"""
Pattern 247: Quantum Agent

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 QuantumAgentConfig:
    """Configuration for Quantum Agent."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 247: 247 Quantum Agent – Line-by-Line Explanation

Overview

Pattern 247: Quantum Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 247
• Pattern Name: 247 Quantum Agent
• File Path: patterns\emerging\247_quantum_agent.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 247: Quantum Agent

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

Explanation: This module docstring describes the purpose and key features of the 247 Quantum Agent 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

QuantumAgentConfig (Line 18)

Description: Configuration for Quantum Agent.

QuantumAgentState (Line 27)

Description: State information for Quantum Agent.

QuantumAgentAgent (Line 36)

Description: Agent implementing Quantum Agent.

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 Quantum Agent."""

Purpose: Process input according to Quantum Agent.

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

QuantumAgentPattern (Line 85)

Description: Quantum Agent – Enhanced implementation

This pattern provides comprehensive functionality for quantum agent 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 = QuantumAgentConfig(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 246: Green Ai

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 GreenAiConfig:
    """Configuration for Green Ai."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 246: 246 Green Ai – Line-by-Line Explanation

Overview

Pattern 246: Green Ai

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 246
• Pattern Name: 246 Green Ai
• File Path: patterns\emerging\246_green_ai.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 246: Green Ai

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

Explanation: This module docstring describes the purpose and key features of the 246 Green Ai 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

GreenAiConfig (Line 18)

Description: Configuration for Green Ai.

GreenAiState (Line 27)

Description: State information for Green Ai.

GreenAiAgent (Line 36)

Description: Agent implementing Green Ai.

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 Green Ai."""

Purpose: Process input according to Green Ai.

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

GreenAiPattern (Line 85)

Description: Green Ai – Enhanced implementation

This pattern provides comprehensive functionality for green ai 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 = GreenAiConfig(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 245: Sustainable Agent

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 SustainableAgentConfig:
    """Configuration for Sustainable Agent."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 245: 245 Sustainable Agent – Line-by-Line Explanation

Overview

Pattern 245: Sustainable Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 245
• Pattern Name: 245 Sustainable Agent
• File Path: patterns\emerging\245_sustainable_agent.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 245: Sustainable Agent

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

Explanation: This module docstring describes the purpose and key features of the 245 Sustainable Agent 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

SustainableAgentConfig (Line 18)

Description: Configuration for Sustainable Agent.

SustainableAgentState (Line 27)

Description: State information for Sustainable Agent.

SustainableAgentAgent (Line 36)

Description: Agent implementing Sustainable Agent.

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 Sustainable Agent."""

Purpose: Process input according to Sustainable Agent.

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

SustainableAgentPattern (Line 85)

Description: Sustainable Agent – Enhanced implementation

This pattern provides comprehensive functionality for sustainable agent 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 = SustainableAgentConfig(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 244: Responsible Ai

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 ResponsibleAiConfig:
    """Configuration for Responsible Ai."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 244: 244 Responsible Ai – Line-by-Line Explanation

Overview

Pattern 244: Responsible Ai

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 244
• Pattern Name: 244 Responsible Ai
• File Path: patterns\emerging\244_responsible_ai.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 244: Responsible Ai

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

Explanation: This module docstring describes the purpose and key features of the 244 Responsible Ai 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

ResponsibleAiConfig (Line 18)

Description: Configuration for Responsible Ai.

ResponsibleAiState (Line 27)

Description: State information for Responsible Ai.

ResponsibleAiAgent (Line 36)

Description: Agent implementing Responsible Ai.

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 Responsible Ai."""

Purpose: Process input according to Responsible Ai.

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

ResponsibleAiPattern (Line 85)

Description: Responsible Ai – Enhanced implementation

This pattern provides comprehensive functionality for responsible ai 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 = ResponsibleAiConfig(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 243: Ethical Agent

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 EthicalAgentConfig:
    """Configuration for Ethical Agent."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 243: 243 Ethical Agent – Line-by-Line Explanation

Overview

Pattern 243: Ethical Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 243
• Pattern Name: 243 Ethical Agent
• File Path: patterns\emerging\243_ethical_agent.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 243: Ethical Agent

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

Explanation: This module docstring describes the purpose and key features of the 243 Ethical Agent 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

EthicalAgentConfig (Line 18)

Description: Configuration for Ethical Agent.

EthicalAgentState (Line 27)

Description: State information for Ethical Agent.

EthicalAgentAgent (Line 36)

Description: Agent implementing Ethical Agent.

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 Ethical Agent."""

Purpose: Process input according to Ethical Agent.

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

EthicalAgentPattern (Line 85)

Description: Ethical Agent – Enhanced implementation

This pattern provides comprehensive functionality for ethical agent 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 = EthicalAgentConfig(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 242: Value Aligned

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 ValueAlignedConfig:
    """Configuration for Value Aligned."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 242: 242 Value Aligned – Line-by-Line Explanation

Overview

Pattern 242: Value Aligned

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 242
• Pattern Name: 242 Value Aligned
• File Path: patterns\emerging\242_value_aligned.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 242: Value Aligned

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

Explanation: This module docstring describes the purpose and key features of the 242 Value Aligned 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

ValueAlignedConfig (Line 18)

Description: Configuration for Value Aligned.

ValueAlignedState (Line 27)

Description: State information for Value Aligned.

ValueAlignedAgent (Line 36)

Description: Agent implementing Value Aligned.

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 Value Aligned."""

Purpose: Process input according to Value Aligned.

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

ValueAlignedPattern (Line 85)

Description: Value Aligned – Enhanced implementation

This pattern provides comprehensive functionality for value aligned 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 = ValueAlignedConfig(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 241: Constitutional Agent

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 ConstitutionalAgentConfig:
    """Configuration for Constitutional Agent."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 241: 241 Constitutional Agent – Line-by-Line Explanation

Overview

Pattern 241: Constitutional Agent

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

• Pattern Number: 241
• Pattern Name: 241 Constitutional Agent
• File Path: patterns\emerging\241_constitutional_agent.py
• Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 241: Constitutional Agent

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

Explanation: This module docstring describes the purpose and key features of the 241 Constitutional Agent 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

ConstitutionalAgentConfig (Line 18)

Description: Configuration for Constitutional Agent.

ConstitutionalAgentState (Line 27)

Description: State information for Constitutional Agent.

ConstitutionalAgentAgent (Line 36)

Description: Agent implementing Constitutional Agent.

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 Constitutional Agent."""

Purpose: Process input according to Constitutional Agent.

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

ConstitutionalAgentPattern (Line 85)

Description: Constitutional Agent – Enhanced implementation

This pattern provides comprehensive functionality for constitutional agent 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 = ConstitutionalAgentConfig(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 240: Decision Support Agent

Purpose: Multi-criteria decision analysis with risk assessment and recommendations.
Helps evaluate alternatives, assess risks, and generate actionable decisions.
"""

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


class DecisionType(Enum):
    """Types of decisions."""
    STRATEGIC = "strategic"  # Long-term strategic decisions
    OPERATIONAL = "operational"  # Day-to-day operations
    INVESTMENT = "investment"  # Financial investments
    RISK_ASSESSMENT = "risk_assessment"  # Risk evaluation
    PRIORITIZATION = "prioritization"  # Priority ranking
    SCENARIO_ANALYSIS = "scenario_analysis"  # What-if scenarios


class RiskLevel(Enum):
    """Risk level assessment."""
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"


@dataclass
class DecisionCriteria:
    """Decision criteria with weight."""
    name: str
    weight: float  # 0.0 to 1.0
    description: str = ""
    is_benefit: bool = True  # True if higher is better


@dataclass
class Alternative:
    """Decision alternative."""
    name: str
    description: str
    scores: Dict[str, float] = field(default_factory=dict)  # Criteria -> score
    cost: float = 0.0
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class DecisionResult:
    """Decision analysis result."""
    decision_type: DecisionType
    alternatives: List[Alternative]
    criteria: List[DecisionCriteria]
    ranked_alternatives: List[tuple[str, float]] = field(default_factory=list)
    recommendation: str = ""
    risks: List[str] = field(default_factory=list)
    risk_level: RiskLevel = RiskLevel.MEDIUM
    confidence: float = 0.0
    reasoning: str = ""


class DecisionSupportAgent:
    """
    Decision Support Agent - Multi-criteria decision analysis.
    
