Tag: agentic

Agentic Architecture Pattern: Real Time Processing


    """
Pattern 180: Real Time Processing

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 RealTimeProcessingConfig:
    """Configuration for Real Time Processing."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 180: 180 Real Time Processing – Line-by-Line Explanation

Overview

Pattern 180: Real Time Processing

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 180
Pattern Name: 180 Real Time Processing
File Path: patterns\quality_performance\180_real_time_processing.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 180: Real Time Processing

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

Explanation: This module docstring describes the purpose and key features of the 180 Real Time Processing 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

RealTimeProcessingConfig (Line 18)

Description: Configuration for Real Time Processing.

RealTimeProcessingState (Line 27)

Description: State information for Real Time Processing.

RealTimeProcessingAgent (Line 36)

Description: Agent implementing Real Time Processing.

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 Real Time Processing."""

Purpose: Process input according to Real Time Processing.

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

RealTimeProcessingPattern (Line 85)

Description: Real Time Processing – Enhanced implementation

This pattern provides comprehensive functionality for real time processing 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 = RealTimeProcessingConfig(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

November 4, 2025

Agentic Architecture Pattern: Stream Processing


    """
Pattern 179: Stream Processing

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 StreamProcessingConfig:
    """Configuration for Stream Processing."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 179: 179 Stream Processing – Line-by-Line Explanation

Overview

Pattern 179: Stream Processing

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 179
Pattern Name: 179 Stream Processing
File Path: patterns\quality_performance\179_stream_processing.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 179: Stream Processing

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

Explanation: This module docstring describes the purpose and key features of the 179 Stream Processing 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

Classes

StreamProcessingConfig (Line 18)

Description: Configuration for Stream Processing.

StreamProcessingState (Line 27)

Description: State information for Stream Processing.

StreamProcessingAgent (Line 36)

Description: Agent implementing Stream Processing.

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 Stream Processing."""

Purpose: Process input according to Stream Processing.

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

StreamProcessingPattern (Line 85)

Description: Stream Processing – Enhanced implementation

This pattern provides comprehensive functionality for stream processing 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 = StreamProcessingConfig(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

November 4, 2025

Agentic Architecture Pattern: Batch Processing


    """
Pattern 178: Batch Processing

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 BatchProcessingConfig:
    """Configuration for Batch Processing."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 178: 178 Batch Processing – Line-by-Line Explanation

Overview

Pattern 178: Batch Processing

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 178
Pattern Name: 178 Batch Processing
File Path: patterns\quality_performance\178_batch_processing.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 178: Batch Processing

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

Explanation: This module docstring describes the purpose and key features of the 178 Batch Processing 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

Classes

BatchProcessingConfig (Line 18)

Description: Configuration for Batch Processing.

BatchProcessingState (Line 27)

Description: State information for Batch Processing.

BatchProcessingAgent (Line 36)

Description: Agent implementing Batch Processing.

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 Batch Processing."""

Purpose: Process input according to Batch Processing.

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

BatchProcessingPattern (Line 85)

Description: Batch Processing – Enhanced implementation

This pattern provides comprehensive functionality for batch processing 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 = BatchProcessingConfig(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

November 4, 2025

Agentic Architecture Pattern: Eager Evaluation


    """
Pattern 177: Eager Evaluation

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 EagerEvaluationConfig:
    """Configuration for Eager Evaluation."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 177: 177 Eager Evaluation – Line-by-Line Explanation

Overview

Pattern 177: Eager Evaluation

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 177
Pattern Name: 177 Eager Evaluation
File Path: patterns\quality_performance\177_eager_evaluation.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 177: Eager Evaluation

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

Explanation: This module docstring describes the purpose and key features of the 177 Eager Evaluation 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

Classes

EagerEvaluationConfig (Line 18)

Description: Configuration for Eager Evaluation.

EagerEvaluationState (Line 27)

Description: State information for Eager Evaluation.

EagerEvaluationAgent (Line 36)

Description: Agent implementing Eager Evaluation.

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 Eager Evaluation."""

Purpose: Process input according to Eager Evaluation.

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

EagerEvaluationPattern (Line 85)

Description: Eager Evaluation – Enhanced implementation

This pattern provides comprehensive functionality for eager evaluation 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 = EagerEvaluationConfig(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

November 4, 2025

Agentic Architecture Pattern: Lazy Evaluation


    """
Pattern 176: Lazy Evaluation

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 LazyEvaluationConfig:
    """Configuration for Lazy Evaluation."""
    model_name: str = "gpt-4"
    temperature: float = 0.7
    max_iterations: int = 10
    enable_logging: bool = True


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

Pattern 176: 176 Lazy Evaluation – Line-by-Line Explanation

Overview

Pattern 176: Lazy Evaluation

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 176
Pattern Name: 176 Lazy Evaluation
File Path: patterns\quality_performance\176_lazy_evaluation.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 176: Lazy Evaluation

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

Explanation: This module docstring describes the purpose and key features of the 176 Lazy Evaluation 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

Classes

LazyEvaluationConfig (Line 18)

Description: Configuration for Lazy Evaluation.

LazyEvaluationState (Line 27)

Description: State information for Lazy Evaluation.

LazyEvaluationAgent (Line 36)

Description: Agent implementing Lazy Evaluation.

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 Lazy Evaluation."""

Purpose: Process input according to Lazy Evaluation.

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

LazyEvaluationPattern (Line 85)

Description: Lazy Evaluation – Enhanced implementation

This pattern provides comprehensive functionality for lazy evaluation 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 = LazyEvaluationConfig(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

November 4, 2025

Agentic Architecture Pattern: Caching


    """
Pattern 175: Caching

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


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

Pattern 175: 175 Caching – Line-by-Line Explanation

Overview

Pattern 175: Caching

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 175
Pattern Name: 175 Caching
File Path: patterns\quality_performance\175_caching.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 175: Caching

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

Explanation: This module docstring describes the purpose and key features of the 175 Caching 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

Classes

CachingConfig (Line 18)

Description: Configuration for Caching.

CachingState (Line 27)

Description: State information for Caching.

CachingAgent (Line 36)

Description: Agent implementing Caching.

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 Caching."""

Purpose: Process input according to Caching.

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

CachingPattern (Line 85)

Description: Caching – Enhanced implementation

This pattern provides comprehensive functionality for caching 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 = CachingConfig(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

November 4, 2025

Agentic Architecture Pattern: Performance Optimization


    """
Pattern 174: Performance Optimization Pattern with LangChain/LangGraph

Purpose: Optimize agent performance through profiling, tuning, and LLM-powered insights.
"""

from typing import Dict, Any, List, Optional, Callable, TypedDict
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import time
from functools import wraps
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
import statistics


class OptimizationStrategy(Enum):
    """Performance optimization strategies."""
    CACHING = "caching"
    BATCHING = "batching"
    PARALLELIZATION = "parallelization"
    LAZY_LOADING = "lazy_loading"
    INDEX_OPTIMIZATION = "index_optimization"


class BottleneckType(Enum):
    """Types of performance bottlenecks."""
    CPU_BOUND = "cpu_bound"
    IO_BOUND = "io_bound"
    MEMORY_BOUND = "memory_bound"
    NETWORK_BOUND = "network_bound"
    DATABASE_BOUND = "database_bound"


class ProfileLevel(Enum):
    """Profiling detail levels."""
    BASIC = "basic"
    DETAILED = "detailed"
    COMPREHENSIVE = "comprehensive"


class PriorityLevel(Enum):
    """Optimization priority levels."""
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    CRITICAL = 4


@dataclass
class PerformanceProfile:
    """Performance profile for an operation."""
    operation: str
    execution_count: int = 0
    total_time: float = 0.0
    min_time: float = float('inf')
    max_time: float = 0.0
    errors: int = 0
    last_execution: Optional[datetime] = None
    
    def avg_time(self) -> float:
        """Average execution time."""
        return self.total_time / self.execution_count if self.execution_count > 0 else 0.0
    
    def record(self, duration: float, error: bool = False):
        """Record an execution."""
        self.execution_count += 1
        self.total_time += duration
        self.min_time = min(self.min_time, duration)
        self.max_time = max(self.max_time, duration)
        self.last_execution = datetime.now()
        if error:
            self.errors += 1


@dataclass
class Bottleneck:
    """Identified performance bottleneck."""
    operation: str
    bottleneck_type: BottleneckType
    severity: float  # 0.0 to 1.0
    impact: str
    measured_time_ms: float
    expected_time_ms: float


@dataclass
class OptimizationSuggestion:
    """Performance optimization suggestion."""
    operation: str
    strategy: OptimizationStrategy
    issue: str
    suggestion: str
    priority: PriorityLevel
    expected_improvement: str


@dataclass
class OptimizationResult:
    """Result of applying an optimization."""
    operation: str
    strategy: OptimizationStrategy
    before_avg_ms: float
    after_avg_ms: float
    improvement_percent: float
    success: bool


@dataclass
class PerformanceReport:
    """Comprehensive performance report."""
    total_operations: int
    total_execution_time_ms: float
    avg_execution_time_ms: float
    bottlenecks: List[Bottleneck]
    suggestions: List[OptimizationSuggestion]
    top_slowest_operations: List[str]


class OptimizationState(TypedDict):
    """State for optimization workflow."""
    profiles: Dict[str, Dict[str, Any]]
    bottlenecks: List[Dict[str, Any]]
    analysis: str
    suggestions: List[str]
    strategies: List[str]


class PerformanceOptimizer:
    """
    Advanced performance optimizer with LangChain/LangGraph integration.
    """
    
    def __init__(self, model_name: str = "gpt-4", temperature: float = 0.3):
        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
        self.profiles: Dict[str, PerformanceProfile] = {}
        self.bottlenecks: List[Bottleneck] = []
        self.optimizations: List[OptimizationResult] = []
        self.workflow = self._build_optimization_workflow()
        
        # Statistics
        self.stats = {
            "total_operations_profiled": 0,
            "total_optimizations_applied": 0,
            "bottlenecks_identified": 0,
            "avg_improvement_percent": 0.0,
            "strategies_used": {strategy: 0 for strategy in OptimizationStrategy}
        }
    
    def _build_optimization_workflow(self) -> StateGraph:
        """Build LangGraph workflow for performance optimization."""
        workflow = StateGraph(OptimizationState)
        
        # Add nodes
        workflow.add_node("analyze_profiles", self._analyze_profiles_node)
        workflow.add_node("identify_bottlenecks", self._identify_bottlenecks_node)
        workflow.add_node("generate_insights", self._generate_insights_node)
        workflow.add_node("recommend_strategies", self._recommend_strategies_node)
        workflow.add_node("prioritize_optimizations", self._prioritize_optimizations_node)
        
        # Define edges
        workflow.set_entry_point("analyze_profiles")
        workflow.add_edge("analyze_profiles", "identify_bottlenecks")
        workflow.add_edge("identify_bottlenecks", "generate_insights")
        workflow.add_edge("generate_insights", "recommend_strategies")
        workflow.add_edge("recommend_strategies", "prioritize_optimizations")
        workflow.add_edge("prioritize_optimizations", END)
        
        return workflow.compile()
    
    def _analyze_profiles_node(self, state: OptimizationState) -> OptimizationState:
        """Analyze performance profiles."""
        profiles_data = {}
        
        for operation, profile in self.profiles.items():
            profiles_data[operation] = {
                "execution_count": profile.execution_count,
                "avg_time_ms": profile.avg_time() * 1000,
                "min_time_ms": profile.min_time * 1000,
                "max_time_ms": profile.max_time * 1000,
                "error_rate": (profile.errors / profile.execution_count * 100) 
                             if profile.execution_count > 0 else 0.0
            }
        
        state["profiles"] = profiles_data
        return state
    
    def _identify_bottlenecks_node(self, state: OptimizationState) -> OptimizationState:
        """Identify performance bottlenecks."""
        profiles = state.get("profiles", {})
        bottlenecks_data = []
        
