):
        # Use a dictionary of MemoryCells
        self._memory: Dict[str, MemoryCell] = {}
        self._global_lock = Lock()

    def write(self, key: str, value: Any, metadata: Optional[Dict[str, Any]] = None):
        """
        Write a value to shared memory with optional metadata.
        
        Args:
            key (str): Unique identifier for the memory cell
            value (Any): Value to be stored
            metadata (Optional[Dict[str, Any]]): Additional metadata
        """
        with self._global_lock:
            if key not in self._memory:
                self._memory[key] = MemoryCell()
            
            cell = self._memory[key]
            cell.update(value, metadata)

    def read(self, key: str, max_age: Optional[float] = None) -> Optional[Any]:
        """
        Read a value from shared memory with optional staleness check.
        
        Args:
            key (str): Unique identifier for the memory cell
            max_age (Optional[float]): Maximum acceptable age of data
        
        Returns:
            Optional[Any]: Stored value or None if not found/stale
        """
        with self._global_lock:
            if key not in self._memory:
                return None
            
            cell = self._memory[key]
            
            # Use cell's max_age if not explicitly provided
            check_age = max_age if max_age is not None else cell.max_age
            
            if cell.is_stale(max_age=check_age):
                return None
            
            return cell.value

    def compare_and_swap(self, key: str, expected: Any, new_value: Any) -> bool:
        """
        Atomic compare-and-swap operation for thread-safe updates.
        
        Args:
            key (str): Unique identifier for the memory cell
            expected (Any): Expected current value
            new_value (Any): New value to set if current matches expected
        
        Returns:
            bool: Whether the swap was successful
        """
        with self._global_lock:
            if key not in self._memory:
                return False
            
            cell = self._memory[key]
            
            with cell._lock:
                if cell.value == expected:
                    cell.update(new_value)
                    return True
                
                return False

    def get_cell_info(self, key: str) -> Optional[Dict[str, Any]]:
        """
        Retrieve comprehensive information about a memory cell.
        
        Args:
            key (str): Unique identifier for the memory cell
        
        Returns:
            Optional[Dict[str, Any]]: Cell information or None if not found
        """
        with self._global_lock:
            if key not in self._memory:
                return None
            
            cell = self._memory[key]
            return {
                "value": cell.value,
                "version": cell.version,
                "last_updated": cell.last_updated,
                "is_stale": cell.is_stale(),
                "metadata": cell.metadata
            }

2. Base Agent with Shared Memory Integration:

# src/shared_memory_system/core/agent_base.py
import asyncio
import uuid
from abc import ABC, abstractmethod
from .shared_memory import SharedMemorySpace

class Agent(ABC):
    """
    Abstract base class for agents interacting with shared memory.
    Provides standard interface and shared memory access methods.
    """
    def __init__(self, memory_space: SharedMemorySpace, agent_type: str):
        """
        Initialize an agent with a shared memory space.
        
        Args:
            memory_space (SharedMemorySpace): Centralized shared memory
            agent_type (str): Type of agent for identification
        """
        self.id = str(uuid.uuid4())
        self.type = agent_type
        self.memory = memory_space
        self.running = False
        self._cycle_delay = 0.1  # Default update frequency

    async def start(self):
        """
        Start the agent's main processing loop.
        """
        self.running = True
        try:
            while self.running:
                await self.cycle()
                await asyncio.sleep(self._cycle_delay)
        except Exception as e:
            print(f"Agent {self.id} encountered an error: {e}")

    def stop(self):
        """
        Gracefully stop the agent's processing.
        """
        self.running = False

    @abstractmethod
    async def cycle(self):
        """
        Abstract method to define agent's primary processing logic.
        Must be implemented by specific agent types.
        """
        pass

    def observe(self, key: str, max_age: float = None):
        """
        Observe a value from shared memory.
        
        Args:
            key (str): Memory cell key to observe
            max_age (float, optional): Maximum acceptable data age
        
        Returns:
            Any: Current value in shared memory
        """
        return self.memory.read(key, max_age)

    def update(self, key: str, value: Any):
        """
        Update a value in shared memory.
        
        Args:
            key (str): Memory cell key to update
            value (Any): New value to store
        """
        self.memory.write(key, value)

3. Sensor Agent Example:

# src/shared_memory_system/agents/sensor_agent.py
import random
import asyncio
from ..core.agent_base import Agent
from ..core.shared_memory import SharedMemorySpace

class TemperatureSensorAgent(Agent):
    """
    Agent simulating a temperature sensor that periodically updates shared memory.
    """
    def __init__(self, memory_space: SharedMemorySpace, location: str):
        """
        Initialize temperature sensor for a specific location.
        
        Args:
            memory_space (SharedMemorySpace): Shared memory system
            location (str): Physical location of the sensor
        """
        super().__init__(memory_space, "temperature_sensor")
        self.location = location

    async def cycle(self):
        """
        Simulate temperature measurement and update shared memory.
        """
        # Simulate realistic temperature measurement
        temperature = round(
            random.uniform(18.0, 25.0),  # Realistic indoor temperature range
            1
        )
        
        # Update shared memory with location-specific temperature
        key = f"temperature:{self.location}"
        self.update(key, {
            "value": temperature,
            "timestamp": asyncio.get_running_loop().time(),
            "location": self.location
        })
        
        print(f"Sensor {self.id} at {self.location}: {temperature}°C")

4. Processor Agent Example:

# src/shared_memory_system/agents/processor_agent.py
import asyncio
from ..core.agent_base import Agent
from ..core.shared_memory import SharedMemorySpace

class EnvironmentControlAgent(Agent):
    """
    Agent that processes temperature data and makes control decisions.
    """
    def __init__(self, memory_space: SharedMemorySpace, locations: list[str]):
        """
        Initialize environment control agent.
        
        Args:
            memory_space (SharedMemorySpace): Shared memory system
            locations (list[str]): Locations to monitor
        """
        super().__init__(memory_space, "environment_control")
        self.monitored_locations = locations
        self.target_temperature = 22.0  # Ideal comfort temperature

    async def cycle(self):
        """
        Process temperature data and make control decisions.
        """
        for location in self.monitored_locations:
            key = f"temperature:{location}"
            temp_data = self.observe(key, max_age=5.0)
            
            if temp_data:
                current_temp = temp_data.get('value')
                
                # Simple temperature control logic
                if current_temp < self.target_temperature - 1:
                    control_action = "HEAT"
                elif current_temp > self.target_temperature + 1:
                    control_action = "COOL"
                else:
                    control_action = "MAINTAIN"
                
                # Update control decision in shared memory
                self.update(f"control:{location}", {
                    "action": control_action,
                    "target": self.target_temperature,
                    "current": current_temp
                })
                
                print(f"Control decision for {location}: {control_action}")

5. Simulation Example:

# examples/industrial_control_simulation.py
import asyncio
from src.shared_memory_system.core.shared_memory import SharedMemorySpace
from src.shared_memory_system.agents.sensor_agent import TemperatureSensorAgent
from src.shared_memory_system.agents.processor_agent import EnvironmentControlAgent

async def main():
    """
    Demonstrate shared memory multi-agent system for environment control.
    """
    # Create shared memory space
    shared_memory = SharedMemorySpace()

    # Create sensor agents for different locations
    locations = ['server_room', 'warehouse', 'office']
    sensor_agents = [
        TemperatureSensorAgent(shared_memory, location) 
        for location in locations
    ]

    # Create environment control agent
    control_agent = EnvironmentControlAgent(shared_memory, locations)

    # Start all agents concurrently
    tasks = [
        *[agent.start() for agent in sensor_agents],
        control_agent.start()
    ]

    # Run for a specific duration
    await asyncio.sleep(60)  # Run for 1 minute

    # Stop all agents
    for agent in sensor_agents + [control_agent]:
        agent.stop()

if __name__ == "__main__":
    asyncio.run(main())

Shared Memory Systems: Deep Dive

Key Design Principles

1. Centralized Memory Management

   • Single SharedMemorySpace acts as a central data repository
   • Provides atomic operations (read, write, compare-and-swap)
   • Ensures thread-safe data access

2. Versioning and Staleness

   • Each memory cell tracks version and last update time
   • Supports data freshness validation
   • Prevents using outdated information

3. Flexible Agent Interactions

   • Agents can read and write to shared memory
   • No direct agent-to-agent communication
   • Decoupled, event-driven architecture

Advantages Demonstrated

1. High-Performance Communication

   • Minimal overhead for data exchange
   • Constant-time read and write operations
   • Suitable for real-time systems

2. Simplified Coordination

   • Agents coordinate through shared state
   • No complex messaging protocols required
   • Easy to add or remove agents

Challenges and Mitigation

1. Scalability Limitations

   • Central memory becomes a potential bottleneck
   • Implemented fine-grained locking
   • Added versioning to reduce contention

2. Data Consistency

   • Implemented compare-and-swap for atomic updates
   • Added max age and versioning to manage data freshness
   • Supports optimistic concurrency control

Research and Extension Opportunities

1. Implement distributed shared memory
2. Add more advanced synchronization mechanisms
3. Create more complex agent interaction patterns
4. Develop performance monitoring tools

[MichaelDecent]

Collaborative Systems Category:

Collaborative systems rely on efficient communication and shared understanding among agents to achieve a common goal. Below is an in-depth explanation of the Blackboard Pattern, Agent Communication Languages (ACLs), and the Publish-Subscribe Pattern within this context.

