"""
Pattern 123: Master-Slave Pattern

Master coordinates slave agents for parallel execution
Implements distributed agent coordination using LangChain/LangGraph.
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict
import random


class AgentStatus(Enum):
    """Status of distributed agent."""
    ACTIVE = "active"
    IDLE = "idle"
    BUSY = "busy"
    FAILED = "failed"


@dataclass
class DistributedAgent:
    """Represents a distributed agent node."""
    agent_id: str
    status: AgentStatus = AgentStatus.IDLE
    load: float = 0.0  # 0.0 to 1.0
    capabilities: List[str] = field(default_factory=list)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Task:
    """Task to be executed."""
    task_id: str
    description: str
    assigned_to: Optional[str] = None
    status: str = "pending"
    result: Optional[str] = None


class MasterSlavePattern:
    """
    Master-Slave Pattern - Master coordinates slave agents for parallel execution
    
    Coordinates multiple distributed agents for scalable and fault-tolerant
    execution of tasks across a distributed system.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.agents: Dict[str, DistributedAgent] = {}
        self.tasks: List[Task] = []
    
    def register_agent(self, agent_id: str, capabilities: List[str] = None):
        """Register a new distributed agent."""
        agent = DistributedAgent(
            agent_id=agent_id,
            capabilities=capabilities or ["general"],
            status=AgentStatus.IDLE
        )
        self.agents[agent_id] = agent
        return agent
    
    def select_agent(self, task: Task) -> Optional[str]:
        """Select best agent for task."""
        available = [
            (aid, agent) for aid, agent in self.agents.items()
            if agent.status in [AgentStatus.IDLE, AgentStatus.ACTIVE] and agent.load  bool:
        """Assign task to an agent."""
        agent_id = self.select_agent(task)
        if not agent_id:
            return False
        
        task.assigned_to = agent_id
        task.status = "assigned"
        self.agents[agent_id].load += 0.2
        self.agents[agent_id].status = AgentStatus.BUSY
        
        return True
    
    def execute_task(self, task: Task) -> Dict[str, Any]:
        """Execute task on assigned agent."""
        if not task.assigned_to:
            if not self.assign_task(task):
                return {"status": "failed", "reason": "no_agent_available"}
        
        agent = self.agents[task.assigned_to]
        
        # Simulate task execution with LLM
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are agent {agent.agent_id} in a Master-Slave system."),
            ("human", "{task_description}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"task_description": task.description})
        
        task.result = str(response.content)
        task.status = "completed"
        
        # Update agent status
        agent.load = max(0.0, agent.load - 0.2)
        if agent.load  Dict[str, Any]:
        """Main execution method."""
        task = Task(
            task_id=f"task_{len(self.tasks) + 1}",
            description=task_description
        )
        self.tasks.append(task)
        
        result = self.execute_task(task)
        
        return {
            "pattern": "Master-Slave",
            "task_id": task.task_id,
            "assigned_to": task.assigned_to,
            "status": result["status"],
            "result": result.get("result", ""),
            "total_agents": len(self.agents),
            "active_agents": sum(1 for a in self.agents.values() if a.status == AgentStatus.ACTIVE or a.status == AgentStatus.BUSY)
        }


# Example usage
if __name__ == "__main__":
    print("=== Master-Slave Pattern Demo ===\n")
    
    pattern = MasterSlavePattern()
    
    # Register agents
    for i in range(3):
        pattern.register_agent(f"agent_{i+1}", ["compute", "process"])
    
    print(f"Registered {len(pattern.agents)} agents")
    
    # Execute tasks
    tasks = [
        "Process data batch 1",
        "Analyze results",
        "Generate report"
    ]
    
    for task_desc in tasks:
        result = pattern.run(task_desc)
        print(f"\nTask: {task_desc}")
        print(f"  Assigned to: {result['assigned_to']}")
        print(f"  Status: {result['status']}")
        print(f"  Result: {result['result'][:100]}...")
    
    print(f"\nTotal tasks executed: {len(pattern.tasks)}")
    print(f"Active agents: {result['active_agents']}/{result['total_agents']}")

    

Agentic Architecture Pattern: Master Slave

Pattern 123: 123 Master Slave Pattern – Line-by-Line Explanation

Overview

Pattern 123: Master-Slave Pattern

Master coordinates slave agents for parallel execution Implements distributed agent coordination using LangChain/LangGraph.

Code Structure Analysis

File Information

• Pattern Number: 123
• Pattern Name: 123 Master Slave Pattern
• File Path: patterns\distributed_agent\123_master_slave_pattern.py
• Total Lines: 178

Detailed Code Explanation

Module Docstring

"""
Pattern 123: Master-Slave Pattern

Master coordinates slave agents for parallel execution
Implements distributed agent coordination using LangChain/LangGraph.
"""

Explanation: This module docstring describes the purpose and key features of the 123 Master Slave Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict
import random  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Enumerations

AgentStatus (Line 19)

class AgentStatus(Enum):
    ACTIVE
    IDLE
    BUSY
    FAILED

Purpose: Status of distributed agent.

Values: – ACTIVE: Currently operational – IDLE: Not currently active – BUSY: Represents busy – FAILED: Execution failed

Classes

AgentStatus (Line 19)

Description: Status of distributed agent.

DistributedAgent (Line 28)

Description: Represents a distributed agent node.

Task (Line 38)

Description: Task to be executed.

