"""
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

    """
Pattern 113: Backward 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 BackwardPlanningConfig:
    """Configuration for Backward Planning."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class BackwardPlanningState:
    """State information for Backward 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 BackwardPlanningAgent:
    """Agent implementing Backward Planning."""
    
    def __init__(self, name: str, config: BackwardPlanningConfig = None):
        self.name = name
        self.config = config or BackwardPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = BackwardPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Backward Planning."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Backward 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 BackwardPlanningPattern:
    """
    Backward Planning - Enhanced implementation
    
    This pattern provides comprehensive functionality for backward planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: BackwardPlanningConfig = None):
        self.config = config or BackwardPlanningConfig()
        self.agents: Dict[str, BackwardPlanningAgent] = {}
        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 Backward 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: BackwardPlanningAgent):
        """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": "Backward 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": "Backward 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("=== Backward Planning Demo ===\n")
    
    # Create configuration
    config = BackwardPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = BackwardPlanningPattern(config)
    
    # Create and register agents
    agent1 = BackwardPlanningAgent("Agent1", config)
    agent2 = BackwardPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate backward 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: Backward Planning

Pattern 113: 113 Backward Planning Pattern – Line-by-Line Explanation

Overview

Pattern 113: Backward Planning Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 113
• Pattern Name: 113 Backward Planning Pattern
• File Path: patterns\planning_strategy\113_backward_planning_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 113: Backward Planning Pattern

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

Explanation: This module docstring describes the purpose and key features of the 113 Backward 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

BackwardPlanningConfig (Line 18)

Description: Configuration for Backward Planning Pattern.

BackwardPlanningState (Line 27)

Description: State information for Backward Planning Pattern.

BackwardPlanningAgent (Line 36)

Description: Agent implementing Backward 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 Backward Planning Pattern."""

Purpose: Process input according to Backward 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

BackwardPlanningPattern (Line 85)

Description: Backward Planning Pattern – Enhanced implementation

This pattern provides comprehensive functionality for backward 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 = BackwardPlanningConfig(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 112: Forward 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 ForwardPlanningConfig:
    """Configuration for Forward Planning."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class ForwardPlanningState:
    """State information for Forward 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 ForwardPlanningAgent:
    """Agent implementing Forward Planning."""
    
    def __init__(self, name: str, config: ForwardPlanningConfig = None):
        self.name = name
        self.config = config or ForwardPlanningConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = ForwardPlanningState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Forward Planning."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Forward 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 ForwardPlanningPattern:
    """
    Forward Planning - Enhanced implementation
    
    This pattern provides comprehensive functionality for forward planning
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: ForwardPlanningConfig = None):
        self.config = config or ForwardPlanningConfig()
        self.agents: Dict[str, ForwardPlanningAgent] = {}
        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 Forward 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: ForwardPlanningAgent):
        """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": "Forward 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": "Forward 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("=== Forward Planning Demo ===\n")
    
    # Create configuration
    config = ForwardPlanningConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = ForwardPlanningPattern(config)
    
    # Create and register agents
    agent1 = ForwardPlanningAgent("Agent1", config)
    agent2 = ForwardPlanningAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate forward 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: Forward Planning

Pattern 112: 112 Forward Planning Pattern – Line-by-Line Explanation

Overview

Pattern 112: Forward Planning Pattern

Forward planning starts from the initial state and applies actions to reach the goal state. Plans by progressively building toward the goal through valid state transitions.

Code Structure Analysis

File Information

• Pattern Number: 112
• Pattern Name: 112 Forward Planning Pattern
• File Path: patterns\planning_strategy\112_forward_planning_pattern.py
• Total Lines: 281

Detailed Code Explanation

Module Docstring

"""
Pattern 112: Forward Planning Pattern

Forward planning starts from the initial state and applies actions to reach the goal state.
Plans by progressively building toward the goal through valid state transitions.
"""

Explanation: This module docstring describes the purpose and key features of the 112 Forward Planning 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
        from collections import deque  # 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

State (Line 16)

Description: World state representation.

Methods:

__hash__ (Line 21)

def __hash__(self):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference

__eq__ (Line 24)

def __eq__(self, other):
    """No method docstring."""

Purpose: No method docstring.

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

satisfies (Line 27)

def satisfies(self, conditions):
    """Check if state satisfies conditions."""

Purpose: Check if state satisfies conditions.

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

Action (Line 33)

Description: Planning action.

Methods:

is_applicable (Line 41)

def is_applicable(self, state):
    """Check if action can be applied in given state."""

Purpose: Check if action can be applied in given state.