---

1. Blackboard Pattern

The Blackboard Pattern is inspired by real-world brainstorming sessions where participants write ideas on a shared board visible to everyone. This mechanism is widely used for collaborative problem-solving.

Key Characteristics:

• Centralized Repository: A shared blackboard acts as a centralized knowledge base.
• Collaborative Problem-Solving: Agents iteratively contribute solutions or modify the shared state to refine the outcome.
• Trigger Mechanisms: Agents monitor the blackboard for relevant updates, triggering their specific problem-solving actions.

Strengths:

• Flexibility: Agents don’t need direct communication channels; the blackboard mediates interaction.
• Shared Knowledge: Ensures all agents operate with a consistent data view, crucial for complex tasks like multi-agent planning.
• Loose Coupling: Agents are decoupled and only need to understand the blackboard structure.

Weaknesses:

• Bottleneck: The centralized nature can limit scalability and introduce latency in high-load scenarios.
• Fault Tolerance: The blackboard can become a single point of failure.
• Implementation Complexity: Requires robust synchronization and conflict resolution mechanisms to manage simultaneous agent actions.

Use Cases:

• Multi-robot coordination in manufacturing.
• Intelligent tutoring systems (sharing user progress and actions among teaching agents).
• Distributed diagnostic systems in fields like network management or healthcare.

---

2. Agent Communication Languages (ACLs)

ACLs provide a structured, semantic way for agents to communicate. Examples include FIPA-ACL and KQML (Knowledge Query and Manipulation Language).

Key Characteristics:

• Semantic Layer: Messages are enriched with meaning, allowing agents to perform complex interactions like negotiations, planning, or queries.
• Standardized Protocols: Defined structures and performatives (e.g., request, inform, propose) ensure predictable interactions.
• Content Agnostic: The language defines how to communicate, not what to communicate, making it versatile.

Strengths:

• Expressiveness: Supports advanced behaviors like knowledge sharing, negotiation, and multi-agent task allocation.
• Interoperability: Different agent platforms can communicate using a shared ACL standard.
• Fault Tolerance: Semantic messaging allows agents to interpret partial or incomplete information.

Weaknesses:

• Complexity: High learning curve for both developers and agents, especially in designing message parsers and interpreters.
• Latency: Semantic processing can introduce delays, making it less suitable for real-time systems.
• Implementation Overhead: Requires additional components like ontology definitions and reasoning engines.

Use Cases:

• Smart grid systems (negotiating power usage between consumers and suppliers).
• Multi-agent simulations in logistics (coordinating supply chain actors).
• Autonomous vehicle coordination (sharing plans and traffic data).

---

3. Publish-Subscribe Pattern

The Publish-Subscribe Pattern is a message-driven mechanism where agents (subscribers) register interest in specific topics, and publishers send relevant updates.

Key Characteristics:

• Broker-Based: A central broker (like Kafka or RabbitMQ) handles message routing.
• Topic-Based Filtering: Subscribers only receive messages relevant to their registered topics.
• Asynchronous Communication: Publishers and subscribers operate independently, enhancing scalability.

Strengths:

• Decoupling: Publishers and subscribers don’t need to know about each other, simplifying agent interactions.
• Scalability: Handles a dynamic number of agents without significant changes.
• Fault Tolerance: Brokers often have built-in features like replication and recovery.

Weaknesses:

• Broker Dependency: The system's reliability depends on the broker's stability and capacity.
• Complex Debugging: Tracing issues can be challenging due to asynchronous communication.
• Latency: Can vary based on broker performance and network conditions.

Use Cases:

• IoT systems (e.g., sensors publishing data, analytics agents subscribing to it).
• Collaborative robots in warehouses (subscribing to updates on inventory status).
• Disaster management systems (publishing real-time alerts and coordinating responders).

---

Comparative Analysis

Criteria	Blackboard Pattern	ACLs	Publish-Subscribe
Communication Style	Centralized, Shared Repository	Semantic, Structured Messages	Decentralized, Broker-Based Topics
Scalability	Moderate	Moderate	High
Fault Tolerance	Low (centralized)	High	Moderate to High
Latency	Moderate to High	Moderate to High	Low to Moderate
Ease of Use	Moderate to High (complex synchronization)	Low (high learning curve)	Moderate
Interoperability	High	Very High	High
Best Fit Use Cases	Collaborative problem-solving	Negotiations, Semantic Planning	Dynamic and Event-Driven Systems

---

Conclusion

• Blackboard Pattern is best for tightly integrated systems that need a shared, central knowledge base.
• ACLs shine in semantically rich and complex interactions requiring structured negotiation and reasoning.
• Publish-Subscribe is ideal for loosely coupled, dynamic, and scalable systems where agents have varied roles.

[MichaelDecent]

Blackboard Pattern Communication System

Blackboard Pattern

[MichaelDecent]

Below are basic implementations of the Blackboard Pattern, Agent Communication Languages (ACLs), and Publish-Subscribe Pattern using Python. These examples are simplified and can be extended for real-world applications.

---

1. Blackboard Pattern

A shared blackboard is implemented as a centralized data structure (e.g., a dictionary). Agents interact with the blackboard by reading and writing.

import threading
import time

# Shared blackboard
blackboard = {}
blackboard_lock = threading.Lock()

# Blackboard agent
class Agent(threading.Thread):
    def __init__(self, name, task):
        super().__init__()
        self.name = name
        self.task = task

    def run(self):
        while True:
            with blackboard_lock:
                if self.task in blackboard:
                    print(f"{self.name} reads: {self.task} -> {blackboard[self.task]}")
                    blackboard[self.task] += 1  # Simulating some work
                else:
                    print(f"{self.name} writes: {self.task} -> 1")
                    blackboard[self.task] = 1
            time.sleep(1)

# Create agents
agent1 = Agent("Agent1", "Task1")
agent2 = Agent("Agent2", "Task1")

agent1.start()
agent2.start()

---

2. Agent Communication Languages (ACLs)

Using JSON for message encoding and decoding simulates structured semantic communication.

import json
import queue
import threading

# Message broker
message_queue = queue.Queue()

# Define ACL Message
def create_acl_message(sender, receiver, performative, content):
    return json.dumps({
        "sender": sender,
        "receiver": receiver,
        "performative": performative,
        "content": content
    })

# Agent class
class ACLAgent(threading.Thread):
    def __init__(self, name):
        super().__init__()
        self.name = name

    def send_message(self, message):
        message_queue.put(message)

    def run(self):
        while True:
            if not message_queue.empty():
                message = json.loads(message_queue.get())
                if message["receiver"] == self.name:
                    print(f"{self.name} received: {message}")

# Create agents
agent1 = ACLAgent("Agent1")
agent2 = ACLAgent("Agent2")

agent1.start()
agent2.start()

# Send messages
message = create_acl_message("Agent1", "Agent2", "inform", "Hello, Agent2!")
agent1.send_message(message)

---

3. Publish-Subscribe Pattern

Using the paho-mqtt library for MQTT-based Pub-Sub communication.