MasterSlavePattern (Line 47)

Description: Master-Slave Pattern – Master coordinates slave agents for parallel execution

Coordinates multiple distributed agents for scalable and fault-tolerant execution of tasks across a distributed system.

Methods:

__init__ (Line 55)

def __init__(self, model_name):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

register_agent (Line 60)

def register_agent(self, agent_id, capabilities):
    """Register a new distributed agent."""

Purpose: Register a new distributed agent.

Parameters: – self: Instance reference – agent_id: Parameter for agent_id – capabilities: Parameter for capabilities

select_agent (Line 70)

def select_agent(self, task):
    """Select best agent for task."""

Purpose: Select best agent for task.

Parameters: – self: Instance reference – task: Parameter for task

assign_task (Line 84)

def assign_task(self, task):
    """Assign task to an agent."""

Purpose: Assign task to an agent.

Parameters: – self: Instance reference – task: Parameter for task

execute_task (Line 97)

def execute_task(self, task):
    """Execute task on assigned agent."""

Purpose: Execute task on assigned agent.

Parameters: – self: Instance reference – task: Parameter for task

run (Line 129)

def run(self, task_description):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – task_description: Parameter for task_description

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = AgentStatus(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 122: Client-Server Pattern

Purpose: Request-response architecture with centralized service coordination
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


class RequestType(Enum):
    """Types of client requests."""
    QUERY = "query"
    COMMAND = "command"
    STREAM = "stream"


@dataclass
class ClientRequest:
    """Client request to server."""
    client_id: str
    request_type: RequestType
    payload: Any
    request_id: str = field(default_factory=lambda: f"req_{datetime.now().timestamp()}")
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class ServerResponse:
    """Server response to client."""
    request_id: str
    status: str  # success, error, pending
    data: Any
    server_id: str
    processing_time: float = 0.0
    timestamp: datetime = field(default_factory=datetime.now)


class Client:
    """Client that sends requests to server."""
    
    def __init__(self, client_id: str, model_name: str = "gpt-4"):
        self.client_id = client_id
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.requests_sent: List[ClientRequest] = []
        self.responses_received: List[ServerResponse] = []
    
    def send_request(self, request_type: RequestType, payload: Any) -> ClientRequest:
        """Send request to server."""
        request = ClientRequest(
            client_id=self.client_id,
            request_type=request_type,
            payload=payload
        )
        self.requests_sent.append(request)
        return request
    
    def receive_response(self, response: ServerResponse):
        """Receive response from server."""
        self.responses_received.append(response)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get client statistics."""
        success_count = sum(1 for r in self.responses_received if r.status == "success")
        return {
            "client_id": self.client_id,
            "requests_sent": len(self.requests_sent),
            "responses_received": len(self.responses_received),
            "success_rate": success_count / len(self.responses_received) if self.responses_received else 0.0
        }


class Server:
    """Central server that processes client requests."""
    
    def __init__(self, server_id: str, model_name: str = "gpt-4"):
        self.server_id = server_id
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.requests_processed: List[ClientRequest] = []
        self.active_connections: Dict[str, Client] = {}
    
    def register_client(self, client: Client):
        """Register a client connection."""
        self.active_connections[client.client_id] = client
    
    def process_request(self, request: ClientRequest) -> ServerResponse:
        """Process client request."""
        start_time = datetime.now()
        
        # Use LLM to process request based on type
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are a server processing {request.request_type.value} requests."),
            ("human", "Client {client_id} requests: {payload}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "client_id": request.client_id,
            "payload": str(request.payload)
        })
        
        end_time = datetime.now()
        processing_time = (end_time - start_time).total_seconds()
        
        self.requests_processed.append(request)
        
        return ServerResponse(
            request_id=request.request_id,
            status="success",
            data=response.content,
            server_id=self.server_id,
            processing_time=processing_time
        )
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get server statistics."""
        avg_processing_time = (
            sum(r.processing_time for r in self.requests_processed) / len(self.requests_processed)
            if self.requests_processed else 0.0
        )
        return {
            "server_id": self.server_id,
            "active_clients": len(self.active_connections),
            "requests_processed": len(self.requests_processed),
            "avg_processing_time": avg_processing_time
        }


class ClientServerPattern:
    """
    Client-Server Pattern - Centralized request-response architecture
    
    Implements client-server communication with request routing and response management.
    """
    
    def __init__(self):
        self.servers: Dict[str, Server] = {}
        self.clients: Dict[str, Client] = {}
        self.message_log: List[Dict[str, Any]] = []
    
    def register_server(self, server: Server):
        """Register a server."""
        self.servers[server.server_id] = server
    
    def register_client(self, client: Client):
        """Register a client."""
        self.clients[client.client_id] = client
    
    def route_request(self, client_id: str, server_id: str, 
                     request_type: RequestType, payload: Any) -> ServerResponse:
        """Route client request to server."""
        client = self.clients.get(client_id)
        server = self.servers.get(server_id)
        
        if not client or not server:
            raise ValueError("Invalid client or server ID")
        
        # Client sends request
        request = client.send_request(request_type, payload)
        
        # Server processes request
        response = server.process_request(request)
        
        # Client receives response
        client.receive_response(response)
        
        # Log the transaction
        self.message_log.append({
            "client_id": client_id,
            "server_id": server_id,
            "request_id": request.request_id,
            "request_type": request_type.value,
            "status": response.status,
            "timestamp": datetime.now()
        })
        
        return response
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "total_servers": len(self.servers),
            "total_clients": len(self.clients),
            "total_requests": len(self.message_log),
            "servers": {sid: server.get_statistics() for sid, server in self.servers.items()},
            "clients": {cid: client.get_statistics() for cid, client in self.clients.items()}
        }