    Features:
    - Multi-criteria evaluation
    - Alternative ranking
    - Risk assessment
    - Scenario analysis
    - AI-powered recommendations
    - Trade-off analysis
    - Sensitivity analysis
    - Decision justification
    """
    
    def __init__(self, name: str):
        self.name = name
        self.llm = ChatOpenAI(model="gpt-4", temperature=0.3)
        
        # Statistics
        self.statistics: Dict[str, Any] = {
            "total_decisions": 0,
            "decision_types": {t.value: 0 for t in DecisionType},
            "recommendations_made": 0,
            "avg_alternatives": 0.0
        }
    
    def evaluate_decision(
        self,
        alternatives: List[Alternative],
        criteria: List[DecisionCriteria],
        decision_type: DecisionType = DecisionType.STRATEGIC
    ) -> DecisionResult:
        """Evaluate alternatives based on criteria."""
        
        # Rank alternatives
        ranked = self._rank_alternatives(alternatives, criteria)
        
        # Assess risks
        risks, risk_level = self._assess_risks(alternatives, ranked, decision_type)
        
        # Generate recommendation
        recommendation = self._generate_recommendation(ranked, risks, decision_type)
        
        # Generate reasoning
        reasoning = self._generate_reasoning(alternatives, criteria, ranked)
        
        # Calculate confidence
        confidence = self._calculate_confidence(alternatives, criteria, ranked)
        
        # Update statistics
        self.statistics["total_decisions"] += 1
        self.statistics["decision_types"][decision_type.value] += 1
        self.statistics["recommendations_made"] += 1
        
        # Update average alternatives
        prev_avg = self.statistics["avg_alternatives"]
        n = self.statistics["total_decisions"]
        self.statistics["avg_alternatives"] = (prev_avg * (n - 1) + len(alternatives)) / n
        
        return DecisionResult(
            decision_type=decision_type,
            alternatives=alternatives,
            criteria=criteria,
            ranked_alternatives=ranked,
            recommendation=recommendation,
            risks=risks,
            risk_level=risk_level,
            confidence=confidence,
            reasoning=reasoning
        )
    
    def _rank_alternatives(
        self,
        alternatives: List[Alternative],
        criteria: List[DecisionCriteria]
    ) -> List[tuple[str, float]]:
        """Rank alternatives using weighted scoring."""
        
        # Normalize weights
        total_weight = sum(c.weight for c in criteria)
        if total_weight == 0:
            total_weight = 1.0
        
        scores = {}
        
        for alt in alternatives:
            score = 0.0
            
            for criterion in criteria:
                if criterion.name in alt.scores:
                    value = alt.scores[criterion.name]
                    weight = criterion.weight / total_weight
                    
                    # Adjust for benefit/cost criteria
                    if criterion.is_benefit:
                        score += value * weight
                    else:
                        # For cost criteria, lower is better
                        score += (1.0 - value) * weight
            
            scores[alt.name] = score
        
        # Sort by score (descending)
        ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
        
        return ranked
    
    def _assess_risks(
        self,
        alternatives: List[Alternative],
        ranked: List[tuple[str, float]],
        decision_type: DecisionType
    ) -> tuple[List[str], RiskLevel]:
        """Assess risks for decision."""
        
        risks = []
        
        # Check for close alternatives (uncertainty)
        if len(ranked) >= 2:
            diff = ranked[0][1] - ranked[1][1]
            if diff  50000:
            risks.append(f"Recommended option has high cost: ${top_alt.cost:,.2f}")
        
        # Decision type specific risks
        if decision_type == DecisionType.INVESTMENT:
            risks.append("Investment decisions carry inherent market risks")
        elif decision_type == DecisionType.STRATEGIC:
            risks.append("Strategic decisions have long-term implications")
        
        # Determine risk level
        if len(risks) >= 3 or (top_alt and top_alt.cost > 100000):
            risk_level = RiskLevel.HIGH
        elif len(risks) >= 2:
            risk_level = RiskLevel.MEDIUM
        else:
            risk_level = RiskLevel.LOW
        
        return risks, risk_level
    
    def _generate_recommendation(
        self,
        ranked: List[tuple[str, float]],
        risks: List[str],
        decision_type: DecisionType
    ) -> str:
        """Generate recommendation using LLM."""
        
        if not ranked:
            return "Unable to make recommendation - no alternatives provided"
        
        top_alt = ranked[0]
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are a decision support expert. Provide a clear, actionable "
                      f"recommendation for a {decision_type.value} decision."),
            ("human", f"Top Alternative: {top_alt[0]} (Score: {top_alt[1]:.3f})\n"
                     f"Identified Risks: {', '.join(risks) if risks else 'None'}\n\n"
                     f"Provide a concise recommendation (2-3 sentences):")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        return str(response.content).strip()
    
    def _generate_reasoning(
        self,
        alternatives: List[Alternative],
        criteria: List[DecisionCriteria],
        ranked: List[tuple[str, float]]
    ) -> str:
        """Generate detailed reasoning."""
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Explain the decision reasoning clearly and objectively."),
            ("human", f"Decision Analysis:\n"
                     f"- Alternatives: {', '.join(a.name for a in alternatives)}\n"
                     f"- Criteria: {', '.join(c.name for c in criteria)}\n"
                     f"- Top Choice: {ranked[0][0]} ({ranked[0][1]:.3f})\n\n"
                     f"Explain why this is the best choice (3-4 sentences):")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        return str(response.content).strip()
    
    def _calculate_confidence(
        self,
        alternatives: List[Alternative],
        criteria: List[DecisionCriteria],
        ranked: List[tuple[str, float]]
    ) -> float:
        """Calculate confidence in recommendation."""
        
        if not ranked:
            return 0.0
        
        # Base confidence
        confidence = 0.7
        
        # Boost if clear leader
        if len(ranked) >= 2:
            diff = ranked[0][1] - ranked[1][1]
            if diff > 0.2:
                confidence += 0.2
            elif diff = 4:
            confidence += 0.1
        
        # Clamp to [0, 1]
        return max(0.0, min(1.0, confidence))
    
    def compare_scenarios(
        self,
        scenarios: Dict[str, List[Alternative]],
        criteria: List[DecisionCriteria]
    ) -> Dict[str, Any]:
        """Compare different scenarios."""
        
        scenario_results = {}
        
        for scenario_name, alts in scenarios.items():
            result = self.evaluate_decision(
                alts,
                criteria,
                DecisionType.SCENARIO_ANALYSIS
            )
            scenario_results[scenario_name] = {
                "top_choice": result.ranked_alternatives[0] if result.ranked_alternatives else ("None", 0.0),
                "risk_level": result.risk_level.value,
                "confidence": result.confidence
            }
        
        return scenario_results
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get decision support statistics."""
        return {
            "name": self.name,
            **self.statistics
        }


class DecisionSupportPattern:
    """
    Decision Support Pattern - Manage decision support agents.
    