Installation:

pip install paho-mqtt

Code:

import paho.mqtt.client as mqtt
import time

# Define the MQTT broker
BROKER = "test.mosquitto.org"
PORT = 1883
TOPIC = "multiagent/demo"

# Publisher
def publisher():
    client = mqtt.Client()
    client.connect(BROKER, PORT, 60)
    for i in range(5):
        message = f"Message {i}"
        client.publish(TOPIC, message)
        print(f"Published: {message}")
        time.sleep(1)
    client.disconnect()

# Subscriber
def subscriber():
    def on_message(client, userdata, msg):
        print(f"Received: {msg.payload.decode()}")

    client = mqtt.Client()
    client.connect(BROKER, PORT, 60)
    client.subscribe(TOPIC)
    client.on_message = on_message
    client.loop_forever()

# Run publisher and subscriber
if __name__ == "__main__":
    import threading

    pub_thread = threading.Thread(target=publisher)
    sub_thread = threading.Thread(target=subscriber)

    sub_thread.start()
    time.sleep(1)  # Ensure subscriber is ready before publishing
    pub_thread.start()

---

Key Notes:

1. Blackboard Pattern:

   • Simplifies shared data but may bottleneck with many agents.
   • Suitable for educational setups or conceptual understanding.

2. ACLs:

   • Encodes structured communication but needs a robust framework for large-scale use.
   • Can integrate with semantic libraries like rdflib for more advanced use cases.

3. Publish-Subscribe:

   • Leverages existing broker technologies (e.g., MQTT, Kafka).
   • Great for distributed systems and scalability.

[MichaelDecent]

The Blackboard Pattern, Agent Communication Languages (ACLs), and Publish-Subscribe Pattern share several common attributes and methods, as they all deal with communication and coordination in multi-agent systems. Despite their unique structures, their commonalities can be leveraged to design reusable components.

---

Common Attributes

1. Agents:

   • All patterns involve independent entities (agents) that act autonomously.
   • Agents are typically characterized by:
     • Unique Identifier: To distinguish between agents.
     • State: Internal information or context that affects their behavior.
     • Capabilities: Tasks or roles they perform in the system.

2. Message or Data Representation:

   • All patterns rely on a standardized way to represent information exchanged between agents:
     • Blackboard: Shared structured data (e.g., key-value pairs or tasks).
     • ACL: Structured messages with performative actions (e.g., inform, request).
     • Publish-Subscribe: Topic-based messages.

3. Communication Medium:

   • A shared resource facilitates communication:
     • Blackboard: Centralized repository.
     • ACL: Message queue or direct communication.
     • Publish-Subscribe: Broker or intermediary service.

4. Synchronization:

   • Mechanisms for ensuring agents operate harmoniously:
     • Blackboard: Data locking to prevent conflicts.
     • ACL: Message delivery and acknowledgment.
     • Publish-Subscribe: Broker-managed topic synchronization.

5. Scalability and Extensibility:

   • All patterns aim to support an increasing number of agents or adapt to new ones.
   • Each provides methods to dynamically manage agent participation.

---

Common Methods

Here are the shared methods or concepts that can unify the implementation of these patterns:

1. Agent Registration/Discovery

• Purpose: Identify or add agents to the system.
• Example Methods:
  • Blackboard: Register a new agent with access rights.
  • ACL: Establish protocols to recognize message senders/receivers.
  • Publish-Subscribe: Subscribe to or unsubscribe from topics.

def register_agent(agent_id, system):
    system['agents'].append(agent_id)

---

2. Message/Task Posting

• Purpose: Allow agents to contribute data or messages to the system.
• Example Methods:
  • Blackboard: Write data to the shared repository.
  • ACL: Send a structured message.
  • Publish-Subscribe: Publish a message to a topic.

def post_message(agent_id, data, system):
    if system['type'] == "blackboard":
        system['repository'].append((agent_id, data))
    elif system['type'] == "acl":
        system['queue'].put(data)
    elif system['type'] == "pubsub":
        system['broker'].publish(data)

---

3. Subscription/Listening

• Purpose: Allow agents to actively monitor changes or receive messages.
• Example Methods:
  • Blackboard: Poll or get notified of data changes.
  • ACL: Listen to incoming messages in a queue.
  • Publish-Subscribe: Subscribe to specific topics.

def subscribe(agent_id, topic, system):
    if system['type'] == "pubsub":
        system['subscriptions'][topic].append(agent_id)

---

4. Processing or Acting on Messages

• Purpose: Interpret received messages and act accordingly.
• Example Methods:
  • Blackboard: Process tasks or respond to changes in the shared data.
  • ACL: Parse message performatives and act on their intent.
  • Publish-Subscribe: React to published events.

def process_message(agent_id, message):
    print(f"Agent {agent_id} processing message: {message}")

---

5. Fault Handling

• Purpose: Gracefully manage errors or inconsistencies in communication.
• Example Methods:
  • Blackboard: Retry or roll back changes.
  • ACL: Re-send or negotiate failed actions.
  • Publish-Subscribe: Buffer messages for disconnected subscribers.

def handle_fault(agent_id, error):
    print(f"Agent {agent_id} encountered error: {error}")

---

Unified Class Design

A unified class for agents could look like this:

class Agent:
    def __init__(self, agent_id, system_type):
        self.agent_id = agent_id
        self.system_type = system_type

    def register(self, system):
        register_agent(self.agent_id, system)

    def post(self, data, system):
        post_message(self.agent_id, data, system)

    def subscribe(self, topic, system):
        subscribe(self.agent_id, topic, system)

    def process(self, message):
        process_message(self.agent_id, message)

    def handle_error(self, error):
        handle_fault(self.agent_id, error)

This approach abstracts the underlying mechanism, enabling agents to interact with different communication patterns using common methods.

[3rd-Son]

Below is a directory structure for implementing a unified system supporting Blackboard Pattern, ACLs, and Publish-Subscribe Pattern. It also includes the code.

---

Directory Structure

multiagent_system/
├── __init__.py
├── main.py                     # Entry point to run the system
├── agents/
│   ├── __init__.py
│   ├── agent.py                # Generic Agent class
│   ├── acl_agent.py            # Specialized Agent for ACL
│   └── pubsub_agent.py         # Specialized Agent for Pub-Sub
├── systems/
│   ├── __init__.py
│   ├── blackboard.py           # Blackboard system implementation
│   ├── acl.py                  # ACL system implementation
│   └── pubsub.py               # Publish-Subscribe system implementation
├── utils/
│   ├── __init__.py
│   ├── message_broker.py       # Simple in-memory Pub-Sub broker
│   └── message_queue.py        # Simple message queue for ACLs
└── README.md                   # Documentation

---

Code Implementation

1. main.py

from agents.agent import Agent
from systems.blackboard import BlackboardSystem
from systems.acl import ACLSystem
from systems.pubsub import PubSubSystem

if __name__ == "__main__":
    # Initialize systems
    blackboard = BlackboardSystem()
    acl = ACLSystem()
    pubsub = PubSubSystem()

    # Create agents
    agent1 = Agent("Agent1", blackboard)
    agent2 = Agent("Agent2", acl)
    agent3 = Agent("Agent3", pubsub)

    # Interact with systems
    agent1.post("Task1", "Initialize Task")
    agent2.post("Message1", {"performative": "inform", "content": "Hello"})
    agent3.publish("topic1", "Event Triggered")

---

2. agents/agent.py

class Agent:
    def __init__(self, agent_id, system):
        self.agent_id = agent_id
        self.system = system

    def post(self, key, value):
        self.system.post(self.agent_id, key, value)

    def subscribe(self, topic):
        if hasattr(self.system, "subscribe"):
            self.system.subscribe(self.agent_id, topic)

    def publish(self, topic, message):
        if hasattr(self.system, "publish"):
            self.system.publish(self.agent_id, topic, message)

---

3. systems/blackboard.py

import threading

class BlackboardSystem:
    def __init__(self):
        self.blackboard = {}
        self.lock = threading.Lock()

    def post(self, agent_id, task, data):
        with self.lock:
            self.blackboard[task] = data
            print(f"{agent_id} posted to Blackboard: {task} -> {data}")

---

4. systems/acl.py

import queue

class ACLSystem:
    def __init__(self):
        self.message_queue = queue.Queue()

    def post(self, agent_id, message_id, data):
        message = {"sender": agent_id, "message_id": message_id, "data": data}
        self.message_queue.put(message)
        print(f"{agent_id} sent ACL message: {message}")

    def receive(self):
        while not self.message_queue.empty():
            message = self.message_queue.get()
            print(f"Received ACL message: {message}")