# Example usage
if __name__ == "__main__":
    print("=== Client-Server Pattern Demo ===\n")
    
    # Create pattern
    pattern = ClientServerPattern()
    
    # Create server
    server = Server("MainServer")
    pattern.register_server(server)
    
    # Create clients
    client1 = Client("Client1")
    client2 = Client("Client2")
    pattern.register_client(client1)
    pattern.register_client(client2)
    
    # Register clients with server
    server.register_client(client1)
    server.register_client(client2)
    
    # Send requests
    print("Sending requests...\n")
    
    response1 = pattern.route_request(
        "Client1", "MainServer", 
        RequestType.QUERY, 
        "Get user data for ID 123"
    )
    print(f"Response 1: {response1.status} (took {response1.processing_time:.2f}s)")
    
    response2 = pattern.route_request(
        "Client2", "MainServer",
        RequestType.COMMAND,
        "Update database record"
    )
    print(f"Response 2: {response2.status} (took {response2.processing_time:.2f}s)")
    
    response3 = pattern.route_request(
        "Client1", "MainServer",
        RequestType.QUERY,
        "Search for documents"
    )
    print(f"Response 3: {response3.status} (took {response3.processing_time:.2f}s)")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    
    print(f"\nServer Statistics:")
    for server_id, server_stats in stats["servers"].items():
        print(f"  {server_id}:")
        for key, value in server_stats.items():
            print(f"    {key}: {value}")
    
    print(f"\nClient Statistics:")
    for client_id, client_stats in stats["clients"].items():
        print(f"  {client_id}:")
        for key, value in client_stats.items():
            print(f"    {key}: {value}")

    

Agentic Architecture Pattern: Client Server

Pattern 122: 122 Client Server Pattern – Line-by-Line Explanation

Overview

Pattern 122: Client Server Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 122
• Pattern Name: 122 Client Server Pattern
• File Path: patterns\distributed_agent\122_client_server_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 122: Client Server Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 122 Client Server Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

ClientServerConfig (Line 18)

Description: Configuration for Client Server Pattern.

ClientServerState (Line 27)

Description: State information for Client Server Pattern.

ClientServerAgent (Line 36)

Description: Agent implementing Client Server Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Client Server Pattern."""

Purpose: Process input according to Client Server Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

ClientServerPattern (Line 85)

Description: Client Server Pattern – Enhanced implementation

This pattern provides comprehensive functionality for client server with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = ClientServerConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 121: Peer-to-Peer Pattern

Decentralized peer-to-peer agent network where agents communicate directly.
Implements P2P communication with LangChain for agent logic and message passing.
"""

from dataclasses import dataclass, field
from datetime import datetime
from typing import Dict, Any, List, Optional, Set
from enum import Enum
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate


class PeerStatus(Enum):
    """Status of peer in network."""
    ONLINE = "online"
    OFFLINE = "offline"
    BUSY = "busy"


@dataclass
class Message:
    """Message between peers."""
    message_id: str
    from_peer: str
    to_peer: str
    content: str
    message_type: str = "direct"  # direct, broadcast, query, response
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class PeerAgent:
    """Peer agent in P2P network."""
    peer_id: str
    name: str
    capabilities: List[str]
    status: PeerStatus = PeerStatus.ONLINE
    known_peers: Set[str] = field(default_factory=set)
    message_queue: List[Message] = field(default_factory=list)
    processed_messages: int = 0


class PeerToPeerNetwork:
    """Decentralized peer-to-peer agent network."""
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.7)
        self.peers: Dict[str, PeerAgent] = {}
        self.message_history: List[Message] = []
        
    def register_peer(self, peer: PeerAgent):
        """Register a new peer in the network."""
        self.peers[peer.peer_id] = peer
        
        # Announce to other peers
        for existing_peer_id in list(self.peers.keys()):
            if existing_peer_id != peer.peer_id:
                peer.known_peers.add(existing_peer_id)
                self.peers[existing_peer_id].known_peers.add(peer.peer_id)
    
    def send_message(self, from_peer_id: str, to_peer_id: str, content: str, message_type: str = "direct"):
        """Send message from one peer to another."""
        if from_peer_id not in self.peers or to_peer_id not in self.peers:
            return False
        
        message = Message(
            message_id=f"msg-{len(self.message_history)}",
            from_peer=from_peer_id,
            to_peer=to_peer_id,
            content=content,
            message_type=message_type
        )
        
        self.peers[to_peer_id].message_queue.append(message)
        self.message_history.append(message)
        return True
    
    def broadcast(self, from_peer_id: str, content: str):
        """Broadcast message to all peers."""
        if from_peer_id not in self.peers:
            return
        
        for peer_id in self.peers:
            if peer_id != from_peer_id:
                self.send_message(from_peer_id, peer_id, content, "broadcast")
    
    def process_peer_messages(self, peer_id: str) -> List[Dict[str, Any]]:
        """Process messages for a specific peer."""
        if peer_id not in self.peers:
            return []
        
        peer = self.peers[peer_id]
        results = []
        
        while peer.message_queue:
            message = peer.message_queue.pop(0)
            