        # Identify slow operations
        if profiles:
            avg_times = [p["avg_time_ms"] for p in profiles.values()]
            if len(avg_times) > 1:
                mean_time = statistics.mean(avg_times)
                stdev_time = statistics.stdev(avg_times)
                
                for operation, profile in profiles.items():
                    avg_time = profile["avg_time_ms"]
                    
                    # Operation is significantly slower than average
                    if avg_time > mean_time + 2 * stdev_time:
                        bottlenecks_data.append({
                            "operation": operation,
                            "avg_time_ms": avg_time,
                            "deviation": avg_time - mean_time,
                            "severity": min((avg_time - mean_time) / mean_time, 1.0)
                        })
        
        state["bottlenecks"] = bottlenecks_data
        return state
    
    def _generate_insights_node(self, state: OptimizationState) -> OptimizationState:
        """Generate insights using LLM."""
        profiles = state.get("profiles", {})
        bottlenecks = state.get("bottlenecks", [])
        
        # Use LLM to analyze performance data
        prompt = ChatPromptTemplate.from_template(
            "Analyze the following performance data and provide insights:\n"
            "Performance Profiles: {profiles_summary}\n"
            "Identified Bottlenecks: {bottlenecks_summary}\n\n"
            "Provide detailed insights about performance patterns and issues."
        )
        
        chain = prompt | self.llm
        response = chain.invoke({
            "profiles_summary": f"{len(profiles)} operations profiled",
            "bottlenecks_summary": f"{len(bottlenecks)} bottlenecks identified"
        })
        
        state["analysis"] = str(response.content)
        return state
    
    def _recommend_strategies_node(self, state: OptimizationState) -> OptimizationState:
        """Recommend optimization strategies using LLM."""
        bottlenecks = state.get("bottlenecks", [])
        analysis = state.get("analysis", "")
        
        suggestions = []
        
        if bottlenecks:
            # Use LLM to recommend strategies
            prompt = ChatPromptTemplate.from_template(
                "Based on the performance analysis, recommend specific optimization strategies:\n"
                "Analysis: {analysis}\n"
                "Bottlenecks: {bottlenecks}\n\n"
                "Provide 3-5 specific, actionable optimization strategies."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({
                "analysis": analysis,
                "bottlenecks": str(bottlenecks)
            })
            
            # Parse suggestions
            suggestions = [
                line.strip()
                for line in str(response.content).split('\n')
                if line.strip() and len(line.strip()) > 10
            ][:5]
        
        state["suggestions"] = suggestions
        return state
    
    def _prioritize_optimizations_node(self, state: OptimizationState) -> OptimizationState:
        """Prioritize optimization strategies."""
        suggestions = state.get("suggestions", [])
        bottlenecks = state.get("bottlenecks", [])
        
        # Map common strategies
        strategies = []
        
        for bottleneck in bottlenecks:
            severity = bottleneck.get("severity", 0.0)
            if severity > 0.7:
                strategies.append(f"CRITICAL: Optimize '{bottleneck['operation']}'")
            elif severity > 0.4:
                strategies.append(f"HIGH: Improve '{bottleneck['operation']}'")
        
        # Add LLM suggestions
        strategies.extend(suggestions)
        
        state["strategies"] = strategies[:10]  # Top 10 strategies
        return state
    
    def profile_operation(self, operation: str) -> Callable:
        """Decorator to profile an operation."""
        def decorator(func: Callable) -> Callable:
            @wraps(func)
            def wrapper(*args, **kwargs):
                start_time = time.time()
                error_occurred = False
                
                try:
                    result = func(*args, **kwargs)
                    return result
                except Exception as e:
                    error_occurred = True
                    raise e
                finally:
                    duration = time.time() - start_time
                    
                    # Record profile
                    if operation not in self.profiles:
                        self.profiles[operation] = PerformanceProfile(operation=operation)
                    
                    self.profiles[operation].record(duration, error_occurred)
                    self.stats["total_operations_profiled"] += 1
            
            return wrapper
        return decorator
    
    def record_execution(
        self,
        operation: str,
        duration_ms: float,
        error: bool = False
    ):
        """Manually record an operation execution."""
        if operation not in self.profiles:
            self.profiles[operation] = PerformanceProfile(operation=operation)
        
        self.profiles[operation].record(duration_ms / 1000.0, error)
        self.stats["total_operations_profiled"] += 1
    
    def identify_bottleneck(
        self,
        operation: str,
        bottleneck_type: BottleneckType,
        measured_time_ms: float,
        expected_time_ms: float
    ) -> Bottleneck:
        """Manually identify a bottleneck."""
        severity = min((measured_time_ms - expected_time_ms) / expected_time_ms, 1.0)
        
        bottleneck = Bottleneck(
            operation=operation,
            bottleneck_type=bottleneck_type,
            severity=max(0.0, severity),
            impact=f"{measured_time_ms - expected_time_ms:.2f}ms slower than expected",
            measured_time_ms=measured_time_ms,
            expected_time_ms=expected_time_ms
        )
        
        self.bottlenecks.append(bottleneck)
        self.stats["bottlenecks_identified"] += 1
        
        return bottleneck
    
    def apply_optimization(
        self,
        operation: str,
        strategy: OptimizationStrategy,
        before_avg_ms: float,
        after_avg_ms: float
    ) -> OptimizationResult:
        """Record an applied optimization."""
        improvement = ((before_avg_ms - after_avg_ms) / before_avg_ms * 100) if before_avg_ms > 0 else 0.0
        
        result = OptimizationResult(
            operation=operation,
            strategy=strategy,
            before_avg_ms=before_avg_ms,
            after_avg_ms=after_avg_ms,
            improvement_percent=improvement,
            success=improvement > 0
        )
        
        self.optimizations.append(result)
        self.stats["total_optimizations_applied"] += 1
        self.stats["strategies_used"][strategy] += 1
        
        # Update average improvement
        if self.optimizations:
            self.stats["avg_improvement_percent"] = statistics.mean(
                opt.improvement_percent for opt in self.optimizations
            )
        
        return result
    
    def analyze_performance(self) -> PerformanceReport:
        """Analyze performance using workflow."""
        initial_state: OptimizationState = {
            "profiles": {},
            "bottlenecks": [],
            "analysis": "",
            "suggestions": [],
            "strategies": []
        }
        
        # Run workflow
        result = self.workflow.invoke(initial_state)
        
        # Create report
        total_ops = len(self.profiles)
        total_time = sum(p.total_time for p in self.profiles.values()) * 1000
        avg_time = total_time / total_ops if total_ops > 0 else 0.0
        
        # Get top slowest operations
        sorted_profiles = sorted(
            self.profiles.items(),
            key=lambda x: x[1].avg_time(),
            reverse=True
        )
        top_slowest = [op for op, _ in sorted_profiles[:5]]
        
        # Convert suggestions to proper format
        suggestions = [
            OptimizationSuggestion(
                operation="general",
                strategy=OptimizationStrategy.CACHING,
                issue="Performance improvement needed",
                suggestion=sugg,
                priority=PriorityLevel.MEDIUM,
                expected_improvement="TBD"
            )
            for sugg in result["suggestions"]
        ]
        
        return PerformanceReport(
            total_operations=total_ops,
            total_execution_time_ms=total_time,
            avg_execution_time_ms=avg_time,
            bottlenecks=self.bottlenecks,
            suggestions=suggestions,
            top_slowest_operations=top_slowest
        )
    
    def get_profile(self, operation: str) -> Optional[PerformanceProfile]:
        """Get profile for an operation."""
        return self.profiles.get(operation)
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get optimization statistics."""
        return {
            "total_operations_profiled": self.stats["total_operations_profiled"],
            "unique_operations": len(self.profiles),
            "total_optimizations_applied": self.stats["total_optimizations_applied"],
            "bottlenecks_identified": self.stats["bottlenecks_identified"],
            "avg_improvement_percent": round(self.stats["avg_improvement_percent"], 2),
            "strategies_used": {
                strategy.value: count
                for strategy, count in self.stats["strategies_used"].items()
                if count > 0
            },
            "successful_optimizations": len([o for o in self.optimizations if o.success])
        }


class PerformanceOptimizationPattern:
    """
    Comprehensive performance optimization pattern coordinator with LangChain/LangGraph.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.optimizer = PerformanceOptimizer(model_name=model_name)
    
    def profile(self, operation: str) -> Callable:
        """Profile an operation using decorator."""
        return self.optimizer.profile_operation(operation)
    
    def record(
        self,
        operation: str,
        duration_ms: float,
        error: bool = False
    ):
        """Record operation execution."""
        self.optimizer.record_execution(operation, duration_ms, error)
    
    def identify_bottleneck(
        self,
        operation: str,
        bottleneck_type: BottleneckType,
        measured_ms: float,
        expected_ms: float
    ) -> Bottleneck:
        """Identify a performance bottleneck."""
        return self.optimizer.identify_bottleneck(
            operation, bottleneck_type, measured_ms, expected_ms
        )
    
    def optimize(
        self,
        operation: str,
        strategy: OptimizationStrategy,
        before_ms: float,
        after_ms: float
    ) -> OptimizationResult:
        """Record an optimization."""
        return self.optimizer.apply_optimization(
            operation, strategy, before_ms, after_ms
        )
    
    def analyze(self) -> Dict[str, Any]:
        """Analyze performance."""
        report = self.optimizer.analyze_performance()
        return {
            "total_operations": report.total_operations,
            "total_time_ms": report.total_execution_time_ms,
            "avg_time_ms": report.avg_execution_time_ms,
            "bottlenecks_count": len(report.bottlenecks),
            "suggestions_count": len(report.suggestions),
            "top_slowest": report.top_slowest_operations
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get statistics."""
        return self.optimizer.get_statistics()



if __name__ == "__main__":
    print("=== Performance Optimization Pattern with LangChain/LangGraph Demo ===\n")
    
    # Note: This demo shows the structure. In production, you'd need:
    # - export OPENAI_API_KEY="your-key-here"
    # - Actual LLM calls would be made
    
    print("Scenario 1: Operation Profiling")
    print("-" * 50)
    optimizer = PerformanceOptimizationPattern(model_name="gpt-4")
    
    # Record multiple operations
    operations = [
        ("database_query", 45.5, False),
        ("api_call", 120.3, False),
        ("data_processing", 230.8, False),
        ("cache_lookup", 5.2, False),
        ("database_query", 52.1, False),
        ("api_call", 115.7, False),
        ("data_processing", 245.2, False),
        ("database_query", 48.9, True),  # Error
    ]
    
    print("Recording operation executions:")
    for operation, duration, error in operations:
        optimizer.record(operation, duration, error)
        status = "ERROR" if error else "OK"
        print(f"  {operation}: {duration:.1f}ms [{status}]")
    
    print("\nScenario 2: Bottleneck Identification")
    print("-" * 50)
    
    # Identify bottlenecks
    bottlenecks = [
        ("data_processing", BottleneckType.CPU_BOUND, 240.0, 100.0),
        ("api_call", BottleneckType.NETWORK_BOUND, 118.0, 50.0),
        ("database_query", BottleneckType.DATABASE_BOUND, 50.0, 20.0),
    ]
    
    print("Identifying bottlenecks:")
    for operation, bt_type, measured, expected in bottlenecks:
        bottleneck = optimizer.identify_bottleneck(
            operation, bt_type, measured, expected
        )
        print(f"  {operation} ({bt_type.value}):")
        print(f"    Measured: {measured:.1f}ms, Expected: {expected:.1f}ms")
        print(f"    Severity: {bottleneck.severity:.2f}, Impact: {bottleneck.impact}")
    
    print("\nScenario 3: Optimization Application")
    print("-" * 50)
    