---

5. systems/pubsub.py

from utils.message_broker import MessageBroker

class PubSubSystem:
    def __init__(self):
        self.broker = MessageBroker()

    def publish(self, agent_id, topic, message):
        self.broker.publish(topic, message)
        print(f"{agent_id} published: {topic} -> {message}")

    def subscribe(self, agent_id, topic):
        self.broker.subscribe(agent_id, topic)
        print(f"{agent_id} subscribed to: {topic}")

---

6. utils/message_broker.py

from collections import defaultdict

class MessageBroker:
    def __init__(self):
        self.subscribers = defaultdict(list)

    def subscribe(self, agent_id, topic):
        self.subscribers[topic].append(agent_id)

    def publish(self, topic, message):
        if topic in self.subscribers:
            for subscriber in self.subscribers[topic]:
                print(f"Message to {subscriber}: {message}")

---

7. utils/message_queue.py

import queue

class MessageQueue:
    def __init__(self):
        self.queue = queue.Queue()

    def send_message(self, sender, receiver, content):
        message = {"sender": sender, "receiver": receiver, "content": content}
        self.queue.put(message)

    def receive_message(self):
        if not self.queue.empty():
            return self.queue.get()
        return None

---

Usage

• Run main.py to see all three communication patterns in action.
• Customize main.py to experiment with agents and systems.

NB: We can rename the files to whatever we like, this is just an overview of my idea

[MichaelDecent]

Proposed Directory Structure for the Multi-Agent Framework

Here’s a directory structure to organize your framework effectively, aligning with the goals of modularity, reusability, and extensibility:

multi_agent_framework/
├── __init__.py                 # Package initializer
├── main.py                     # Entry point for running the system
├── core/                       # Core abstractions and base classes
│   ├── __init__.py
│   ├── agent_factory.py        # Abstract base class for Agent Factory
│   ├── agent_registry.py       # Abstract base class for Agent Registry
│   ├── communication_layer.py  # Abstract base class for Communication Layer
│   ├── order_resolution.py     # Abstract base class for Order Resolution
│   └── base_agent.py                # Base Agent class
├── systems/                    # Implementations of abstract components
│   ├── __init__.py
│   ├── blackboard.py           # Blackboard system implementation
│   ├── acl.py                  # ACL-based communication implementation
│   ├── pubsub.py               # Publish-Subscribe implementation
│   ├── hierarchical_planner.py # Hierarchical Task Planner implementation
│   └── distributed_queue.py    # Distributed Queue for task allocation
├── factories/                  # Agent Factory implementations
│   ├── __init__.py
│   ├── simple_factory.py       # Simple Agent Factory for basic agents
│   ├── role_based_factory.py   # Factory for role-based agents
│   └── hybrid_factory.py       # Factory for heterogeneous agents
├── registries/                 # Agent Registry implementations
│   ├── __init__.py
│   ├── simple_registry.py      # Simple in-memory registry
│   ├── distributed_registry.py # Registry for distributed systems
│   └── scalable_registry.py    # Registry with load-balancing capabilities
├── utils/                      # Utility modules
│   ├── __init__.py
│   ├── message_broker.py       # Simple Pub-Sub broker
│   └── message_queue.py        # Queue utility for ACLs
└── README.md                   # Documentation

---

Code Overview for Each Component

1. core/ - Core Abstract Classes

Contains the base definitions for the framework.

• agent_factory.py

  from abc import ABC, abstractmethod
  
  class AgentFactory(ABC):
      @abstractmethod
      def create_agent(self, agent_id, agent_type):
          pass

• agent_registry

from abc import ABC, abstractmethod

class AgentRegistry(ABC):
    @abstractmethod
    def register_agent(self, agent):
        pass

    @abstractmethod
    def get_agent(self, agent_id):
        pass

• communication_layer.py

  from abc import ABC, abstractmethod
  
  class CommunicationLayer(ABC):
      @abstractmethod
      def broadcast(self, message):
          pass
  
      @abstractmethod
      def multicast(self, message, group_ids):
          pass
  
      @abstractmethod
      def unicast(self, message, agent_id):
          pass

• order_resolution

  from abc import ABC, abstractmethod

class OrderResolution(ABC):
    @abstractmethod
    def assign_task(self, agent_id, task):
        pass

    @abstractmethod
    def resolve_order(self):
        pass

• base_agent

  from abc import ABC, abstractmethod

class BaseAgent(ABC):

    @abstractmethod
    def run(self): # yet to determine a generic name for the abstract method 
        pass

2. systems/ - Implementations

Implement the core abstractions for specific systems.

• blackboard.py

  from core.communication_layer import CommunicationLayer
  
  class BlackboardSystem(CommunicationLayer):
      def __init__(self):
          self.blackboard = {}
  
      def broadcast(self, message):
          self.blackboard["broadcast"] = message
  
      def multicast(self, message, group_ids):
          for group in group_ids:
              self.blackboard[group] = message
  
      def unicast(self, message, agent_id):
          self.blackboard[agent_id] = message

3. factories/ - Agent Factories

Create agents dynamically based on type or role.

• simple_factory.py

  from core.agent_factory import AgentFactory
  from core.base_agent import BaseAgent
  
  class SimpleFactory(AgentFactory):
      def create_agent(self, agent_id, agent_type):
          return BaseAgent(agent_id, agent_type)

4. registries/ - Agent Registries

Manage agent lifecycles.

• simple_registry.py

  from core.agent_registry import AgentRegistry
  
  class SimpleRegistry(AgentRegistry):
      def __init__(self):
          self.registry = {}
  
      def register_agent(self, agent):
          self.registry[agent.agent_id] = agent
  
      def get_agent(self, agent_id):
          return self.registry.get(agent_id)

5. Use Cases

Provide practical demonstrations of the framework.

• example_blackboard.py

  from systems.blackboard import BlackboardSystem
  
  blackboard = BlackboardSystem()
  blackboard.broadcast("System Initialization")
  print(blackboard.blackboard)

---

Why This Structure Works

1. Modularity:

   • Keeps core abstractions separate from their implementations.
   • Allows easy replacement or extension of components.

2. Scalability:

   • New patterns or mechanisms can be added to systems/.
   • Different agent behaviors can be implemented in factories/.

3. Ease of Understanding:

   • Each layer (core, systems, factories, registries) has a clear responsibility.
   • Example scripts help demonstrate practical usage.

4. Extensibility:

   • New systems (e.g., for distributed task allocation) can be plugged in without modifying the framework's core logic.

[MichaelDecent]

The directory structure is designed to organize a multi-agent framework in a modular and extensible manner. Each component or concern of the framework is encapsulated in its own directory or module, allowing the framework to scale, evolve, and remain maintainable. Here's how it works:

---

3. Core Components (core/)

• Purpose: Contains abstract base classes that define the essential interfaces and contracts for core framework functionalities.
• Files:
  • base_agent.py: Defines the base agent class with common attributes and methods (e.g., perceive, decide, act, communicate).
  • agent_factory.py: Abstracts the logic for creating agents, allowing different implementations (e.g., role-based, hybrid).
  • agent_registry.py: Defines the interface for managing and retrieving registered agents.
  • communication_layer.py: Establishes the base structure for communication mechanisms like publish-subscribe or ACL.
  • order_resolution.py: Abstracts task management, ensuring tasks are allocated and resolved systematically.

---

4. Systems (systems/)

• Purpose: Houses concrete implementations of communication mechanisms and task allocation strategies.
• Files:
  • blackboard.py: Implements the Blackboard communication model for collaborative problem-solving.
  • acl.py: Implements ACL-based communication for structured message exchange.
  • pubsub.py: Implements the Publish-Subscribe model for decoupled communication.
  • hierarchical_planner.py: Implements a task planning system using a hierarchical approach.
  • distributed_queue.py: Implements distributed queue logic for scalable task allocation.

---

5. Factories (factories/)

• Purpose: Provides concrete implementations for creating different types of agents dynamically.
• Files:
  • simple_factory.py: Creates basic agents.
  • role_based_factory.py: Creates agents with predefined roles (e.g., picker, packer).
  • hybrid_factory.py: Creates heterogeneous agents, combining multiple agent types.