Parameters: – self: Instance reference – state: Current workflow state

apply (Line 45)

def apply(self, state):
    """Apply action to state, producing new state."""

Purpose: Apply action to state, producing new state.

Parameters: – self: Instance reference – state: Current workflow state

PlanStep (Line 52)

Description: Step in a plan.

Plan (Line 61)

Description: Complete plan from initial to goal state.

ForwardPlanningPattern (Line 69)

Description: Forward Planning Pattern – State-space forward search.

Methods:

__init__ (Line 74)

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_action (Line 79)

def add_action(self, action):
    """Register an action."""

Purpose: Register an action.

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

get_applicable_actions (Line 84)

def get_applicable_actions(self, state):
    """Get all actions applicable in current state."""

Purpose: Get all actions applicable in current state.

Parameters: – self: Instance reference – state: Current workflow state

forward_search (Line 88)

def forward_search(self, initial_state, goal_conditions, max_depth):
    """Forward search from initial state to goal.
Uses breadth-first search."""

Purpose: Forward search from initial state to goal. Uses breadth-first search.

Parameters: – self: Instance reference – initial_state: Parameter for initial_state – goal_conditions: Parameter for goal_conditions – max_depth: Parameter for max_depth

run (Line 149)

def run(self, initial_facts, goal_facts, max_depth):
    """Execute forward planning."""

Purpose: Execute forward planning.

Parameters: – self: Instance reference – initial_facts: Parameter for initial_facts – goal_facts: Parameter for goal_facts – max_depth: Parameter for max_depth

get_statistics (Line 205)

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

Purpose: Get planning 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 = State(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 111: Hierarchical Task Network (HTN) Pattern

Decomposes high-level tasks into hierarchical subtasks until primitive actions are reached.
Implements HTN planning with LangChain for task decomposition.
"""

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


class TaskType(Enum):
    """Type of task in HTN."""
    PRIMITIVE = "primitive"  # Directly executable action
    COMPOUND = "compound"    # Needs decomposition


@dataclass
class Task:
    """Represents a task in the HTN."""
    task_id: str
    name: str
    task_type: TaskType
    parameters: Dict[str, Any] = field(default_factory=dict)
    subtasks: List['Task'] = field(default_factory=list)
    preconditions: List[str] = field(default_factory=list)
    effects: List[str] = field(default_factory=list)
    status: str = "pending"  # pending, executing, completed, failed
    result: Optional[Any] = None


@dataclass
class Method:
    """Decomposition method for compound tasks."""
    method_id: str
    name: str
    task_name: str  # Which compound task this decomposes
    preconditions: List[str]
    subtasks_template: List[Dict[str, Any]]  # Template for subtasks


@dataclass
class Operator:
    """Primitive operator that can be executed."""
    operator_id: str
    name: str
    parameters: List[str]
    preconditions: List[str]
    effects: List[str]
    execute_func: Optional[callable] = None


class HTNPlanner:
    """
    Hierarchical Task Network planner that decomposes tasks using methods.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.llm = ChatOpenAI(model=model_name, temperature=0.5)
        self.methods: Dict[str, List[Method]] = {}  # task_name -> methods
        self.operators: Dict[str, Operator] = {}
        self.world_state: Dict[str, bool] = {}
        
    def add_method(self, method: Method):
        """Add a decomposition method."""
        if method.task_name not in self.methods:
            self.methods[method.task_name] = []
        self.methods[method.task_name].append(method)
        
    def add_operator(self, operator: Operator):
        """Add a primitive operator."""
        self.operators[operator.name] = operator
        
    def set_world_state(self, state: Dict[str, bool]):
        """Set the current world state."""
        self.world_state = state.copy()
        
    def check_preconditions(self, preconditions: List[str]) -> bool:
        """Check if preconditions are satisfied."""
        for precond in preconditions:
            if precond.startswith("not "):
                pred = precond[4:]
                if self.world_state.get(pred, False):
                    return False
            else:
                if not self.world_state.get(precond, False):
                    return False
        return True
    
    def apply_effects(self, effects: List[str]):
        """Apply effects to world state."""
        for effect in effects:
            if effect.startswith("not "):
                pred = effect[4:]
                self.world_state[pred] = False
            else:
                self.world_state[effect] = True
    
    def decompose_task(self, task: Task) -> Optional[List[Task]]:
        """Decompose a compound task using applicable methods."""
        if task.task_type == TaskType.PRIMITIVE:
            return [task]
        
        # Find applicable methods
        methods = self.methods.get(task.name, [])
        applicable_methods = [
            m for m in methods
            if self.check_preconditions(m.preconditions)
        ]
        
        if not applicable_methods:
            # Use LLM to suggest decomposition
            return self._llm_decompose(task)
        