    # Apply optimizations
    optimizations = [
        ("data_processing", OptimizationStrategy.PARALLELIZATION, 240.0, 85.0),
        ("api_call", OptimizationStrategy.CACHING, 118.0, 12.0),
        ("database_query", OptimizationStrategy.INDEX_OPTIMIZATION, 50.0, 18.0),
    ]
    
    print("Applying optimizations:")
    for operation, strategy, before, after in optimizations:
        result = optimizer.optimize(operation, strategy, before, after)
        print(f"  {operation} ({strategy.value}):")
        print(f"    Before: {before:.1f}ms, After: {after:.1f}ms")
        print(f"    Improvement: {result.improvement_percent:.1f}%")
        print(f"    Success: {result.success}")
    
    print("\nScenario 4: Performance Analysis")
    print("-" * 50)
    
    print("Analyzing performance with LLM insights...")
    analysis = optimizer.analyze()
    
    print(f"\nAnalysis Results:")
    print(f"  Total Operations: {analysis['total_operations']}")
    print(f"  Total Execution Time: {analysis['total_time_ms']:.1f}ms")
    print(f"  Average Time per Operation: {analysis['avg_time_ms']:.1f}ms")
    print(f"  Bottlenecks Identified: {analysis['bottlenecks_count']}")
    print(f"  Optimization Suggestions: {analysis['suggestions_count']}")
    
    if analysis['top_slowest']:
        print(f"\nTop Slowest Operations:")
        for i, operation in enumerate(analysis['top_slowest'], 1):
            print(f"    {i}. {operation}")
    
    print("\nScenario 5: Using Profile Decorator")
    print("-" * 50)
    
    # Example of using decorator
    @optimizer.profile("heavy_computation")
    def heavy_computation(n: int) -> int:
        """Simulate heavy computation."""
        result = 0
        for i in range(n):
            result += i * i
        return result
    
    print("Profiling decorated function:")
    for i in range(3):
        result = heavy_computation(1000)
        print(f"  Iteration {i+1}: computed {result}")
    
    # Get profile for decorated function
    profile = optimizer.optimizer.get_profile("heavy_computation")
    if profile:
        print(f"\nProfile for 'heavy_computation':")
        print(f"  Executions: {profile.execution_count}")
        print(f"  Avg Time: {profile.avg_time()*1000:.2f}ms")
        print(f"  Min Time: {profile.min_time*1000:.2f}ms")
        print(f"  Max Time: {profile.max_time*1000:.2f}ms")
    
    print("\nScenario 6: Statistics")
    print("-" * 50)
    
    stats = optimizer.get_statistics()
    print(f"Total Operations Profiled: {stats['total_operations_profiled']}")
    print(f"Unique Operations: {stats['unique_operations']}")
    print(f"Optimizations Applied: {stats['total_optimizations_applied']}")
    print(f"Bottlenecks Identified: {stats['bottlenecks_identified']}")
    print(f"Average Improvement: {stats['avg_improvement_percent']:.1f}%")
    print(f"Successful Optimizations: {stats['successful_optimizations']}")
    
    if stats['strategies_used']:
        print(f"\nOptimization Strategies Used:")
        for strategy, count in stats['strategies_used'].items():
            print(f"  {strategy}: {count}")
    
    print("\n" + "=" * 50)
    print("Performance optimization pattern demonstration complete!")
    print("=" * 50)

Agentic Architecture Pattern: Performance Optimization

Pattern 174: 174 Performance Optimization – Line-by-Line Explanation

Overview

Pattern 174: Performance Optimization Pattern with LangChain/LangGraph

Purpose: Optimize agent performance through profiling, tuning, and LLM-powered insights.

Code Structure Analysis

File Information

Pattern Number: 174
Pattern Name: 174 Performance Optimization
File Path: patterns\quality_performance\174_performance_optimization.py
Total Lines: 648

Detailed Code Explanation

Module Docstring

"""
Pattern 174: Performance Optimization Pattern with LangChain/LangGraph

Purpose: Optimize agent performance through profiling, tuning, and LLM-powered insights.
"""

Explanation: This module docstring describes the purpose and key features of the 174 Performance Optimization pattern.

Imports Section

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

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, Callable, TypedDict: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime, timedelta: 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

Enumerations

OptimizationStrategy (Line 19)

class OptimizationStrategy(Enum):
    CACHING
    BATCHING
    PARALLELIZATION
    LAZY_LOADING
    INDEX_OPTIMIZATION

Purpose: Performance optimization strategies.

Values: – CACHING: Represents caching – BATCHING: Represents batching – PARALLELIZATION: Represents parallelization – LAZY_LOADING: Represents lazy loading – INDEX_OPTIMIZATION: Represents index optimization

BottleneckType (Line 28)

class BottleneckType(Enum):
    CPU_BOUND
    IO_BOUND
    MEMORY_BOUND
    NETWORK_BOUND
    DATABASE_BOUND

Purpose: Types of performance bottlenecks.

Values: – CPU_BOUND: Represents cpu bound – IO_BOUND: Represents io bound – MEMORY_BOUND: Represents memory bound – NETWORK_BOUND: Represents network bound – DATABASE_BOUND: Represents database bound

ProfileLevel (Line 37)

class ProfileLevel(Enum):
    BASIC
    DETAILED
    COMPREHENSIVE

Purpose: Profiling detail levels.

Values: – BASIC: Represents basic – DETAILED: Represents detailed – COMPREHENSIVE: Represents comprehensive

PriorityLevel (Line 44)

class PriorityLevel(Enum):
    LOW
    MEDIUM
    HIGH
    CRITICAL

Purpose: Optimization priority levels.

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

Classes

OptimizationStrategy (Line 19)

Description: Performance optimization strategies.

BottleneckType (Line 28)

Description: Types of performance bottlenecks.

ProfileLevel (Line 37)

Description: Profiling detail levels.

PriorityLevel (Line 44)

Description: Optimization priority levels.

PerformanceProfile (Line 53)

Description: Performance profile for an operation.

Methods:

`avg_time` (Line 63)

def avg_time(self):
    """Average execution time."""

Purpose: Average execution time.

Parameters: – self: Instance reference

`record` (Line 67)

def record(self, duration, error):
    """Record an execution."""

Purpose: Record an execution.

Parameters: – self: Instance reference – duration: Parameter for duration – error: Parameter for error

Bottleneck (Line 79)

Description: Identified performance bottleneck.

OptimizationSuggestion (Line 90)

Description: Performance optimization suggestion.

OptimizationResult (Line 101)

Description: Result of applying an optimization.

PerformanceReport (Line 112)

Description: Comprehensive performance report.

OptimizationState (Line 122)

Description: State for optimization workflow.

PerformanceOptimizer (Line 131)

Description: Advanced performance optimizer with LangChain/LangGraph integration.

Methods:

`init` (Line 136)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”) – temperature: Randomness in LLM responses (0.0-1.0)

`_build_optimization_workflow` (Line 152)

def _build_optimization_workflow(self):
    """Build LangGraph workflow for performance optimization."""

Purpose: Build LangGraph workflow for performance optimization.

Parameters: – self: Instance reference

`_analyze_profiles_node` (Line 173)

def _analyze_profiles_node(self, state):
    """Analyze performance profiles."""

Purpose: Analyze performance profiles.

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

`_identify_bottlenecks_node` (Line 190)

def _identify_bottlenecks_node(self, state):
    """Identify performance bottlenecks."""

Purpose: Identify performance bottlenecks.

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

`_generate_insights_node` (Line 217)

def _generate_insights_node(self, state):
    """Generate insights using LLM."""

Purpose: Generate insights using LLM.

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

`_recommend_strategies_node` (Line 239)

def _recommend_strategies_node(self, state):
    """Recommend optimization strategies using LLM."""

Purpose: Recommend optimization strategies using LLM.

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

`_prioritize_optimizations_node` (Line 271)

def _prioritize_optimizations_node(self, state):
    """Prioritize optimization strategies."""

Purpose: Prioritize optimization strategies.

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

`profile_operation` (Line 292)

def profile_operation(self, operation):
    """Decorator to profile an operation."""

Purpose: Decorator to profile an operation.

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

`record_execution` (Line 319)

def record_execution(self, operation, duration_ms, error):
    """Manually record an operation execution."""

Purpose: Manually record an operation execution.

Parameters: – self: Instance reference – operation: Parameter for operation – duration_ms: Parameter for duration_ms – error: Parameter for error

`identify_bottleneck` (Line 332)

def identify_bottleneck(self, operation, bottleneck_type, measured_time_ms, expected_time_ms):
    """Manually identify a bottleneck."""

Purpose: Manually identify a bottleneck.

Parameters: – self: Instance reference – operation: Parameter for operation – bottleneck_type: Parameter for bottleneck_type – measured_time_ms: Parameter for measured_time_ms – expected_time_ms: Parameter for expected_time_ms

`apply_optimization` (Line 356)

def apply_optimization(self, operation, strategy, before_avg_ms, after_avg_ms):
    """Record an applied optimization."""

Purpose: Record an applied optimization.

Parameters: – self: Instance reference – operation: Parameter for operation – strategy: Parameter for strategy – before_avg_ms: Parameter for before_avg_ms – after_avg_ms: Parameter for after_avg_ms

`analyze_performance` (Line 387)

def analyze_performance(self):
    """Analyze performance using workflow."""

Purpose: Analyze performance using workflow.

Parameters: – self: Instance reference

`get_profile` (Line 435)

def get_profile(self, operation):
    """Get profile for an operation."""

Purpose: Get profile for an operation.

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

`get_statistics` (Line 439)

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

Purpose: Get optimization statistics.

Parameters: – self: Instance reference

PerformanceOptimizationPattern (Line 456)

Description: Comprehensive performance optimization pattern coordinator with LangChain/LangGraph.

Methods:

`init` (Line 461)

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

Purpose: No method docstring.

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

`profile` (Line 464)

def profile(self, operation):
    """Profile an operation using decorator."""

Purpose: Profile an operation using decorator.

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

`record` (Line 468)

def record(self, operation, duration_ms, error):
    """Record operation execution."""

Purpose: Record operation execution.

Parameters: – self: Instance reference – operation: Parameter for operation – duration_ms: Parameter for duration_ms – error: Parameter for error

`identify_bottleneck` (Line 477)

def identify_bottleneck(self, operation, bottleneck_type, measured_ms, expected_ms):
    """Identify a performance bottleneck."""

Purpose: Identify a performance bottleneck.

Parameters: – self: Instance reference – operation: Parameter for operation – bottleneck_type: Parameter for bottleneck_type – measured_ms: Parameter for measured_ms – expected_ms: Parameter for expected_ms

`optimize` (Line 489)

def optimize(self, operation, strategy, before_ms, after_ms):
    """Record an optimization."""

Purpose: Record an optimization.

Parameters: – self: Instance reference – operation: Parameter for operation – strategy: Parameter for strategy – before_ms: Parameter for before_ms – after_ms: Parameter for after_ms

`analyze` (Line 501)

def analyze(self):
    """Analyze performance."""

Purpose: Analyze performance.

Parameters: – self: Instance reference

`get_statistics` (Line 513)

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

Purpose: Get statistics.