            # Use LLM to generate response
            response_content = self._generate_peer_response(peer, message)
            
            # Send response if needed
            if message.message_type in ["query", "direct"]:
                self.send_message(peer_id, message.from_peer, response_content, "response")
            
            peer.processed_messages += 1
            
            results.append({
                "message_id": message.message_id,
                "from": message.from_peer,
                "type": message.message_type,
                "content": message.content,
                "response": response_content
            })
        
        return results
    
    def _generate_peer_response(self, peer: PeerAgent, message: Message) -> str:
        """Generate response using LLM."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"""You are {peer.name} (ID: {peer.peer_id}), a peer in a P2P network.
Your capabilities: {', '.join(peer.capabilities)}
Known peers: {len(peer.known_peers)}"""),
            ("human", """Message from {from_peer}:
{content}

Type: {message_type}

Provide a helpful response:""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "from_peer": message.from_peer,
            "content": message.content,
            "message_type": message.message_type
        })
        
        return str(response.content)
    
    def get_network_status(self) -> Dict[str, Any]:
        """Get status of the P2P network."""
        return {
            "total_peers": len(self.peers),
            "online_peers": len([p for p in self.peers.values() if p.status == PeerStatus.ONLINE]),
            "total_messages": len(self.message_history),
            "peers": [
                {
                    "peer_id": p.peer_id,
                    "name": p.name,
                    "status": p.status.value,
                    "known_peers": len(p.known_peers),
                    "processed_messages": p.processed_messages,
                    "queued_messages": len(p.message_queue)
                }
                for p in self.peers.values()
            ]
        }


class PeerToPeerPattern:
    """Complete P2P pattern implementation."""
    
    def __init__(self, model_name: str = "gpt-4"):
        self.network = PeerToPeerNetwork(model_name=model_name)
        
    def run(self, scenario: str = "data_sharing") -> Dict[str, Any]:
        """Run P2P scenario."""
        # Create peers
        peers = [
            PeerAgent("peer-1", "DataCollector", ["data_collection", "storage"]),
            PeerAgent("peer-2", "DataProcessor", ["processing", "analysis"]),
            PeerAgent("peer-3", "DataVisualizer", ["visualization", "reporting"]),
            PeerAgent("peer-4", "QualityChecker", ["validation", "quality_check"])
        ]
        
        for peer in peers:
            self.network.register_peer(peer)
        
        # Simulate P2P interactions
        self.network.broadcast("peer-1", "I have collected new data. Who can process it?")
        
        # Process messages
        for peer_id in list(self.network.peers.keys()):
            self.network.process_peer_messages(peer_id)
        
        # Peer 2 requests data
        self.network.send_message("peer-2", "peer-1", "Send me the data for processing", "query")
        self.network.process_peer_messages("peer-1")
        
        # Peer 3 asks for results
        self.network.send_message("peer-3", "peer-2", "Are the processing results ready?", "query")
        self.network.process_peer_messages("peer-2")
        
        return self.network.get_network_status()


def main():
    """Example usage of Peer-to-Peer Pattern."""
    print("=" * 80)
    print("Peer-to-Peer Pattern Demo")
    print("=" * 80)
    
    pattern = PeerToPeerPattern()
    
    print("\n🌐 Starting P2P Network...")
    result = pattern.run("data_sharing")
    
    print(f"\n📊 Network Status:")
    print(f"Total Peers: {result['total_peers']}")
    print(f"Online Peers: {result['online_peers']}")
    print(f"Total Messages: {result['total_messages']}")
    
    print(f"\n👥 Peer Details:")
    for peer in result['peers']:
        print(f"\n  {peer['name']} ({peer['peer_id']})")
        print(f"    Status: {peer['status']}")
        print(f"    Known Peers: {peer['known_peers']}")
        print(f"    Processed: {peer['processed_messages']} messages")
        print(f"    Queued: {peer['queued_messages']} messages")


if __name__ == "__main__":
    main()

    

Agentic Architecture Pattern: Peer To Peer

Pattern 121: 121 Peer To Peer Pattern – Line-by-Line Explanation

Overview

Pattern 121: Peer-to-Peer Pattern

Decentralized network where agents are equals, communicating directly with each other without a central coordinator.

Code Structure Analysis

File Information

• Pattern Number: 121
• Pattern Name: 121 Peer To Peer Pattern
• File Path: patterns\distributed_agent\121_peer_to_peer_pattern.py
• Total Lines: 163

Detailed Code Explanation

Module Docstring

"""
Pattern 121: Peer-to-Peer Pattern

Decentralized network where agents are equals, communicating directly
with each other without a central coordinator.
"""

Explanation: This module docstring describes the purpose and key features of the 121 Peer To Peer Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional, Set
from dataclasses import dataclass, field
from datetime import datetime
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Set: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Classes

Peer (Line 16)

Description: Peer agent in P2P network.

Message (Line 26)

Description: P2P message.

PeerToPeerPattern (Line 34)

Description: Peer-to-Peer Pattern – Decentralized agent communication.

Methods:

__init__ (Line 39)

def __init__(self, model_name):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”)

add_peer (Line 44)

def add_peer(self, peer):
    """Add a peer to the network."""

Purpose: Add a peer to the network.

Parameters: – self: Instance reference – peer: Parameter for peer

connect_peers (Line 48)

def connect_peers(self, peer1_id, peer2_id):
    """Establish bidirectional connection between peers."""