---

6. Registries (registries/)

• Purpose: Provides mechanisms to register, track, and retrieve agents in the system.
• Files:
  • simple_registry.py: Basic in-memory registry for small-scale systems.
  • distributed_registry.py: Registry designed for distributed systems, ensuring scalability.
  • scalable_registry.py: Advanced registry with load-balancing and fault-tolerance capabilities.

---

7. Utilities (utils/)

• Purpose: Provides auxiliary modules for supporting various system functionalities.
• Files:
  • message_broker.py: Implements a simple message broker for the Pub-Sub communication layer.
  • message_queue.py: Utility for queuing and processing messages, used by ACL-based systems.

---

How It All Fits Together

1. Core defines the rules:
   • Abstract base classes in core/ define the expectations for agents, factories, registries, and communication layers.
2. Systems implement specific strategies:
   • Concrete systems (e.g., Pub-Sub, ACL, Blackboard) in systems/ implement communication and task allocation strategies.
3. Factories create agents:
   • Factories in factories/ generate agents of specific types or roles as needed by the use case.
4. Registries manage agents:
   • Registries in registries/ handle agent tracking and retrieval for collaboration.
5. Utilities support the system:
   • Utility modules in utils/ enable communication, logging, and other auxiliary functions.
6. Main orchestrates:
   • main.py ties everything together, combining agents, systems, and registries to simulate or run the framework.

---

Advantages

• Modularity: Each concern is encapsulated in its own directory/module.
• Extensibility: New communication mechanisms or agent types can be added without modifying the core.
• Reusability: Core abstractions can be reused across multiple implementations.
• Scalability: Different implementations (e.g., scalable registries, distributed systems) can be swapped as needed.

This structure supports both homogeneous and heterogeneous agent frameworks with interchangeable components.

[3rd-Son]

@cobycloud please look at this structure
What do you have to say?

[MichaelDecent]

Real-life use case

Here is is a real-life use case example utilizing the provided Blackboard implementation while adhering to the proposed directory structure

---

Directory Structure Integration

• The use case involves a smart city traffic monitoring system, where multiple agents collaborate to:
  1. Collect traffic data from sensors.
  2. Analyze traffic patterns.
  3. Propose solutions like adjusting traffic lights or suggesting alternative routes.

---

Modified Blackboard System

core/base_agent.py

Defines the base agent interface.

from abc import ABC, abstractmethod

class BaseAgent(ABC):
    def __init__(self, name: str, capabilities: list):
        self.name = name
        self.capabilities = capabilities

    @abstractmethod
    async def process(self, communication_layer, problem: dict):
        pass

---

core/agent_factory.py

Defines the base factory interface.

from abc import ABC, abstractmethod

class AgentFactory(ABC):
    @abstractmethod
    def create_agents(self):
        pass

---

core/agent_registry.py

Defines the base registry interface.

from abc import ABC, abstractmethod

class AgentRegistry(ABC):
    @abstractmethod
    def register(self, agent):
        pass

    @abstractmethod
    def get_agents(self):
        pass

---

core/communication_layer.py

from abc import ABC, abstractmethod

class CommunicationLayer(ABC):

    @abstractmethod
    async def solve_problem(self, problem):
        pass

    @abstractmethod
    def add_knowledge(self, key, content, contributor):
        pass

systems/blackboard.py
Updated Blackboard system inheriting from CommunicationLayer.

import asyncio
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from enum import Enum, auto
from core.communication_layer import CommunicationLayer

class KnowledgeState(Enum):
    INCOMPLETE = auto()
    PARTIAL = auto()
    COMPLETE = auto()

@dataclass
class BlackboardData:
    content: Any
    state: KnowledgeState = field(default=KnowledgeState.INCOMPLETE)
    contributor: Optional[str] = None
    confidence_level: float = 0.0

class Blackboard(CommunicationLayer):
    def __init__(self, agents: List[Subclasses(BaseAgent)]  ):
        self._knowledge: Dict[str, BlackboardData] = {}
        self._agents: List[Any] = agents
        self._solution_lock = asyncio.Lock()
        self._problem_state: KnowledgeState = KnowledgeState.INCOMPLETE

    def add_knowledge(self, key: str, content: Any, contributor: str):
        existing = self._knowledge.get(key)
        confidence = self._calculate_confidence(existing, content)

        new_data = BlackboardData(
            content=content,
            contributor=contributor,
            confidence_level=confidence,
            state=self._determine_state(content)
        )
        self._knowledge[key] = new_data
        self._update_problem_state()

    def _calculate_confidence(self, existing: Optional[BlackboardData], new_content: Any) -> float:
        if not existing:
            return 0.5
        return min(existing.confidence_level + 0.2, 1.0)

    def _determine_state(self, content: Any) -> KnowledgeState:
        if content is None:
            return KnowledgeState.INCOMPLETE
        elif isinstance(content, list) and len(content) < 2:
            return KnowledgeState.PARTIAL
        return KnowledgeState.COMPLETE

    def _update_problem_state(self):
        states = [data.state for data in self._knowledge.values()]
        if all(state == KnowledgeState.COMPLETE for state in states):
            self._problem_state = KnowledgeState.COMPLETE
        elif any(state == KnowledgeState.COMPLETE for state in states):
            self._problem_state = KnowledgeState.PARTIAL
        else:
            self._problem_state = KnowledgeState.INCOMPLETE

    async def solve_problem(self, problem: Dict[str, Any]):
        print(f"Starting problem-solving process: {problem}")
        tasks = [asyncio.create_task(agent.process(self, problem)) for agent in self._agents]
        await asyncio.gather(*tasks)

        if self._problem_state == KnowledgeState.COMPLETE:
            print("Problem solved successfully!")
            self._print_solution()
        else:
            print("Problem solving incomplete.")

    def _print_solution(self):
        print("\n--- Final Blackboard Solution ---")
        for key, data in self._knowledge.items():
            print(f"{key}: {data.content} (Contributor: {data.contributor}, "
                  f"Confidence: {data.confidence_level:.2f})")

---

factories/simple_factory.py

Implements the factory interface.

from core.agent_factory import AgentFactory
from core.base_agent import BaseAgent

class TrafficDataCollector(BaseAgent):
    async def process(self, communication_layer, problem):
        await asyncio.sleep(1)
        data = {"traffic_density": {"intersection_a": 80, "intersection_b": 60}}
        communication_layer.add_knowledge("traffic_data", data, self.name)

class TrafficAnalyzer(BaseAgent):
    async def process(self, communication_layer, problem):
        await asyncio.sleep(1.5)
        traffic_data = communication_layer._knowledge.get("traffic_data")
        if traffic_data:
            analysis = {"busiest_intersection": "intersection_a"}
            communication_layer.add_knowledge("traffic_analysis", analysis, self.name)

class TrafficSolutionGenerator(BaseAgent):
    async def process(self, communication_layer, problem):
        await asyncio.sleep(2)
        analysis = communication_layer._knowledge.get("traffic_analysis")
        if analysis:
            solution = {"action": "Adjust signals to favor intersection_a"}
            communication_layer.add_knowledge("traffic_solution", solution, self.name)

class SimpleAgentFactory(AgentFactory):
    def create_agents(self):
        return [
            TrafficDataCollector("TrafficDataCollector", ["data_collection"]),
            TrafficAnalyzer("TrafficAnalyzer", ["analysis"]),
            TrafficSolutionGenerator("TrafficSolutionGenerator", ["solution_generation"]),
        ]

---

registries/simple_registry.py

Implements the registry interface.

from core.agent_registry import AgentRegistry

class SimpleAgentRegistry(AgentRegistry):
    def __init__(self):
        self._agents = []

    def register(self, agent):
        self._agents.append(agent)

    def get_agents(self):
        return self._agents

---

main.py

Integrates everything into the use case.

import asyncio
from systems.blackboard import Blackboard
from factories.simple_factory import SimpleAgentFactory
from registries.simple_registry import SimpleAgentRegistry

async def main():

    # Create agent registry
    registry = SimpleAgentRegistry()

    # Create and register agents using the factory
    factory = SimpleAgentFactory()
    agents = factory.create_agents()
    for agent in agents:
        registry.register(agent)
    
    # Create Blackboard communication layer
    blackboard = Blackboard(agents=registry.get_agents())

    # Define a traffic optimization problem
    problem = {"description": "Optimize traffic flow in the city"}
    await blackboard.solve_problem(problem)

if __name__ == "__main__":
    asyncio.run(main())

---

How Core Interfaces Were Utilized

1. BaseAgent:

   • All agents (e.g., TrafficDataCollector) inherit from the BaseAgent class.
   • Standardized attributes (name, capabilities) and the process() method ensure consistency.