        # Choose first applicable method
        method = applicable_methods[0]
        
        # Create subtasks from template
        subtasks = []
        for subtask_template in method.subtasks_template:
            subtask = Task(
                task_id=f"{task.task_id}-{len(subtasks)+1}",
                name=subtask_template["name"],
                task_type=TaskType(subtask_template.get("type", "primitive")),
                parameters=subtask_template.get("parameters", {}),
                preconditions=subtask_template.get("preconditions", []),
                effects=subtask_template.get("effects", [])
            )
            subtasks.append(subtask)
        
        task.subtasks = subtasks
        return subtasks
    
    def _llm_decompose(self, task: Task) -> List[Task]:
        """Use LLM to decompose a task when no methods match."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are an HTN planner. Decompose tasks into subtasks."),
            ("human", """Task: {task_name}
Parameters: {parameters}
Current State: {state}

Decompose this task into 2-5 concrete subtasks.
Format each as: TaskName|type|description

Where type is either 'primitive' or 'compound'""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "task_name": task.name,
            "parameters": task.parameters,
            "state": self.world_state
        })
        
        # Parse response
        lines = str(response.content).strip().split('\n')
        subtasks = []
        
        for i, line in enumerate(lines[:5]):
            parts = line.split('|')
            if len(parts) >= 2:
                subtask = Task(
                    task_id=f"{task.task_id}-{i+1}",
                    name=parts[0].strip(),
                    task_type=TaskType.PRIMITIVE if "primitive" in parts[1].lower() else TaskType.COMPOUND,
                    parameters=task.parameters
                )
                subtasks.append(subtask)
        
        task.subtasks = subtasks
        return subtasks
    
    def plan(self, root_task: Task) -> List[Task]:
        """Create a plan by decomposing the root task."""
        plan = []
        queue = [root_task]
        
        while queue:
            task = queue.pop(0)
            
            if task.task_type == TaskType.PRIMITIVE:
                plan.append(task)
            else:
                # Decompose compound task
                subtasks = self.decompose_task(task)
                if subtasks:
                    # Add subtasks to front of queue
                    queue = subtasks + queue
        
        return plan
    
    def execute_plan(self, plan: List[Task]) -> List[Dict[str, Any]]:
        """Execute a plan of primitive tasks."""
        results = []
        
        for task in plan:
            if not self.check_preconditions(task.preconditions):
                results.append({
                    "task_id": task.task_id,
                    "name": task.name,
                    "status": "failed",
                    "reason": "Preconditions not met"
                })
                break
            
            # Execute task
            operator = self.operators.get(task.name)
            
            if operator and operator.execute_func:
                try:
                    result = operator.execute_func(task.parameters)
                    task.status = "completed"
                    task.result = result
                    
                    # Apply effects
                    self.apply_effects(task.effects)
                    
                    results.append({
                        "task_id": task.task_id,
                        "name": task.name,
                        "status": "completed",
                        "result": result
                    })
                except Exception as e:
                    results.append({
                        "task_id": task.task_id,
                        "name": task.name,
                        "status": "failed",
                        "error": str(e)
                    })
                    break
            else:
                # Simulate execution with LLM
                prompt = ChatPromptTemplate.from_messages([
                    ("system", "You are executing a primitive task."),
                    ("human", "Execute: {task_name} with parameters {parameters}")
                ])
                
                chain = prompt | self.llm
                response = chain.invoke({
                    "task_name": task.name,
                    "parameters": task.parameters
                })
                
                task.status = "completed"
                task.result = str(response.content)
                self.apply_effects(task.effects)
                
                results.append({
                    "task_id": task.task_id,
                    "name": task.name,
                    "status": "completed",
                    "result": task.result
                })
        
        return results


class HTNPattern:
    """
    Complete HTN planning pattern with method library.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.planner = HTNPlanner(model_name=model_name)
        self._setup_default_methods()
        
    def _setup_default_methods(self):
        """Setup default methods for common tasks."""
        # Example: Travel method
        self.planner.add_method(Method(
            method_id="travel-1",
            name="Travel by Air",
            task_name="travel",
            preconditions=["has_airport"],
            subtasks_template=[
                {"name": "book_flight", "type": "primitive", "effects": ["flight_booked"]},
                {"name": "go_to_airport", "type": "primitive", "preconditions": ["flight_booked"]},
                {"name": "board_plane", "type": "primitive", "preconditions": ["at_airport"]},
                {"name": "fly", "type": "primitive", "effects": ["at_destination"]}
            ]
        ))
        
    def run(self, task: Task, initial_state: Dict[str, bool]) -> Dict[str, Any]:
        """Run HTN planning and execution."""
        self.planner.set_world_state(initial_state)
        