Purpose: Establish bidirectional connection between peers.

Parameters: – self: Instance reference – peer1_id: Parameter for peer1_id – peer2_id: Parameter for peer2_id

send_message (Line 54)

def send_message(self, from_peer, to_peer, content):
    """Send message from one peer to another."""

Purpose: Send message from one peer to another.

Parameters: – self: Instance reference – from_peer: Parameter for from_peer – to_peer: Parameter for to_peer – content: Parameter for content

broadcast (Line 73)

def broadcast(self, from_peer, content):
    """Broadcast message to all connected peers."""

Purpose: Broadcast message to all connected peers.

Parameters: – self: Instance reference – from_peer: Parameter for from_peer – content: Parameter for content

run (Line 87)

def run(self, scenario):
    """Execute P2P scenario."""

Purpose: Execute P2P scenario.

Parameters: – self: Instance reference – scenario: Parameter for scenario

get_statistics (Line 105)

def get_statistics(self):
    """Get network statistics."""

Purpose: Get network statistics.

Parameters: – self: Instance reference

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = Peer(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 120: Resource Constrained Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class ResourceConstrainedPlanningConfig:
    """Configuration for Resource Constrained Planning Pattern."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class ResourceConstrainedPlanningState:
    """State information for Resource Constrained Planning Pattern."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class ResourceConstrainedPlanningAgent:
    """Agent implementing Resource Constrained Planning Pattern."""
    
    def __init__(self, name: str, config: ResourceConstrainedPlanningConfig = None):
        self.name = name
        self.config = config or ResourceConstrainedPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = ResourceConstrainedPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Resource Constrained Planning Pattern."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Resource Constrained Planning Pattern. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class ResourceConstrainedPlanningPattern:
    """
    Resource Constrained Planning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for resource constrained planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: ResourceConstrainedPlanningConfig = None):
        self.config = config or ResourceConstrainedPlanningConfig()
        self.agents: Dict[str, ResourceConstrainedPlanningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Resource Constrained Planning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: ResourceConstrainedPlanningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Resource Constrained Planning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Resource Constrained Planning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Resource Constrained Planning Pattern Demo ===\n")
    
    # Create configuration
    config = ResourceConstrainedPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = ResourceConstrainedPlanningPattern(config)
    
    # Create and register agents
    agent1 = ResourceConstrainedPlanningAgent("Agent1", config)
    agent2 = ResourceConstrainedPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate resource constrained planning functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Resource Constrained Planning

Pattern 120: 120 Resource Constrained Planning Pattern – Line-by-Line Explanation

Overview

Pattern 120: Resource Constrained Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 120
• Pattern Name: 120 Resource Constrained Planning Pattern
• File Path: patterns\planning_strategy\120_resource_constrained_planning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 120: Resource Constrained Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 120 Resource Constrained Planning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

ResourceConstrainedPlanningConfig (Line 18)

Description: Configuration for Resource Constrained Planning Pattern.

ResourceConstrainedPlanningState (Line 27)

Description: State information for Resource Constrained Planning Pattern.

ResourceConstrainedPlanningAgent (Line 36)

Description: Agent implementing Resource Constrained Planning Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Resource Constrained Planning Pattern."""

Purpose: Process input according to Resource Constrained Planning Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

ResourceConstrainedPlanningPattern (Line 85)

Description: Resource Constrained Planning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for resource constrained planning with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = ResourceConstrainedPlanningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 119: Temporal Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class TemporalPlanningConfig:
    """Configuration for Temporal Planning Pattern."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class TemporalPlanningState:
    """State information for Temporal Planning Pattern."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class TemporalPlanningAgent:
    """Agent implementing Temporal Planning Pattern."""
    
    def __init__(self, name: str, config: TemporalPlanningConfig = None):
        self.name = name
        self.config = config or TemporalPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = TemporalPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Temporal Planning Pattern."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Temporal Planning Pattern. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class TemporalPlanningPattern:
    """
    Temporal Planning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for temporal planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: TemporalPlanningConfig = None):
        self.config = config or TemporalPlanningConfig()
        self.agents: Dict[str, TemporalPlanningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Temporal Planning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: TemporalPlanningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Temporal Planning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Temporal Planning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Temporal Planning Pattern Demo ===\n")
    
    # Create configuration
    config = TemporalPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = TemporalPlanningPattern(config)
    
    # Create and register agents
    agent1 = TemporalPlanningAgent("Agent1", config)
    agent2 = TemporalPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate temporal planning functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Temporal Planning

Pattern 119: 119 Temporal Planning Pattern – Line-by-Line Explanation

Overview

Pattern 119: Temporal Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 119
• Pattern Name: 119 Temporal Planning Pattern
• File Path: patterns\planning_strategy\119_temporal_planning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 119: Temporal Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 119 Temporal Planning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

TemporalPlanningConfig (Line 18)

Description: Configuration for Temporal Planning Pattern.

TemporalPlanningState (Line 27)

Description: State information for Temporal Planning Pattern.

TemporalPlanningAgent (Line 36)

Description: Agent implementing Temporal Planning Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Temporal Planning Pattern."""