2. AgentFactory:

   • The SimpleAgentFactory implements the create_agents() method as defined in the AgentFactory interface.

3. AgentRegistry:

   • The SimpleAgentRegistry implements register(agent) and get_agents() methods per the AgentRegistry interface.

---

Benefits of this Approach

• Scalability: Future additions of factories, registries, or agents follow the same design contract.
• Interchangeability: Any factory or registry implementation can be swapped with another as long as it adheres to the core interface.
• Code Reusability: Standardized components can be reused across different systems or projects.

[MichaelDecent]

Just trying to see how i can i align a real life implementation on the current directory structure

[MichaelDecent]

Here’s a simplified directory structure for Contract Net Manager communication pattern. It includes only the necessary files based on the functionality described in the code.

multiagent_framework/
│
├── agents/
│   ├── __init__.py
│   ├── agent.py               # Contains the `Agent` class and its methods
│
├── tasks/
│   ├── __init__.py
│   ├── task.py                # Contains the `Task` class and `TaskStatus` enum
│
├── managers/
│   ├── __init__.py
│   ├── contract_net_manager.py # Contains the `ContractNetManager` class
│
├── main.py                     # Entry point to run the framework
└── requirements.txt            # Python dependencies

File Details

1. agents/agent.py:

   • Implements the Agent class, its methods (evaluate_task, perform_task), and any agent-related logic.

2. tasks/task.py:

   • Defines the Task class and the TaskStatus enumeration.

3. managers/contract_net_manager.py:

   • Implements the ContractNetManager class, which manages task allocation using the Contract Net Protocol.

4. main.py:

   • Entry point of the application. Contains the main() function to initialize agents, create tasks, and run the allocation process.

---

multiagent_framework/agents/agent.py

import random
from typing import List, Optional, Dict
from pydantic import BaseModel, Field, ConfigDict

from tasks.task import Task


class Agent(BaseModel):
    """
    Represents an agent with specializations and workload constraints
    """
    name: str
    specializations: List[str]
    max_load: float = Field(default=1.0, ge=0, le=1.0)
    
    # Runtime attributes (not validated by Pydantic)
    current_load: float = 0.0
    completed_tasks: List[Task] = []
    failed_tasks: List[Task] = []

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
        validate_assignment=True
    )

    async def evaluate_task(self, task: Task) -> Optional[float]:
        """
        Evaluate a task's suitability and potential bid
        """
        # Check specialization match
        if not any(spec in self.specializations for spec in task.description.split(',')):
            return None
        
        # Check remaining capacity
        remaining_capacity = self.max_load - self.current_load
        
        # Reject if insufficient capacity
        if task.complexity > remaining_capacity:
            return None
        
        # Calculate bid (lower is better)
        bid = (task.complexity / remaining_capacity) / task.reward
        
        return bid

    async def perform_task(self, task: Task) -> Dict:
        """
        Simulate task execution
        """
        # Simulate task complexity and potential failure
        await asyncio.sleep(task.complexity)
        
        # Random chance of task failure
        if random.random() < 0.1:  # 10% failure rate
            return {"status": "failed", "reason": "Unexpected complexity"}
        
        return {
            "status": "completed",
            "details": f"Task {task.id} completed by {self.name}",
            "reward": task.reward
        }

---

multiagent_framework/tasks/task.py

import random
from enum import Enum
from typing import Optional, Dict
from pydantic import BaseModel, Field, ConfigDict, validator


class TaskStatus(str, Enum):
    OPEN = "open"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"


class Task(BaseModel):
    """
    Represents a task to be allocated in the Contract Net Protocol
    """
    id: str = Field(default_factory=lambda: f"task_{random.randint(1000, 9999)}")
    description: str
    complexity: float = Field(ge=0.0, le=1.0)
    reward: float = Field(gt=0)
    status: TaskStatus = TaskStatus.OPEN
    assigned_agent: Optional[str] = None
    result: Optional[Dict] = None

    model_config = ConfigDict(
        extra='forbid',  # Prevent additional fields
        frozen=False,    # Allow modification after creation
        validate_assignment=True  # Validate on attribute change
    )

    @validator('complexity')
    def validate_complexity(cls, v):
        """
        Ensure complexity is between 0 and 1
        """
        if not (0 <= v <= 1):
            raise ValueError("Complexity must be between 0 and 1")
        return v

---

multiagent_framework/managers/contract_net_manager.py

import asyncio
from typing import List, Optional, Tuple, Dict
from pydantic import BaseModel, ConfigDict

from agents.agent import Agent
from tasks.task import Task, TaskStatus


class ContractNetManager(BaseModel):
    """
    Manages task allocation using Contract Net Protocol
    """
    agents: List[Agent] = []
    tasks: List[Task] = []
    task_history: List[Task] = []

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
        validate_assignment=True
    )

    def register_agent(self, agent: Agent):
        """
        Register an agent in the contract net
        """
        self.agents.append(agent)

    def create_task(self, description: str, complexity: float, reward: float) -> Task:
        """
        Create a new task for allocation
        """
        task = Task(
            description=description,
            complexity=complexity,
            reward=reward
        )
        self.tasks.append(task)
        return task

    async def allocate_task(self, task: Task) -> Optional[Tuple[Agent, Dict]]:
        """
        Allocate a task using Contract Net Protocol
        """
        print(f"\nAllocating Task: {task.description}")
        
        # Call for proposals
        bids = []
        for agent in self.agents:
            bid = await agent.evaluate_task(task)
            if bid is not None:
                bids.append((agent, bid))
        
        # No suitable agents found
        if not bids:
            print(f"No agents available for task: {task.description}")
            return None
        
        # Select agent with lowest bid (best fit)
        selected_agent, _ = min(bids, key=lambda x: x[1])
        
        # Assign and execute task
        task.assigned_agent = selected_agent.name
        task.status = TaskStatus.IN_PROGRESS
        
        try:
            result = await selected_agent.perform_task(task)
            
            if result['status'] == 'completed':
                task.status = TaskStatus.COMPLETED
                task.result = result
                selected_agent.completed_tasks.append(task)
                print(f"Task {task.id} completed by {selected_agent.name}")
            else:
                task.status = TaskStatus.FAILED
                selected_agent.failed_tasks.append(task)
                print(f"Task {task.id} failed for {selected_agent.name}")
            
            return (selected_agent, result)
        
        except Exception as e:
            print(f"Task allocation error: {e}")
            task.status = TaskStatus.FAILED
            return None

    async def run_task_allocation(self):
        """
        Run task allocation for all tasks
        """
        task_allocation_tasks = [
            self.allocate_task(task) for task in self.tasks
        ]
        
        await asyncio.gather(*task_allocation_tasks)
        
        # Print allocation summary
        self.print_allocation_summary()

    def print_allocation_summary(self):
        """
        Print summary of task allocations
        """
        print("\n--- Task Allocation Summary ---")
        for task in self.tasks:
            print(f"Task {task.id}: {task.description}")
            print(f"  Status: {task.status}")
            print(f"  Assigned Agent: {task.assigned_agent or 'Unassigned'}")
            print(f"  Result: {task.result or 'N/A'}\n")

---

multiagent_framework/main.py

import asyncio
from managers.contract_net_manager import ContractNetManager
from agents.agent import Agent


async def main():
    # Create Contract Net Manager
    contract_net = ContractNetManager()

    # Create specialized agents
    data_agent = Agent(name="DataAnalyst", specializations=["data_processing", "analytics"], max_load=0.7)
    ml_agent = Agent(name="MLSpecialist", specializations=["machine_learning", "model_training"], max_load=0.5)
    cloud_agent = Agent(name="CloudEngineer", specializations=["cloud_deployment", "infrastructure"], max_load=0.6)

    # Register agents
    contract_net.register_agent(data_agent)
    contract_net.register_agent(ml_agent)
    contract_net.register_agent(cloud_agent)

    # Create diverse tasks
    tasks = [
        contract_net.create_task("data_processing", complexity=0.3, reward=100),
        contract_net.create_task("machine_learning,model_training", complexity=0.5, reward=250),
        contract_net.create_task("cloud_deployment", complexity=0.4, reward=150),
        contract_net.create_task("analytics", complexity=0.2, reward=80)
    ]

    # Run task allocation
    await contract_net.run_task_allocation()


if __name__ == "__main__":
    asyncio.run(main())

---

This layout ensures that all components are modular, easy to maintain, and scalable.