        # Create plan
        plan = self.planner.plan(task)
        
        # Execute plan
        results = self.planner.execute_plan(plan)
        
        return {
            "task": task.name,
            "plan_length": len(plan),
            "plan": [{"id": t.task_id, "name": t.name} for t in plan],
            "execution_results": results,
            "final_state": self.planner.world_state
        }


def main():
    """Example usage of HTN Pattern."""
    print("=" * 80)
    print("Hierarchical Task Network (HTN) Pattern Demo")
    print("=" * 80)
    
    # Create HTN pattern
    pattern = HTNPattern()
    planner = pattern.planner
    
    # Add methods for "prepare_meal"
    planner.add_method(Method(
        method_id="meal-1",
        name="Prepare Pasta Meal",
        task_name="prepare_meal",
        preconditions=["has_ingredients"],
        subtasks_template=[
            {"name": "boil_water", "type": "primitive", "effects": ["water_boiling"]},
            {"name": "cook_pasta", "type": "primitive", "preconditions": ["water_boiling"], "effects": ["pasta_cooked"]},
            {"name": "prepare_sauce", "type": "primitive", "effects": ["sauce_ready"]},
            {"name": "combine", "type": "primitive", "preconditions": ["pasta_cooked", "sauce_ready"], "effects": ["meal_ready"]}
        ]
    ))
    
    # Add operators
    planner.add_operator(Operator(
        operator_id="op-1",
        name="boil_water",
        parameters=["amount"],
        preconditions=[],
        effects=["water_boiling"]
    ))
    
    # Define root task
    root_task = Task(
        task_id="task-1",
        name="prepare_meal",
        task_type=TaskType.COMPOUND,
        parameters={"meal_type": "pasta", "servings": 2}
    )
    
    # Initial world state
    initial_state = {
        "has_ingredients": True,
        "has_kitchen": True
    }
    
    # Run HTN planning
    print("\n🎯 Root Task:")
    print(f"  {root_task.name} (type: {root_task.task_type.value})")
    print(f"  Parameters: {root_task.parameters}")
    
    print("\n📍 Initial State:")
    for key, value in initial_state.items():
        print(f"  {key}: {value}")
    
    print("\n" + "=" * 80)
    print("🔄 Planning and Execution")
    print("=" * 80)
    
    result = pattern.run(root_task, initial_state)
    
    # Display plan
    print("\n📋 Generated Plan:")
    for i, step in enumerate(result["plan"], 1):
        print(f"  {i}. {step['name']} ({step['id']})")
    
    # Display execution results
    print("\n✅ Execution Results:")
    for exec_result in result["execution_results"]:
        print(f"\n  {exec_result['name']} ({exec_result['task_id']})")
        print(f"    Status: {exec_result['status']}")
        if exec_result.get('result'):
            print(f"    Result: {str(exec_result['result'])[:100]}...")
    
    # Display final state
    print("\n📍 Final State:")
    for key, value in result["final_state"].items():
        print(f"  {key}: {value}")
    
    print(f"\n✨ Plan completed with {len(result['plan'])} steps")


if __name__ == "__main__":
    main()

    

Agentic Architecture Pattern: Hierarchical Task Network

Pattern 111: 111 Hierarchical Task Network Htn Pattern – Line-by-Line Explanation

Overview

Pattern 111: Hierarchical Task Network (HTN) Pattern

Planning approach that decomposes high-level tasks into subtasks recursively until primitive actions are reached. Uses methods to define decompositions.

Code Structure Analysis

File Information

• Pattern Number: 111
• Pattern Name: 111 Hierarchical Task Network Htn Pattern
• File Path: patterns\planning_strategy\111_hierarchical_task_network_htn_pattern.py
• Total Lines: 249

Detailed Code Explanation

Module Docstring

"""
Pattern 111: Hierarchical Task Network (HTN) Pattern

Planning approach that decomposes high-level tasks into subtasks recursively
until primitive actions are reached. Uses methods to define decompositions.
"""

Explanation: This module docstring describes the purpose and key features of the 111 Hierarchical Task Network Htn Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
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: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – 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

Enumerations

TaskType (Line 15)

class TaskType(Enum):
    COMPOUND
    PRIMITIVE

Purpose: Type of task in HTN.

Values: – COMPOUND: Represents compound – PRIMITIVE: Represents primitive

Classes

TaskType (Line 15)

Description: Type of task in HTN.

Task (Line 22)

Description: Task in HTN.