    Coordinates multiple decision support agents for different contexts.
    """
    
    def __init__(self):
        self.agents: Dict[str, DecisionSupportAgent] = {}
    
    def create_agent(self, name: str) -> DecisionSupportAgent:
        """Create a new decision support agent."""
        agent = DecisionSupportAgent(name)
        self.agents[name] = agent
        print(f"   Created decision support agent: {name}")
        return agent
    
    def run(
        self,
        agent_name: str,
        alternatives: List[Alternative],
        criteria: List[DecisionCriteria],
        decision_type: DecisionType = DecisionType.STRATEGIC
    ) -> Dict[str, Any]:
        """Perform decision analysis using specified agent."""
        if agent_name not in self.agents:
            return {"error": f"Agent {agent_name} not found"}
        
        agent = self.agents[agent_name]
        result = agent.evaluate_decision(alternatives, criteria, decision_type)
        
        print(f"\n=== Decision Analysis by {agent_name} ===")
        print(f"Decision Type: {result.decision_type.value}")
        print(f"Alternatives Evaluated: {len(result.alternatives)}")
        print(f"Criteria Used: {len(result.criteria)}\n")
        
        print("Rankings:")
        for i, (name, score) in enumerate(result.ranked_alternatives, 1):
            print(f"  {i}. {name}: {score:.3f}")
        
        print(f"\nRecommendation: {result.recommendation}")
        print(f"\nReasoning: {result.reasoning}")
        
        if result.risks:
            print(f"\nRisks ({result.risk_level.value} level):")
            for risk in result.risks:
                print(f"  - {risk}")
        
        print(f"\nConfidence: {result.confidence:.1%}")
        
        return {
            "agent": agent_name,
            "decision_type": result.decision_type.value,
            "top_choice": result.ranked_alternatives[0] if result.ranked_alternatives else ("None", 0.0),
            "risk_level": result.risk_level.value,
            "confidence": result.confidence,
            "statistics": agent.get_statistics()
        }


# Example usage
if __name__ == "__main__":
    print("=== Decision Support Agent Pattern Demo ===\n")
    
    # Create pattern
    pattern = DecisionSupportPattern()
    
    # Create agents
    print("1. Setting up decision support agents...")
    
    advisor = pattern.create_agent("StrategicAdvisor")
    
    # Define criteria
    print("\n2. Defining decision criteria...")
    
    criteria = [
        DecisionCriteria(name="ROI", weight=0.3, description="Return on Investment", is_benefit=True),
        DecisionCriteria(name="Risk", weight=0.2, description="Risk Level", is_benefit=False),
        DecisionCriteria(name="Timeline", weight=0.2, description="Implementation Time", is_benefit=False),
        DecisionCriteria(name="Impact", weight=0.3, description="Business Impact", is_benefit=True),
    ]
    
    # Define alternatives
    print("   Defining alternatives...")
    
    alternatives = [
        Alternative(
            name="Cloud Migration",
            description="Migrate infrastructure to cloud",
            scores={"ROI": 0.85, "Risk": 0.6, "Timeline": 0.7, "Impact": 0.9},
            cost=150000
        ),
        Alternative(
            name="In-House Development",
            description="Build custom solution internally",
            scores={"ROI": 0.7, "Risk": 0.4, "Timeline": 0.3, "Impact": 0.75},
            cost=200000
        ),
        Alternative(
            name="SaaS Solution",
            description="Adopt third-party SaaS",
            scores={"ROI": 0.8, "Risk": 0.2, "Timeline": 0.9, "Impact": 0.7},
            cost=50000
        ),
    ]
    
    # Perform analysis
    print("\n3. Performing decision analysis...")
    
    result = pattern.run("StrategicAdvisor", alternatives, criteria, DecisionType.STRATEGIC)
    
    print("\n" + "="*60 + "\n")
    
    # Investment decision
    print("4. Evaluating investment decision...")
    
    investment_alternatives = [
        Alternative(
            name="Stock Portfolio",
            description="Diversified stock investments",
            scores={"ROI": 0.75, "Risk": 0.7, "Timeline": 0.5, "Impact": 0.8},
            cost=100000
        ),
        Alternative(
            name="Real Estate",
            description="Commercial property",
            scores={"ROI": 0.65, "Risk": 0.4, "Timeline": 0.2, "Impact": 0.85},
            cost=500000
        ),
        Alternative(
            name="Bonds",
            description="Government bonds",
            scores={"ROI": 0.4, "Risk": 0.1, "Timeline": 0.8, "Impact": 0.5},
            cost=75000
        ),
    ]
    
    result2 = pattern.run("StrategicAdvisor", investment_alternatives, criteria, DecisionType.INVESTMENT)
    
    # Print statistics
    print("\n" + "="*60)
    print("\n5. Decision Support Statistics:")
    stats = result2["statistics"]
    for key, value in stats.items():
        print(f"   {key}: {value}")
    
    print("\n✓ Multi-criteria decision analysis with risk assessment complete!")

    

Agentic Architecture Pattern: Decision Support Agent

Pattern 240: 240 Decision Support Agent – Line-by-Line Explanation

Overview

Pattern 240: Decision Support Agent

Purpose: Multi-criteria decision analysis with risk assessment and recommendations. Helps evaluate alternatives, assess risks, and generate actionable decisions.

Code Structure Analysis

File Information

• Pattern Number: 240
• Pattern Name: 240 Decision Support Agent
• File Path: patterns\domain_specific\240_decision_support_agent.py
• Total Lines: 469

Detailed Code Explanation

Module Docstring

"""
Pattern 240: Decision Support Agent

Purpose: Multi-criteria decision analysis with risk assessment and recommendations.
Helps evaluate alternatives, assess risks, and generate actionable decisions.
"""

Explanation: This module docstring describes the purpose and key features of the 240 Decision Support Agent pattern.

Imports Section

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

Enumerations

DecisionType (Line 18)

class DecisionType(Enum):
    STRATEGIC
    OPERATIONAL
    INVESTMENT
    RISK_ASSESSMENT
    PRIORITIZATION
    SCENARIO_ANALYSIS

Purpose: Types of decisions.

Values: – STRATEGIC: Represents strategic – OPERATIONAL: Represents operational – INVESTMENT: Represents investment – RISK_ASSESSMENT: Represents risk assessment – PRIORITIZATION: Represents prioritization – SCENARIO_ANALYSIS: Represents scenario analysis

RiskLevel (Line 28)

class RiskLevel(Enum):
    LOW
    MEDIUM
    HIGH
    CRITICAL

Purpose: Risk level assessment.

Values: – LOW: Represents low – MEDIUM: Represents medium – HIGH: Represents high – CRITICAL: Represents critical

Classes

DecisionType (Line 18)

Description: Types of decisions.

RiskLevel (Line 28)

Description: Risk level assessment.

DecisionCriteria (Line 37)

Description: Decision criteria with weight.

Alternative (Line 46)

Description: Decision alternative.

DecisionResult (Line 56)

Description: Decision analysis result.

DecisionSupportAgent (Line 69)

Description: Decision Support Agent – Multi-criteria decision analysis.

Features: – Multi-criteria evaluation – Alternative ranking – Risk assessment – Scenario analysis – AI-powered recommendations – Trade-off analysis – Sensitivity analysis – Decision justification

Methods:

__init__ (Line 84)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label

evaluate_decision (Line 96)

def evaluate_decision(self, alternatives, criteria, decision_type):
    """Evaluate alternatives based on criteria."""

Purpose: Evaluate alternatives based on criteria.

Parameters: – self: Instance reference – alternatives: Parameter for alternatives – criteria: Parameter for criteria – decision_type: Parameter for decision_type

_rank_alternatives (Line 141)

def _rank_alternatives(self, alternatives, criteria):
    """Rank alternatives using weighted scoring."""

Purpose: Rank alternatives using weighted scoring.

Parameters: – self: Instance reference – alternatives: Parameter for alternatives – criteria: Parameter for criteria

_assess_risks (Line 177)

def _assess_risks(self, alternatives, ranked, decision_type):
    """Assess risks for decision."""

Purpose: Assess risks for decision.

Parameters: – self: Instance reference – alternatives: Parameter for alternatives – ranked: Parameter for ranked – decision_type: Parameter for decision_type

_generate_recommendation (Line 214)

def _generate_recommendation(self, ranked, risks, decision_type):
    """Generate recommendation using LLM."""

Purpose: Generate recommendation using LLM.

Parameters: – self: Instance reference – ranked: Parameter for ranked – risks: Parameter for risks – decision_type: Parameter for decision_type

_generate_reasoning (Line 240)

def _generate_reasoning(self, alternatives, criteria, ranked):
    """Generate detailed reasoning."""

Purpose: Generate detailed reasoning.