Parameters: – self: Instance reference

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = OptimizationStrategy(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

November 4, 2025

Agentic Architecture Pattern: Telemetry


    """
Pattern 173: Telemetry Pattern with LangChain/LangGraph

Purpose: Collect detailed operational data for analysis and optimization with LLM insights.
"""

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


class EventType(Enum):
    """Telemetry event types."""
    USER_ACTION = "user_action"
    SYSTEM_EVENT = "system_event"
    ERROR = "error"
    PERFORMANCE = "performance"
    BUSINESS = "business"


class TraceLevel(Enum):
    """Trace detail levels."""
    CRITICAL = "critical"
    ERROR = "error"
    WARNING = "warning"
    INFO = "info"
    DEBUG = "debug"


class AggregationPeriod(Enum):
    """Data aggregation periods."""
    MINUTE = "minute"
    HOUR = "hour"
    DAY = "day"
    WEEK = "week"


@dataclass
class TelemetryEvent:
    """Telemetry event."""
    event_id: str
    event_type: EventType
    agent_id: str
    properties: Dict[str, Any]
    severity: TraceLevel = TraceLevel.INFO
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Span:
    """Distributed tracing span."""
    span_id: str
    parent_id: Optional[str]
    operation: str
    start_time: datetime
    end_time: Optional[datetime] = None
    tags: Dict[str, Any] = field(default_factory=dict)
    status: str = "in_progress"
    error: Optional[str] = None
    
    def duration_ms(self) -> float:
        """Get span duration in milliseconds."""
        if self.end_time:
            return (self.end_time - self.start_time).total_seconds() * 1000
        return 0.0


@dataclass
class MetricPoint:
    """Custom metric data point."""
    metric_name: str
    value: float
    dimensions: Dict[str, str]
    unit: str = ""
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Trace:
    """Distributed trace containing multiple spans."""
    trace_id: str
    root_span_id: str
    spans: List[Span]
    start_time: datetime
    end_time: Optional[datetime] = None
    service_name: str = ""


@dataclass
class AggregatedMetrics:
    """Aggregated metrics summary."""
    period: AggregationPeriod
    start_time: datetime
    end_time: datetime
    metrics: Dict[str, Dict[str, float]]
    event_counts: Dict[EventType, int]
    error_rate: float


class TelemetryState(TypedDict):
    """State for telemetry analysis workflow."""
    events: List[Dict[str, Any]]
    traces: List[Dict[str, Any]]
    metrics: Dict[str, Any]
    insights: str
    recommendations: List[str]
    anomalies: List[str]


class TelemetryCollector:
    """
    Advanced telemetry collector with LangChain/LangGraph integration.
    """
    
    def __init__(self, model_name: str = "gpt-4", temperature: float = 0.3):
        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
        self.events: List[TelemetryEvent] = []
        self.spans: Dict[str, Span] = {}
        self.traces: Dict[str, Trace] = {}
        self.metrics: Dict[str, List[MetricPoint]] = defaultdict(list)
        self.active_spans: Dict[str, Span] = {}
        self.workflow = self._build_telemetry_workflow()
        
        # Statistics
        self.stats = {
            "total_events": 0,
            "total_spans": 0,
            "total_traces": 0,
            "total_metrics": 0,
            "events_by_type": {event_type: 0 for event_type in EventType},
            "errors_tracked": 0,
            "avg_span_duration_ms": 0.0
        }
    
    def _build_telemetry_workflow(self) -> StateGraph:
        """Build LangGraph workflow for telemetry analysis."""
        workflow = StateGraph(TelemetryState)
        
        # Add nodes
        workflow.add_node("collect_data", self._collect_data_node)
        workflow.add_node("analyze_patterns", self._analyze_patterns_node)
        workflow.add_node("detect_anomalies", self._detect_anomalies_node)
        workflow.add_node("generate_insights", self._generate_insights_node)
        workflow.add_node("provide_recommendations", self._provide_recommendations_node)
        
        # Define edges
        workflow.set_entry_point("collect_data")
        workflow.add_edge("collect_data", "analyze_patterns")
        workflow.add_edge("analyze_patterns", "detect_anomalies")
        workflow.add_edge("detect_anomalies", "generate_insights")
        workflow.add_edge("generate_insights", "provide_recommendations")
        workflow.add_edge("provide_recommendations", END)
        
        return workflow.compile()
    
    def _collect_data_node(self, state: TelemetryState) -> TelemetryState:
        """Collect and organize telemetry data."""
        # Get recent events
        recent_events = self.events[-50:]
        state["events"] = [
            {
                "event_id": e.event_id,
                "type": e.event_type.value,
                "agent_id": e.agent_id,
                "severity": e.severity.value,
                "timestamp": e.timestamp.isoformat()
            }
            for e in recent_events
        ]
        
        # Get recent traces
        recent_traces = list(self.traces.values())[-20:]
        state["traces"] = [
            {
                "trace_id": t.trace_id,
                "spans": len(t.spans),
                "duration_ms": sum(s.duration_ms() for s in t.spans),
                "service": t.service_name
            }
            for t in recent_traces
        ]
        
        # Get metrics summary
        metrics_summary = {}
        for metric_name, points in self.metrics.items():
            recent_points = points[-20:]
            if recent_points:
                metrics_summary[metric_name] = {
                    "count": len(recent_points),
                    "avg": statistics.mean(p.value for p in recent_points),
                    "min": min(p.value for p in recent_points),
                    "max": max(p.value for p in recent_points)
                }
        
        state["metrics"] = metrics_summary
        return state
    
    def _analyze_patterns_node(self, state: TelemetryState) -> TelemetryState:
        """Analyze telemetry patterns using LLM."""
        events = state.get("events", [])
        traces = state.get("traces", [])
        metrics = state.get("metrics", {})
        
        # Use LLM to analyze patterns
        prompt = ChatPromptTemplate.from_template(
            "Analyze the following telemetry data and identify patterns:\n"
            "Recent Events: {events_summary}\n"
            "Trace Data: {traces_summary}\n"
            "Metrics: {metrics_summary}\n\n"
            "Identify key patterns, trends, and noteworthy observations."
        )
        
        chain = prompt | self.llm
        response = chain.invoke({
            "events_summary": f"{len(events)} events tracked",
            "traces_summary": f"{len(traces)} traces with avg {statistics.mean([t['duration_ms'] for t in traces]):.2f}ms" if traces else "No traces",
            "metrics_summary": str(metrics)
        })
        
        state["insights"] = str(response.content)
        return state
    
    def _detect_anomalies_node(self, state: TelemetryState) -> TelemetryState:
        """Detect anomalies in telemetry data."""
        anomalies = []
        
        # Detect error spikes
        recent_events = state.get("events", [])
        error_events = [e for e in recent_events if e.get("type") == EventType.ERROR.value]
        if len(error_events) > len(recent_events) * 0.1:
            anomalies.append(f"High error rate: {len(error_events)}/{len(recent_events)} events are errors")
        
        # Detect slow traces
        traces = state.get("traces", [])
        if traces:
            durations = [t["duration_ms"] for t in traces]
            avg_duration = statistics.mean(durations)
            stdev_duration = statistics.stdev(durations) if len(durations) > 1 else 0
            
            slow_traces = [t for t in traces if t["duration_ms"] > avg_duration + 2 * stdev_duration]
            if slow_traces:
                anomalies.append(f"Detected {len(slow_traces)} slow traces (>2 std dev)")
        
        # Detect metric anomalies
        metrics = state.get("metrics", {})
        for metric_name, stats in metrics.items():
            if stats["max"] > stats["avg"] * 3:
                anomalies.append(f"Metric '{metric_name}' has outlier: max={stats['max']:.2f}, avg={stats['avg']:.2f}")
        
        state["anomalies"] = anomalies
        return state
    
    def _generate_insights_node(self, state: TelemetryState) -> TelemetryState:
        """Generate insights from telemetry data."""
        insights = state.get("insights", "")
        anomalies = state.get("anomalies", [])
        
        if anomalies:
            # Use LLM to interpret anomalies
            prompt = ChatPromptTemplate.from_template(
                "Based on the following telemetry anomalies, provide detailed insights:\n"
                "Anomalies: {anomalies}\n"
                "Context: {insights}\n\n"
                "Provide actionable insights about what these anomalies might indicate."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({
                "anomalies": "\n".join(anomalies),
                "insights": insights
            })
            
            state["insights"] = str(response.content)
        
        return state
    
    def _provide_recommendations_node(self, state: TelemetryState) -> TelemetryState:
        """Provide recommendations based on telemetry analysis."""
        insights = state.get("insights", "")
        anomalies = state.get("anomalies", [])
        
        recommendations = []
        
        if anomalies or insights:
            # Use LLM to generate recommendations
            prompt = ChatPromptTemplate.from_template(
                "Based on the telemetry analysis, provide 3-5 specific recommendations:\n"
                "Insights: {insights}\n"
                "Anomalies: {anomalies}\n\n"
                "Provide actionable recommendations for improvement."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({
                "insights": insights,
                "anomalies": "\n".join(anomalies) if anomalies else "No anomalies detected"
            })
            
            # Parse recommendations
            recommendations = [
                line.strip()
                for line in str(response.content).split('\n')
                if line.strip() and len(line.strip()) > 10
            ][:5]
        
        state["recommendations"] = recommendations
        return state
    
    def track_event(
        self,
        event_type: EventType,
        agent_id: str,
        properties: Dict[str, Any],
        severity: TraceLevel = TraceLevel.INFO
    ) -> TelemetryEvent:
        """Track a telemetry event."""
        event = TelemetryEvent(
            event_id=str(uuid.uuid4()),
            event_type=event_type,
            agent_id=agent_id,
            properties=properties,
            severity=severity
        )
        
        self.events.append(event)
        self.stats["total_events"] += 1
        self.stats["events_by_type"][event_type] += 1
        
        if event_type == EventType.ERROR:
            self.stats["errors_tracked"] += 1
        
        return event
    
    def start_span(
        self,
        operation: str,
        parent_id: Optional[str] = None,
        trace_id: Optional[str] = None,
        tags: Optional[Dict[str, Any]] = None
    ) -> Span:
        """Start a new tracing span."""
        span = Span(
            span_id=str(uuid.uuid4()),
            parent_id=parent_id,
            operation=operation,
            start_time=datetime.now(),
            tags=tags or {}
        )
        
        self.spans[span.span_id] = span
        self.active_spans[span.span_id] = span
        self.stats["total_spans"] += 1
        
        # Create or update trace
        if trace_id:
            if trace_id in self.traces:
                self.traces[trace_id].spans.append(span)
            else:
                self.traces[trace_id] = Trace(
                    trace_id=trace_id,
                    root_span_id=span.span_id,
                    spans=[span],
                    start_time=span.start_time
                )
                self.stats["total_traces"] += 1
        
        return span
    
    def end_span(self, span_id: str, status: str = "success", error: Optional[str] = None):
        """End a tracing span."""
        if span_id in self.active_spans:
            span = self.active_spans[span_id]
            span.end_time = datetime.now()
            span.status = status
            span.error = error
            
            # Update average duration
            total_durations = sum(s.duration_ms() for s in self.spans.values() if s.end_time)
            completed_spans = sum(1 for s in self.spans.values() if s.end_time)
            if completed_spans > 0:
                self.stats["avg_span_duration_ms"] = total_durations / completed_spans
            
            del self.active_spans[span_id]
    
    def record_metric(
        self,
        metric_name: str,
        value: float,
        dimensions: Optional[Dict[str, str]] = None,
        unit: str = ""
    ):
        """Record a custom metric."""
        metric = MetricPoint(
            metric_name=metric_name,
            value=value,
            dimensions=dimensions or {},
            unit=unit
        )
        
        self.metrics[metric_name].append(metric)
        self.stats["total_metrics"] += 1
    
    def get_aggregated_metrics(
        self,
        period: AggregationPeriod,
        start_time: Optional[datetime] = None,
        end_time: Optional[datetime] = None
    ) -> AggregatedMetrics:
        """Get aggregated metrics for a period."""
        if not end_time:
            end_time = datetime.now()
        if not start_time:
            # Default to last hour
            start_time = end_time - timedelta(hours=1)
        