Method (Line 32)

Description: Method for decomposing compound tasks.

TaskNetwork (Line 41)

Description: Network of tasks to execute.

HTNPattern (Line 47)

Description: Hierarchical Task Network Pattern – Recursive task decomposition.

Methods:

__init__ (Line 52)

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_task (Line 58)

def add_task(self, task):
    """Register a task."""

Purpose: Register a task.

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

add_method (Line 64)

def add_method(self, method):
    """Register a decomposition method."""

Purpose: Register a decomposition method.

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

decompose (Line 70)

def decompose(self, task_name, state):
    """Decompose a compound task into subtasks."""

Purpose: Decompose a compound task into subtasks.

Parameters: – self: Instance reference – task_name: Parameter for task_name – state: Current workflow state

_check_preconditions (Line 96)

def _check_preconditions(self, preconditions, state):
    """Check if preconditions are met."""

Purpose: Check if preconditions are met.

Parameters: – self: Instance reference – preconditions: Parameter for preconditions – state: Current workflow state

plan (Line 103)

def plan(self, goal_task, initial_state):
    """Create a plan by decomposing the goal task."""

Purpose: Create a plan by decomposing the goal task.

Parameters: – self: Instance reference – goal_task: Parameter for goal_task – initial_state: Parameter for initial_state

execute_plan (Line 118)

def execute_plan(self, plan):
    """Execute the primitive tasks in the plan."""

Purpose: Execute the primitive tasks in the plan.

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

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 = TaskType(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 110: Sensor Fusion 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 SensorFusionConfig:
    """Configuration for Sensor Fusion Pattern."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


@dataclass
class SensorFusionState:
    """State information for Sensor Fusion 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 SensorFusionAgent:
    """Agent implementing Sensor Fusion Pattern."""
    
    def __init__(self, name: str, config: SensorFusionConfig = None):
        self.name = name
        self.config = config or SensorFusionConfig()
        self.llm = ChatOpenAI(
            model=self.config.model_name,
            temperature=self.config.temperature
        )
        self.state = SensorFusionState()
        self.execution_history: List[Dict[str, Any]] = []
    
    def process(self, input_data: Any) -> Dict[str, Any]:
        """Process input according to Sensor Fusion Pattern."""
        self.state.current_step += 1
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are {self.name}, implementing Sensor Fusion 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 SensorFusionPattern:
    """
    Sensor Fusion Pattern - Enhanced implementation
    
    This pattern provides comprehensive functionality for sensor fusion
    with full LangChain/LangGraph integration.
    
    Features:
    - Multi-agent coordination
    - State management
    - Execution tracking
    - LangGraph workflow integration
    """
    
    def __init__(self, config: SensorFusionConfig = None):
        self.config = config or SensorFusionConfig()
        self.agents: Dict[str, SensorFusionAgent] = {}
        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 Sensor Fusion 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: SensorFusionAgent):
        """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": "Sensor Fusion 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": "Sensor Fusion 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("=== Sensor Fusion Pattern Demo ===\n")
    
    # Create configuration
    config = SensorFusionConfig(
        model_name="gpt-4",
        temperature=0.7,
        enable_logging=True
    )
    
    # Create pattern
    pattern = SensorFusionPattern(config)
    
    # Create and register agents
    agent1 = SensorFusionAgent("Agent1", config)
    agent2 = SensorFusionAgent("Agent2", config)
    pattern.register_agent(agent1)
    pattern.register_agent(agent2)
    
    print("\nExecuting pattern workflow...")
    
    # Execute with workflow
    result1 = pattern.run("Demonstrate sensor fusion 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: Sensor Fusion

Pattern 110: 110 Sensor Fusion Pattern – Line-by-Line Explanation

Overview

Pattern 110: Sensor Fusion Pattern

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 110
• Pattern Name: 110 Sensor Fusion Pattern
• File Path: patterns\perception_sensing\110_sensor_fusion_pattern.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 110: Sensor Fusion Pattern

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

Explanation: This module docstring describes the purpose and key features of the 110 Sensor Fusion 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

SensorFusionConfig (Line 18)

Description: Configuration for Sensor Fusion Pattern.

SensorFusionState (Line 27)

Description: State information for Sensor Fusion Pattern.

SensorFusionAgent (Line 36)

Description: Agent implementing Sensor Fusion 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 Sensor Fusion Pattern."""

Purpose: Process input according to Sensor Fusion 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

SensorFusionPattern (Line 85)

Description: Sensor Fusion Pattern – Enhanced implementation

This pattern provides comprehensive functionality for sensor fusion 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 = SensorFusionConfig(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