Parameters: – self: Instance reference – alternatives: Parameter for alternatives – criteria: Parameter for criteria – ranked: Parameter for ranked

_calculate_confidence (Line 262)

def _calculate_confidence(self, alternatives, criteria, ranked):
    """Calculate confidence in recommendation."""

Purpose: Calculate confidence in recommendation.

Parameters: – self: Instance reference – alternatives: Parameter for alternatives – criteria: Parameter for criteria – ranked: Parameter for ranked

compare_scenarios (Line 291)

def compare_scenarios(self, scenarios, criteria):
    """Compare different scenarios."""

Purpose: Compare different scenarios.

Parameters: – self: Instance reference – scenarios: Parameter for scenarios – criteria: Parameter for criteria

get_statistics (Line 314)

def get_statistics(self):
    """Get decision support statistics."""

Purpose: Get decision support statistics.

Parameters: – self: Instance reference

DecisionSupportPattern (Line 322)

Description: Decision Support Pattern – Manage decision support agents.

Coordinates multiple decision support agents for different contexts.

Methods:

__init__ (Line 329)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_agent (Line 332)

def create_agent(self, name):
    """Create a new decision support agent."""

Purpose: Create a new decision support agent.

Parameters: – self: Instance reference – name: Identifier or label

run (Line 339)

def run(self, agent_name, alternatives, criteria, decision_type):
    """Perform decision analysis using specified agent."""

Purpose: Perform decision analysis using specified agent.

Parameters: – self: Instance reference – agent_name: Parameter for agent_name – alternatives: Parameter for alternatives – criteria: Parameter for criteria – decision_type: Parameter for decision_type

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 = DecisionType(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 239: Data Analysis Agent

Purpose: Statistical data analysis with insights, visualizations, and reporting.
Performs descriptive statistics, correlation analysis, trend detection, and generates insights.
"""

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


class AnalysisType(Enum):
    """Types of data analysis."""
    DESCRIPTIVE = "descriptive"  # Mean, median, std dev
    CORRELATION = "correlation"  # Relationships between variables
    TREND = "trend"  # Time series trends
    DISTRIBUTION = "distribution"  # Data distribution
    ANOMALY = "anomaly"  # Outlier detection
    COMPARATIVE = "comparative"  # Compare groups


@dataclass
class Dataset:
    """Dataset for analysis."""
    name: str
    data: List[Dict[str, Any]]
    columns: List[str] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def __post_init__(self):
        if not self.columns and self.data:
            self.columns = list(self.data[0].keys())


@dataclass
class AnalysisResult:
    """Analysis result."""
    dataset_name: str
    analysis_type: AnalysisType
    statistics: Dict[str, Any]
    insights: List[str]
    visualizations: List[str] = field(default_factory=list)
    recommendations: List[str] = field(default_factory=list)
    confidence: float = 0.0


class DataAnalysisAgent:
    """
    Data Analysis Agent - Statistical analysis and insights.
    
    Features:
    - Descriptive statistics
    - Correlation analysis
    - Trend detection
    - Anomaly detection
    - Distribution analysis
    - AI-powered insights
    - Recommendations
    - Visualization suggestions
    """
    
    def __init__(self, name: str):
        self.name = name
        self.llm = ChatOpenAI(model="gpt-4", temperature=0.3)
        
        # Statistics
        self.statistics: Dict[str, Any] = {
            "total_analyses": 0,
            "analysis_types": {t.value: 0 for t in AnalysisType},
            "datasets_analyzed": 0,
            "insights_generated": 0
        }
    
    def analyze(self, dataset: Dataset, analysis_type: AnalysisType = AnalysisType.DESCRIPTIVE) -> AnalysisResult:
        """Perform analysis on dataset."""
        
        # Perform statistical analysis
        if analysis_type == AnalysisType.DESCRIPTIVE:
            stats = self._descriptive_statistics(dataset)
        elif analysis_type == AnalysisType.CORRELATION:
            stats = self._correlation_analysis(dataset)
        elif analysis_type == AnalysisType.TREND:
            stats = self._trend_analysis(dataset)
        elif analysis_type == AnalysisType.DISTRIBUTION:
            stats = self._distribution_analysis(dataset)
        elif analysis_type == AnalysisType.ANOMALY:
            stats = self._anomaly_detection(dataset)
        else:  # COMPARATIVE
            stats = self._comparative_analysis(dataset)
        
        # Generate insights using LLM
        insights = self._generate_insights(dataset, stats, analysis_type)
        
        # Generate recommendations
        recommendations = self._generate_recommendations(dataset, stats, insights)
        
        # Suggest visualizations
        visualizations = self._suggest_visualizations(dataset, analysis_type)
        
        # Calculate confidence
        confidence = self._calculate_confidence(dataset, stats)
        
        # Update statistics
        self.statistics["total_analyses"] += 1
        self.statistics["analysis_types"][analysis_type.value] += 1
        self.statistics["datasets_analyzed"] = self.statistics.get("datasets_analyzed", 0) + 1
        self.statistics["insights_generated"] += len(insights)
        
        return AnalysisResult(
            dataset_name=dataset.name,
            analysis_type=analysis_type,
            statistics=stats,
            insights=insights,
            visualizations=visualizations,
            recommendations=recommendations,
            confidence=confidence
        )
    
    def _descriptive_statistics(self, dataset: Dataset) -> Dict[str, Any]:
        """Calculate descriptive statistics."""
        stats = {}
        
        for column in dataset.columns:
            # Extract numeric values
            values = []
            for row in dataset.data:
                val = row.get(column)
                if isinstance(val, (int, float)):
                    values.append(val)
            
            if values:
                stats[column] = {
                    "count": len(values),
                    "mean": statistics.mean(values),
                    "median": statistics.median(values),
                    "std_dev": statistics.stdev(values) if len(values) > 1 else 0,
                    "min": min(values),
                    "max": max(values),
                    "range": max(values) - min(values)
                }
        
        return stats
    
    def _correlation_analysis(self, dataset: Dataset) -> Dict[str, Any]:
        """Analyze correlations between variables."""
        correlations = {}
        
        # Get numeric columns
        numeric_cols = []
        for col in dataset.columns:
            values = [row.get(col) for row in dataset.data if isinstance(row.get(col), (int, float))]
            if values:
                numeric_cols.append((col, values))
        
        # Calculate pairwise correlations
        for i in range(len(numeric_cols)):
            for j in range(i + 1, len(numeric_cols)):
                col1, vals1 = numeric_cols[i]
                col2, vals2 = numeric_cols[j]
                
                if len(vals1) == len(vals2) and len(vals1) > 1:
                    corr = self._pearson_correlation(vals1, vals2)
                    correlations[f"{col1}_vs_{col2}"] = corr
        
        return {"correlations": correlations}
    
    def _trend_analysis(self, dataset: Dataset) -> Dict[str, Any]:
        """Detect trends in data."""
        trends = {}
        
        for column in dataset.columns:
            values = [row.get(column) for row in dataset.data if isinstance(row.get(column), (int, float))]
            
            if len(values) >= 3:
                # Simple trend detection
                if values[-1] > values[0]:
                    direction = "increasing"
                    change_pct = ((values[-1] - values[0]) / values[0]) * 100 if values[0] != 0 else 0
                elif values[-1]  Dict[str, Any]:
        """Analyze data distribution."""
        distributions = {}
        
        for column in dataset.columns:
            values = [row.get(column) for row in dataset.data]
            
            # Frequency distribution
            freq = defaultdict(int)
            for val in values:
                freq[str(val)] += 1
            
            # Find mode
            mode = max(freq.items(), key=lambda x: x[1]) if freq else (None, 0)
            
            distributions[column] = {
                "unique_values": len(freq),
                "mode": mode[0],
                "mode_frequency": mode[1],
                "distribution": dict(freq)
            }
        
        return {"distributions": distributions}
    
    def _anomaly_detection(self, dataset: Dataset) -> Dict[str, Any]:
        """Detect anomalies/outliers."""
        anomalies = {}
        
        for column in dataset.columns:
            values = [row.get(column) for row in dataset.data if isinstance(row.get(column), (int, float))]
            
            if len(values) > 3:
                mean = statistics.mean(values)
                std_dev = statistics.stdev(values)
                