        # Filter events in time range
        period_events = [
            e for e in self.events
            if start_time <= e.timestamp  0 else 0.0
        
        # Aggregate metrics
        metrics_summary = {}
        for metric_name, points in self.metrics.items():
            period_points = [
                p for p in points
                if start_time <= p.timestamp  1 else 0.0
                }
        
        return AggregatedMetrics(
            period=period,
            start_time=start_time,
            end_time=end_time,
            metrics=metrics_summary,
            event_counts=event_counts,
            error_rate=error_rate
        )
    
    def analyze_telemetry(self) -> Dict[str, Any]:
        """Analyze telemetry data using workflow."""
        initial_state: TelemetryState = {
            "events": [],
            "traces": [],
            "metrics": {},
            "insights": "",
            "recommendations": [],
            "anomalies": []
        }
        
        # Run workflow
        result = self.workflow.invoke(initial_state)
        
        return {
            "insights": result["insights"],
            "anomalies": result["anomalies"],
            "recommendations": result["recommendations"],
            "events_analyzed": len(result["events"]),
            "traces_analyzed": len(result["traces"])
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get telemetry statistics."""
        return {
            "total_events": self.stats["total_events"],
            "total_spans": self.stats["total_spans"],
            "total_traces": self.stats["total_traces"],
            "total_metrics": self.stats["total_metrics"],
            "events_by_type": {
                event_type.value: count
                for event_type, count in self.stats["events_by_type"].items()
            },
            "errors_tracked": self.stats["errors_tracked"],
            "avg_span_duration_ms": round(self.stats["avg_span_duration_ms"], 2),
            "active_spans": len(self.active_spans)
        }


class TelemetryPattern:
    """
    Comprehensive telemetry pattern coordinator with LangChain/LangGraph.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.collector = TelemetryCollector(model_name=model_name)
    
    def track(
        self,
        event_type: EventType,
        agent_id: str,
        properties: Dict[str, Any]
    ) -> str:
        """Track an event."""
        event = self.collector.track_event(event_type, agent_id, properties)
        return event.event_id
    
    def trace_operation(
        self,
        operation: str,
        tags: Optional[Dict[str, Any]] = None
    ) -> Span:
        """Start tracing an operation."""
        return self.collector.start_span(operation, tags=tags)
    
    def complete_trace(self, span: Span, success: bool = True):
        """Complete a trace."""
        status = "success" if success else "error"
        self.collector.end_span(span.span_id, status)
    
    def measure(
        self,
        metric_name: str,
        value: float,
        dimensions: Optional[Dict[str, str]] = None
    ):
        """Measure a metric."""
        self.collector.record_metric(metric_name, value, dimensions)
    
    def analyze(self) -> Dict[str, Any]:
        """Analyze telemetry data."""
        return self.collector.analyze_telemetry()
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get telemetry statistics."""
        return self.collector.get_statistics()



if __name__ == "__main__":
    print("=== Telemetry Pattern with LangChain/LangGraph Demo ===\n")
    
    # Note: This demo shows the structure. In production, you'd need:
    # - export OPENAI_API_KEY="your-key-here"
    # - Actual LLM calls would be made
    
    print("Scenario 1: Event Tracking")
    print("-" * 50)
    telemetry = TelemetryPattern(model_name="gpt-4")
    
    # Track various events
    events = [
        (EventType.USER_ACTION, "user-123", {"action": "login", "success": True}),
        (EventType.SYSTEM_EVENT, "api-server", {"event": "startup", "version": "1.2.3"}),
        (EventType.PERFORMANCE, "database", {"query_time_ms": 45.2, "query": "SELECT"}),
        (EventType.ERROR, "payment-service", {"error": "timeout", "retry_count": 3}),
        (EventType.BUSINESS, "checkout", {"order_id": "ORD-123", "amount": 99.99}),
    ]
    
    print("Tracking events:")
    for event_type, agent_id, properties in events:
        event_id = telemetry.track(event_type, agent_id, properties)
        print(f"  ✓ Tracked {event_type.value} event {event_id[:8]}...")
    
    print("\nScenario 2: Distributed Tracing")
    print("-" * 50)
    
    # Trace a complex operation
    print("Tracing order processing:")
    order_span = telemetry.trace_operation("process_order", tags={"order_id": "ORD-456"})
    print(f"  Started trace: {order_span.span_id[:8]}...")
    
    # Simulate child operations
    validate_span = telemetry.collector.start_span(
        "validate_payment",
        parent_id=order_span.span_id,
        trace_id=order_span.span_id
    )
    telemetry.collector.end_span(validate_span.span_id, "success")
    print(f"  Completed validation: {validate_span.duration_ms():.2f}ms")
    
    inventory_span = telemetry.collector.start_span(
        "check_inventory",
        parent_id=order_span.span_id,
        trace_id=order_span.span_id
    )
    telemetry.collector.end_span(inventory_span.span_id, "success")
    print(f"  Completed inventory check: {inventory_span.duration_ms():.2f}ms")
    
    telemetry.complete_trace(order_span, success=True)
    print(f"  Total order processing: {order_span.duration_ms():.2f}ms")
    
    print("\nScenario 3: Custom Metrics")
    print("-" * 50)
    
    # Record various metrics
    metrics_data = [
        ("cpu_usage", 65.5, {"host": "server-1", "service": "api"}),
        ("memory_mb", 2048.0, {"host": "server-1", "service": "api"}),
        ("request_count", 1543.0, {"endpoint": "/api/users", "status": "200"}),
        ("response_time_ms", 125.3, {"endpoint": "/api/orders", "status": "200"}),
        ("error_rate", 2.5, {"service": "payment", "type": "timeout"}),
    ]
    
    print("Recording metrics:")
    for metric_name, value, dimensions in metrics_data:
        telemetry.measure(metric_name, value, dimensions)
        print(f"  ✓ {metric_name}: {value} {dimensions}")
    
    print("\nScenario 4: Telemetry Analysis")
    print("-" * 50)
    
    print("Analyzing telemetry data...")
    analysis = telemetry.analyze()
    
    print(f"\nInsights:")
    print(f"  {analysis['insights'][:200]}...")
    
    print(f"\nAnomalies Detected: {len(analysis['anomalies'])}")
    for anomaly in analysis['anomalies']:
        print(f"  ⚠️ {anomaly}")
    
    print(f"\nRecommendations:")
    for i, recommendation in enumerate(analysis['recommendations'][:3], 1):
        print(f"  {i}. {recommendation[:100]}...")
    
    print(f"\nEvents Analyzed: {analysis['events_analyzed']}")
    print(f"Traces Analyzed: {analysis['traces_analyzed']}")
    
    print("\nScenario 5: Aggregated Metrics")
    print("-" * 50)
    
    aggregated = telemetry.collector.get_aggregated_metrics(
        AggregationPeriod.HOUR,
        start_time=datetime.now() - timedelta(hours=1)
    )
    
    print(f"Period: {aggregated.period.value}")
    print(f"Error Rate: {aggregated.error_rate:.2f}%")
    
    print("\nEvent Counts:")
    for event_type, count in aggregated.event_counts.items():
        if count > 0:
            print(f"  {event_type.value}: {count}")
    
    print("\nMetric Aggregations:")
    for metric_name, stats in list(aggregated.metrics.items())[:3]:
        print(f"  {metric_name}:")
        print(f"    avg={stats['avg']:.2f}, min={stats['min']:.2f}, max={stats['max']:.2f}")
    
    print("\nScenario 6: Statistics")
    print("-" * 50)
    
    stats = telemetry.get_statistics()
    print(f"Total Events: {stats['total_events']}")
    print(f"Total Spans: {stats['total_spans']}")
    print(f"Total Traces: {stats['total_traces']}")
    print(f"Total Metrics: {stats['total_metrics']}")
    print(f"Errors Tracked: {stats['errors_tracked']}")
    print(f"Avg Span Duration: {stats['avg_span_duration_ms']:.2f}ms")
    
    print("\nEvents by Type:")
    for event_type, count in stats['events_by_type'].items():
        if count > 0:
            print(f"  {event_type}: {count}")
    
    print("\n" + "=" * 50)
    print("Telemetry pattern demonstration complete!")
    print("=" * 50)

Agentic Architecture Pattern: Telemetry

Pattern 173: 173 Telemetry – Line-by-Line Explanation

Overview

Pattern 173: Telemetry Pattern with LangChain/LangGraph

Purpose: Collect detailed operational data for analysis and optimization with LLM insights.

Code Structure Analysis

File Information

Pattern Number: 173
Pattern Name: 173 Telemetry
File Path: patterns\quality_performance\173_telemetry.py
Total Lines: 687

Detailed Code Explanation

Module Docstring

"""
Pattern 173: Telemetry Pattern with LangChain/LangGraph

Purpose: Collect detailed operational data for analysis and optimization with LLM insights.
"""

Explanation: This module docstring describes the purpose and key features of the 173 Telemetry pattern.

Imports Section

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

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional, TypedDict: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime, timedelta: 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

Enumerations

EventType (Line 19)

class EventType(Enum):
    USER_ACTION
    SYSTEM_EVENT
    ERROR
    PERFORMANCE
    BUSINESS

Purpose: Telemetry event types.

Values: – USER_ACTION: Represents user action – SYSTEM_EVENT: Represents system event – ERROR: Error severity level – PERFORMANCE: Represents performance – BUSINESS: Represents business

TraceLevel (Line 28)

class TraceLevel(Enum):
    CRITICAL
    ERROR
    WARNING
    INFO
    DEBUG

Purpose: Trace detail levels.

Values: – CRITICAL: Represents critical – ERROR: Error severity level – WARNING: Warning severity level – INFO: Informational severity level – DEBUG: Represents debug

AggregationPeriod (Line 37)

class AggregationPeriod(Enum):
    MINUTE
    HOUR
    DAY
    WEEK

Purpose: Data aggregation periods.

Values: – MINUTE: Represents minute – HOUR: Represents hour – DAY: Represents day – WEEK: Represents week

Classes

EventType (Line 19)

Description: Telemetry event types.

TraceLevel (Line 28)

Description: Trace detail levels.

AggregationPeriod (Line 37)

Description: Data aggregation periods.

TelemetryEvent (Line 46)

Description: Telemetry event.

Span (Line 57)

Description: Distributed tracing span.

Methods:

`duration_ms` (Line 68)

def duration_ms(self):
    """Get span duration in milliseconds."""

Purpose: Get span duration in milliseconds.

Parameters: – self: Instance reference

MetricPoint (Line 76)

Description: Custom metric data point.

Trace (Line 86)

Description: Distributed trace containing multiple spans.

AggregatedMetrics (Line 97)

Description: Aggregated metrics summary.

TelemetryState (Line 107)

Description: State for telemetry analysis workflow.

TelemetryCollector (Line 117)

Description: Advanced telemetry collector with LangChain/LangGraph integration.

Methods:

`init` (Line 122)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”) – temperature: Randomness in LLM responses (0.0-1.0)

`_build_telemetry_workflow` (Line 142)

def _build_telemetry_workflow(self):
    """Build LangGraph workflow for telemetry analysis."""

Purpose: Build LangGraph workflow for telemetry analysis.

Parameters: – self: Instance reference

`_collect_data_node` (Line 163)

def _collect_data_node(self, state):
    """Collect and organize telemetry data."""

Purpose: Collect and organize telemetry data.

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

`_analyze_patterns_node` (Line 205)

def _analyze_patterns_node(self, state):
    """Analyze telemetry patterns using LLM."""