Purpose: Process input according to Temporal Planning Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

TemporalPlanningPattern (Line 85)

Description: Temporal Planning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for temporal planning with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = TemporalPlanningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 118: Distributed Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class DistributedPlanningConfig:
    """Configuration for Distributed Planning Pattern."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class DistributedPlanningState:
    """State information for Distributed Planning Pattern."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class DistributedPlanningAgent:
    """Agent implementing Distributed Planning Pattern."""
    
    def __init__(self, name: str, config: DistributedPlanningConfig = None):
        self.name = name
        self.config = config or DistributedPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = DistributedPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Distributed Planning Pattern."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Distributed Planning Pattern. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class DistributedPlanningPattern:
    """
    Distributed Planning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for distributed planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: DistributedPlanningConfig = None):
        self.config = config or DistributedPlanningConfig()
        self.agents: Dict[str, DistributedPlanningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Distributed Planning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: DistributedPlanningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Distributed Planning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Distributed Planning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Distributed Planning Pattern Demo ===\n")
    
    # Create configuration
    config = DistributedPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = DistributedPlanningPattern(config)
    
    # Create and register agents
    agent1 = DistributedPlanningAgent("Agent1", config)
    agent2 = DistributedPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate distributed planning functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Distributed Planning

Pattern 118: 118 Distributed Planning Pattern – Line-by-Line Explanation

Overview

Pattern 118: Distributed Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 118
• Pattern Name: 118 Distributed Planning Pattern
• File Path: patterns\planning_strategy\118_distributed_planning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 118: Distributed Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 118 Distributed Planning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

DistributedPlanningConfig (Line 18)

Description: Configuration for Distributed Planning Pattern.

DistributedPlanningState (Line 27)

Description: State information for Distributed Planning Pattern.

DistributedPlanningAgent (Line 36)

Description: Agent implementing Distributed Planning Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Distributed Planning Pattern."""

Purpose: Process input according to Distributed Planning Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

DistributedPlanningPattern (Line 85)

Description: Distributed Planning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for distributed planning with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = DistributedPlanningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 117: Multi Agent Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class MultiAgentPlanningConfig:
    """Configuration for Multi Agent Planning Pattern."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class MultiAgentPlanningState:
    """State information for Multi Agent Planning Pattern."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class MultiAgentPlanningAgent:
    """Agent implementing Multi Agent Planning Pattern."""
    
    def __init__(self, name: str, config: MultiAgentPlanningConfig = None):
        self.name = name
        self.config = config or MultiAgentPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = MultiAgentPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Multi Agent Planning Pattern."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Multi Agent Planning Pattern. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class MultiAgentPlanningPattern:
    """
    Multi Agent Planning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for multi agent planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: MultiAgentPlanningConfig = None):
        self.config = config or MultiAgentPlanningConfig()
        self.agents: Dict[str, MultiAgentPlanningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Multi Agent Planning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: MultiAgentPlanningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Multi Agent Planning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Multi Agent Planning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Multi Agent Planning Pattern Demo ===\n")
    
    # Create configuration
    config = MultiAgentPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = MultiAgentPlanningPattern(config)
    
    # Create and register agents
    agent1 = MultiAgentPlanningAgent("Agent1", config)
    agent2 = MultiAgentPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate multi agent planning functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Multi Agent Planning

Pattern 117: 117 Multi Agent Planning Pattern – Line-by-Line Explanation

Overview

Pattern 117: Multi Agent Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 117
• Pattern Name: 117 Multi Agent Planning Pattern
• File Path: patterns\planning_strategy\117_multi_agent_planning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 117: Multi Agent Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 117 Multi Agent Planning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

MultiAgentPlanningConfig (Line 18)

Description: Configuration for Multi Agent Planning Pattern.

MultiAgentPlanningState (Line 27)

Description: State information for Multi Agent Planning Pattern.

MultiAgentPlanningAgent (Line 36)

Description: Agent implementing Multi Agent Planning Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Multi Agent Planning Pattern."""

Purpose: Process input according to Multi Agent Planning Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

MultiAgentPlanningPattern (Line 85)

Description: Multi Agent Planning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for multi agent planning with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = MultiAgentPlanningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 116: Replanning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class ReplanningConfig:
    """Configuration for Replanning Pattern."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class ReplanningState:
    """State information for Replanning Pattern."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class ReplanningAgent:
    """Agent implementing Replanning Pattern."""
    
    def __init__(self, name: str, config: ReplanningConfig = None):
        self.name = name
        self.config = config or ReplanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = ReplanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Replanning Pattern."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Replanning Pattern. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class ReplanningPattern:
    """
    Replanning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for replanning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: ReplanningConfig = None):
        self.config = config or ReplanningConfig()
        self.agents: Dict[str, ReplanningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Replanning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: ReplanningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Replanning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Replanning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Replanning Pattern Demo ===\n")
    
    # Create configuration
    config = ReplanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = ReplanningPattern(config)
    
    # Create and register agents
    agent1 = ReplanningAgent("Agent1", config)
    agent2 = ReplanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate replanning functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Replanning

Pattern 116: 116 Replanning Pattern – Line-by-Line Explanation

Overview

Pattern 116: Replanning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 116
• Pattern Name: 116 Replanning Pattern
• File Path: patterns\planning_strategy\116_replanning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 116: Replanning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 116 Replanning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

ReplanningConfig (Line 18)

Description: Configuration for Replanning Pattern.

ReplanningState (Line 27)

Description: State information for Replanning Pattern.

ReplanningAgent (Line 36)

Description: Agent implementing Replanning Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Replanning Pattern."""