                # Values beyond 2 standard deviations
                outliers = [v for v in values if abs(v - mean) > 2 * std_dev]
                
                anomalies[column] = {
                    "outlier_count": len(outliers),
                    "outliers": outliers[:10],  # Limit to 10
                    "threshold": 2 * std_dev
                }
        
        return {"anomalies": anomalies}
    
    def _comparative_analysis(self, dataset: Dataset) -> Dict[str, Any]:
        """Compare groups or categories."""
        comparisons = {}
        
        # Group by categorical columns and compare numeric columns
        categorical_cols = []
        numeric_cols = []
        
        for col in dataset.columns:
            sample_vals = [row.get(col) for row in dataset.data[:10]]
            if all(isinstance(v, (int, float)) for v in sample_vals if v is not None):
                numeric_cols.append(col)
            else:
                categorical_cols.append(col)
        
        if categorical_cols and numeric_cols:
            cat_col = categorical_cols[0]
            num_col = numeric_cols[0]
            
            # Group by category
            groups = defaultdict(list)
            for row in dataset.data:
                groups[str(row.get(cat_col))].append(row.get(num_col))
            
            # Calculate stats for each group
            group_stats = {}
            for group, values in groups.items():
                numeric_values = [v for v in values if isinstance(v, (int, float))]
                if numeric_values:
                    group_stats[group] = {
                        "mean": statistics.mean(numeric_values),
                        "count": len(numeric_values)
                    }
            
            comparisons[f"{cat_col}_by_{num_col}"] = group_stats
        
        return {"comparisons": comparisons}
    
    def _pearson_correlation(self, x: List[float], y: List[float]) -> float:
        """Calculate Pearson correlation coefficient."""
        if len(x) != len(y) or len(x)  List[str]:
        """Generate insights using LLM."""
        stats_summary = str(stats)[:500]  # Limit length
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are a data analysis expert. Generate 3-5 clear, actionable insights "
                      f"from the {analysis_type.value} analysis results. "
                      f"Focus on patterns, trends, and important findings."),
            ("human", f"Dataset: {dataset.name}\n\nAnalysis Results:\n{stats_summary}\n\nInsights:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        # Parse insights
        content = str(response.content)
        insights = [line.strip() for line in content.split('\n') if line.strip()]
        insights = [re.sub(r'^\d+[\.\)]\s*', '', ins) for ins in insights]
        
        return insights[:5]
    
    def _generate_recommendations(self, dataset: Dataset, stats: Dict[str, Any], insights: List[str]) -> List[str]:
        """Generate recommendations."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Based on the analysis insights, provide 2-3 actionable recommendations."),
            ("human", f"Insights:\n" + "\n".join(f"- {ins}" for ins in insights) + "\n\nRecommendations:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        content = str(response.content)
        recommendations = [line.strip() for line in content.split('\n') if line.strip()]
        recommendations = [re.sub(r'^\d+[\.\)]\s*', '', rec) for rec in recommendations]
        
        return recommendations[:3]
    
    def _suggest_visualizations(self, dataset: Dataset, analysis_type: AnalysisType) -> List[str]:
        """Suggest appropriate visualizations."""
        viz_map = {
            AnalysisType.DESCRIPTIVE: ["Box plot", "Histogram", "Summary table"],
            AnalysisType.CORRELATION: ["Scatter plot", "Correlation matrix", "Heatmap"],
            AnalysisType.TREND: ["Line chart", "Area chart", "Trend lines"],
            AnalysisType.DISTRIBUTION: ["Histogram", "Bar chart", "Pie chart"],
            AnalysisType.ANOMALY: ["Scatter plot with outliers", "Box plot", "Time series"],
            AnalysisType.COMPARATIVE: ["Bar chart", "Grouped bar chart", "Box plot comparison"]
        }
        
        return viz_map.get(analysis_type, ["Bar chart", "Line chart"])
    
    def _calculate_confidence(self, dataset: Dataset, stats: Dict[str, Any]) -> float:
        """Calculate confidence in analysis."""
        sample_size = len(dataset.data)
        
        # Base confidence on sample size
        if sample_size >= 100:
            confidence = 0.9
        elif sample_size >= 30:
            confidence = 0.8
        elif sample_size >= 10:
            confidence = 0.7
        else:
            confidence = 0.5
        
        return confidence
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get analysis statistics."""
        return {
            "name": self.name,
            **self.statistics
        }


class DataAnalysisPattern:
    """
    Data Analysis Pattern - Manage data analysis agents.
    
    Coordinates multiple analysis agents for different purposes.
    """
    
    def __init__(self):
        self.agents: Dict[str, DataAnalysisAgent] = {}
    
    def create_agent(self, name: str) -> DataAnalysisAgent:
        """Create a new data analysis agent."""
        agent = DataAnalysisAgent(name)
        self.agents[name] = agent
        print(f"   Created data analysis agent: {name}")
        return agent
    
    def run(self, agent_name: str, dataset: Dataset, analysis_type: AnalysisType = AnalysisType.DESCRIPTIVE) -> Dict[str, Any]:
        """Perform analysis using specified agent."""
        if agent_name not in self.agents:
            return {"error": f"Agent {agent_name} not found"}
        
        agent = self.agents[agent_name]
        result = agent.analyze(dataset, analysis_type)
        
        print(f"\n=== Analysis by {agent_name} ===")
        print(f"Dataset: {result.dataset_name} ({len(dataset.data)} rows)")
        print(f"Analysis Type: {result.analysis_type.value}")
        print(f"Confidence: {result.confidence:.1%}\n")
        
        print("Key Statistics:")
        for key, value in list(result.statistics.items())[:3]:
            print(f"  {key}: {str(value)[:100]}")
        
        print(f"\nInsights ({len(result.insights)}):")
        for i, insight in enumerate(result.insights, 1):
            print(f"  {i}. {insight}")
        
        if result.recommendations:
            print(f"\nRecommendations:")
            for i, rec in enumerate(result.recommendations, 1):
                print(f"  {i}. {rec}")
        
        if result.visualizations:
            print(f"\nSuggested Visualizations: {', '.join(result.visualizations)}")
        
        return {
            "agent": agent_name,
            "analysis_type": result.analysis_type.value,
            "insights_count": len(result.insights),
            "confidence": result.confidence,
            "statistics": agent.get_statistics()
        }


# Example usage
if __name__ == "__main__":
    print("=== Data Analysis Agent Pattern Demo ===\n")
    
    # Create pattern
    pattern = DataAnalysisPattern()
    
    # Create agents
    print("1. Setting up data analysis agents...")
    
    analyst = pattern.create_agent("DataAnalyst")
    
    # Create sample datasets
    print("\n2. Creating sample datasets...")
    
    sales_data = Dataset(
        name="Monthly Sales",
        data=[
            {"month": "Jan", "sales": 45000, "costs": 30000, "customers": 120},
            {"month": "Feb", "sales": 52000, "costs": 32000, "customers": 145},
            {"month": "Mar", "sales": 48000, "costs": 31000, "customers": 135},
            {"month": "Apr", "sales": 61000, "costs": 35000, "customers": 170},
            {"month": "May", "sales": 58000, "costs": 34000, "customers": 160},
            {"month": "Jun", "sales": 65000, "costs": 36000, "customers": 180},
        ]
    )
    
    # Perform analyses
    print("\n3. Performing analyses...")
    
    # Descriptive statistics
    result1 = pattern.run("DataAnalyst", sales_data, AnalysisType.DESCRIPTIVE)
    
    print("\n" + "="*60 + "\n")
    
    # Trend analysis
    result2 = pattern.run("DataAnalyst", sales_data, AnalysisType.TREND)
    
    print("\n" + "="*60 + "\n")
    
    # Correlation analysis
    result3 = pattern.run("DataAnalyst", sales_data, AnalysisType.CORRELATION)
    
    # Print statistics
    print("\n" + "="*60)
    print("\n4. Analysis Statistics:")
    stats = result3["statistics"]
    for key, value in stats.items():
        print(f"   {key}: {value}")
    
    print("\n✓ Comprehensive data analysis with AI-powered insights complete!")

    

Agentic Architecture Pattern: Data Analysis Agent

Pattern 239: 239 Data Analysis Agent – Line-by-Line Explanation

Overview

Pattern 239: Data Analysis Agent

Purpose: Statistical data analysis with insights, visualizations, and reporting. Performs descriptive statistics, correlation analysis, trend detection, and generates insights.

Code Structure Analysis

File Information

• Pattern Number: 239
• Pattern Name: 239 Data Analysis Agent
• File Path: patterns\domain_specific\239_data_analysis_agent.py
• Total Lines: 483

Detailed Code Explanation

Module Docstring

"""
Pattern 239: Data Analysis Agent

Purpose: Statistical data analysis with insights, visualizations, and reporting.
Performs descriptive statistics, correlation analysis, trend detection, and generates insights.
"""

Explanation: This module docstring describes the purpose and key features of the 239 Data Analysis Agent pattern.

Imports Section

from typing import Dict, Any, List, Optional, Union
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
import statistics
import re  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Union: 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

Enumerations

AnalysisType (Line 19)

class AnalysisType(Enum):
    DESCRIPTIVE
    CORRELATION
    TREND
    DISTRIBUTION
    ANOMALY
    COMPARATIVE

Purpose: Types of data analysis.

Values: – DESCRIPTIVE: Represents descriptive – CORRELATION: Represents correlation – TREND: Represents trend – DISTRIBUTION: Represents distribution – ANOMALY: Represents anomaly – COMPARATIVE: Represents comparative

Classes

AnalysisType (Line 19)

Description: Types of data analysis.

Dataset (Line 30)

Description: Dataset for analysis.

Methods:

__post_init__ (Line 37)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

AnalysisResult (Line 43)

Description: Analysis result.

DataAnalysisAgent (Line 54)

Description: Data Analysis Agent – Statistical analysis and insights.

Features: – Descriptive statistics – Correlation analysis – Trend detection – Anomaly detection – Distribution analysis – AI-powered insights – Recommendations – Visualization suggestions

Methods:

__init__ (Line 69)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label

analyze (Line 81)

def analyze(self, dataset, analysis_type):
    """Perform analysis on dataset."""

Purpose: Perform analysis on dataset.

Parameters: – self: Instance reference – dataset: Parameter for dataset – analysis_type: Parameter for analysis_type

_descriptive_statistics (Line 126)

def _descriptive_statistics(self, dataset):
    """Calculate descriptive statistics."""

Purpose: Calculate descriptive statistics.

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

_correlation_analysis (Line 151)

def _correlation_analysis(self, dataset):
    """Analyze correlations between variables."""

Purpose: Analyze correlations between variables.

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

_trend_analysis (Line 174)

def _trend_analysis(self, dataset):
    """Detect trends in data."""

Purpose: Detect trends in data.

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

_distribution_analysis (Line 202)

def _distribution_analysis(self, dataset):
    """Analyze data distribution."""

Purpose: Analyze data distribution.

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

_anomaly_detection (Line 226)

def _anomaly_detection(self, dataset):
    """Detect anomalies/outliers."""

Purpose: Detect anomalies/outliers.

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

_comparative_analysis (Line 248)

def _comparative_analysis(self, dataset):
    """Compare groups or categories."""

Purpose: Compare groups or categories.

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

_pearson_correlation (Line 286)

def _pearson_correlation(self, x, y):
    """Calculate Pearson correlation coefficient."""

Purpose: Calculate Pearson correlation coefficient.

Parameters: – self: Instance reference – x: Parameter for x – y: Parameter for y

_generate_insights (Line 303)

def _generate_insights(self, dataset, stats, analysis_type):
    """Generate insights using LLM."""

Purpose: Generate insights using LLM.

Parameters: – self: Instance reference – dataset: Parameter for dataset – stats: Parameter for stats – analysis_type: Parameter for analysis_type

_generate_recommendations (Line 324)

def _generate_recommendations(self, dataset, stats, insights):
    """Generate recommendations."""

Purpose: Generate recommendations.

Parameters: – self: Instance reference – dataset: Parameter for dataset – stats: Parameter for stats – insights: Parameter for insights

_suggest_visualizations (Line 340)

def _suggest_visualizations(self, dataset, analysis_type):
    """Suggest appropriate visualizations."""

Purpose: Suggest appropriate visualizations.

Parameters: – self: Instance reference – dataset: Parameter for dataset – analysis_type: Parameter for analysis_type

_calculate_confidence (Line 353)

def _calculate_confidence(self, dataset, stats):
    """Calculate confidence in analysis."""

Purpose: Calculate confidence in analysis.

Parameters: – self: Instance reference – dataset: Parameter for dataset – stats: Parameter for stats

get_statistics (Line 369)

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

Purpose: Get analysis statistics.

Parameters: – self: Instance reference

DataAnalysisPattern (Line 377)

Description: Data Analysis Pattern – Manage data analysis agents.

Coordinates multiple analysis agents for different purposes.

Methods:

__init__ (Line 384)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_agent (Line 387)

def create_agent(self, name):
    """Create a new data analysis agent."""

Purpose: Create a new data analysis agent.

Parameters: – self: Instance reference – name: Identifier or label

run (Line 394)

def run(self, agent_name, dataset, analysis_type):
    """Perform analysis using specified agent."""

Purpose: Perform analysis using specified agent.

Parameters: – self: Instance reference – agent_name: Parameter for agent_name – dataset: Parameter for dataset – analysis_type: Parameter for analysis_type

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 = AnalysisType(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 238: Code Generation Agent

Purpose: AI-powered code generation with language detection, testing, and documentation.
Generates code from natural language descriptions with quality checks and best practices.
"""

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
import re


class ProgrammingLanguage(Enum):
    """Supported programming languages."""
    PYTHON = "python"
    JAVASCRIPT = "javascript"
    TYPESCRIPT = "typescript"
    JAVA = "java"
    CSHARP = "csharp"
    CPP = "cpp"
    GO = "go"
    RUST = "rust"
    RUBY = "ruby"
    PHP = "php"


class CodeQuality(Enum):
    """Code quality levels."""
    QUICK = "quick"  # Fast generation, basic functionality
    STANDARD = "standard"  # Good quality with comments
    PRODUCTION = "production"  # High quality, well-documented
    ENTERPRISE = "enterprise"  # Best practices, fully tested


@dataclass
class CodeRequest:
    """Code generation request."""
    description: str
    language: ProgrammingLanguage
    quality: CodeQuality = CodeQuality.STANDARD
    include_tests: bool = False
    include_docs: bool = True
    requirements: List[str] = field(default_factory=list)


@dataclass
class GeneratedCode:
    """Generated code result."""
    request: CodeRequest
    code: str
    tests: Optional[str] = None
    documentation: Optional[str] = None
    language: ProgrammingLanguage = ProgrammingLanguage.PYTHON
    complexity_score: float = 0.0
    line_count: int = 0
    has_error_handling: bool = False
    best_practices_applied: List[str] = field(default_factory=list)


class CodeGenerationAgent:
    """
    Code Generation Agent - AI-powered code synthesis.
    
    Features:
    - Natural language to code translation
    - Multi-language support
    - Test generation
    - Documentation generation
    - Code quality levels
    - Best practices enforcement
    - Error handling
    - Complexity analysis
    """
    
    def __init__(self, name: str, default_language: ProgrammingLanguage = ProgrammingLanguage.PYTHON):
        self.name = name
        self.default_language = default_language
        self.llm = ChatOpenAI(model="gpt-4", temperature=0.2)
        
        # Statistics
        self.statistics: Dict[str, Any] = {
            "total_generations": 0,
            "languages_used": {lang.value: 0 for lang in ProgrammingLanguage},
            "quality_distribution": {q.value: 0 for q in CodeQuality},
            "avg_line_count": 0,
            "tests_generated": 0
        }
    
    def generate(self, request: CodeRequest) -> GeneratedCode:
        """Generate code from request."""
        
        # Generate main code
        code = self._generate_code(request)
        
        # Generate tests if requested
        tests = None
        if request.include_tests:
            tests = self._generate_tests(code, request)
            self.statistics["tests_generated"] += 1
        
        # Generate documentation if requested
        documentation = None
        if request.include_docs:
            documentation = self._generate_documentation(code, request)
        
        # Analyze code
        line_count = len(code.split('\n'))
        complexity_score = self._calculate_complexity(code)
        has_error_handling = self._check_error_handling(code, request.language)
        best_practices = self._identify_best_practices(code, request.language)
        
        # Update statistics
        self.statistics["total_generations"] += 1
        self.statistics["languages_used"][request.language.value] += 1
        self.statistics["quality_distribution"][request.quality.value] += 1
        
        old_avg = self.statistics["avg_line_count"]
        total = self.statistics["total_generations"]
        self.statistics["avg_line_count"] = (old_avg * (total - 1) + line_count) / total
        
        return GeneratedCode(
            request=request,
            code=code,
            tests=tests,
            documentation=documentation,
            language=request.language,
            complexity_score=complexity_score,
            line_count=line_count,
            has_error_handling=has_error_handling,
            best_practices_applied=best_practices
        )
    
    def _generate_code(self, request: CodeRequest) -> str:
        """Generate code from description."""
        
        quality_instructions = {
            CodeQuality.QUICK: "Generate working code quickly. Basic functionality is sufficient.",
            CodeQuality.STANDARD: "Generate clean, readable code with inline comments.",
            CodeQuality.PRODUCTION: "Generate production-quality code with comprehensive error handling, "
                                   "logging, and documentation.",
            CodeQuality.ENTERPRISE: "Generate enterprise-grade code following all best practices, "
                                   "with complete error handling, logging, type hints, and comprehensive documentation."
        }
        
        requirements_text = ""
        if request.requirements:
            requirements_text = f"\n\nSpecific requirements:\n" + "\n".join(f"- {req}" for req in request.requirements)
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are an expert {request.language.value} developer. "
                      f"Generate high-quality, idiomatic code. "
                      f"{quality_instructions.get(request.quality, '')} "
                      f"Include appropriate error handling and follow best practices. "
                      f"Return ONLY the code, no explanations."),
            ("human", f"Generate {request.language.value} code for:\n\n{request.description}{requirements_text}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        # Extract code from response
        code = str(response.content).strip()
        code = self._extract_code_block(code)
        
        return code
    
    def _generate_tests(self, code: str, request: CodeRequest) -> str:
        """Generate unit tests for code."""
        
        test_frameworks = {
            ProgrammingLanguage.PYTHON: "pytest",
            ProgrammingLanguage.JAVASCRIPT: "Jest",
            ProgrammingLanguage.TYPESCRIPT: "Jest",
            ProgrammingLanguage.JAVA: "JUnit",
            ProgrammingLanguage.CSHARP: "xUnit",
            ProgrammingLanguage.GO: "testing package",
            ProgrammingLanguage.RUST: "Rust testing framework"
        }
        
        framework = test_frameworks.get(request.language, "standard testing framework")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"You are an expert in {request.language.value} testing. "
                      f"Generate comprehensive unit tests using {framework}. "
                      f"Include edge cases and error scenarios. "
                      f"Return ONLY the test code, no explanations."),
            ("human", f"Generate tests for this code:\n\n{code}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        tests = str(response.content).strip()
        tests = self._extract_code_block(tests)
        
        return tests
    
    def _generate_documentation(self, code: str, request: CodeRequest) -> str:
        """Generate documentation for code."""
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", f"Generate clear, comprehensive documentation for {request.language.value} code. "
                      f"Include: purpose, parameters, return values, usage examples, and edge cases."),
            ("human", f"Document this code:\n\n{code}")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({})
        
        return str(response.content).strip()
    
    def _extract_code_block(self, text: str) -> str:
        """Extract code from markdown code blocks."""
        # Try to find code block with language specifier
        pattern = r'```(?:\w+)?\n(.*?)```'
        matches = re.findall(pattern, text, re.DOTALL)
        
        if matches:
            return matches[0].strip()
        
        # If no code block found, return as is
        return text
    
    def _calculate_complexity(self, code: str) -> float:
        """Calculate code complexity score (simplified)."""
        lines = [line for line in code.split('\n') if line.strip() and not line.strip().startswith('#')]
        
        # Count complexity indicators
        complexity = 0
        complexity += len(re.findall(r'\bif\b|\bfor\b|\bwhile\b', code)) * 2
        complexity += len(re.findall(r'\bdef\b|\bclass\b|\bfunction\b', code)) * 3
        complexity += len(re.findall(r'\btry\b|\bcatch\b|\bexcept\b', code)) * 1
        complexity += len(re.findall(r'\band\b|\bor\b|\b&&\b|\b\|\|\b', code)) * 1
        
        # Normalize by line count
        if lines:
            return min(10.0, complexity / len(lines) * 10)
        return 0.0
    
    def _check_error_handling(self, code: str, language: ProgrammingLanguage) -> bool:
        """Check if code includes error handling."""
        error_keywords = {
            ProgrammingLanguage.PYTHON: ['try', 'except', 'raise', 'finally'],
            ProgrammingLanguage.JAVASCRIPT: ['try', 'catch', 'throw', 'finally'],
            ProgrammingLanguage.JAVA: ['try', 'catch', 'throw', 'throws'],
            ProgrammingLanguage.CSHARP: ['try', 'catch', 'throw', 'finally'],
            ProgrammingLanguage.GO: ['defer', 'panic', 'recover', 'error'],
            ProgrammingLanguage.RUST: ['Result', 'Option', 'unwrap', 'expect']
        }
        
        keywords = error_keywords.get(language, ['try', 'catch', 'error'])
        return any(keyword in code for keyword in keywords)
    
    def _identify_best_practices(self, code: str, language: ProgrammingLanguage) -> List[str]:
        """Identify applied best practices."""
        practices = []
        
        # Common practices
        if re.search(r'""".*?"""', code, re.DOTALL) or re.search(r'/\*\*.*?\*/', code, re.DOTALL):
            practices.append("Documentation strings")
        
        if self._check_error_handling(code, language):
            practices.append("Error handling")
        
        if re.search(r'#.*|//.*', code):
            practices.append("Inline comments")
        
        if language == ProgrammingLanguage.PYTHON:
            if 'def ' in code and '->' in code:
                practices.append("Type hints")
            if 'logging' in code:
                practices.append("Logging")
        
        if 'test' in code.lower() or 'assert' in code:
            practices.append("Testing utilities")
        
        return practices
    
    def generate_from_description(self, description: str, language: Optional[ProgrammingLanguage] = None) -> GeneratedCode:
        """Quick generation from description."""
        request = CodeRequest(
            description=description,
            language=language or self.default_language,
            quality=CodeQuality.STANDARD,
            include_tests=False,
            include_docs=True
        )
        return self.generate(request)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get generation statistics."""
        return {
            "name": self.name,
            "default_language": self.default_language.value,
            **self.statistics
        }


class CodeGenerationPattern:
    """
    Code Generation Pattern - Manage code generation agents.
    
    Coordinates multiple agents for different languages and purposes.
    """
    
    def __init__(self):
        self.agents: Dict[str, CodeGenerationAgent] = {}
    
    def create_agent(self, name: str, default_language: ProgrammingLanguage = ProgrammingLanguage.PYTHON) -> CodeGenerationAgent:
        """Create a new code generation agent."""
        agent = CodeGenerationAgent(name, default_language)
        self.agents[name] = agent
        print(f"   Created code generation agent: {name} ({default_language.value})")
        return agent
    
    def run(self, agent_name: str, request: CodeRequest) -> Dict[str, Any]:
        """Generate code using specified agent."""
        if agent_name not in self.agents:
            return {"error": f"Agent {agent_name} not found"}
        
        agent = self.agents[agent_name]
        result = agent.generate(request)
        
        print(f"\n=== Code Generated by {agent_name} ===")
        print(f"Language: {result.language.value}")
        print(f"Quality: {result.request.quality.value}")
        print(f"Lines: {result.line_count} | Complexity: {result.complexity_score:.1f}/10")
        print(f"Error Handling: {'✓' if result.has_error_handling else '✗'}")
        
        if result.best_practices_applied:
            print(f"Best Practices: {', '.join(result.best_practices_applied)}")
        
        print(f"\nGenerated Code:\n{result.code[:300]}...")
        
        if result.tests:
            print(f"\nTests Generated: ✓ ({len(result.tests.split(chr(10)))} lines)")
        
        if result.documentation:
            print(f"\nDocumentation: {result.documentation[:150]}...")
        
        return {
            "agent": agent_name,
            "language": result.language.value,
            "line_count": result.line_count,
            "complexity": result.complexity_score,
            "statistics": agent.get_statistics()
        }


# Example usage
if __name__ == "__main__":
    print("=== Code Generation Agent Pattern Demo ===\n")
    
    # Create pattern
    pattern = CodeGenerationPattern()
    
    # Create agents
    print("1. Setting up code generation agents...")
    
    python_agent = pattern.create_agent("PythonCodeGen", ProgrammingLanguage.PYTHON)
    js_agent = pattern.create_agent("JSCodeGen", ProgrammingLanguage.JAVASCRIPT)
    
    # Generate code examples
    print("\n2. Generating code...")
    
    # Python function
    request1 = CodeRequest(
        description="Create a function to calculate the Fibonacci sequence up to n terms using memoization",
        language=ProgrammingLanguage.PYTHON,
        quality=CodeQuality.PRODUCTION,
        include_tests=True,
        include_docs=True
    )
    result1 = pattern.run("PythonCodeGen", request1)
    
    print("\n" + "="*60 + "\n")
    
    # JavaScript class
    request2 = CodeRequest(
        description="Create a Task class with methods to add, complete, and list tasks",
        language=ProgrammingLanguage.JAVASCRIPT,
        quality=CodeQuality.STANDARD,
        include_tests=False,
        include_docs=True
    )
    result2 = pattern.run("JSCodeGen", request2)
    
    print("\n" + "="*60 + "\n")
    
    # Python data processing
    request3 = CodeRequest(
        description="Create a function to parse CSV data and calculate statistics (mean, median, std dev)",
        language=ProgrammingLanguage.PYTHON,
        quality=CodeQuality.PRODUCTION,
        include_tests=True,
        requirements=["Handle missing values", "Return dict with statistics"]
    )
    result3 = pattern.run("PythonCodeGen", request3)
    
    # Print statistics
    print("\n" + "="*60)
    print("\n3. Code Generation Statistics:")
    stats = result3["statistics"]
    for key, value in stats.items():
        print(f"   {key}: {value}")
    
    print("\n✓ AI-powered code generation with quality checks complete!")

    

Agentic Architecture Pattern: Code Generation Agent

Pattern 238: 238 Code Generation Agent – Line-by-Line Explanation

Overview

Pattern 238: Code Generation Agent

Purpose: AI-powered code generation with language detection, testing, and documentation. Generates code from natural language descriptions with quality checks and best practices.

Code Structure Analysis

File Information

• Pattern Number: 238
• Pattern Name: 238 Code Generation Agent
• File Path: patterns\domain_specific\238_code_generation_agent.py
• Total Lines: 412

Detailed Code Explanation

Module Docstring

"""
Pattern 238: Code Generation Agent

Purpose: AI-powered code generation with language detection, testing, and documentation.
Generates code from natural language descriptions with quality checks and best practices.
"""

Explanation: This module docstring describes the purpose and key features of the 238 Code Generation Agent 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
import re  # 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

Enumerations

ProgrammingLanguage (Line 17)

class ProgrammingLanguage(Enum):
    PYTHON
    JAVASCRIPT
    TYPESCRIPT
    JAVA
    CSHARP
    CPP
    GO
    RUST
    RUBY
    PHP

Purpose: Supported programming languages.

Values: – PYTHON: Represents python – JAVASCRIPT: Represents javascript – TYPESCRIPT: Represents typescript – JAVA: Represents java – CSHARP: Represents csharp – CPP: Represents cpp – GO: Represents go – RUST: Represents rust – RUBY: Represents ruby – PHP: Represents php

CodeQuality (Line 31)

class CodeQuality(Enum):
    QUICK
    STANDARD
    PRODUCTION
    ENTERPRISE

Purpose: Code quality levels.

Values: – QUICK: Represents quick – STANDARD: Represents standard – PRODUCTION: Represents production – ENTERPRISE: Represents enterprise

Classes

ProgrammingLanguage (Line 17)

Description: Supported programming languages.

CodeQuality (Line 31)

Description: Code quality levels.

CodeRequest (Line 40)

Description: Code generation request.

GeneratedCode (Line 51)

Description: Generated code result.

CodeGenerationAgent (Line 64)

Description: Code Generation Agent – AI-powered code synthesis.

Features: – Natural language to code translation – Multi-language support – Test generation – Documentation generation – Code quality levels – Best practices enforcement – Error handling – Complexity analysis

Methods:

__init__ (Line 79)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – default_language: Parameter for default_language

generate (Line 93)

def generate(self, request):
    """Generate code from request."""

Purpose: Generate code from request.

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

_generate_code (Line 137)

def _generate_code(self, request):
    """Generate code from description."""

Purpose: Generate code from description.

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

_generate_tests (Line 171)

def _generate_tests(self, code, request):
    """Generate unit tests for code."""

Purpose: Generate unit tests for code.

Parameters: – self: Instance reference – code: Parameter for code – request: Parameter for request

_generate_documentation (Line 202)

def _generate_documentation(self, code, request):
    """Generate documentation for code."""

Purpose: Generate documentation for code.

Parameters: – self: Instance reference – code: Parameter for code – request: Parameter for request

_extract_code_block (Line 216)

def _extract_code_block(self, text):
    """Extract code from markdown code blocks."""

Purpose: Extract code from markdown code blocks.

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

_calculate_complexity (Line 228)

def _calculate_complexity(self, code):
    """Calculate code complexity score (simplified)."""

Purpose: Calculate code complexity score (simplified).

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

_check_error_handling (Line 244)

def _check_error_handling(self, code, language):
    """Check if code includes error handling."""

Purpose: Check if code includes error handling.

Parameters: – self: Instance reference – code: Parameter for code – language: Parameter for language

_identify_best_practices (Line 258)

def _identify_best_practices(self, code, language):
    """Identify applied best practices."""

Purpose: Identify applied best practices.

Parameters: – self: Instance reference – code: Parameter for code – language: Parameter for language

generate_from_description (Line 283)

def generate_from_description(self, description, language):
    """Quick generation from description."""

Purpose: Quick generation from description.

Parameters: – self: Instance reference – description: Parameter for description – language: Parameter for language

get_statistics (Line 294)

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

Purpose: Get generation statistics.

Parameters: – self: Instance reference

CodeGenerationPattern (Line 303)

Description: Code Generation Pattern – Manage code generation agents.

Coordinates multiple agents for different languages and purposes.

Methods:

__init__ (Line 310)

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

Purpose: No method docstring.

Parameters: – self: Instance reference

create_agent (Line 313)

def create_agent(self, name, default_language):
    """Create a new code generation agent."""

Purpose: Create a new code generation agent.

Parameters: – self: Instance reference – name: Identifier or label – default_language: Parameter for default_language

run (Line 320)

def run(self, agent_name, request):
    """Generate code using specified agent."""

Purpose: Generate code using specified agent.

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

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 = ProgrammingLanguage(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