Purpose: Analyze telemetry patterns using LLM.

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

`_detect_anomalies_node` (Line 230)

def _detect_anomalies_node(self, state):
    """Detect anomalies in telemetry data."""

Purpose: Detect anomalies in telemetry data.

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

`_generate_insights_node` (Line 260)

def _generate_insights_node(self, state):
    """Generate insights from telemetry data."""

Purpose: Generate insights from telemetry data.

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

`_provide_recommendations_node` (Line 284)

def _provide_recommendations_node(self, state):
    """Provide recommendations based on telemetry analysis."""

Purpose: Provide recommendations based on telemetry analysis.

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

`track_event` (Line 316)

def track_event(self, event_type, agent_id, properties, severity):
    """Track a telemetry event."""

Purpose: Track a telemetry event.

Parameters: – self: Instance reference – event_type: Parameter for event_type – agent_id: Parameter for agent_id – properties: Parameter for properties – severity: Parameter for severity

`start_span` (Line 341)

def start_span(self, operation, parent_id, trace_id, tags):
    """Start a new tracing span."""

Purpose: Start a new tracing span.

Parameters: – self: Instance reference – operation: Parameter for operation – parent_id: Parameter for parent_id – trace_id: Parameter for trace_id – tags: Parameter for tags

`end_span` (Line 376)

def end_span(self, span_id, status, error):
    """End a tracing span."""

Purpose: End a tracing span.

Parameters: – self: Instance reference – span_id: Parameter for span_id – status: Parameter for status – error: Parameter for error

`record_metric` (Line 392)

def record_metric(self, metric_name, value, dimensions, unit):
    """Record a custom metric."""

Purpose: Record a custom metric.

Parameters: – self: Instance reference – metric_name: Parameter for metric_name – value: Parameter for value – dimensions: Parameter for dimensions – unit: Parameter for unit

`get_aggregated_metrics` (Line 410)

def get_aggregated_metrics(self, period, start_time, end_time):
    """Get aggregated metrics for a period."""

Purpose: Get aggregated metrics for a period.

Parameters: – self: Instance reference – period: Parameter for period – start_time: Parameter for start_time – end_time: Parameter for end_time

`analyze_telemetry` (Line 468)

def analyze_telemetry(self):
    """Analyze telemetry data using workflow."""

Purpose: Analyze telemetry data using workflow.

Parameters: – self: Instance reference

`get_statistics` (Line 490)

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

Purpose: Get telemetry statistics.

Parameters: – self: Instance reference

TelemetryPattern (Line 507)

Description: Comprehensive telemetry pattern coordinator with LangChain/LangGraph.

Methods:

`init` (Line 512)

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

Purpose: No method docstring.

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

`track` (Line 515)

def track(self, event_type, agent_id, properties):
    """Track an event."""

Purpose: Track an event.

Parameters: – self: Instance reference – event_type: Parameter for event_type – agent_id: Parameter for agent_id – properties: Parameter for properties

`trace_operation` (Line 525)

def trace_operation(self, operation, tags):
    """Start tracing an operation."""

Purpose: Start tracing an operation.

Parameters: – self: Instance reference – operation: Parameter for operation – tags: Parameter for tags

`complete_trace` (Line 533)

def complete_trace(self, span, success):
    """Complete a trace."""

Purpose: Complete a trace.

Parameters: – self: Instance reference – span: Parameter for span – success: Parameter for success

`measure` (Line 538)

def measure(self, metric_name, value, dimensions):
    """Measure a metric."""

Purpose: Measure a metric.

Parameters: – self: Instance reference – metric_name: Parameter for metric_name – value: Parameter for value – dimensions: Parameter for dimensions

`analyze` (Line 547)

def analyze(self):
    """Analyze telemetry data."""

Purpose: Analyze telemetry data.

Parameters: – self: Instance reference

`get_statistics` (Line 551)

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

Purpose: Get telemetry statistics.

Parameters: – self: Instance reference

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = EventType(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

November 4, 2025

Agentic Architecture Pattern: Logging


    """
Pattern 172: Logging

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


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

Pattern 172: 172 Logging – Line-by-Line Explanation

Overview

Pattern 172: Logging

Purpose: Enhanced implementation with full LangChain/LangGraph integration

Code Structure Analysis

File Information

Pattern Number: 172
Pattern Name: 172 Logging
File Path: patterns\quality_performance\172_logging.py
Total Lines: 247

Detailed Code Explanation

Module Docstring

"""
Pattern 172: Logging

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

Explanation: This module docstring describes the purpose and key features of the 172 Logging 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

Classes

LoggingConfig (Line 18)

Description: Configuration for Logging.

LoggingState (Line 27)

Description: State information for Logging.

LoggingAgent (Line 36)

Description: Agent implementing Logging.

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 Logging."""

Purpose: Process input according to Logging.

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

LoggingPattern (Line 85)

Description: Logging – Enhanced implementation

This pattern provides comprehensive functionality for logging 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 = LoggingConfig(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

November 4, 2025

Agentic Architecture Pattern: Monitoring


    """
Pattern 171: Monitoring Pattern with LangChain/LangGraph

Purpose: Track agent performance, health, and metrics in real-time with LangGraph integration.
"""

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


class HealthStatus(Enum):
    """Health status levels."""
    HEALTHY = "healthy"
    DEGRADED = "degraded"
    UNHEALTHY = "unhealthy"


class MonitoringStrategy(Enum):
    """Monitoring strategies."""
    REAL_TIME = "real_time"
    PERIODIC = "periodic"
    THRESHOLD_BASED = "threshold_based"
    ANOMALY_DETECTION = "anomaly_detection"


class AlertLevel(Enum):
    """Alert severity levels."""
    INFO = "info"
    WARNING = "warning"
    ERROR = "error"
    CRITICAL = "critical"


class ThresholdType(Enum):
    """Threshold comparison types."""
    GREATER_THAN = "greater_than"
    LESS_THAN = "less_than"
    EQUALS = "equals"
    RANGE = "range"


@dataclass
class Metric:
    """Performance metric."""
    name: str
    value: float
    unit: str
    tags: Dict[str, str] = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class HealthCheck:
    """Health check result."""
    component: str
    status: HealthStatus
    message: str
    metrics: List[Metric] = field(default_factory=list)
    timestamp: datetime = field(default_factory=datetime.now)


@dataclass
class Alert:
    """Monitoring alert."""
    alert_id: str
    level: AlertLevel
    component: str
    message: str
    metric_name: str
    metric_value: float
    threshold: float
    triggered_at: datetime = field(default_factory=datetime.now)
    acknowledged: bool = False


@dataclass
class Threshold:
    """Metric threshold configuration."""
    metric_name: str
    threshold_type: ThresholdType
    value: float
    alert_level: AlertLevel
    max_value: Optional[float] = None  # For range thresholds


@dataclass
class TimeSeriesData:
    """Time-series data for a metric."""
    metric_name: str
    values: List[float]
    timestamps: List[datetime]
    window_size: int = 10


@dataclass
class Dashboard:
    """Monitoring dashboard data."""
    components: Dict[str, HealthStatus]
    total_metrics: int
    active_alerts: int
    alert_distribution: Dict[AlertLevel, int]
    recent_anomalies: List[str]
    system_health: str


class MonitoringState(TypedDict):
    """State for monitoring workflow."""
    component: str
    metrics: List[Dict[str, Any]]
    health_status: str
    alerts: List[str]
    analysis: str
    recommendations: List[str]


class MonitoringEngine:
    """
    Core monitoring engine with LangChain/LangGraph integration.
    """
    
    def __init__(self, model_name: str = "gpt-4", temperature: float = 0.3):
        self.llm = ChatOpenAI(model=model_name, temperature=temperature)
        self.metrics: Dict[str, List[Metric]] = {}
        self.health_checks: Dict[str, HealthCheck] = {}
        self.alerts: Dict[str, Alert] = {}
        self.thresholds: Dict[str, Threshold] = {}
        self.time_series: Dict[str, TimeSeriesData] = {}
        self.monitoring_strategies: Dict[str, MonitoringStrategy] = {}
        self.anomaly_history: List[Dict[str, Any]] = []
        self.workflow = self._build_monitoring_workflow()
        
        # Statistics
        self.stats = {
            "total_metrics_collected": 0,
            "alerts_triggered": 0,
            "anomalies_detected": 0,
            "health_checks_performed": 0,
            "alerts_by_level": {level: 0 for level in AlertLevel},
            "components_monitored": set()
        }
    
    def _build_monitoring_workflow(self) -> StateGraph:
        """Build LangGraph workflow for intelligent monitoring."""
        workflow = StateGraph(MonitoringState)
        
        # Add nodes
        workflow.add_node("collect_metrics", self._collect_metrics_node)
        workflow.add_node("analyze_health", self._analyze_health_node)
        workflow.add_node("detect_anomalies", self._detect_anomalies_node)
        workflow.add_node("generate_alerts", self._generate_alerts_node)
        workflow.add_node("provide_recommendations", self._provide_recommendations_node)
        
        # Define edges
        workflow.set_entry_point("collect_metrics")
        workflow.add_edge("collect_metrics", "analyze_health")
        workflow.add_edge("analyze_health", "detect_anomalies")
        workflow.add_edge("detect_anomalies", "generate_alerts")
        workflow.add_edge("generate_alerts", "provide_recommendations")
        workflow.add_edge("provide_recommendations", END)
        
        return workflow.compile()
    
    def _collect_metrics_node(self, state: MonitoringState) -> MonitoringState:
        """Collect and organize metrics."""
        component = state["component"]
        
        # Get recent metrics for component
        metrics_data = []
        if component in self.metrics:
            recent_metrics = self.metrics[component][-10:]
            metrics_data = [
                {
                    "name": m.name,
                    "value": m.value,
                    "unit": m.unit,
                    "timestamp": m.timestamp.isoformat()
                }
                for m in recent_metrics
            ]
        
        state["metrics"] = metrics_data
        return state
    
    def _analyze_health_node(self, state: MonitoringState) -> MonitoringState:
        """Analyze component health using LLM."""
        metrics = state["metrics"]
        
        if not metrics:
            state["health_status"] = HealthStatus.HEALTHY.value
            state["analysis"] = "No metrics available for analysis"
            return state
        
        # Use LLM to analyze metrics
        prompt = ChatPromptTemplate.from_template(
            "Analyze the following metrics and determine system health:\n"
            "Metrics: {metrics}\n\n"
            "Provide a health status (HEALTHY, DEGRADED, or UNHEALTHY) and brief analysis."
        )
        
        chain = prompt | self.llm
        response = chain.invoke({"metrics": str(metrics)})
        analysis = response.content
        
        # Determine health status
        analysis_lower = analysis.lower()
        if "unhealthy" in analysis_lower:
            health_status = HealthStatus.UNHEALTHY.value
        elif "degraded" in analysis_lower:
            health_status = HealthStatus.DEGRADED.value
        else:
            health_status = HealthStatus.HEALTHY.value
        
        state["health_status"] = health_status
        state["analysis"] = analysis
        
        return state
    
    def _detect_anomalies_node(self, state: MonitoringState) -> MonitoringState:
        """Detect anomalies in metrics using statistical analysis."""
        component = state["component"]
        alerts = []
        
        # Check thresholds
        if component in self.metrics:
            recent_metrics = self.metrics[component][-5:]
            for metric in recent_metrics:
                if metric.name in self.thresholds:
                    threshold = self.thresholds[metric.name]
                    if self._check_threshold(metric.value, threshold):
                        alerts.append(
                            f"Threshold exceeded: {metric.name} = {metric.value} "
                            f"(threshold: {threshold.value})"
                        )
        
        # Statistical anomaly detection
        if component in self.time_series:
            ts_data = self.time_series[component]
            if len(ts_data.values) >= 5:
                mean = statistics.mean(ts_data.values)
                stdev = statistics.stdev(ts_data.values)
                latest = ts_data.values[-1]
                
                # Check if latest value is an outlier (> 2 standard deviations)
                if abs(latest - mean) > 2 * stdev:
                    alerts.append(
                        f"Anomaly detected: {ts_data.metric_name} = {latest:.2f} "
                        f"(mean: {mean:.2f}, stdev: {stdev:.2f})"
                    )
        
        state["alerts"] = alerts
        return state
    
    def _generate_alerts_node(self, state: MonitoringState) -> MonitoringState:
        """Generate and categorize alerts."""
        alerts = state.get("alerts", [])
        
        if alerts:
            # Use LLM to categorize alert severity
            prompt = ChatPromptTemplate.from_template(
                "Categorize these monitoring alerts by severity (INFO, WARNING, ERROR, CRITICAL):\n"
                "Alerts: {alerts}\n\n"
                "Provide severity level for each alert."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({"alerts": str(alerts)})
            
            # Update alerts with LLM categorization
            categorized_alerts = []
            for alert in alerts:
                severity = "WARNING"  # Default
                if "critical" in response.content.lower():
                    severity = "CRITICAL"
                elif "error" in response.content.lower():
                    severity = "ERROR"
                categorized_alerts.append(f"[{severity}] {alert}")
            
            state["alerts"] = categorized_alerts
        
        return state
    
    def _provide_recommendations_node(self, state: MonitoringState) -> MonitoringState:
        """Provide recommendations based on analysis."""
        health_status = state.get("health_status", "HEALTHY")
        alerts = state.get("alerts", [])
        analysis = state.get("analysis", "")
        
        recommendations = []
        
        if health_status != HealthStatus.HEALTHY.value or alerts:
            # Use LLM to generate recommendations
            prompt = ChatPromptTemplate.from_template(
                "Based on the following system analysis, provide 3-5 actionable recommendations:\n"
                "Health Status: {health_status}\n"
                "Analysis: {analysis}\n"
                "Alerts: {alerts}\n\n"
                "Provide specific, actionable recommendations."
            )
            
            chain = prompt | self.llm
            response = chain.invoke({
                "health_status": health_status,
                "analysis": analysis,
                "alerts": str(alerts)
            })
            
            # Parse recommendations
            recommendations = [
                line.strip() 
                for line in response.content.split('\n') 
                if line.strip() and len(line.strip()) > 10
            ][:5]
        
        state["recommendations"] = recommendations
        return state
    
    def _check_threshold(self, value: float, threshold: Threshold) -> bool:
        """Check if value breaches threshold."""
        if threshold.threshold_type == ThresholdType.GREATER_THAN:
            return value > threshold.value
        elif threshold.threshold_type == ThresholdType.LESS_THAN:
            return value < threshold.value
        elif threshold.threshold_type == ThresholdType.EQUALS:
            return abs(value - threshold.value) < 0.01
        elif threshold.threshold_type == ThresholdType.RANGE:
            return not (threshold.value <= value  ts_data.window_size:
            ts_data.values = ts_data.values[-ts_data.window_size:]
            ts_data.timestamps = ts_data.timestamps[-ts_data.window_size:]
        
        # Update statistics
        self.stats["total_metrics_collected"] += 1
        self.stats["components_monitored"].add(component)
    
    def set_threshold(
        self,
        metric_name: str,
        value: float,
        threshold_type: ThresholdType = ThresholdType.GREATER_THAN,
        alert_level: AlertLevel = AlertLevel.WARNING,
        max_value: Optional[float] = None
    ):
        """Set threshold for a metric."""
        self.thresholds[metric_name] = Threshold(
            metric_name=metric_name,
            threshold_type=threshold_type,
            value=value,
            alert_level=alert_level,
            max_value=max_value
        )
    
    def check_health(self, component: str) -> HealthCheck:
        """Perform comprehensive health check using workflow."""
        initial_state: MonitoringState = {
            "component": component,
            "metrics": [],
            "health_status": HealthStatus.HEALTHY.value,
            "alerts": [],
            "analysis": "",
            "recommendations": []
        }
        
        # Run workflow
        result = self.workflow.invoke(initial_state)
        
        # Create health check
        status = HealthStatus(result["health_status"])
        health_check = HealthCheck(
            component=component,
            status=status,
            message=result["analysis"],
            metrics=self.metrics.get(component, [])[-5:]
        )
        
        self.health_checks[component] = health_check
        self.stats["health_checks_performed"] += 1
        
        # Create alerts if needed
        for alert_msg in result["alerts"]:
            alert = Alert(
                alert_id=f"alert_{len(self.alerts)}",
                level=AlertLevel.WARNING,
                component=component,
                message=alert_msg,
                metric_name=component,
                metric_value=0.0,
                threshold=0.0
            )
            self.alerts[alert.alert_id] = alert
            self.stats["alerts_triggered"] += 1
            self.stats["alerts_by_level"][AlertLevel.WARNING] += 1
        
        return health_check
    
    def get_metric_stats(self, component: str, metric_name: str) -> Dict[str, Any]:
        """Get statistics for a metric."""
        if component not in self.metrics:
            return {}
        
        values = [
            m.value for m in self.metrics[component]
            if m.name == metric_name
        ]
        
        if not values:
            return {}
        
        return {
            "count": len(values),
            "min": min(values),
            "max": max(values),
            "avg": statistics.mean(values),
            "median": statistics.median(values),
            "stdev": statistics.stdev(values) if len(values) > 1 else 0,
            "latest": values[-1]
        }
    
    def get_dashboard(self) -> Dashboard:
        """Get comprehensive monitoring dashboard."""
        components = {
            component: check.status
            for component, check in self.health_checks.items()
        }
        
        total_metrics = sum(len(metrics) for metrics in self.metrics.values())
        active_alerts = len([a for a in self.alerts.values() if not a.acknowledged])
        
        alert_distribution = {
            level: sum(
                1 for a in self.alerts.values()
                if a.level == level and not a.acknowledged
            )
            for level in AlertLevel
        }
        
        recent_anomalies = [
            a["message"] for a in self.anomaly_history[-5:]
        ]
        
        # Determine overall system health
        unhealthy_count = sum(
            1 for status in components.values()
            if status == HealthStatus.UNHEALTHY
        )
        degraded_count = sum(
            1 for status in components.values()
            if status == HealthStatus.DEGRADED
        )
        
        if unhealthy_count > 0:
            system_health = "UNHEALTHY"
        elif degraded_count > 0:
            system_health = "DEGRADED"
        else:
            system_health = "HEALTHY"
        
        return Dashboard(
            components=components,
            total_metrics=total_metrics,
            active_alerts=active_alerts,
            alert_distribution=alert_distribution,
            recent_anomalies=recent_anomalies,
            system_health=system_health
        )
    
    def acknowledge_alert(self, alert_id: str):
        """Acknowledge an alert."""
        if alert_id in self.alerts:
            self.alerts[alert_id].acknowledged = True
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get monitoring statistics."""
        return {
            "total_metrics_collected": self.stats["total_metrics_collected"],
            "alerts_triggered": self.stats["alerts_triggered"],
            "anomalies_detected": self.stats["anomalies_detected"],
            "health_checks_performed": self.stats["health_checks_performed"],
            "alerts_by_level": {
                level.value: count
                for level, count in self.stats["alerts_by_level"].items()
            },
            "components_monitored": len(self.stats["components_monitored"]),
            "active_alerts": len([a for a in self.alerts.values() if not a.acknowledged]),
            "thresholds_configured": len(self.thresholds)
        }


class MonitoringPattern:
    """
    Comprehensive monitoring pattern coordinator with LangChain/LangGraph.
    """
    
    def __init__(self, model_name: str = "gpt-4"):
        self.engine = MonitoringEngine(model_name=model_name)
    
    def monitor(
        self,
        component: str,
        metric_name: str,
        value: float,
        unit: str = ""
    ) -> Dict[str, Any]:
        """Monitor a component with automated analysis."""
        # Record metric
        self.engine.record_metric(component, metric_name, value, unit)
        
        # Perform health check
        health_check = self.engine.check_health(component)
        
        return {
            "component": component,
            "health_status": health_check.status.value,
            "message": health_check.message,
            "alerts": [
                alert.message
                for alert in self.engine.alerts.values()
                if alert.component == component and not alert.acknowledged
            ]
        }
    
    def configure_monitoring(
        self,
        metric_name: str,
        threshold: float,
        threshold_type: ThresholdType = ThresholdType.GREATER_THAN
    ):
        """Configure monitoring thresholds."""
        self.engine.set_threshold(metric_name, threshold, threshold_type)
    
    def get_dashboard(self) -> Dict[str, Any]:
        """Get monitoring dashboard."""
        dashboard = self.engine.get_dashboard()
        return {
            "system_health": dashboard.system_health,
            "components": {
                comp: status.value
                for comp, status in dashboard.components.items()
            },
            "total_metrics": dashboard.total_metrics,
            "active_alerts": dashboard.active_alerts,
            "recent_anomalies": dashboard.recent_anomalies
        }
    
    def get_statistics(self) -> Dict[str, Any]:
        """Get monitoring statistics."""
        return self.engine.get_statistics()


if __name__ == "__main__":
    print("=== Monitoring Pattern with LangChain/LangGraph Demo ===\n")
    
    # Note: This demo shows the structure. In production, you'd need:
    # - export OPENAI_API_KEY="your-key-here"
    # - Actual LLM calls would be made
    
    print("Scenario 1: Basic Monitoring")
    print("-" * 50)
    monitor = MonitoringPattern(model_name="gpt-4")
    
    # Configure thresholds
    monitor.configure_monitoring("cpu_usage", 80.0, ThresholdType.GREATER_THAN)
    monitor.configure_monitoring("memory_usage", 90.0, ThresholdType.GREATER_THAN)
    monitor.configure_monitoring("response_time", 1000.0, ThresholdType.GREATER_THAN)
    
    print("✓ Configured monitoring thresholds")
    
    # Monitor different components
    components = [
        ("api_server", "cpu_usage", 65.0, "%"),
        ("api_server", "memory_usage", 78.0, "%"),
        ("api_server", "response_time", 250.0, "ms"),
        ("database", "cpu_usage", 45.0, "%"),
        ("database", "query_time", 150.0, "ms"),
        ("cache", "hit_rate", 92.0, "%"),
    ]
    
    print("\nMonitoring components:")
    for component, metric, value, unit in components:
        result = monitor.monitor(component, metric, value, unit)
        print(f"  {component}.{metric}: {value}{unit} -> {result['health_status']}")
    
    print("\nScenario 2: Threshold Violations")
    print("-" * 50)
    
    # Simulate threshold violations
    violations = [
        ("api_server", "cpu_usage", 85.0, "%"),
        ("database", "cpu_usage", 92.0, "%"),
        ("api_server", "response_time", 1500.0, "ms"),
    ]
    
    print("Simulating threshold violations:")
    for component, metric, value, unit in violations:
        result = monitor.monitor(component, metric, value, unit)
        print(f"\n  {component}.{metric}: {value}{unit}")
        print(f"    Status: {result['health_status']}")
        print(f"    Message: {result['message'][:100]}...")
        if result['alerts']:
            print(f"    Alerts: {len(result['alerts'])} active")
    
    print("\nScenario 3: Monitoring Dashboard")
    print("-" * 50)
    
    dashboard = monitor.get_dashboard()
    print(f"System Health: {dashboard['system_health']}")
    print(f"Total Metrics: {dashboard['total_metrics']}")
    print(f"Active Alerts: {dashboard['active_alerts']}")
    
    print("\nComponent Health:")
    for component, status in dashboard['components'].items():
        print(f"  {component}: {status}")
    
    if dashboard['recent_anomalies']:
        print("\nRecent Anomalies:")
        for anomaly in dashboard['recent_anomalies']:
            print(f"  - {anomaly}")
    
    print("\nScenario 4: Statistics")
    print("-" * 50)
    
    stats = monitor.get_statistics()
    print(f"Total Metrics Collected: {stats['total_metrics_collected']}")
    print(f"Alerts Triggered: {stats['alerts_triggered']}")
    print(f"Health Checks Performed: {stats['health_checks_performed']}")
    print(f"Components Monitored: {stats['components_monitored']}")
    print(f"Thresholds Configured: {stats['thresholds_configured']}")
    
    print("\nAlerts by Level:")
    for level, count in stats['alerts_by_level'].items():
        print(f"  {level}: {count}")
    
    print("\n" + "=" * 50)
    print("Monitoring pattern demonstration complete!")
    print("=" * 50)

Agentic Architecture Pattern: Monitoring

Pattern 171: 171 Monitoring – Line-by-Line Explanation

Overview

Pattern 171: Monitoring Pattern with LangChain/LangGraph

Purpose: Track agent performance, health, and metrics in real-time with LangGraph integration.

Code Structure Analysis

File Information

Pattern Number: 171
Pattern Name: 171 Monitoring
File Path: patterns\quality_performance\171_monitoring.py
Total Lines: 677

Detailed Code Explanation

Module Docstring

"""
Pattern 171: Monitoring Pattern with LangChain/LangGraph

Purpose: Track agent performance, health, and metrics in real-time with LangGraph integration.
"""

Explanation: This module docstring describes the purpose and key features of the 171 Monitoring pattern.

Imports Section

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

Enumerations

HealthStatus (Line 17)

class HealthStatus(Enum):
    HEALTHY
    DEGRADED
    UNHEALTHY

Purpose: Health status levels.

Values: – HEALTHY: Represents healthy – DEGRADED: Represents degraded – UNHEALTHY: Represents unhealthy

MonitoringStrategy (Line 24)

class MonitoringStrategy(Enum):
    REAL_TIME
    PERIODIC
    THRESHOLD_BASED
    ANOMALY_DETECTION

Purpose: Monitoring strategies.

Values: – REAL_TIME: Represents real time – PERIODIC: Represents periodic – THRESHOLD_BASED: Represents threshold based – ANOMALY_DETECTION: Represents anomaly detection

AlertLevel (Line 32)

class AlertLevel(Enum):
    INFO
    WARNING
    ERROR
    CRITICAL

Purpose: Alert severity levels.

Values: – INFO: Informational severity level – WARNING: Warning severity level – ERROR: Error severity level – CRITICAL: Represents critical

ThresholdType (Line 40)

class ThresholdType(Enum):
    GREATER_THAN
    LESS_THAN
    EQUALS
    RANGE

Purpose: Threshold comparison types.

Values: – GREATER_THAN: Represents greater than – LESS_THAN: Represents less than – EQUALS: Represents equals – RANGE: Represents range

Classes

HealthStatus (Line 17)

Description: Health status levels.

MonitoringStrategy (Line 24)

Description: Monitoring strategies.

AlertLevel (Line 32)

Description: Alert severity levels.

ThresholdType (Line 40)

Description: Threshold comparison types.

Metric (Line 49)

Description: Performance metric.

HealthCheck (Line 59)

Description: Health check result.

Alert (Line 69)

Description: Monitoring alert.

Threshold (Line 83)

Description: Metric threshold configuration.

TimeSeriesData (Line 93)

Description: Time-series data for a metric.

Dashboard (Line 102)

Description: Monitoring dashboard data.

MonitoringState (Line 112)

Description: State for monitoring workflow.

MonitoringEngine (Line 122)

Description: Core monitoring engine with LangChain/LangGraph integration.

Methods:

`init` (Line 127)

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

Purpose: No method docstring.

Parameters: – self: Instance reference – model_name: LLM model to use (e.g., “gpt-4”) – temperature: Randomness in LLM responses (0.0-1.0)

`_build_monitoring_workflow` (Line 148)

def _build_monitoring_workflow(self):
    """Build LangGraph workflow for intelligent monitoring."""

Purpose: Build LangGraph workflow for intelligent monitoring.

Parameters: – self: Instance reference

`_collect_metrics_node` (Line 169)

def _collect_metrics_node(self, state):
    """Collect and organize metrics."""

Purpose: Collect and organize metrics.

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

`_analyze_health_node` (Line 190)

def _analyze_health_node(self, state):
    """Analyze component health using LLM."""

Purpose: Analyze component health using LLM.

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

`_detect_anomalies_node` (Line 224)

def _detect_anomalies_node(self, state):
    """Detect anomalies in metrics using statistical analysis."""

Purpose: Detect anomalies in metrics using statistical analysis.

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

`_generate_alerts_node` (Line 259)

def _generate_alerts_node(self, state):
    """Generate and categorize alerts."""

Purpose: Generate and categorize alerts.

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

`_provide_recommendations_node` (Line 288)

def _provide_recommendations_node(self, state):
    """Provide recommendations based on analysis."""

Purpose: Provide recommendations based on analysis.

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

`_check_threshold` (Line 323)

def _check_threshold(self, value, threshold):
    """Check if value breaches threshold."""

Purpose: Check if value breaches threshold.

Parameters: – self: Instance reference – value: Parameter for value – threshold: Parameter for threshold

`record_metric` (Line 335)

def record_metric(self, component, name, value, unit, tags):
    """Record a metric value."""

Purpose: Record a metric value.

Parameters: – self: Instance reference – component: Parameter for component – name: Identifier or label – value: Parameter for value – unit: Parameter for unit – tags: Parameter for tags

`set_threshold` (Line 377)

def set_threshold(self, metric_name, value, threshold_type, alert_level, max_value):
    """Set threshold for a metric."""

Purpose: Set threshold for a metric.

Parameters: – self: Instance reference – metric_name: Parameter for metric_name – value: Parameter for value – threshold_type: Parameter for threshold_type – alert_level: Parameter for alert_level – max_value: Parameter for max_value

`check_health` (Line 394)

def check_health(self, component):
    """Perform comprehensive health check using workflow."""

Purpose: Perform comprehensive health check using workflow.

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

`get_metric_stats` (Line 437)

def get_metric_stats(self, component, metric_name):
    """Get statistics for a metric."""

Purpose: Get statistics for a metric.

Parameters: – self: Instance reference – component: Parameter for component – metric_name: Parameter for metric_name

`get_dashboard` (Line 460)

def get_dashboard(self):
    """Get comprehensive monitoring dashboard."""

Purpose: Get comprehensive monitoring dashboard.

Parameters: – self: Instance reference

`acknowledge_alert` (Line 508)

def acknowledge_alert(self, alert_id):
    """Acknowledge an alert."""

Purpose: Acknowledge an alert.

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

`get_statistics` (Line 513)

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

Purpose: Get monitoring statistics.

Parameters: – self: Instance reference

MonitoringPattern (Line 530)

Description: Comprehensive monitoring pattern coordinator with LangChain/LangGraph.

Methods:

`init` (Line 535)

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

Purpose: No method docstring.

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

`monitor` (Line 538)

def monitor(self, component, metric_name, value, unit):
    """Monitor a component with automated analysis."""

Purpose: Monitor a component with automated analysis.

Parameters: – self: Instance reference – component: Parameter for component – metric_name: Parameter for metric_name – value: Parameter for value – unit: Parameter for unit

`configure_monitoring` (Line 563)

def configure_monitoring(self, metric_name, threshold, threshold_type):
    """Configure monitoring thresholds."""

Purpose: Configure monitoring thresholds.

Parameters: – self: Instance reference – metric_name: Parameter for metric_name – threshold: Parameter for threshold – threshold_type: Parameter for threshold_type

`get_dashboard` (Line 572)

def get_dashboard(self):
    """Get monitoring dashboard."""

Purpose: Get monitoring dashboard.

Parameters: – self: Instance reference

`get_statistics` (Line 586)

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

Purpose: Get monitoring statistics.

Parameters: – self: Instance reference

Design Patterns Used

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

Usage Example

# Initialize the pattern
pattern = HealthStatus(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

November 4, 2025

Tag: agentic

Agentic Architecture Pattern: Real Time Processing

Agentic Architecture Pattern: Real Time Processing

Pattern 180: 180 Real Time Processing – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Classes

RealTimeProcessingConfig (Line 18)

RealTimeProcessingState (Line 27)

RealTimeProcessingAgent (Line 36)

__init__ (Line 39)

process (Line 49)

get_statistics (Line 75)

RealTimeProcessingPattern (Line 85)

__init__ (Line 99)

_build_workflow (Line 105)

register_agent (Line 150)

execute (Line 156)

run (Line 188)

get_statistics (Line 192)

WorkflowState (Line 108)

Design Patterns Used

Usage Example

Key Concepts

Extension Points

Agentic Architecture Pattern: Stream Processing

Agentic Architecture Pattern: Stream Processing

Pattern 179: 179 Stream Processing – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Classes

StreamProcessingConfig (Line 18)

StreamProcessingState (Line 27)

StreamProcessingAgent (Line 36)

__init__ (Line 39)

process (Line 49)

get_statistics (Line 75)

StreamProcessingPattern (Line 85)

__init__ (Line 99)

_build_workflow (Line 105)

register_agent (Line 150)

execute (Line 156)

run (Line 188)

get_statistics (Line 192)

WorkflowState (Line 108)

Design Patterns Used

Usage Example

Key Concepts

Extension Points

Agentic Architecture Pattern: Batch Processing

Agentic Architecture Pattern: Batch Processing

Pattern 178: 178 Batch Processing – Line-by-Line Explanation

Overview

Code Structure Analysis

File Information

Detailed Code Explanation

Module Docstring

Imports Section

Classes

BatchProcessingConfig (Line 18)

BatchProcessingState (Line 27)

BatchProcessingAgent (Line 36)

__init__ (Line 39)

process (Line 49)

get_statistics (Line 75)

BatchProcessingPattern (Line 85)

__init__ (Line 99)

_build_workflow (Line 105)

register_agent (Line 150)

execute (Line 156)

run (Line 188)

get_statistics (Line 192)

`init` (Line 39)

`process` (Line 49)

`get_statistics` (Line 75)

`init` (Line 99)

`_build_workflow` (Line 105)

`register_agent` (Line 150)

`execute` (Line 156)

`run` (Line 188)

`get_statistics` (Line 192)

`init` (Line 39)

`process` (Line 49)

`get_statistics` (Line 75)

`init` (Line 99)

`_build_workflow` (Line 105)

`register_agent` (Line 150)

`execute` (Line 156)

`run` (Line 188)

`get_statistics` (Line 192)

`init` (Line 39)

`process` (Line 49)

`get_statistics` (Line 75)

`init` (Line 99)

`_build_workflow` (Line 105)

`register_agent` (Line 150)

`execute` (Line 156)

`run` (Line 188)

`get_statistics` (Line 192)

`init` (Line 39)

`process` (Line 49)

`get_statistics` (Line 75)

`init` (Line 99)

`_build_workflow` (Line 105)

`register_agent` (Line 150)

`execute` (Line 156)

`run` (Line 188)

`get_statistics` (Line 192)

`init` (Line 39)

`process` (Line 49)

`get_statistics` (Line 75)

`init` (Line 99)

`_build_workflow` (Line 105)

`register_agent` (Line 150)

`execute` (Line 156)

`run` (Line 188)

`get_statistics` (Line 192)