Purpose: Process input according to Replanning Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

ReplanningPattern (Line 85)

Description: Replanning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for replanning with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = ReplanningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 115: Continuous Planning

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class ContinuousPlanningConfig:
    """Configuration for Continuous Planning."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class ContinuousPlanningState:
    """State information for Continuous Planning."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class ContinuousPlanningAgent:
    """Agent implementing Continuous Planning."""
    
    def __init__(self, name: str, config: ContinuousPlanningConfig = None):
        self.name = name
        self.config = config or ContinuousPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = ContinuousPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Continuous Planning."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Continuous Planning. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class ContinuousPlanningPattern:
    """
    Continuous Planning - Enhanced implementation
    
    This pattern provides comprehensive functionality for continuous planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: ContinuousPlanningConfig = None):
        self.config = config or ContinuousPlanningConfig()
        self.agents: Dict[str, ContinuousPlanningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Continuous Planning. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: ContinuousPlanningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Continuous Planning",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Continuous Planning",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Continuous Planning Demo ===\n")
    
    # Create configuration
    config = ContinuousPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = ContinuousPlanningPattern(config)
    
    # Create and register agents
    agent1 = ContinuousPlanningAgent("Agent1", config)
    agent2 = ContinuousPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate continuous planning functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Continuous Planning

Pattern 115: 115 Continuous Planning Pattern – Line-by-Line Explanation

Overview

Pattern 115: Continuous Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 115
• Pattern Name: 115 Continuous Planning Pattern
• File Path: patterns\planning_strategy\115_continuous_planning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 115: Continuous Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 115 Continuous Planning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

ContinuousPlanningConfig (Line 18)

Description: Configuration for Continuous Planning Pattern.

ContinuousPlanningState (Line 27)

Description: State information for Continuous Planning Pattern.

ContinuousPlanningAgent (Line 36)

Description: Agent implementing Continuous Planning Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Continuous Planning Pattern."""

Purpose: Process input according to Continuous Planning Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

ContinuousPlanningPattern (Line 85)

Description: Continuous Planning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for continuous planning with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = ContinuousPlanningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

    """
Pattern 114: Partial Order Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict


@dataclass
class PartialOrderPlanningConfig:
    """Configuration for Partial Order Planning Pattern."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class PartialOrderPlanningState:
    """State information for Partial Order Planning Pattern."""
    current_step: int = 0
    data: Dict[str, Any] = field(default_factory=dict)
    results: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


class PartialOrderPlanningAgent:
    """Agent implementing Partial Order Planning Pattern."""
    
    def __init__(self, name: str, config: PartialOrderPlanningConfig = None):
        self.name = name
        self.config = config or PartialOrderPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = PartialOrderPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Partial Order Planning Pattern."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Partial Order Planning Pattern. "
                      f"Provide clear, structured responses."),
            ("human", "Process this input: {input_data}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"input_data": str(input_data)})
        
        result = {
            "agent": self.name,
            "step": self.state.current_step,
            "input": input_data,
            "output": str(response.content),
            "timestamp": datetime.now()
        }
        
        self.execution_history.append(result)
        self.state.results.append(result)
        
        return result
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get agent statistics."""
        return {
            "name": self.name,
            "total_executions": len(self.execution_history),
            "current_step": self.state.current_step,
            "results_count": len(self.state.results)
        }


class PartialOrderPlanningPattern:
    """
    Partial Order Planning Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for partial order planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: PartialOrderPlanningConfig = None):
        self.config = config or PartialOrderPlanningConfig()
        self.agents: Dict[str, PartialOrderPlanningAgent] = {}
        self.workflow = self._build_workflow()
        self.execution_log: List[Dict[str, Any]] = []
    
    def _build_workflow(self) -> StateGraph:
        """Build LangGraph workflow for pattern execution."""
        
        class WorkflowState(TypedDict):
            input: str
            processed: str
            output: str
            metadata: Dict[str, Any]
        
        def process_input(state: WorkflowState) -> WorkflowState:
            """Process input node."""
            return {
                **state,
                "processed": f"Processed: {state['input']}",
                "metadata": {"step": "process_input"}
            }
        
        def generate_output(state: WorkflowState) -> WorkflowState:
            """Generate output node."""
            llm = ChatOpenAI(model=self.config.model_name, temperature=self.config.temperature)
            
            prompt = ChatPromptTemplate.from_messages([
                ("system", "You are implementing Partial Order Planning Pattern. Generate comprehensive output."),
                ("human", "{input}")
            ])
            
            chain = prompt | llm
            response = chain.invoke({"input": state["processed"]})
            
            return {
                **state,
                "output": str(response.content),
                "metadata": {**state.get("metadata", {}), "step": "generate_output"}
            }
        
        # Build workflow graph
        workflow = StateGraph(WorkflowState)
        workflow.add_node("process", process_input)
        workflow.add_node("generate", generate_output)
        workflow.set_entry_point("process")
        workflow.add_edge("process", "generate")
        workflow.add_edge("generate", END)
        
        return workflow.compile()
    
    def register_agent(self, agent: PartialOrderPlanningAgent):
        """Register an agent with the pattern."""
        self.agents[agent.name] = agent
        if self.config.enable_logging:
            print(f"Registered agent: {agent.name}")
    
    def execute(self, input_data: Any, agent_name: str = None) -> Dict[str, Any]:
        """Execute the pattern with given input."""
        