[MichaelDecent]

Contract Net Manager

[MichaelDecent]

This pattern is more of a like a control panel acting as Task allocation manager, which falls under the Centralized MAS :

Methods to prioritize include the following:

def create_task(self, description: str, complexity: float, reward: float) -> Task:
        """
        Create a new task for allocation
        """
        task = Task(
            description=description,
            complexity=complexity,
            reward=reward
        )
        self.tasks.append(task)
        return task

    async def allocate_task(self, task: Task) -> Optional[Tuple[Agent, Dict]]:
        """
        Allocate a task using Contract Net Protocol
        """
        print(f"\nAllocating Task: {task.description}")
        
        # Call for proposals
        bids = []
        for agent in self.agents:
            bid = await agent.evaluate_task(task)
            if bid is not None:
                bids.append((agent, bid))
        
        # No suitable agents found
        if not bids:
            print(f"No agents available for task: {task.description}")
            return None
        
        # Select agent with lowest bid (best fit)
        selected_agent, _ = min(bids, key=lambda x: x[1])
        
        # Assign and execute task
        task.assigned_agent = selected_agent.name
        task.status = TaskStatus.IN_PROGRESS
        
        try:
            result = await selected_agent.perform_task(task)
            
            if result['status'] == 'completed':
                task.status = TaskStatus.COMPLETED
                task.result = result
                selected_agent.completed_tasks.append(task)
                print(f"Task {task.id} completed by {selected_agent.name}")
            else:
                task.status = TaskStatus.FAILED
                selected_agent.failed_tasks.append(task)
                print(f"Task {task.id} failed for {selected_agent.name}")
            
            return (selected_agent, result)
        
        except Exception as e:
            print(f"Task allocation error: {e}")
            task.status = TaskStatus.FAILED
            return None

[MichaelDecent]

Even though it clearly written here that Contract Net Protocol works in a Decentralized approach, i still do not fully agree, because there is a Manager Agent, the allocates tasks to other agent based on condition (i.e bids)"

.Contract Net Protocol (CNP)

• Concept:
  • Inspired by economic models, CNP is a decentralized approach where agents negotiate over task allocation using a bidding mechanism.
  • One agent (the "manager") announces a task, and other agents (the "bidders") propose solutions or costs for completing it. The manager selects the best bidder.
• Strengths:
  • Flexibility and scalability in dynamic environments.
• Weaknesses:
  • Communication overhead in systems with many agents.
• Use Case:
  • Task allocation in distributed environments, such as a fleet of drones coordinating delivery tasks.

[MichaelDecent]

Here is the directory structure for PubSub Broker System and how the files will be organized:

---

Directory Structure

pubsub_system/
│
├── broker/
│   ├── __init__.py
│   ├── pubsub_broker.py
│
├── agents/
│   ├── __init__.py
│   ├── agent.py
│
├── main.py
└── requirements.txt

---

File Contents

pubsub_system/broker/pubsub_broker.py

import asyncio
from typing import Any, Dict, Set
from uuid import uuid4


class PubSubBroker:
    def __init__(self):
        """
        Initialize the Publish-Subscribe Broker.
        Manages topics and subscriptions for agents.
        """
        self._topics: Dict[str, Set[str]] = {}  # Topic to subscriber mappings
        self._subscribers: Dict[str, asyncio.Queue] = {}  # Subscriber ID to message queue
        self._topic_queues: Dict[str, asyncio.Queue] = {}  # Topic to message queue

    async def subscribe(self, topic: str) -> str:
        """
        Subscribe an agent to a specific topic.
        
        Args:
            topic (str): The topic to subscribe to
        
        Returns:
            str: Unique subscriber ID
        """
        subscriber_id = str(uuid4())
        
        # Create message queue for this subscriber
        self._subscribers[subscriber_id] = asyncio.Queue()
        
        # Add subscriber to topic
        if topic not in self._topics:
            self._topics[topic] = set()
        self._topics[topic].add(subscriber_id)
        
        return subscriber_id

    async def unsubscribe(self, topic: str, subscriber_id: str):
        """
        Unsubscribe an agent from a topic.
        
        Args:
            topic (str): The topic to unsubscribe from
            subscriber_id (str): Unique identifier of the subscriber
        """
        if topic in self._topics and subscriber_id in self._topics[topic]:
            self._topics[topic].remove(subscriber_id)
            
            # Optional: Clean up if no subscribers remain
            if not self._topics[topic]:
                del self._topics[topic]

    async def publish(self, topic: str, message: Any):
        """
        Publish a message to a specific topic.
        
        Args:
            topic (str): The topic to publish to
            message (Any): The message to be published
        """
        if topic not in self._topics:
            return
        
        # Distribute message to all subscribers of this topic
        for subscriber_id in self._topics[topic]:
            await self._subscribers[subscriber_id].put(message)

    async def receive(self, subscriber_id: str) -> Any:
        """
        Receive messages for a specific subscriber.
        
        Args:
            subscriber_id (str): Unique identifier of the subscriber
        
        Returns:
            Any: Received message
        """
        return await self._subscribers[subscriber_id].get()

---

pubsub_system/agents/agent.py

from typing import Any, List

from broker.pubsub_broker import PubSubBroker


class Agent:
    def __init__(self, broker: PubSubBroker, name: str):
        """
        Initialize an agent with a name and connection to the broker.
        
        Args:
            broker (PubSubBroker): The publish-subscribe broker
            name (str): Name of the agent
        """
        self.broker = broker
        self.name = name
        self.subscribed_topics: List[str] = []
        self.subscriber_id: str = None

    async def subscribe(self, topic: str):
        """
        Subscribe the agent to a topic.
        
        Args:
            topic (str): The topic to subscribe to
        """
        self.subscriber_id = await self.broker.subscribe(topic)
        self.subscribed_topics.append(topic)
        print(f"{self.name} subscribed to {topic}")

    async def publish(self, topic: str, message: Any):
        """
        Publish a message to a topic.
        
        Args:
            topic (str): The topic to publish to
            message (Any): The message to be published
        """
        print(f"{self.name} publishing to {topic}: {message}")
        await self.broker.publish(topic, message)

    async def listen(self):
        """
        Continuously listen for messages on subscribed topics.
        """
        while True:
            try:
                message = await self.broker.receive(self.subscriber_id)
                print(f"{self.name} received: {message}")
            except Exception as e:
                print(f"{self.name} listening error: {e}")
                break

---

pubsub_system/main.py

import asyncio

from broker.pubsub_broker import PubSubBroker
from agents.agent import Agent


async def main():
    # Create a broker
    broker = PubSubBroker()

    # Create agents
    data_agent = Agent(broker, "Data Collector")
    analysis_agent = Agent(broker, "Data Analyzer")
    visualization_agent = Agent(broker, "Visualization Agent")

    # Subscribe agents to topics
    await data_agent.subscribe("raw_data")
    await analysis_agent.subscribe("processed_data")
    await visualization_agent.subscribe("visualization_request")

    # Simulate listening and publishing
    listen_tasks = [
        asyncio.create_task(data_agent.listen()),
        asyncio.create_task(analysis_agent.listen()),
        asyncio.create_task(visualization_agent.listen())
    ]

    # Simulate some interactions
    await data_agent.publish("raw_data", {"sensor_id": 1, "temperature": 25.5})
    await analysis_agent.publish("processed_data", {"avg_temp": 25.5, "trend": "stable"})
    await visualization_agent.publish("visualization_request", "generate_temperature_chart")

    # Wait a bit to see messages
    await asyncio.sleep(2)

    # Clean up tasks
    for task in listen_tasks:
        task.cancel()


if __name__ == "__main__":
    asyncio.run(main())

---

This layout ensures modularity, clarity, and ease of maintenance for the PubSubBroker system and its agents.

[MichaelDecent]

In this pattern there will a broker, which will serve as the communication layer.

methods to prioritize for the Communication layer are:

 async def subscribe(self, topic: str) -> str:
        """
        Subscribe an agent to a specific topic.
        
        Args:
            topic (str): The topic to subscribe to
        
        Returns:
            str: Unique subscriber ID
        """
        subscriber_id = str(uuid4())
        
        # Create message queue for this subscriber
        self._subscribers[subscriber_id] = asyncio.Queue()
        
        # Add subscriber to topic
        if topic not in self._topics:
            self._topics[topic] = set()
        self._topics[topic].add(subscriber_id)
        
        return subscriber_id

    async def unsubscribe(self, topic: str, subscriber_id: str):
        """
        Unsubscribe an agent from a topic.
        
        Args:
            topic (str): The topic to unsubscribe from
            subscriber_id (str): Unique identifier of the subscriber
        """
        if topic in self._topics and subscriber_id in self._topics[topic]:
            self._topics[topic].remove(subscriber_id)
            
            # Optional: Clean up if no subscribers remain
            if not self._topics[topic]:
                del self._topics[topic]

    async def publish(self, topic: str, message: Any):
        """
        Publish a message to a specific topic.
        
        Args:
            topic (str): The topic to publish to
            message (Any): The message to be published
        """
        if topic not in self._topics:
            return
        
        # Distribute message to all subscribers of this topic
        for subscriber_id in self._topics[topic]:
            await self._subscribers[subscriber_id].put(message)

    async def receive(self, subscriber_id: str) -> Any:
        """
        Receive messages for a specific subscriber.
        
        Args:
            subscriber_id (str): Unique identifier of the subscriber
        
        Returns:
            Any: Received message
        """
        return await self._subscribers[subscriber_id].get()

[MichaelDecent]

PubSub Broker pattern

[MichaelDecent]

Service-Oriented Architecture (SOA)

Service-Oriented Architecture (SOA) in the context of a multi-agent framework, along with a code implementation to illustrate the concept.

import asyncio
import json
from typing import Dict, Any
from fastapi import FastAPI, HTTPException
import httpx
import uvicorn

class Agent:
    def __init__(self, name: str, base_url: str):
        self.name = name
        self.base_url = base_url
        self.services: Dict[str, callable] = {}
        self.registry_url = "http://localhost:8000/register"
        self.known_agents: Dict[str, str] = {}

    def expose_service(self, service_name: str, handler: callable):
        """
        Expose a service that can be called by other agents.
        
        :param service_name: Unique name of the service
        :param handler: Function that implements the service logic
        """
        self.services[service_name] = handler

    async def register_with_registry(self):
        """
        Register this agent's services with the central registry.
        """
        services_list = list(self.services.keys())
        async with httpx.AsyncClient() as client:
            try:
                response = await client.post(self.registry_url, json={
                    "agent_name": self.name,
                    "base_url": self.base_url,
                    "services": services_list
                })
                response.raise_for_status()
            except httpx.RequestError as e:
                print(f"Error registering with registry: {e}")

    async def discover_agents(self):
        """
        Discover other agents and their available services.
        """
        async with httpx.AsyncClient() as client:
            try:
                response = await client.get(self.registry_url + "/agents")
                response.raise_for_status()
                self.known_agents = response.json()
            except httpx.RequestError as e:
                print(f"Error discovering agents: {e}")

    async def call_service(self, agent_name: str, service_name: str, payload: Dict[str, Any]):
        """
        Call a service on another agent.
        
        :param agent_name: Name of the target agent
        :param service_name: Name of the service to call
        :param payload: Data to send with the service call
        :return: Response from the service
        """
        if agent_name not in self.known_agents:
            await self.discover_agents()
        
        if agent_name not in self.known_agents:
            raise HTTPException(status_code=404, detail=f"Agent {agent_name} not found")
        
        agent_url = self.known_agents[agent_name]
        async with httpx.AsyncClient() as client:
            try:
                response = await client.post(
                    f"{agent_url}/service/{service_name}", 
                    json=payload
                )
                response.raise_for_status()
                return response.json()
            except httpx.RequestError as e:
                print(f"Error calling service: {e}")
                raise HTTPException(status_code=500, detail=str(e))

def create_agent_app(agent: Agent):
    """
    Create a FastAPI application for an agent.
    
    :param agent: Agent instance to create app for
    :return: FastAPI application
    """
    app = FastAPI(title=f"{agent.name} Agent Service")

    @app.post("/service/{service_name}")
    async def handle_service(service_name: str, payload: Dict[str, Any]):
        if service_name not in agent.services:
            raise HTTPException(status_code=404, detail="Service not found")
        
        return await agent.services[service_name](payload)

    @app.on_event("startup")
    async def startup_event():
        await agent.register_with_registry()

    return app

# Central Registry Service
registry_app = FastAPI(title="Multi-Agent Registry")
registered_agents: Dict[str, str] = {}

@registry_app.post("/register")
async def register_agent(agent_data: Dict[str, Any]):
    registered_agents[agent_data['agent_name']] = agent_data['base_url']
    return {"status": "success"}

@registry_app.get("/agents")
async def get_registered_agents():
    return registered_agents

# Example Usage Demonstration
async def data_processing_service(payload: Dict[str, Any]):
    """
    Example service that processes data.
    """
    data = payload.get('data', [])
    processed_data = [x * 2 for x in data]
    return {"processed_data": processed_data}

async def machine_learning_service(payload: Dict[str, Any]):
    """
    Example service that simulates a machine learning task.
    """
    input_data = payload.get('input', [])
    prediction = sum(input_data) / len(input_data) if input_data else 0
    return {"prediction": prediction}

async def main():
    # Create agents
    data_agent = Agent("DataProcessingAgent", "http://localhost:8001")
    ml_agent = Agent("MachineLearningAgent", "http://localhost:8002")

    # Expose services
    data_agent.expose_service("process_data", data_processing_service)
    ml_agent.expose_service("predict", machine_learning_service)

    # Run the applications
    config_registry = uvicorn.Config(registry_app, host="localhost", port=8000)
    config_data_agent = uvicorn.Config(create_agent_app(data_agent), host="localhost", port=8001)
    config_ml_agent = uvicorn.Config(create_agent_app(ml_agent), host="localhost", port=8002)

    server_registry = uvicorn.Server(config_registry)
    server_data_agent = uvicorn.Server(config_data_agent)
    server_ml_agent = uvicorn.Server(config_ml_agent)

    await asyncio.gather(
        server_registry.serve(),
        server_data_agent.serve(),
        server_ml_agent.serve()
    )

if __name__ == "__main__":
    asyncio.run(main())

Let me break down the Service-Oriented Architecture (SOA) for Multi-Agent Systems:

Key Concepts of SOA in Multi-Agent Systems

1. Service Exposure

   • Agents define and expose specific services they can provide
   • Services are well-defined, discrete functional units
   • Each service has a unique name and clear input/output contract

2. Service Discovery

   • Central registry allows agents to discover and locate available services
   • Agents can dynamically find and interact with other agents' services
   • Enables loose coupling and flexibility

3. Service Invocation

   • Services are called over network protocols (HTTP/gRPC)
   • Uses a request-response communication model
   • Language and platform agnostic

Implementation Details

The code demonstrates a SOA multi-agent framework with:

1. Agent Class

   • expose_service(): Registers services
   • register_with_registry(): Announces available services
   • discover_agents(): Finds other agents and their services
   • call_service(): Invokes services on other agents

2. Central Registry

   • Tracks all registered agents
   • Provides service and agent discovery
   • Enables dynamic service location

3. Service Exposition

   • FastAPI used to create service endpoints
   • Each agent has its own HTTP server
   • Services can be called via POST requests

4. Example Services

   • data_processing_service: Demonstrates a simple data transformation service
   • machine_learning_service: Simulates a prediction service

Advantages Demonstrated

• Flexibility: Agents can dynamically add/remove services
• Scalability: Easy to add new agents and services
• Decoupling: Agents don't need to know implementation details
• Interoperability: Services can be consumed across different platforms

Technologies Used

• FastAPI for creating service endpoints
• httpx for async HTTP requests
• uvicorn as the ASGI server
• asyncio for asynchronous operations

Potential Real-World Applications

• Distributed computing
• Microservices architectures
• IoT systems
• Complex simulation environments

Running the Framework

To use this framework, you would:

1. Start the central registry
2. Create agents
3. Expose services
4. Discover and call services across agents

The code provides a complete, runnable example of how SOA principles can be implemented in a multi-agent system.