        # Use workflow if no specific agent requested
        if agent_name is None:
            result = self.workflow.invoke({
                "input": str(input_data),
                "processed": "",
                "output": "",
                "metadata": {}
            })
            
            execution_record = {
                "pattern": "Partial Order Planning Pattern",
                "input": input_data,
                "output": result.get("output", ""),
                "metadata": result.get("metadata", {}),
                "timestamp": datetime.now()
            }
            
            self.execution_log.append(execution_record)
            return execution_record
        
        # Execute with specific agent
        agent = self.agents.get(agent_name)
        if not agent:
            raise ValueError(f"Agent {agent_name} not found")
        
        result = agent.process(input_data)
        self.execution_log.append(result)
        return result
    
    def run(self, input_data: Any) -> Dict[str, Any]:
        """Main execution method."""
        return self.execute(input_data)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get pattern statistics."""
        return {
            "pattern": "Partial Order Planning Pattern",
            "total_executions": len(self.execution_log),
            "registered_agents": len(self.agents),
            "agents": [agent.get_statistics() for agent in self.agents.values()]
        }


# Example usage
if __name__ == "__main__":
    print("=== Partial Order Planning Pattern Demo ===\n")
    
    # Create configuration
    config = PartialOrderPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = PartialOrderPlanningPattern(config)
    
    # Create and register agents
    agent1 = PartialOrderPlanningAgent("Agent1", config)
    agent2 = PartialOrderPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate partial order planning functionality")
    print(f"\nWorkflow Result:")
    print(f"  Output: {result1['output'][:150]}...")
    
    # Execute with specific agents
    result2 = pattern.execute("Specific task for Agent1", agent_name="Agent1")
    print(f"\nAgent1 Result:")
    print(f"  Output: {result2['output'][:150]}...")
    
    result3 = pattern.execute("Specific task for Agent2", agent_name="Agent2")
    print(f"\nAgent2 Result:")
    print(f"  Output: {result3['output'][:150]}...")
    
    # Show statistics
    print("\n=== Statistics ===")
    stats = pattern.get_statistics()
    print(f"Pattern: {stats['pattern']}")
    print(f"Total Executions: {stats['total_executions']}")
    print(f"Registered Agents: {stats['registered_agents']}")
    print(f"\nAgent Statistics:")
    for agent_stats in stats['agents']:
        print(f"  {agent_stats['name']}: {agent_stats['total_executions']} executions")

    

Agentic Architecture Pattern: Partial Order Planning

Pattern 114: 114 Partial Order Planning Pattern – Line-by-Line Explanation

Overview

Pattern 114: Partial Order Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 114
• Pattern Name: 114 Partial Order Planning Pattern
• File Path: patterns\planning_strategy\114_partial_order_planning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 114: Partial Order Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration
"""

Explanation: This module docstring describes the purpose and key features of the 114 Partial Order Planning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from typing_extensions import TypedDict  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration – from langgraph.graph import StateGraph, END: LangGraph for workflow orchestration – from typing_extensions import TypedDict: Type hints for better code documentation

Classes

PartialOrderPlanningConfig (Line 18)

Description: Configuration for Partial Order Planning Pattern.

PartialOrderPlanningState (Line 27)

Description: State information for Partial Order Planning Pattern.

PartialOrderPlanningAgent (Line 36)

Description: Agent implementing Partial Order Planning Pattern.

Methods:

__init__ (Line 39)

def __init__(self, name, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – config: Configuration settings

process (Line 49)

def process(self, input_data):
    """Process input according to Partial Order Planning Pattern."""

Purpose: Process input according to Partial Order Planning Pattern.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 75)

def get_statistics(self):
    """Get agent statistics."""

Purpose: Get agent statistics.

Parameters: – self: Instance reference

PartialOrderPlanningPattern (Line 85)

Description: Partial Order Planning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for partial order planning with full LangChain/LangGraph integration.

Features: – Multi-agent coordination – State management – Execution tracking – LangGraph workflow integration

Methods:

__init__ (Line 99)

def __init__(self, config):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – config: Configuration settings

_build_workflow (Line 105)

def _build_workflow(self):
    """Build LangGraph workflow for pattern execution."""

Purpose: Build LangGraph workflow for pattern execution.

Parameters: – self: Instance reference

register_agent (Line 150)

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

Purpose: Register an agent with the pattern.

Parameters: – self: Instance reference – agent: Parameter for agent

execute (Line 156)

def execute(self, input_data, agent_name):
    """Execute the pattern with given input."""

Purpose: Execute the pattern with given input.

Parameters: – self: Instance reference – input_data: Parameter for input_data – agent_name: Parameter for agent_name

run (Line 188)

def run(self, input_data):
    """Main execution method."""

Purpose: Main execution method.

Parameters: – self: Instance reference – input_data: Parameter for input_data

get_statistics (Line 192)

def get_statistics(self):
    """Get pattern statistics."""

Purpose: Get pattern statistics.

Parameters: – self: Instance reference

WorkflowState (Line 108)

Description: No class docstring.

Design Patterns Used

• Strategy Pattern: Different execution strategies for various scenarios
• State Pattern: Managing different states in the workflow
• Chain of Responsibility: Sequential processing through workflow nodes
• Observer Pattern: Tracking and monitoring pattern execution
• Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = PartialOrderPlanningConfig(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

• LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
• Workflow Orchestration: LangGraph manages execution flow through nodes
• State Management: TypedDict defines workflow state structure
• Error Handling: Comprehensive error handling and fallback mechanisms
• Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities